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The Faculty of Medicine of Harvard University

Curriculum Vitae

Date Prepared: 08/19/2019

Name: Oleg S. Pianykh

Office Address: Massachusetts General Hospital 25 New Chardon Str., Suite 470, Boston, MA 02114

Home Address: 10 Hargrave Circle, Unit B,

Newton, MA 02461

Work Phone: (617) 724-2618

Work Email: opianykh@mgh.harvard.edu

Work FAX: (866) 604-2482

Place of Birth: Nizhny Novgorod, Russian Federation

Education: 1994 M.S. with honors and

Gold Medal Applied Mathematics and Physics

Moscow State University, Moscow, Russia

1994 D.E.R. (Diplôme d'Etudes et de Recherches)

Philosophy French University College & Sorbonne University, Paris, France

1998 Ph.D. Computer Science (advisor: John M. Tyler, PhD)

Louisiana State University, Baton Rouge, LA

Postdoctoral Training: 12/05-04/06

Trainee Computer Security Certificate Program, Center for Professional Development

Stanford University

Faculty Academic Appointments: (current HMS appointment in bold) 08/99-07/01

Instructor Radiology Louisiana State University Health Sciences Center

07/01-05/06

Assistant Professor Radiology Louisiana State University Health Sciences Center

05/06-09/06

Associate Professor Radiology Louisiana State University Health Sciences Center

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09/06-05/07

Lector Radiology Harvard Medical School

05/07- Assistant Professor Radiology Harvard Medical School 12/11- Instructor Harvard Extension School Harvard University

Appointments at Hospitals/Affiliated Institutions: 07/98-07/99

Information Analyst IV Radiology Louisiana State University Health Sciences Center

08/05-09/06

Chief Information Officer

Louisiana State University Healthcare Network

Louisiana State University Health Sciences Center

05/07-03/08

Healthcare IT Consultant

Harvard Medical Faculty Physicians Corporation

Beth Israel Deaconess Medical Center

09/06-09/12

Lead Imaging Scientist

Radiology Beth Israel Deaconess Medical Center

10/12 - Senior Scientist Radiology Massachusetts General Hospital

Other Professional Positions: 2004-2007 Teleradiology project consultant Russian Cardiology Research Center,

Ministry of Health of the Russian Federation

2005-2010 HIT Advisor, Surgical Quality Alliance American College of Surgeons 2011 DICOM Advisor Perceptive Informatics Inc. 2012 DICOM and teleradiology consultant CV-Sight Inc. 2016-2017 Operations management advisor Research and Practice Center of Medical

Radiology 2017- DICOM advisor RADLogics Inc. 2017 - Medical imaging advisor EMTensor GmbH

Major Administrative Leadership Positions:

Local 2001-2006 Teleradiology Administrator Louisiana State University Health Care

Network 2011- Medical Informatics Program (graduate

level) Course Director Harvard Extension School, Harvard University

2015 - Director of Medical Analytics Group (MAG)

Massachusetts General Hospital, Department of Radiology

Regional 2005-2006 Chief Information Officer Louisiana State University Healthcare

Network (LSUHN)

National 2002-2007 Chief Executive Officer Universal PACS Inc.

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Committee Service:

Local 2002-2005 Doctoral Dissertation Committee Louisiana State University Computer

Science Department, Baton Rouge, LA 2002-2005 Committee member 2002-2006 PACS Committee Louisiana State University Health

Sciences Center, Baton Rouge, LA 2002-2005 Committee member, advising on PACS

and DICOM functionality, working with PACS vendors

2010-2012 Image Lightly Committee Beth Israel Deaconess Medical Center 2010-2012 Committee member, working on radiation

dose reduction in CT imaging. 2015- IT Committee Massachusetts General Hospital,

Department of Radiology 2015- Committee member, advising on DICOM,

PACS and operation management projects

Regional

National 2005 - 2010 HIT Advisor, Surgical Quality Alliance American College of Surgeons 2005 – 2010 Committee member, advising on PACS

and DICOM functionality, computer applications in healthcare

International 2008 - 2013

Member of DICOM Standard Web Services workgroup (WADO)

International DICOM Standard Committee

2008 – 2013 Committee member, working on standardizing DICOM Web Services (WADO), currently part of DICOM standard

2011-present Committee member, reviewing and advising on MS and PhD theses.

2018 - Member of DICOM WG-20, Integration of Imaging and Information Systems

International DICOM Standard Committee

Working on integrating AI algorithms in radiology workflow

Professional Societies: 2006 - European Society of Radiology

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2006 – 2013 Associate Member 2014 – present Corresponding Member

Editorial Activities:

Ad hoc Reviewer Reviewer:

Pattern Recognition Journal Journal of Computer Assisted Tomography American Journal of Radiology

Other Editorial Roles 2014- Book series editor: I founded a new book

series dedicated to clinical applications of information sciences. The first book was published in 2014.

“Understanding Medical Informatics” book series, Springer

Honors and Prizes: 1993 Summer internship

grant French University College, Paris, France

1994 Gold Medal and Honors Diploma

Moscow State University, Moscow, Russia

1994 Tuition Award Louisiana State University 2008 Cum Laude Award for

outstanding scientific paper

Beth Israel Deaconess Medical Center

Paper co-author

2008- 5-star readers’ rating of my DICOM book

Google books and Amazon Book author

2013 Trainee Research Prize for outstanding scientific paper

Radiological Society of North America

Paper co-author

2015 Top paper in perfusion domain

"Who Is Publishing In My Domain?" review by BioMedUpdater science group in biomedical sciences

Paper author

Report of Local Teaching and Training

Teaching of Students in Courses: 2011-2015 Medical Informatics Harvard Extension School, Harvard

University Graduate students Full semester course: 3-hour sessions per

week for 15 weeks, plus 3-5 hours/month “on demand”

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2016-present

Big Data in Healthcare Applications Harvard Extension School, Harvard University

Graduate students Full semester course: 3-hour sessions per week for 15 weeks, plus 3-5 hours/month “on demand”

Formal Teaching of Residents, Clinical Fellows and Research Fellows (post-docs): 1999-2000 Digital Image Processing, DICOM and

PACS Louisiana State University Medical Center

Residents and Clinical Fellows 3 hours/year 2001-2003 Developing Advanced PACS

Workstations Louisiana State University, Computer Science Department

Graduate students and faculty 6 hours/year 2004 PACS and Medical Imaging Louisiana State University Health

Sciences Center Residents 5 hours/year 2005 CAD in Medical Imaging Louisiana State University Health

Sciences Center Residents 5 hours/year 2005 Digital Image Processing, DICOM and

PACS Louisiana State University Health Sciences Center

Residents 5 hours/year 2007-2012 Digital Image Processing, DICOM and

PACS BIDMC

Residents 3 hours/year

Formally Mentored Harvard Medical, Dental and Graduate Students: 2014 Dileep Monie, graduate student / Harvard Extension School Career stage: Graduate student Mentoring role: research mentor Accomplishments:

under my supervision he was studying predictive modeling for clinical patient workflow. 2014-2016 Mark Stites, MS / Harvard Extension School Career stage: Graduate student Mentoring role: research mentor Accomplishments:

Work on DICOM network security under my supervision, Mark performed the first worldwide scan of digital radiology acceptance and security, a truly unique project revealing worldwide quality of medical imaging; our work was published in American Journal of Roentgenology.

2014-2015, 2017

Jing Ai, MS / Harvard Extension School

Career stage: Graduate student Mentoring role: research mentor Accomplishments: Jing was my graduate student in Harvard Medical informatics class, then intern in my Medical Analytics Group (MAG); Jing worked on clinical operations management problems including optimal wait room size management, her work was published by JACR

2014-2015 Alex Jacobson, graduate student / Harvard Extension School Career stage: Graduate student Mentoring role: research mentor Accomplishments:

Work on radiation dose database project. 2015 David Wihl, graduate student / Harvard Extension School Career stage: Graduate student Mentoring role: research mentor Accomplishments:

David was conducting a study of reference-free image quality predictors.

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2015-2016 Sampson Abiola, Graduate student / Harvard Extension School Career stage: Graduate student Mentoring role: research mentor Accomplishments:

under my supervision Sampson performed a study of worldwide HL7 security, which was then presented at ECR 2016 International meeting.

2015-2016 Minkhorst, Cory / Harvard Business School Career stage: Graduate student Mentoring role: research mentor Accomplishments:

Under my supervision, Michael built full-scale computer simulation model for Yawkey 6 MRI, and used it to analyze various exam scheduling strategies and parameters; the results of this work were presented to MGH leadership and led to several improvements in Yawkey 6 scheduling.

2015-016 Reiche, Michael / Harvard Business School Career stage: Graduate student Mentoring role: research mentor Accomplishments:

Under my supervision, Michael built full-scale computer simulation model for Yawkey 6 MRI, and used it to analyze various exam scheduling strategies and parameters; the results of this work were presented to MGH leadership and led to several improvements in Yawkey 6 scheduling.

2017-2018 Catherine Curtis, Graduate student, Harvard University Career stage: Graduate researcher Mentoring role: research mentor Accomplishments:

Catherine has worked on statistical analysis of MGH operational data which produced a paper published at JACT

2017-2018 Nick H. Kamboj, Graduate student, Harvard Extension School Career stage: Graduate researcher Mentoring role: research mentor Accomplishments:

Researching new concept of objective human perception of digital image quality, published at Journal of Digital Imaging

Other Mentored Trainees and Faculty: 1995-2002 Rahman Tashakkori, PhD / Professor, Appalachian State University Career stage: PhD student Mentoring role: PHD thesis co-adviser Accomplishments:

Supervising work on wavelets applied to medical image representation and compression; coauthoring 4 publications

2002-2005 Monica Trifas, PhD / Assistant Professor, Jacksonville State University Career stage: PhD student Mentoring role: PHD thesis co-adviser Accomplishments:

Supervising work on medical image enhancement; publishing 4 papers 2000-2004 Yesheng Li, MS / Graduate student, Louisiana State University Career stage: PhD student Mentoring role: PHD thesis co-adviser Accomplishments

Supervising work on medical image wavelet compression: developing wavelet compression toolset for medical imaging, used in medical imaging teleradiology software.

2000-2006 Mikhail Milchenko, PhD / Graduate student, Louisiana State University Career stage: PhD student Mentoring role: PHD thesis co-adviser Accomplishments:

As Mikhail’s PHD thesis co-adviser, I was supervising his work on MR image correction, which successfully led to his PhD degree, and a publication in Journal of Magnetic Resonance Imaging

2012-2015, 2018

Xenia Loginova, MS / Graduate student, Higher School of Economics

Career stage: Graduate student Mentoring role: research mentor Accomplishments: I was directing Xenia’s MS thesis work on non-reference image quality quantification; as a result, we discovered a set of major non-reference image quality features, responsible for human perception of image quality. The work was published at JDI.

2014 Badanin Yuri, MS / Higher School of Economics, School of Data Analysis and Artificial Intelligence

Career stage: Graduate student Mentoring role: research mentor Accomplishments: I was directing Yuri’s graduate thesis work on geodesic 3D tumor segmentation:

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dynamic competitive 3D segmentation strategy was developed to extract tumor boundaries in presence of noise and complex anatomy (the strategy was demonstrated to produce results superior to reported previously).

2014-2015 Victor Lopatin, MS / Higher School of Economics, School of Data Analysis and Artificial Intelligence

Career stage: Graduate student Mentoring role: research mentor Accomplishments: under my supervision, Victor has designed an algorithm to segment 3D patient anatomy with anatomically-correct organ curvature.

2015-2016 Kirill Malakhov, PhD student / Higher School of Economics, School of Data Analysis and Artificial Intelligence

Career stage: Graduate student Mentoring role: research mentor Accomplishments: under my supervision Kirill studied the problem of most efficient patient scheduling, resistant to such common disruptions as late patients and delayed exams.

2014-2016 Catherine Oglevee, MS / Hamilton College Career stage: Graduate student Mentoring role: research mentor Accomplishments:

Project as a study of medical images leaks (accidental loss of imaging data) published in Journal of Digital Imaging; a study of optimal waiting room occupancy models (published in JACR), work on patient satisfaction from wait time prediction (published in m J Med Qual.)

2015 Xuefang Xie, PhD / University of Maryland Career stage: Graduate student Mentoring role: research mentor Accomplishments:

Supervising studies on optimal exam delay prediction: Xuefang was working as a member of my Medical Analytics Group, developing predictive models for patient and medical facility delays.

2015-2016 Anna Vorontsova, B.S. / Higher School of Economics, School of Data Analysis and Artificial Intelligence

Career stage: Graduate student Mentoring role: research mentor Accomplishments: Under my supervision, Anna has completed a degree qualification project on digital nearly-lossless digital image compression, using a novel algorithm we have developed.

2015 Michal Rozenwald, B.S. / Higher School of Economics, School of Data Analysis and Artificial Intelligence

Career stage: Graduate student Mentoring role: research mentor Accomplishments: Under my supervision, Michal has completed a degree qualification project on digital imaging frequency rebalancing for image quality improvement.

2015-2016 Anita Kodali, Graduate student, Dartmouth College Career stage: Graduate researcher Mentoring role: research mentor Accomplishments:

Anita has developed several performance metrics for MGH radiology, which were later incorporated into the BIDash project

2016-2017 Connor McCann, Graduate student, Boston University Career stage: Graduate researcher Mentoring role: research mentor Accomplishments:

Under my supervision, Connor has researched and developed a prototype for hospital indoor tracking system.

2016-2017 Catherine Liu, Graduate student, Boston University Career stage: Graduate researcher Mentoring role: research mentor Accomplishments:

Catherine has developed several statistical analyses of MGH workflow data, which lead to 3 publications at major radiology journals

2018-2019 Richard Zhang, Graduate student, MIT Career stage: Graduate student Mentoring role: research mentor Accomplishments:

Analysing ED department bottleneck with GAM model learning (presented to the department). Developing optimal scheduling algorithms.

2019- Keis Bejgo, Graduate Student, MIT Worked on using machine learning algorithms for workflow analysis. Developed code

to run optimal model feature selection 2019- Jing Ai, Graduate Student, Columbia University

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Worked on using algorithmic approaches to identify performance bottlenecks in Emergency Department.

Local Invited Presentations:

No presentations below were sponsored by outside entities

2004 Medical Imaging (invited lecture) Louisiana State University, Department of Physics 2005 CAD in Medical Imaging (invited lecture) Louisiana State University Medical Center 2006 Digital Image Processing, DICOM and PACS (invited lecture) Louisiana State University Medical Center 2008 Advanced Medical Imaging in Radiology (grand rounds) Beth Israel Deaconess Medical Center 2011 Image postprocessing for CT dose reduction (grand rounds) Beth Israel Deaconess Medical Center 2017 Data Analytics in Operations Management (invited lecture) MGH, Boston, USA 2017 Data Analytics in Operations Management (invited lecture) MGH, Boston, USA 2018 Optimizing Clinical Workflow (invited lecture) MGH Data Alliance, Boston, USA

Report of Regional, National and International Invited Teaching and Presentations

No presentations below were sponsored by outside entities

Regional 2006 Introduction to PACS (invited lecture) Pennington Biomedical Center, Baton Rouge, LA 2008-2012 DICOM. PACS. Teleradiology (invited annual lecture for Radiology fellows) Beth Israel Deaconess Medical Center 2014 Lessons Learned During the Development and Deployment of DICOM (invited lecture) Northeastern University, Boston, USA 2016 Developing data-driven healthcare: from science to reality (invited lecture) Massachusetts Institute of Technology, Cambridge, USA 2017 Queueing in Healthcare (invited lecture) Massachusetts Institute of Technology, Cambridge, USA 2017 Optimizing Radiology (invited presentation) MGH/MIT Data Science Group, Boston, USA

National 2011 Automated coronary artery plaque analysis (presentation) American Roentgen Ray Society meeting, Washington DC 2011 Perfusion Analysis: From Popular To Optimal (presentation) American Society of Neuroradiology meeting, New Orleans, LA 2011 Optimal image transformation for thresholding: an initial step for auto-segmentation for

brain tumor volumetry (presentation) American Society of Neuroradiology meeting, New Orleans, LA

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2012 Can We Validate Perfusion Analysis? Introducing Digital Perfusion Phantoms (selected oral abstract)

RSNA 2012, Chicago, USA 2014 Can we predict patient wait time? (selected oral abstract) RSNA 2014, Chicago, USA 2015 Clinical security worldwide: Maps and country ratings (selected oral abstract) RSNA 2015, Chicago, USA 2017 Predictive analytics for patient wait-time management, and on-time clinical workflow

(invited keynote lecture) Chief Analytics Officer conference, Boston, USA 2018 Real-World uses of machine learning in healthcare (invited keynote lecture) Chief Analytics Officer conference, Boston, USA 2019 Data Analytics in Operations Management (invited lecture) Kennedy School of Government, Harvard University (invited lecture)

International 2003 Invited seminar on PACS and medical imaging (invited lecture) Moscow State University, Department of Physics 2005-2012 Digital Medical Imaging and Teleradiology (invited annual lecture) Ministry of Transportation, Department of Healthcare, Russian Railways, Moscow 2008-2011 Medical Digital Imaging Standards (invited lecture) Semi-Annual Telemedicine School, Moscow, Russia 2011 CT Dose Reduction with Nonlinear Image Filters (invited lecture) Madrid (Spain) – MIT (USA) M+Visison Consortium, Boston, MA 2011- present

Medical Informatics course (visiting professorship)

Teaching full semester course to graduate students at Higher School of Economics, School of Data Analysis and Artificial Intelligence (Moscow, Russia)

2011- 2015 Selected Problems in Medical Informatics course (visiting professorship) Teaching full semester course to undergraduate students at Higher School of

Economics, School of Data Analysis and Artificial Intelligence (Moscow, Russia) 2014 CAD and image segmentation (invited section moderator) European Congress of Radiology, Vienna, Austria 2014 Crossroads of diagnostic imaging (invited presentation) European Congress of Radiology, Vienna, Austria 2016- present

Data Analytics course (visiting professorship)

Teaching full semester course to graduate students at Higher School of Economics, School of Data Analysis and Artificial Intelligence (Moscow, Russia)

2013 Introduction to DICOM and its applications (invited lecture) Moscow Institute of Physics and Technology, Moscow, Russia 2014 Development and use of web-based Teleradiology (invited lecture) European Congress of Radiology, Vienna, Austria 2014 Big Data in Healthcare: How can we make it work? (invited lecture) Higher School of Economics, Moscow, Russia 2016 Big Data at work: Monitoring patient workflow in radiology (invited lecture) European Society of Cardiology, Valencia, Spain 2017 How to Optimize Radiology with Big Data (invited presentation) European Congress of Radiology, Vienna, Austria 2018 Machine learning in radiology operations management (invited presentation) European Society of Medical Imaging Informatics, Vienna, Austria 2018 Data Analytics in Operations Management (invited presentation)

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European Society of Medical Imaging Informatics, Vienna, Austria 2019 Continuous Machine Learning in Radiology IS3R Meeting, Budapest, Hungary

Report of Clinical Activities and Innovations

Clinical Innovations: Development of UniPACS: modular, performance-optimized radiology system at Louisiana State University Medical Center (2003)

UniPACS: Easy-to-use PACS system developed and implemented in Louisiana State University Medical Center.

The system was optimized to run on a plain personal computer, fully supporting DICOM image processing and exchange. Due to its robustness and modular design, the system was used during post-Katrina disaster recovery efforts in 2005-2006, running LSUHSC radiology: we were able to connect remote radiologists and imaging archives over plain phone lines, using plain computers run from military tents. The use of UniPACS had a significant impact on post-hurricane radiology.

UniPACS software was also used to run several clinical trials and data projects, such as nationwide West Nile imaging database.

Building lightweight state-wide teleradiology network for Louisiana State University Medical Center (2005)

Building LSU Healthcare Teleradiology Network, connecting 7 hospitals in the state of Louisiana. Working with LSUHSC’s vice-president Dr. F. Opelka, I was responsible for developing the imaging software and integrating with image-acquisition devices.

Implemented solution included advanced image compression for faster image transfer, robust imaging communications, simplicity of access, and advanced image processing functionality. As a result, we were able to transfer large image data volumes from small remote facilities over slow (around 1 Mb/sec) networks. The use of this system greatly contributed to shortening radiology turn-around time, providing timelier healthcare.

Analytically-tractable clustering algorithm

I developed a novel data clustering algorithm which, unlike any other known clustering, can be solved directly, for globally-optimal point, without iterations. The algorithm was used to segment MRI brain anatomy (blood vessels) in real time, with optimal results.

Real-time 3D tumor selection algorithm developed and implemented for routine clinical use at Beth Israel

The goal of this project was to replace cumbersome and time-consuming manual tumor segmentation with a more reliable and faster automated tool. I was responsible for developing the segmentation algorithm, and for optimizing its quality and computing speed (reduced from 10 minutes initial computing

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Deaconess Medical Center (2008)

time to less than a second in the final algorithm version). This work was done in collaboration with the techs of BIDMC 3D lab

The algorithm reduced tumor segmentation time from 20 minutes (manual) to less than one second (automated). The error in volume segmentation was reduced from 12% (manual) to 8% (automated).

Tumor size extraction and measurement algorithm, Beth Israel Deaconess Medical Center (2008)

Tumor size extraction and measurement algorithm, developed to build a tumor metric database at Beth Israel Deaconess Medical Center.

The algorithm automated tumor size extraction and storage, which provided the physicians with robust tumor-tracking database. I have developed a solution which allowed us to extract tumor locations and labels from a proprietary PACS database (capturing radiologists’ input) into our own tumor size-tracking registry. This enabled us, for each patient, to track tumor size changes and monitor the response to provided therapies.

Fast, robust CT dose reduction filter, Beth Israel Deaconess Medical Center (2008)

CT dose reduction filter, implemented to reduce CT imaging irradiation doses. The filter provided for low-dose CT scans by removing low-energy noise.

The algorithm was capable of up to 7X dose reduction, and was found to be better than vendor-specific options available at that time. Implemented at BIDMC.

Lung motion-compensation algorithm, Beth Israel Deaconess Medical Center (2010)

I developed a new algorithm for 3D lung registration (inhale/exhale motion compensation). The algorithm was using a much simpler registration approach (elliptical lung outlines), which resulted in real-time implementation. The main goal of registration was to accurately align lung anatomy and tumor areas, to compare patient’s conditions between different scans.

MRI fetal measurement algorithm and application, Beth Israel Deaconess Medical Center (2011)

Developed an application to perform fetal measurements in MRI images. The application was providing some non-trivial shapes to be overlaid with the structures of the fetal brain, for specific measurements. The application was designed in collaboration with Drs. Levine and Romiro, to enhance their ability to detect brain pathologies.

Organ thickness correction algorithm (2011)

I invented and developed an algorithm for organ thickness correction, which improves small structure detectability in X-ray images of organs with varying

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thickness (such as breast images). The algorithm was published in my “Image Quality in Medicine” book.

Co-development and implementation of radiation dose tracking database, MGH (2012)

This work was done in collaboration with Radimetrics, a private company implementing radiation dose tracking solution. I was responsible for working with them on refining the algorithmic part of their product, making it more robust and less sensitive to various image-acquisition scenarios. I was also leading the clinical implementation of this product at MGH.

DPP: Digital Perfusion Phantoms: (2012)

Digital Perfusion Phantoms (DPP): Inventing and implementing a novel approach to evaluating the quality and reliability of perfusion analysis. This work was largely based on my concept of perfusion linearity – a novel concept relating perfusion values to linear functions of original image intensities.

Digital Perfusion Phantoms are cleverly-designed DICOM images, producing pre-defined perfusion maps. DPPs help avoid the complexity of real patient anatomy, literally visualizing perfusion algorithm errors and accuracy. DPPs are currently used at several institutions/companies to evaluate the quality of their perfusion.

Light web-based PACS viewer (2012)

Light web-based PACS viewer.

I designed and implemented (in collaboration with a software company) a light web-based DICOM image viewer, providing diagnostic image quality. One of the first viewers with zero-footprint technology, providing original image quality of any platform/browser, and optimized for slow networks.

The viewer was used in several teleradiology projects worldwide, including itelerad.com teleradiology service.

Perfusion “key image” visualization algorithm (2012)

I developed and piloted a new algorithm capable of visualizing the contribution of each perfusion sequence image into the final perfusion values (blood flow and volume, mean transit time). As a result, it was shown that only a few images really influence the final perfusion values, and therefore the amount of perfusion images to be acquired can be significantly reduced.

CPU-based, real-time volume rendering algorithm (2012)

Fast 3D medical image rendering algorithm.

Designed and implemented a fast, CPU-based 3D medical image rendering algorithm, providing real-time interactive image rendering on a plain off-the-shelf computer. Due to its speed and robustness, the implementation was

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provided to several clinical centers with time-critical patient care, including the Ministry of Emergency Situations (Russian Federation)

Pixel interpolation algorithm for digital imaging (2012)

New interpolation algorithm for digital imaging.

I discovered and published a new digital image interpolation algorithm, providing superior image interpolation quality. Medical digital images are acquired at fixed resolution (example: 512×512 pixels for CT), and therefore cannot be zoomed without inserting (interpolating) the missing/intermediate pixel values. The choice of this interpolation greatly affects visual image quality. My new interpolation algorithm was specifically designed to be used with medical images, and proved to be superior to known linear and cubic interpolation techniques.

Electronic patient dispatching: Massachusetts General Hospital (2013)

Electronic patient dispatching: Designing and implementing an electronic patient dispatching system for the X-ray scanning facility in Yawkey 6.

The system reduced unproductive manual labor in identifying the next X-ray unit available, reduced facility idling time, and reduced patient wait time. Workload load-balancing ensured that all techs perform the same number of exams.

Patient wait time displays: Massachusetts General Hospital (2013-)

Patient wait time displays: Designing and implementing patient wait time predicting algorithms, to display accurate wait time estimates in MGH waiting areas.

This large project was initially started at MGH orthopedic clinic area (Yawkey 3), then expanded to other MGH facilities (remote sites included). The work was done in collaboration with Dr. Rosenthal, MT Shore, Sharon Gibson, modality and site managers. The displays were programmed to demonstrate current wait time for each patient facility and modality, to improve patient waiting experience.

The novel mathematical analysis of patient time predictability, performed for this project, proved to be crucial not only for the wait time algorithm implementation, but also for identifying workflow bottlenecks and major improvement points. Workflow predictability was studied as a way to enforce deterministic, well-organized healthcare. The results were presented at several major conferences and published in JACR.

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First study and country ratings of worldwide radiology data security (2014)

Designing a methodology for evaluating healthcare information security and risks of patient data leaks worldwide (presented and published).

With recent massive patient security breaches reported, it has become essential to provide a consistent, practical methodology for identifying potential clinical data leaks before they happen. I designed and implemented (in collaboration with my Harvard research assistants, Mark Stites and Sampson Abiola) the first clinical security scanning algorithm, based on DICOM protocol. The implementation of this algorithm was used to scan all 4.3 billion existing IP addresses to identify 2774 unprotected radiology archives (worldwide).

This study also led to a novel healthcare security rating methodology, when each county/region was rated proportionally to its clinical security risks – a significant step forward in enhancing patient data protection, and in raising clinical security awareness.

Eliminating imaging “leaks”, Massachusetts General Hospital (2014)

Designing a methodology and performing a project on eliminating digital imaging data losses.

How can one guarantee that none of the acquired patient images will be lost in transition to the radiology department and diagnostic workstation? This project started when we discovered that as many as one study per day can lose some of its images without anyone knowing about it. Therefore I launched a study of the image “leak” phenomenon, when we meticulously analyzed our digital imaging “pipelines”, workflow, vendor-specific image processing to locate the origins of hidden data leaks. As a result, the main reasons for accidental data losses were identified and eliminated, providing MGH with a “no image lost” workflow.

Tempus Fugit (TF) portal, Massachusetts General Hospital (2015)

Tempus Fugit (“time flies”) portal: Real-time tracking of patient-care critical events for MGH Radiology.

I designed and implemented TF portal as a complex patient exam tracking system, which processes real-time data from several MGH databases (RIS, PACS) to identify all exceptional cases requiring immediate attention. Examples include unread exams, modality images unsent to PACS, patients waiting for too long, and so on. The portal was launched in early 2015 and immediately became an essential tool for identifying real-time bottlenecks; now the portal is routinely used by MGH ops managers and radiologists.

Exam assignment quality metric, Massachusetts

Working with members of my MAG group and Dr. Alkasab, we designed and implemented a new metric for rating the quality of radiology exam assignments. When exams are assigned to radiology divisions to be interpreted, it is imperative to provide the best division match. Given a large number of possible

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General Hospital (2015)

(and sometimes non-trivial) exams, our assignment quality metric was computing a probabilistic quality of exam assignments, which was then used to identify outliers, and to enforce “no exam gets forgotten” assignment policies.

Realistic computer simulation model for MGH MRI Yawkey 6 facility (2015)

Working with two students from Harvard Business School (Michael Reiche and Cory Minkhorst), I developed and studied a realistic computer simulation model for outpatient scanning at MGH.

This complex model was designed to represent Yawkey 6 MRI scanning workflow, to address the most vital “what if” questions regarding timely patient scanning. Model analysis lead to several important conclusions impossible to derive otherwise – such as the precise analysis on how exam duration reduction affects patient wait times and patient throughput. The model also discovered the phenomenon of “late patient penalty” – the effects of late patients on the overall patient processing time.

Time-to-PACS alerting system, Massachusetts General Hospital (2015)

Time-to-PACS exam alerting system: Real-time alerts to techs on delayed exams.

I designed and implemented Time-to-PACS alerting system in collaboration with MT Shore, Dr. Rosenthal, and MGH radiology modality managers as an extension to the original TF portal, which can actively alert modality supervisors about any delayed exams (exams completed but not sent to PACS). As a result, the system ensures that all exams are submitted to MGH PACS right after they are, thus enforcing the standards of timely patient care. Techs/managers are automatically paged in case of delays.

Our recent PACS data analysis demonstrated that Time-to-PACS alerts reduced the number of delayed exams by a factor of three – a remarkable improvement for our clinical workflow. Started with CT imaging, Time-to-PACS alerting is currently being expended to the other modalities, and to the remote MGH sites.

Exam volume predicting algorithm/portal for MGH Radiology, Massachusetts General Hospital (2015)

Working together with Dr. Saini and TJ Bollerman, we designed and implemented a mathematical algorithm predicting the next day exam volume for different MGH Radiology divisions. The goal of this prediction was to help divisions with making right staffing decisions, allocating sufficient workforce for tomorrow’s workload. The algorithm was optimized to ensure that its exam volume prediction would be accurate to a single radiologist’s workload.

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Probabilistic analysis of wait room occupancy (2015)

I developed a new “time-probability” approach to wait room occupancy analysis. Working with my graduate researchers (Jung Ai and Catherine Oglevee), and using MGH Yawkey 6 waiting room as a real-life example, we demonstrated that wait room crowding should be studied in probabilistic way, as a function of longest crowding time and its probability. This significantly improves existing estimates for waiting room capacity, when much smaller rooms may be allocated with negligible risk of overcrowding.

This analysis was provided to guide Yawkey 6 renovations, and published in Journal of the American College of Radiology

Data-driven radiology workflow, Massachusetts General Hospital (2015)

In summer of 2015, with great support from the MGH Radiology department, I founded Medical Analytics Group (MAG) – a group of applied scientists, working on extracting clinical knowledge from data.

The main MAG goal is to develop optimal models for healthcare delivery, letting them develop and grow from the advanced clinical data analysis. Consequently, MAG was started to help the other clinical innovators – managers, radiologists – to verify their ideas, and to convert these ideas into the real-life applications, directly impacting our healthcare. MAG’s work has already led to a number of improvements in the quality of patient care at MGH.

BIDash clinical workflow tracking system, 2016-

Patient tracking system for MGH Breast Imaging department

The system (data, algorithms, software) to track patients within breast imaging workflow, designed in collaboration with MGH Breast Imaging department. It was designed to replace archaic paper-based patient tracking, and deliver real-time information about each patient’s location and procedure timing. New features such as examination duration thresholding were added to alert about procedures taking too long, or patients waiting for too long. Examination scheduling functions were added as well, to record specific recommendations for scheduled examinations. The software resulted in a number of operational improvements at MGH Breast Imaging (such as reduced patient wait times)

3D workload software, 2017-

Software application to predict the volume of 3D imaging cases

Implemented with Steven Guitron (member of my MAG team) and Jennifer McGowan (head of 3D imaging), this software application shows and predicts the number of 3D cases to be processed by MGH 3D lab. The use of this software helps track and allocate 3D resources in the most optimal way.

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Patient wait prediction software, 2.0 (2018)

Patient wait time prediction software implemented at Massachusetts General Hospital

A major remake of the previously used patient wait time prediction software.

The new version was based on the novel machine learning algorithms, “re-learning” the optimal predictor parameters in real time. As a result, a much more accurate patient wait time prediction was achieved. The application was implemented in multiple patient wait rooms at MGH, and received very positive patient feedback.

Tempus Fugit software (2013, major remake in 2018)

Real-time radiology outlier tracking system

Tempus Fugit (TF) was initially designed and implemented by me in 2013, then completely rebuilt in 2018 by my MAG group (Steven Guitron, Darren Parke) in collaboration with MGH operations management. Tempus Fugit identifies, in real time, all abnormal events happening in radiology workflow (delayed patients, over-long examinations, resource utilization). Catching all these problems in real-time and displaying them to radiology decision-makers proved to be extremely efficient for improving radiology workflow, reducing turn-around times, and preventing operational problems before they become worse. New TF relies on complex pattern-learning algorithms for advanced data analysis.

Report of Technological and Other Scientific Innovations

Functional Set Compression (2003)

“Functional Set Compression” patent (number 6,661,925), granted by US Patent office on December 9, 2003.

Functional Set Compression is a new approach to compressing large volumes of similar images – in particular, sets of medical images (slices), acquired during the same scan. It provides better compression performance than state-of-the-art image compression applied to each image separately.

PACS workstation software for diagnostic use (2003)

FDA premarket approval, https://510k.directory/clearances/K023476

The novel lightweight image-analysis workstation software was designed and developed by me, in collaboration with John M. Tyler, my graduate thesis adviser.

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Soon after, I also wrote and received FDA approval for its clinical diagnostic use. The software was later licensed to Universal PACS Inc.

Advanced Radiologist’s Workstation (2005)

“Advanced Radiologist’s Workstation” patent (number 6,909,436), granted by US Patent office on June 21, 2005

Advanced Radiologist’s Workstation invention combined a series of advanced image-processing algorithms, designed to improve the quality of radiology interpretation. The algorithms designed and implemented by me included flicker-free image redrawing, search for similar image patterns, efficient image compression.

Flash PACS (2007)

Software implemented at Beth Israel Deaconess Medical Center and Harrington Hospital.

Flash PACS: A fully-functional PACS system that can be run from a USB flash drive. Flash PACS was used to provide Teleradiology support for Harrington Teleradiology project. The system could be plugged into any existing PACS workstation, without any installation required, providing instantaneous access to remote (teleradiology) imaging servers.

CT streak removal algorithm and software (2007)

Software implemented at Beth Israel Deaconess Medical Center.

Developed and implemented a novel algorithm for CT streak removal, for a team of CT radiologists. The software was automatically removing streak artifacts from low-dose CT images. The results were presented internationally at at ECR 2007, http://posterng.netkey.at/esr/viewing/index.php?module=viewing_poster&pi=12352

CT plaque analysis algorithm and software (2008)

Developed and implemented CT plaque analysis software.

The software provided an accurate real-time coronary artery plaque segmentation tool, to identify and measure plaques. Unlike previously-used techniques, the software was able to identify different types of plaques at the same time (when they are present together), in 3D, and exclude arterial walls from the plaque measurements.

Real-time 3D tumor selection software (2008)

Software implemented at Beth Israel Deaconess Medical Center

Real-time 3D tumor selection software application developed and implemented for routine clinical use at Beth Israel Deaconess Medical Center. The software was running my tumor-selection algorithm (based on competitive 3D flood-fill) and was

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integrated into clinical workstation and PACS (via DICOM protocol) – thus providing a seamless access to its measurement results.

Liver fat measurement software (2009)

Software for measuring liver fat content from CT images

Developed (in collaboration with Dr. V. Raptopoulos and Dr. M. Claus) and

implemented an algorithm for liver fat measurements in CT images.

The software computes relative liver fat content based on Hounsfield units.

CT dose reduction software (2010)

Software for improving diagnostic quality of low-dose CT images

I designed a new CT dose reduction algorithm to reduce CT imaging irradiation

doses by low-energy noise removal; at least 2X dose reduction achieved. Consequently, I have implemented (in collaboration with Drs. Raptopoulos, Bankier, Litmanovich) software based on this algorithm, clinically used to denoise thin CT slices. The software was integrated into a production PACS system.

Digital image interpolation software (2012)

Advanced digital image application implementing fine-grain zooming for medical images.

I developed software to implement my advanced image interpolation algorithm, providing superior image quality for medical image digital zooming. This software was provided to several national and international radiology groups I was working with at that time.

Patient wait prediction software (2013)

Patient wait time prediction software implemented at Massachusetts General Hospital

The software, developed in collaboration with MGH EMI group, predicts waiting

time for the next patient walking into a waiting room. It also predicts delays for the scheduled exams. Implemented in MGH waiting areas (“wait time displays”).

Room timer software (2013-present)

Software implemented at Massachusetts General Hospital

“Room timer” software: a mobile application running on iPod Touch devices and

used to dispatch MGH orthopedic patients to ten X-ray exam rooms in the most optimal, productive way.

Implemented in MGH Yawkey 3 area; was demonstrated to reduce patient wait time, and to provide a more fair distribution of exams between the techs.

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DPing software (2014-present)

Published in AJR; described in my AuntMinnie interview www.auntminnie.com/index.aspx?sec=ser&sub=def&pag=dis&ItemID=110500

Designed and implemented a software application for scanning Radiology security breaches worldwide (DPing)

The application was used to perform a worldwide scan of radiology data security, informing radiology managers about potential security breaches.

HL7Ping software (2015)

Digital software for accessing radiology security worldwide.

Designed and implemented (in collaboration with Sampsong Abiola, my graduate student at Harvard) a software application for scanning Hospital Information System security breaches worldwide (HL7 scan).

The application was used to perform a worldwide scan of clinical data security, informing hospital managers about potential security breaches.

Operational machine learning (2019)

A set of operational features to be used for operational machine learning

Developed and implemented a set of specific features, which can be used as predictors of workflows. These features, when combined with machine learning algorithms, result in accurate operational models.

Report of Education of Patients and Service to the Community

No presentations below were sponsored by outside entities

Activities 2005-2006 LSU Healthcare Network (LSUHN) / Chief Information Officer Post-Katrina disaster relief and PACS recovery: rebuilding state imaging network,

connecting remotely-working radiologists, working with vendors and developing in-house PACS solutions to recover radiology imaging workflow.

2005-2006 Jetson Correctional Facility / Project Lead Teleradiology for Jetson Correctional Facility, LA: providing radiologists with remote

access to Jetson patients. This helped increase patient care quality for Jetson inmates, who had higher risk of TB and other specific diseases.

2006- LSUHC / PACS Consultant Providing technological and educational support for LSUHC radiology system:

consultations, assistance with DICOM-related projects, assistance with vendor-neutral radiology archives

2007-2008 BIDMC / PACS and teleradiology Consultant Harrington Teleradiology Project, MA. I was in charge of establishing teleradiology

connection from BIDMC radiology group to the Harrington radiology imaging, cleaning unread backlog of 600 cases.

2012-2013 Massachusetts General Hospital / Project lead

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Implementing patient radiation exposure tracking database (Radimetrics): keeping track of patient radiation doses, insuring patient safety standards. Developing radiation-related educational materials for the patients.

2014- Boston-area universities (BU, MIT, Harvard)/Data analysis internship program Providing Medical Informatics internships to local universities: BU, MIT, Harvard.

Working with Harvard Business School graduate students on analyzing and improving MGH radiology workflow.

2014- Massachusetts General Hospital / Project lead Providing patient wait time displays in MGH waiting areas: Displaying current wait

times and waiting line sizes to the patients 2015- Massachusetts General Hospital / Project lead Improving patient service metrics in MGH recovery areas: identifying main bottlenecks

(such as the mismatch between staff and patient scheduling), consulting on improvements; helping reduce patient wait time, improving accuracy of procedure scheduling.

2017- Massachusetts General Hospital / Project lead Establishing and running “patient appointment reminder” system at MGH radiology, to

inform outpatients about their scheduled examinations and preparation for them. The system was able to significantly reduce patient no-shows.

2018- Massachusetts General Hospital / Project lead Establishing and running “patient wait reminder” system at MGH radiology, to alert

MGH staff about patients waiting for more than 20 minutes.

Educational Material for Patients and the Lay Community: Patient educational material 2014 Radiation dose leaflet Co-author Patient education pamphlet

describing the effects of radiation doses from radiology exams

Report of Scholarship

Peer-Reviewed Scholarship in print or other media: Research Investigations (** indicates my mentees, who were first authors)

1. Pianykh OS, Tyler JM, Waggenspack WN. "Improved Monte-Carlo Form Factor Integration", Computers & Graphics, Volume 22, Issue 6, December 1998, pp. 723-734

2. Pianykh OS, Tyler JM, Sharman R, “Autoregressive Models for Compressing Similar Data”.

SPIE, Aerospace/Defense Sensing and Controls, 1998, Vol. 3389, pp. 92-103

3. Sharman R, Tyler JM, Pianykh OS, Szu Harold H., “Compensating for wavelet sensitivity to translation in image registration applications”, SPIE series, 1999, vol. 3723, pp. 466-469

4. Tashakkori R, Tyler JM, Pianykh OS, “Construction of optimal wavelet basis for medical

images”, The International Society for Optical Engineering, Volume 3723, 1999, pp. 163-171

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5. Pianykh OS, Tyler JM, Sharman R. "Nearly-Lossless Autoregressive Image Compression". Pattern Recognition Letters, Volume 20, Issue 2, February 1999, pp. 221-228

6. Sharman R, Tyler JM, Pianykh OS, “A Fast and Accurate Method To Register Medical

Images Using Wavelet Modulus Maxima”, Pattern Recognition Letters, Volume 21, Issue 6-7, June 2000, pp. 447-462

7. Tashakkori R, Tyler JM, Pianykh OS, “Prediction of medical images using wavelets”, SPIE - The International Society for Optical Engineering, Volume 4056, 2000, pp. 332-340

8. Qi X, Tyler JM, Pianykh OS, “Diagnostically lossless medical image compression via

wavelet-based background noise removal”, SPIE - The International Society for Optical Engineering, Volume 4056, 2000, pp. 470-480

9. Qi X, Tyler JM, Pianykh OS, “Integer wavelet transformations with predictive coding improves

3-D similar image set compression”, SPIE - The International Society for Optical Engineering, Volume 4391, 2001, pp. 238-249

10. Pianykh OS, Tyler JM. "Compression Ratio Boundaries For Predictive Signal Compression",

IEEE Transactions on Image Processing, Volume 10, Issue 2, February 2001, pp. 323-326

11. Pianykh OS, “DICOM and PACS in Telemedicine”. “Klinicheskaya Vizualizatsia” (Clinical Visualization), #19, December 2001(in Russian) pp. 23-29

12. Qi X, Tyler JM, Pianykh OS, Tashakkori R, “Predictive Coding Integer-Based Wavelet Transform Approach to Diagnostically Lossless Medical Image Volume Compression” International Institute of Informatics and Systemics in Systemics, Cybernetics and Informatics; Image Acoustic Speech and Signal Processing, Part 1, Volume 6, 2001, pp. 375-379

13. Pianykh OS, Introduction to Medical Data Representation and Processing on Internet”.

“Vizualizatsia v Medicine” (Visualization in Medicine), #3, March 2002 (in Russian) pp.130-137

14. Pianykh OS, “Some problems in the creation of the telemedicine network of Russia and

possible ways of their solution”, Vestnnik Rentgenol Radiol. 2004 Nov-Dec; (6): (in Russian) pp. 50-57

15. Brener NE, Iyengar SS, Pianykh OS, “A conclusive methodology for rating OCR

performance”, Journal of the American Society for Information Science and Technology, Volume 56, Issue 12, October 2005, pp. 1274-1287

16. Milchenko MV, Pianykh OS, Tyler JM, “The Fast Automatic Algorithm for Correction of MR

Bias Field”, Journal of Magnetic Resonance Imaging, Volume 24, Issue 4, October 2006, pp. 891-900

17. Pianykh OS. “Analytically tractable case of fuzzy c-means clustering”, Pattern Recognition

Journal, Elsevier, Volume 39, Issue 1, January 2006, pp. 35-46

18. Kornienko V., Pronin I., Pianykh OS, Fadeeva L., “Brain tissue analysis with CT perfusion”, Medical Visualization, February 2007, (in Russian) pp 70-81

19. Litmanovich D, Boiselle PM, Bankier AA, Kataoka ML, Pianykh OS, Raptopoulos V., “Dose

reduction in computed tomographic angiography of pregnant patients with suspected acute

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pulmonary embolism”, Journal of Computer Assisted Tomography, Volume 33, Issue 6, November 2009, pp. 961-966

20. Tognolini A, Schor-Bardach R, Pianykh OS, Wilcox CJ, Raptopoulos V, Goldberg SN. “Body

tumor CT perfusion protocols: Optimization of acquisition scan parameters in a rat tumor model”, Radiology, Volume 251, Issue 3, June 2009, pp. 712-720

21. Pianykh OS, “Perfusion linearity and its applications in perfusion algorithm analysis”,

Computerized Medical Imaging and Graphics, Volume 36, Issue 3, April 2012, pp. 204-214

22. Pianykh OS, “Finitely-supported L2-optimal kernels for digital signal interpolation”, IEEE Transactions on Signal Processing, Volume 60, Issue 1, January 2012, Article number 6035801, pp. 494-498

23. Pianykh OS, “Digital perfusion phantoms for visual perfusion validation”, American Journal of

Roentgenology, Volume 199, Issue 3, September 2012, pp. 627-634

24. Vadvala H, Mayrhofer T, Pianykh O, Kalra M, Hoffmann U, Ghoshhajra B, “Coronary CTA using scout-based automated tube potential and current selection algorithm, with breast displacement results in lower radiation exposure in females compared to males”, Cardiovascular Diagnosis and Therapy, Vo. 4, Num 6., December 2014, pp. 470-480

25. Andrabi Y, Pianykh O, Agrawal M, Kambadakone A, Blake MA, Sahani DV, “Radiation

dose consideration in kidney stone CT examinations: integration of iterative reconstruction algorithms with routine clinical practice”, American Journal of Roentgenology, Volume 204, Issue 5, 1 May 2015, pp. 1055-1063

26. Oglevee C**, Pianykh O, “Losing Images in Digital Radiology: More than You Think”, Journal

of Digital Imaging, Volume 28, Issue 3, 26 June 2015, pp. 264-271

27. Pianykh OS, Rosenthal DI, “Can We Predict Patient Wait Time?”, Journal of the American College of Radiology, 2015, Vol 12, Issue 10, pp.1058-1066

28. States M**, Pianykh OS, "How Secure is Your Radiology Department? Mapping Digital

Radiology Adoption and Security Worldwide", American Journal of Roentgenology 2016 Apr;206(4), pp. 797-804.

29. Ai J, Oglevee C**, Pianykh O, “Determining Waiting Room Occupancy at an Outpatient Clinic

using Simulated Observations and Probability-Duration Curve”, Journal of the American College of Radiology, 2016 Jun;13(6): pp. 620-627

30. Jaworsky C**, Pianykh O, Oglevee C., “Patient Feedback on Waiting Time Displays.”, Am J Med Qual. 2017 Jan/Feb;32(1):108.

31. Pianykh OS, Jaworsky C, Shore MT, Rosenthal DI., “Improving Radiology Workflow with Automated Examination Tracking and Alerts”, J Am Coll Radiol. 2017 Jul;14(7) pp. 937-943

32. Liu C**, Harvey HB, Jaworsky C, Shore MT, Guerrier CE, Pianykh O., “Text Message Reminders Reduce Outpatient Radiology No-Shows But Do Not Improve Arrival Punctuality”, J Am Coll Radiol. 2017 Aug;14(8) pp. 1049-1054.

33. Harvey HB, Liu C, Ai J, Jaworsky C, Guerrier CE, Flores E, Pianykh O., “Predicting No-Shows in Radiology Using Regression Modeling of Data Available in the Electronic Medical Record”, J Am Coll Radiol. 2017 Oct;14(10) pp. 1303-1309

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34. Glover M 4th, Daye D, Khalilzadeh O, Pianykh O, Rosenthal DI, Brink JA, Flores EJ..

“Socioeconomic and Demographic Predictors of Missed Opportunities to Provide Advanced Imaging Services”, J Am Coll Radiol. 2017 Nov;14(11) pp. 1403-141

35. Curtis C**, Liu C, Bollerman TJ, Pianykh OS., “Machine Learning for Predicting Patient Wait Times and Appointment Delays”, J Am Coll Radiol. 2018 Oct 24. Pp.: S1546-1440(17)31014-1

36. Choy G., Khalilzadeh O., Michalski M., Do S., Samir A., Pianykh O., Geis J R, Pandharipande P.V. ; Brink J.A.; Dreyer K., “Current Applications and Future Impact of Machine Learning in Radiology”, Radiology, Aug 2018; Vol 288(2), pp.318-328.

37. Pianykh O., Pospelova K., Kamboj N., “Modeling Human Perception of Image Quality”, Journal of Digital Imaging, Jul 2018, pp.1-8

Other peer-reviewed scholarship

38. Sharman R, Tyler JM, Pianykh OS. “Wavelet-based registration and compression of sets of images”, Proceedings of SPIE - The International Society for Optical Engineering Volume 3078, 1997, pp. 497-505

39. Sharman R, Tyler JM, Pianykh OS. “Registration and set compression of images using

Wavelet Modulus Maxima on massively parallel machines”, Proceedings of SPIE - The International Society for Optical Engineering Volume 3164, 1997, pp. 221-231

40. Pianykh OS, Tyler JM, Sharman R, “Parallel methods for similar image compression and

classification with common models”, Proceedings of SPIE - The International Society for Optical Engineering Volume 3452, 1998, pp. 127-133

41. Sharman R, Tyler JM, Pianykh OS, “Registration and Restoration of Objects in Images

Subjected to Shear Using Wavelets”, Proceedings Of The Society Of Photo-Optical Instrumentation Engineers (SPIE), 1998, Volume: 3391, pp: 37-45

42. Pianykh OS, Tyler JM, Sharman R, “Compressing data sets of similar images with autoregressive models”, Proceedings of The Society Of Photo-Optical Instrumentation Engineers (SPIE), 1998, Volume: 3389 Pages: 49-56

43. Tashakkori R, Pianykh OS, Tyler JM, Qi X, “Decorrelating medical image sets with lifting: A new approach”, Proceedings of the International Conference on Imaging Science, Systems and Technology, CISST'04 2004, pp. 336-341

44. Tashakkori R, Tyler JM, Pianykh OS, “Medical image optimal wavelets”, Proceedings of the

2005 International Conference on Computer Vision, VISION'05, 2005, pp. 205-211

45. Trifas M, Pianykh OS, Tyler JM. "Medical Image Enhancement", WMSCI 2005 - The 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings, Volume 5, 2005, pp. 300-305

46. Trifas M, Pianykh OS, Tyler JM. "Medical Image Enhancement", Proceedings of the 2005

International Conference on Computer Vision, VISION'05 2005, pp. 212-218

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47. Trifas M, Tyler JM., Pianykh OS, “Contrast enhancement of medical images using multiscale decomposition”, Proceedings of SPIE - The International Society for Optical Engineering, Volume 6057, 2006, Article number 60570I, pp. 1-12

48. Trifas M, Tyler JM., Pianykh OS, “Applying multiresolution methods to medical image enhancement”, Proceedings of the Annual Southeast Conference, Volume 2006, 2006, pp. 254-259

Non-peer reviewed scholarship in print or other media: Proceedings of meetings or other non-peer reviewed scholarship

1. Pianykh O., Miltchenko M., Castaneda-Zuniga W.R., “Efficient and reliable web-based DICOM workstation”, Radiology (Supplement), Vol. 221 2001, pp. 118-119

2. Trifas M., Tyler J.M., Pianykh OS, “Multiscale contrast enhancement for medical images”, Electronic Imaging, Vol. 16.2, 2005, p. 2.

3. Ternovoy S., Sinitsyn V., Pianykh O., Ustuzhanin D., “Teleradiology in Russia: current state” (in Russian), Physician Journal, Vol. 3, 2008, pp 3-6

4. Pianykh OS, “Perfusion Linearity and Its Applications”, http://arxiv.org/abs/1006.0168, May 2010

5. Pianykh OS, “L2-optimal image interpolation and its applications to medical imaging”, http://arxiv.org/abs/1006.2368, May 2010

6. Pianykh OS, “Bilateral filters: what they can and cannot do”, http://arxiv.org/abs/1007.1016, July 2010

7. Pianykh OS, “Improving digital signal interpolation: L2-optimal kernels with kernel-invariant interpolation speed”, https://arxiv.org/abs/1104.4295, April 2011

Books/textbooks for the medical or scientific community

1. Pianykh OS (author), “Digital Imaging and Communications in Medicine (DICOM)”, Springer-Verlag, First edition, 2008. ISBN: 9783540745709 This was the first comprehensive book published on DICOM standard. The idea to write it originated from my daily radiology projects, and repetitive problems radiologists and clinical staff kept running into. Therefore the book was developed to provide the most essential DICOM training, and more in-depth guidance on how DICOM and digital medicine are working. This made the book a great success, it was marked with 5/5 stars with major book sellers, and went into many libraries, major medical schools included. It is also used as a textbook in many digital imaging classes, including the one I teach at Harvard. See Journal of Nuclear Medicine review of this book here: http://jnm.snmjournals.org/content/50/8/1384.full.pdf

2. Pianykh OS (author), “Digital Imaging and Communications in Medicine (DICOM)”, Springer-Verlag, Second edition (largely rewritten), 2011, ISBN 978-3-642-10849-5 This was the second rewrite of my DICOM book (415 pages), driven by more requests from the readers of the first edition, and by recent updates in the DICOM standard. In essence, the first edition was completely revised and rewritten, to provide the readers with more up-to-date

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DICOM knowledge to be used in their clinical projects. The book is frequently cited in DICOM publications, and used as a textbook for digital radiology. Pianykh OS (author), “Image Quality in Medicine”, Springer-Verlag, 2014, ISBN: 978-3-319-01759-4 This book was designed to address the most critical questions of digital image quality in medicine. Even after a perfect digital image is acquired, it can still lose its original diagnostic quality if manipulated/viewed incorrectly. Knowing how to preserve and even improve diagnostic content of a digital image has therefore become the main subject of this book. The book was well-met and introduced a new series of similar books, “Understanding medical informatics”, which I am currently working on.

Professional educational materials or reports, in print or other media:

1. Medical Informatics (graduate course, syllabus, set of 14 online lectures hosted at Harvard), taught at Harvard Extension School, 2012-2016 https://canvas.harvard.edu/courses/8247/assignments/syllabus

2. Big Data in Healthcare Applications (graduate course, syllabus, set of 14 online lectures hosted at Harvard), taught at Harvard Extension School, 2017-present https://www.extension.harvard.edu/faculty-directory/oleg-pianykh

3. Online teleradiology portal (web site, medical image database), used for remote image interpretation www.itelerad.com The portal was used to collect imaging cases on West Nile virus outbreaks.

4. Set of 10 educational interviews/reports, posted at www.auntminnie.com (radiology teaching/news web portal). I was asked to give these interviews during RSNA and ECR conferences, and they cover different aspects of current radiology problems

5. Set of Digital Perfusion Phantoms (test image set in DICOM format) for validating quality of perfusion analysis – used by clinical scientists and perfusion software vendors www.droleg.com/Docs/DPP.zip , 2012-present

6. Data Analytics in Operations Management (online video, https://www.youtube.com/watch?v=WpnPQ_4WzMg) – introduction into radiology operational analysis, recorded foe European Society of Imaging Informatics as a part of their eLearning series, 2018

Clinical Guidelines and Reports:

1. DICOM Standard Part 18: Web Access to DICOM Persistent Objects (WADO) http://dicom.nema.org/medical/dicom/current/output/pdf/part18.pdf I was actively participating in the development of this standard as a member of DICOM WADO workgroup. After several years of work we produced a document which has become an official international standard, enabling DICOM applications to communicate their data online.

2. Partners radiology utilization report: A set of guidelines for optimizing radiology workflow and utilization for Partners Healthcare. The report guidelines were based on data analysis, performed based on one year of Partners operational data (2.3 million records). The results were presented and distributed to MGH and Brigham radiology departments, and are being used to implement more efficient workflow patterns.

Thesis:

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PhD Thesis:

Pianykh OS, “Lossless Set Compression of Correlated Information”, Department of Computer Science, Louisiana State University and Agricultural & Mechanical College, 1998

Abstracts, Poster Presentations and Exhibits Presented at Professional Meetings: Selected most recent abstracts:

1. Jaworsky C, Xie X, Pianykh OS, Zurkiya O, Salazar G, Mueller P, Rosenthal D, Liu R, “Large volume data analysis to identify opportunities to improve efficiencies in the interventional radiology pre-procedure and recovery area”, Society of Interventional Radiology (SIR), 2016

2. Pianykh OS., “Radiology Information Security Worldwide”, RSNA 2016

3. Flores E., Bollerman TJ, Jaworsky C, Jing A., Pianykh OS, “The Patient Engagement for Equity in Radiology (PEER) project: Big-Data Driven Predictive Analytics Model to Identify Social Determinants of Health Negatively Impacting Access to Radiology Care and Develop Culturally Sensitive Healthcare Solution”, RSNA 2016

4. Pianykh OS, “Radiology and Cyber-Security: Recent Trends and Changes”, RSNA 2017

5. Guitron S., Siping, You, Pianykh OS, “Radiology Facility Utilization Thresholds”, SIIM 2018

6. Pianykh O.S., “Machine learning in radiology operations management”, European Congress of Radiology, Vienna, Austria, 2018

7. Guitron S., Parke D., Pianykh OS, “Improving Radiology Appointment Wait Time Prediction with Machine Learning”, RSNA 2018

8. Pianykh O.S., Parke D., “Machine learning in radiology management: predicting ED workflow overloads”, European Congress of Radiology, Vienna, Austria, 2019

Narrative Report In the 1990s, medicine appeared to be a very unusual field for a mathematician, but only at the first glance. The recent and on-going transition to all digital imaging presented unlimited possibilities for advanced analysis. I always wanted to use mathematics to help improve lives, and therefore my graduate work focused on medical imaging. I joined a radiology department immediately after graduation and have stayed in radiology ever since.

My first major work was done in the field of medical image postprocessing and analysis. I began by developing several novel imaging algorithms to better visualize 3D data (such as CT), to improve diagnostic image quality (such as my publications on optimal image interpolation, based of variation minimization theory), to remove noise from images and to compress imaging data. This extensive research, reinforced by constant interactions with radiologists around me, helped me become an expert in many practical areas of radiology imaging. It also helped me recognize the repetitiveness of

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many radiology problems, so I decided to structure and to share my expertise. This is how my first book on DICOM was written and published in 2008.

The book was an instant success (second edition released in 2011) and led to a number of new developments in my career. First, I became much more involved with radiology standardization efforts, joining DICOM workgroups and similar initiatives. I always wanted this to be much more than technical work, so I always used this as an opportunity to merge best imaging science with best imaging practices. This led to several successes, and I am currently recognized as an expert in this field, involved in many DICOM-related projects.

Second, my initial achievements in applied science led to more administrative responsibilities, which in turn led to more research. For instance, in 2005 I was appointed CIO of Louisiana Healthcare Network (7 hospitals statewide), and two month later I saw this network completely annihilated by the hurricane Katrina. We were able to rebuild our digital radiology imaging network using simple telephone lines, because we used the advanced image compression algorithms that I had implemented previously. This is how theoretical math has met applied radiology – the experience that made me rethink my entire career path. Although I started as a theoretical scientist, I realized that all medical projects should be driven by the importance of real problems, and algorithms should be implemented to make a real-life difference.

Therefore, I started broadening my work. For example, I saw severe shortcomings of perfusion algorithms, and I have developed, published, and implemented significant research in robust perfusion analysis. This also allowed me to create a unified theory for all perfusion processing (“perfusion linearity”), which, combined with my DICOM experience, led me to the discovery of Digital Perfusion Phantoms (DPPs). Based on convolution theory of blood flow, DPPs represent mathematically-generated sequences of images, optimal for detecting errors in any perfusion algorithm. Similarly, I have developed and published several CT image techniques for removing noise from images, aimed at CT dose reduction. My algorithm was able to achieve up to 7X reduction in CT radiation dose, which was much better than vendor-specific “raw data” methods. Through my work with DICOM committees and vendors I realized the hidden vulnerabilities of radiology networks – and I developed, published and presented a systematic approach to assessing radiology security worldwide. Thus, my projects branched into multiple areas, but they were serving the same single purpose: I have been always using applied science to optimize radiology performance and quality.

Following this approach, I have also made several significant contributions to healthcare operations research. Modern radiology workflow has reached a level of complexity that cannot be managed manually, and the price of a failure is extremely high. At the same time, tiny bits of operational data, just like individual pixels in images, mean nothing separately, but form powerful patterns when combined. With great support from my MGH colleagues, I was able to establish a new research area – operational pattern analysis – which combines classical operational theory (queueing theory, linear programming, optimal scheduling) with modern data science (machine learning, artificial intelligence), and applies it to improve the real radiology workflow. This work produced several successful projects (such as patient wait prediction algorithms), and I was able to start Medical Analytics Group – a group of applied scientists within MGH, working on operational patterns and workflow optimization. We have published and implemented several significant algorithms reducing patient wait time and radiology no-shows, optimizing scheduling, exploring utilization patterns. Although different from my initial imaging work, this area of my current research is driven by the same goal – using math and data sciences to solve the most important problems.

Finally, the 20+ years I spent in the field made me appreciate the importance of teaching. Therefore, in 2011 I launched my first full-semester “Medical Analytics” course, offered as a part of Harvard graduate program. Since then, I have been teaching on an annual basis, locally and internationally, and I have been also presenting the results of my work as an invited/keynote speaker. In addition to

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this, I have been constantly working on implementing all my algorithms into the real clinical workflow. This implementation work consumed a considerable amount of my time, yet I was able to publish my work on a regular basis, in the most recognized journals in my field.

Looking back at all my work, I realize that my main goal – bringing applied mathematics to medicine – has proven its value, and produced many important, vibrant, fully-implemented results. The recognition of this work helps me do even more.