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Imaging Biomarkers Automated Structured Assembly Pipeline (IB-ASAP)
C. Pérez-Castillo1*#, A. Pomar-Nadal, 2*#, G. García-Martí3*#, A. Alberich-Bayarri4*#, R. Sanz-Requena5*#, L. Martí-Bonmatí6*#+.
*Department of Radiology, Hospital Quirón Valencia, Avda. Blasco Ibáñez, 14, 46010 Valencia, Spain. #Consorcio cvREMOD, Programa Cenit-e 2009-2012, Ministerio de Ciencia e Innovación, Madrid, Spain
[email protected] [email protected]
[email protected] [email protected]
[email protected] +Department of Medicine, University of Valencia, Valencia, Spain.
Abstract Purpose: To include imaging biomarkers in the radiological workflow, providing additional quantitative information to radiologists in order to friendly obtain more accurate diagnosis. Materials and Methods: Imaging biomarkers define objective characteristics that are related to normal biological processes, diseases, or the response to treatment. Their implementation is changing the concept and workflow of radiology today. By applying new modeling techniques and computational procedures to medical images, a set of quantitative parameters is obtained. This quantitative information provides accurate and reproducible measures of various processes in individual patients. Their potential to display and measure a wide range of biological and physiological situations, and their non invasive nature, makes imaging biomarkers one of the most active research fields. An automated post-processing platform was developed in order to implement imaging biomarkers in the radiological workflow. The post-processing algorithms quantify biochemical, cellular and structural levels that indicate the presence and magnitude of different conditions and diseases. For instance: indicators of neovascularization in cancer processes (such as prostate cancer, hepatic focal lesions, breast cancer, brain tumors), trabecular bone structure studies on osteoporosis, studies of cartilage degeneration in osteoarthritis, studies of connectivity, volume and morphometry in neurodegenerative diseases and studies of morphology and function of the cardiovascular system. The platform stores the results in a database and generates structured reports that are sent to the PACS. These post-processing reports provide very useful quantitative information to the radiologist for the diagnosis. The platform software is implemented in Java programming language using the open-source NetBeans IDE. Post-processing algorithms are programmed in Matlab and results are stored in a MySQL database. The only hardware requirement is a workstation connected to the hospital network.
Results: The developments have provided an innovative service that follows an organized process, as a proper technological support to leverage the usability and ease the development and implementation of quantitative imaging. In addition, the software is fully automated, vendor independent and compatible with DICOM standards. Conclusion: Imaging biomarkers help establishing the presence of a lesion before it becomes evident, assess the predisposition to suffer it, measure its biological situation, define its progress and evaluate treatment effects. The platform quickly incorporates all these advantages into the radiological workflow.
I. INTRODUCTION
The workspace of radiologists and medical imaging specialists has changed with the development and implementation of digital imaging. The viewing, processing and properties extraction from medical images are some of the parcels of medicine where innovation is most visible.
In just a few decades, the use of magnetic resonance imaging (MRI) scanners has exponentially grown. Clinicians can demand MRI scans to help diagnosing multiple sclerosis, brain tumours, tendonitis, cancer and strokes, to name just a few. An MRI scan is one of the best methods for the in-vivo examination of the human body without opening it. Modern MRI equipments provide non invasive, highly accurate anatomic images and have an excellent spatial resolution that allows to visualize internal structures in detail and to define their main properties. The high quality of MRI images help radiologists to classify diseases by analyzing morphological, structural and physical properties.
On the other hand, the current high capacity of computers can be exploited to improve the quality and to extract information from medical images by means of advanced post-processing algorithms. As a result of the synergy between digital imaging and computer processing, new imaging
biomarkers are being developed to provide quantitative information that cannot be a priori detected or measured by the visualization of the original medical images [1].
Imaging biomarkers are objective characteristics extracted from medical images that act as indicators of normal biological processes, diseases or responses to therapeutic interventions [2]. This quantitative information is obtained before a lesion or biological process becomes evident in the radiological observation, by analyzing properties and multivariate combination of medical images and data. This process requires careful monitoring of acquisition, normalization of data and image preparation, data extraction, analysis and visualization of results. Their enormous potential to display and measure a wide range of biological and physiological situations, and their non invasive nature, makes imaging biomarkers one of the most active research fields. Some examples of imaging biomarkers pictures are shown in Fig. 1.
Fig. 1 Examples of pictures extracted from imaging biomarkers
quantification. From left to right: tractography, morphometry analysis, aortic flow, cardiac study, knee pharmacokinetics and jaw mechanical quantification.
The use of imaging biomarkers opens the field of medical
imaging to other disciplines such as engineering and physics. This multidisciplinary interaction, included in the area of
Biomedical Engineering Knowledge, is defining new radiological workflows.
The adequate technological support required to integrate the use of imaging biomarkers in a radiology service is described in this work. The presented Biomarkers Workflow implements the entire methodology, from the image acquisition to the generation and storage of post-processing reports, making the whole process much more efficient and simple.
II. MATERIALS AND METHODS
The radiological workflow and the image-based clinical practice are tightly related to the PACS (Picture Achiving and Communication System) and the DICOM (Digital Imaging and Communications in Medicine) standard.
A PACS is a hospital computer system that manages acquisition, transmission, storage, distribution, display and interpretation of medical images.
Medical digital images format is defined by DICOM standard [3], which facilitates the exchange of clinical cases and studies between different organizations. A DICOM file encapsulates the image within a structure that includes a data header, which contains relevant information such as patient data and parameters of image acquisition. This information is indexed by pairs of numbers called tags, to be managed and operated by hospital information systems (see Fig. 2).
Fig. 2 Dicom header example
Besides defining the file format, DICOM includes a
network communication protocol that uses TCP/IP (Transfer Control Protocol / Internet Protocol). Thus, DICOM files can be exchanged between two DICOM-compatible entities. This data exchange is managed by several DICOM services: • Dicom Store: It is used to send images and structured
reports to a PACS or workstation. • Storage Commitment: It is used to confirm that an image
has been permanently stored by a device. The user (modality, workstation, etc.) uses the confirmation of the storage station (service provider) to ensure that data exchange was properly done.
• Query/Retrieve: It allows a workstation to search for images in a PACS and retrieve them.
• Other services: Dicom Worklist, Modality Performed Procedure Step, Dicom Print, etc.
In order to include imaging biomarkers in the radiological
workflow, a post-processing platform [4] has been completely integrated in the hospital network. It receives DICOM images from the hospital PACS or any other storage media, and sends complete reports containing the post-processing results to the PACS.
Fig. 3 Radiological workflow and biomarkers improvement
The radiological workflow and the way the imaging
biomarkers complement it, providing additional quantitative information to the radiologist for the diagnosis is observed in Fig. 3 [5].
The IB-ASAP data pipeline is shown in Fig. 4:
Fig. 4 IB-ASAP data pipeline
IB-ASAP consists on several steps that follow an organized
process: data reception, medical images storage, notifications, post-processing algorithms execution and post-processing results management.
A. Data Reception Firstly, input DICOM images reach the platform DICOM
node in three ways: from external devices (CD, DVD, USB storage), from the PACS or other DICOM storage stations by query/retrieve service via the network, or directly from imaging devices (MRI, Computerized Tomography).
B. Medical Images Storage
While being received by the platform DICOM node, medical images are automatically stored in a directory tree structure (according to information extracted from their DICOM headers) and optionally transformed from DICOM to other formats that facilitate computer processing (ANALYZE or NIFTI). For each image sequence, a .txt file is created inside the series folder containing the following information: number of images, patient position, number of temporal positions, echo times, diffusion B-Values and further header information.
C. Notifications
The platform sends an e-mail alert to the users to indicate that a new dataset has arrived to the pipeline, and then allows selecting and launching the proper post-processing algorithm depending on the study type extracted from DICOM headers.
D. Post-processing Algorithms Execution
Post-processing algorithms quantify biochemical, cellular and structural levels of patients and aid early diagnosis, assessment of prognosis, definition of therapeutic options and evaluation of treatment effectiveness.
Some examples of post-processing studies are: Morphometry [6] and volumetry analysis, functional studies, finite element mechanical simulations [7], spectroscopy profile, diffusion [8, 9], perfusion, quantification of water-fat-iron, flow dynamics quantification [10], fiber tracking studies, pharmacokinetic models [11], image correlation with genetic profile, studies of texture and physical properties and multimodal studies.
After start running, post-processing algorithms prompt messages that ask for user interaction when required (processes are automated to require minimal user interaction). The interaction is centralized and managed in the post-processing platform interface.
E. Post-Processing Results Management
Final post-processing results (i.e. imaging biomarkers quantification), including multi-parametric images and data, follow two paths:
1) DICOM Structured Reports: Data is embodied in structured reports [12] that are sent to the PACS, providing very useful complementary quantitative information to the radiologist for the diagnosis. These reports are automatically created by using predesigned HTML (HyperText Markup Language) templates, customized for every post-processing workflow. After being filled with the required data, the HTML templates are transformed to JPG format and embedded in a file with a DICOM header that has been
previously extracted out from one of the patient’s study images. By this procedure, it can be ensured that the post-processing report will be appended to the correct patient and study in the PACS. Then the platform sends the dicomized report to the PACS [13, 14] by using the Dicom Store service, and waits for a Dicom Storage Commitment to confirm that it has been permanently stored.
2) Post-Processing Database: Data is also stored in a MySQL database for further statistical analysis and normality patterns calculation, as well as automatic knowledge extraction by data mining procedures. This process also allows regenerating the report anytime in the future by using different HTML templates.
The following software has been used to implement the
biomarkers workflow: The post-processing platform software is implemented in Java programming language using the Netbeans IDE 7.0.1. The use of Java ensures compatibility with several operative systems by installing the Java Virtual Machine. A screenshot of the post-processing platform interface is shown in Fig. 5.
Fig. 5 Screenshot of the post-processing platform interface
Post-processing algorithms are programmed using Matlab
(The MathWorks Inc., Natick, Massachusetts, USA). However, any other programming language or post-processing suite for the biomarkers quantification can be used, due to the modularity of the biomarkers workflow (i.e. input and output for each workflow is centralized in the database). HTML templates were created with Dreamweaver CS5 suite. The database for storing all the information is programmed using MySQL and PhpMyAdmin. DICOM protocol services and communications are managed with dcm4che2 libraries.
The only hardware requirement is a workstation connected to the hospital network.
III. RESULTS
The developments have provided an innovative service that follows an organized process, implemented in the proper technological support. The post-processing platform leverages the usability and eases the development and implementation of quantitative imaging in the radiological workflow. As a result, IB-ASAP exploits the possibilities offered by technological advances and multidisciplinary collaboration.
An example of the resulting new workflow, in this case for the study of prostate carcinoma, is described below.
So far the techniques used in the study of prostate cancer do not allow in many cases to detect the disease, so there is a need of more accurate diagnostic tools. Useful imaging techniques for the study of patients with prostate cancer, such as ultrasound and conventional MRI, usually fail to detect the disease in its early stages [15, 16]. On ultrasound, the majority of tumors (> 50%) are isoechoic and central gland lesions are not seen, showing a low sensitivity (39-52%). On MRI, the image shows the tumor with sensitivity that does not exceed 67-72% (see Fig. 6). Although widely used for the study of the prostate, MRI conventional sequences have a low accuracy in detecting malignant tumors, since the findings on MRI may mimic or be similar to those of benign prostatic hyperplasia, prostatitis or post-biopsy changes. Therefore MRI conventional sequences have a limited usefulness of as a technique to diagnose cancer.
Fig. 6 58 years old patient prostate adenocarcinoma
In order to improve the diagnosis and monitoring of
malignant tumors, new MRI acquisition techniques have been developed and added to the standard protocol for prostate MRI. Three examples of these new techniques are the dynamic pharmacokinetic modeling, the study of molecular diffusion of water and the clinical evaluation by spectroscopy. The vast amount of images and data generated by these techniques cannot be processed directly, but requires the application of medical image post-processing algorithms to draw relevant conclusions by the quantification of lesion characteristics (imaging biomarkers).
The output of the post-processing algorithms is included in three reports: perfusion, diffusion and spectroscopy. The perfusion report overlays a vascular permeability parametric map on anatomical slices, highlighting differences on the
diffusion of the blood of arterial vessels through the capillaries of the prostate tissue. The diffusion report quantifies intracellular and extracellular mobility and diffusion of the protons of water molecules within the prostatic tissue, showing the areas with increased cellularity [17]. The spectroscopy report gives a biochemical and metabolic profile of the gland, highlighting increased choline and regional reduction in the levels of citrate, which are indicators of tumor presence [18]. An example of perfusion quantification image is shown in Fig. 7.
Fig. 7 Parametric map of perfusion quantification. Red colour indicates more
vascular permeability in prostate tissue
Multivariate combination of the results of these three
techniques (diffusion MRI, perfusion MRI and MR spectroscopy) (see Fig. 8) on a common platform represents a significant increase in sensitivity and specificity for the diagnosis and monitoring of prostate cancer, thereby improving the diagnosis and monitoring of the lesion.
Fig. 8 Examples of perfusion, diffusion and spectroscopy reports
IV. CONCLUSIONS
Imaging biomarkers are powerful tools that quantify bio-physiological properties not yet perceptible in the medical image, provide high sensitivity and specificity, and are non invasive to the patient.
The proposed Assembly Pipeline workflow facilitates the fast inclusion of imaging biomarkers in clinical practice, exploiting the possibilities offered by technological advances and multidisciplinary collaboration. This new process
approaches radiological workflow to the new personalized medicine paradigm, as it allows extracting physical, chemical and biological properties from individual patients. The resulting quantification reports contain additional information that complements traditional radiological diagnosis, while improving its accuracy and the evaluation of the effectiveness of treatments.
ACKNOWLEDGMENT
Supported by grants from SERAM (Sociedad Española de Radiología Médica). The authors also thank the Radiology Department of Hospital Quirón Valencia for their help and continuous support with image acquisition and for the clinical validation.
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