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Application of Pattern Recognition Framework for Quantification of Parkinson’s Disease in DAT SPECT Imaging Saurabh Jain, Yousef Salimpour, Laurent Younes, Gwenn Smith, Zoltan Mari, Vesna Sossi, Arman Rahmim Abstract–Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected parkinsonian syndromes. Visual classification or quantitative analysis of mean regional uptake has been performed in the past. Our objective is to enable further enhanced clinical utility in the diagnosis as well as tracking of progression in Parkinson’s disease via quantification based on pattern recognition. We developed and implemented two such frameworks: first, we utilized shape/texture metrics that did require registration to a common structure/template; e.g. 3D moment-invariants, Haralick texture features, and multiple others. We also used a surface registration algorithm, which falls under the broad class of Large Deformation Diffeomorphic Metric Mapping (LDDMM). In this latter framework, we obtain a common coordinate system for the entire population based on MR images, and compare SPECT intensities across subjects in this common coordinate system. This method has the advantage of estimating population-based templates for each structure individually rather than using a predetermined collective atlas for all regions, as is customary. In this common coordinate system, we then used Principal Component Analysis (PCA) on intensities to obtain sub- regions (set of voxels inside the structure of interest) with highest variance in SPECT intensities across subjects. We show that the healthy and diseased populations can be subsequently distinguished. Via these methods, we also aimed to assess correlations with different clinical measures (e.g. UPDRS score, disease duration). In addition to enabling enhanced diagnostic task performance, these methods have considerable potential as biomarkers of PD progression. I. INTRODUCTION Imaging of the dopaminergic system with SPECT has become widespread in Europe and has entered a new active phase in the US since 123 I-ioflupane-dopamine transporter (DAT) SPECT was approved by the FDA in 2011. Visual interpretation is the common assessment approach [1-3]. However, more objective assessment can be performed using quantitative analysis [4] involving manual or automated ROI drawing and analysis [5, 6]. Our proposed approach is based on the observation that Manuscript received December 06, 2014. S. Jain and L. Younes are with the Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA 21287. Y. Salimpour and Z. Mari are with the Department of Neurology, Johns Hopkins University, Baltimore, MD, USA 21287. G. Smith is with the Department of Psychiatry, Johns Hopkins University, Baltimore, MD, USA 21287. V. Sossi is with the Department of Physics & Astronomy, University of British Columbia, BC, Canada V6T1Z1. A. Rahmim is with the Departments of Radiology, and Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD, USA 21287. SPECT and PET images convey important information at the voxel level, whereas commonly used ROI-averaging vastly oversimplifies the available spatial uptake information. We propose to explore a novel quantification paradigm applied to SPECT neurochemical imaging that utilizes pattern recognition to quantify uptake alterations with disease, expected to detect subtle regional DAT losses: this is especially important as DAT binding can be a marker of PD at the presymptomatic stage [7, 8] and the proposed approach can enhance abilities to discriminate among different populations. Furthermore, we seek to move beyond classification tasks in clinical DAT SPECT imaging [9], to also arrive at enhanced markers of progression. This is a significant step, and is for instance consistent with the aims of the Parkinson’s Progressive Marker Initiative (PPMI) to identity biomarkers of PD progression, a critical step in the development of novel and enhanced treatments for PD [10]. We have in the past pursued pattern recognition techniques, including shape and texture analysis in the context of quantitative brain PET imaging, in studies of PD [11-13] and neuroinflammation [14]. These techniques have the advantage of not requiring normalization/registration of all regions of interest (ROIs) to a common structure. In the present work, we turn our attention to DAT SPECT imaging, given its increasingly popular clinical usage. Furthermore, we have evidence that application of shape and texture metrics can appropriately extend from the higher resolution spectrum of PET imaging to the domain of SPECT imaging [15]. In parallel, we have also utilized a distinct and powerful normalization framework, enabling generation of a population-based template of ROIs, to which individual subject ROIs are mapped, enabling application of other pattern recognition tools, such as principal component analysis (PCA), and generation of sub- structure maps of correlation with clinical manifestations. To do the latter, we investigated the use of surface matching based on Large Deformation Diffeomorphic Metric Mapping (LDDMM) [16], as a method for obtaining a common coordinate system to compare SPECT intensities across subjects. Nonlinear mapping of intensities, commonly known as normalization, is performed in popular neuroimaging analysis packages such as SPM by mapping MRI images to predetermined atlases. Compared to more typical normalization methods, our approach is based on surface matching, as opposed to image matching, which is more robust under noise, since it is not affected by noise once we obtain an

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Page 1: Application of Pattern Recognition Framework for ... of Pattern Recognition Framework for Quantification of Parkinson’s Disease in DAT SPECT Imaging Saurabh Jain, Yousef Salimpour,

Application of Pattern Recognition Framework for Quantification of Parkinson’s Disease in DAT

SPECT Imaging

Saurabh Jain, Yousef Salimpour, Laurent Younes, Gwenn Smith, Zoltan Mari, Vesna Sossi, Arman Rahmim

Abstract–Dopamine transporter (DAT) SPECT imaging is

increasingly utilized for diagnostic purposes in suspected

parkinsonian syndromes. Visual classification or

quantitative analysis of mean regional uptake has been

performed in the past. Our objective is to enable further

enhanced clinical utility in the diagnosis as well as tracking

of progression in Parkinson’s disease via quantification

based on pattern recognition. We developed and

implemented two such frameworks: first, we utilized

shape/texture metrics that did require registration to a

common structure/template; e.g. 3D moment-invariants,

Haralick texture features, and multiple others. We also used

a surface registration algorithm, which falls under the broad

class of Large Deformation Diffeomorphic Metric Mapping

(LDDMM). In this latter framework, we obtain a common

coordinate system for the entire population based on MR

images, and compare SPECT intensities across subjects in

this common coordinate system. This method has the

advantage of estimating population-based templates for each

structure individually rather than using a predetermined

collective atlas for all regions, as is customary. In this

common coordinate system, we then used Principal

Component Analysis (PCA) on intensities to obtain sub-

regions (set of voxels inside the structure of interest) with

highest variance in SPECT intensities across subjects. We

show that the healthy and diseased populations can be

subsequently distinguished. Via these methods, we also

aimed to assess correlations with different clinical measures

(e.g. UPDRS score, disease duration). In addition to enabling

enhanced diagnostic task performance, these methods have

considerable potential as biomarkers of PD progression.

I. INTRODUCTION

Imaging of the dopaminergic system with SPECT has

become widespread in Europe and has entered a new

active phase in the US since 123

I-ioflupane-dopamine

transporter (DAT) SPECT was approved by the FDA in

2011. Visual interpretation is the common assessment

approach [1-3]. However, more objective assessment can

be performed using quantitative analysis [4] involving

manual or automated ROI drawing and analysis [5, 6].

Our proposed approach is based on the observation that

Manuscript received December 06, 2014. S. Jain and L. Younes are with the Center for Imaging Science,

Johns Hopkins University, Baltimore, MD, USA 21287. Y. Salimpour and Z. Mari are with the Department of Neurology,

Johns Hopkins University, Baltimore, MD, USA 21287. G. Smith is with the Department of Psychiatry, Johns Hopkins

University, Baltimore, MD, USA 21287. V. Sossi is with the Department of Physics & Astronomy, University

of British Columbia, BC, Canada V6T1Z1. A. Rahmim is with the Departments of Radiology, and Electrical &

Computer Engineering, Johns Hopkins University, Baltimore, MD, USA 21287.

SPECT and PET images convey important information at

the voxel level, whereas commonly used ROI-averaging

vastly oversimplifies the available spatial uptake

information. We propose to explore a novel quantification

paradigm applied to SPECT neurochemical imaging that

utilizes pattern recognition to quantify uptake alterations

with disease, expected to detect subtle regional DAT

losses: this is especially important as DAT binding can be

a marker of PD at the presymptomatic stage [7, 8] and the

proposed approach can enhance abilities to discriminate

among different populations. Furthermore, we seek to

move beyond classification tasks in clinical DAT SPECT

imaging [9], to also arrive at enhanced markers of

progression. This is a significant step, and is for instance

consistent with the aims of the Parkinson’s Progressive

Marker Initiative (PPMI) to identity biomarkers of PD

progression, a critical step in the development of novel

and enhanced treatments for PD [10].

We have in the past pursued pattern recognition

techniques, including shape and texture analysis in the

context of quantitative brain PET imaging, in studies of

PD [11-13] and neuroinflammation [14]. These

techniques have the advantage of not requiring

normalization/registration of all regions of interest (ROIs)

to a common structure. In the present work, we turn our

attention to DAT SPECT imaging, given its increasingly

popular clinical usage. Furthermore, we have evidence

that application of shape and texture metrics can

appropriately extend from the higher resolution spectrum

of PET imaging to the domain of SPECT imaging [15].

In parallel, we have also utilized a distinct and

powerful normalization framework, enabling generation

of a population-based template of ROIs, to which

individual subject ROIs are mapped, enabling application

of other pattern recognition tools, such as principal

component analysis (PCA), and generation of sub-

structure maps of correlation with clinical manifestations.

To do the latter, we investigated the use of surface

matching based on Large Deformation Diffeomorphic

Metric Mapping (LDDMM) [16], as a method for

obtaining a common coordinate system to compare

SPECT intensities across subjects. Nonlinear mapping of

intensities, commonly known as normalization, is

performed in popular neuroimaging analysis packages

such as SPM by mapping MRI images to predetermined

atlases. Compared to more typical normalization

methods, our approach is based on surface matching, as

opposed to image matching, which is more robust under

noise, since it is not affected by noise once we obtain an

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appropriate segmentation. Furthermore, our method has

the advantage of estimating population-based templates

for each structure individually rather than using a

predetermined collective atlas for all regions, as is

customary. We used our LDDMM framework, described

in Section II, to first obtain a population-based template

from the caudate and putamen segmented from MRI

scans, and then obtain non-linear maps between each

subject and the template. These mappings are then used to

map the SPECT intensities in caudate and putamen to the

common coordinate system of the template. We then

perform PCA of the intensities in the common coordinate

system to obtain low dimensional features, which

correspond to regions in the caudate and putamen in

which the intensities vary the most across subjects.

For consistency, subjects were selected from the PPMI

database such that SPECT data were acquired on the

same kind of scanner, while having high-resolution 3T

MRI images, and age was limited to <70 to minimize

further age-related confounds. With this criteria, we

analyzed 128 subjects, 48 of which were healthy controls

(HCs) while 80 were diagnosed with Parkinson’s Disease

(PD). We show in Section V that for this particular

population, the uptake in putamen is enough to classify

HC from PD using a simple linear classifier in the low

dimensional space of principal components. However,

what is of greatest interest to us in the present work is the

ability to quantify progression and not merely enable

classification, as we discuss later in this work.

II. LDDMM FRAMEWORK

In the LDDMM framework, a non-linear map

between two surfaces, say and , is obtained by

integrating a time varying vector field on the

underlying Euclidean space , so that it is a solution of

the differential equation

, , , with the initial condition , 0 for all . The

solution to this equation for a given vector field , is

denoted by . The optimal mapping is obtained by

minimizing the cost functional

| | | , 1 | , where the first term penalizes the size of the vector field

in an appropriate norm, which in turn, controls the

smoothness of , and the second term penalizes the

mismatch between the mapped surface , 1 and the

target surface T. The details of this algorithm can be

found in [16].

The template is generated via a Bayesian generative

model described in [17], by mapping a hyper-template to

a template using a deformation parameterized with a

random vector field, as above, and maximizing the joint

likelihood of the observed target surfaces, which are

considered to be noisy versions of the deformed template.

III. TEXTURE AND SHAPE ANALYSIS

METHODS

In parallel to utilization of a normalization (LDDMM)

framework, we pursued other pattern recognition tools

that do not require normalization. We have elaborated

upon these in past application to PET imaging [14], and

briefly outline here:

(a) 3D moment invariants (3D-MIs): spatial

descriptors designed to be invariant to scaling, translation

and rotation, with extensive use in areas such as pattern

recognition, and as we have utilized in brain imaging.

These metrics included 2nd

order J1, J2 and J3 as well as

3rd

order B3 and B4 metrics.

(b) Intensity histogram analysis: e.g. standard

deviation, skewness and kurtosis. We also performed

cumulative histogram analysis leading to the intensity-

volume histogram [18].

(c) Neighborhood gray tone difference (NGTD)

analysis: which enables quantification of local variations

in uptake. Five textural metrics, corresponding to visual

properties of texture [19], were computed: (1) coarseness;

(2) contrast; (3) busyness; (4) complexity; (5) strength.

(d) The gray-level spatial-dependence (GLSD) matrix

(the co-occurrence matrix [8]). We considered twelve so-

called Haralick texture measures [20, 21]: (1) energy, (2)

entropy, (3) correlation, (4) contrast (also known as

inertia [22, 23]), (5) variance, (6) sum mean, (7)

agreement [24] (also known as Cohen’s [25] а-statistic),

(8) cluster shade, (9) cluster tendency (or prominence),

(10) homogeneity, (11) max probability, and (12) inverse

variance.

The latter approach (Haralick texture features; d) were

utilized in a recent study [9] for automatic detection of

PD. We supplement this with other methods, as outlined

above (a, b, c), and furthermore move beyond

classification tasks, to identify features that best capture

progression.

IV. MRI-DRIVEN SEGMENTATION AND

MAPPING

Both abovementioned overall frameworks (Sec. II and

III) utilized the following MRI-bases analyses:

1. Segment the high-resolution MRI images to obtain

the boundaries of the caudate and putamen (both left

and right), utilizing a multi-atlas segmentation

method [26].

2. Resample the SPECT image on the MRI grid

performing rigid mapping, using the FSL utility

FLIRT [27].

Figure 1 shows the output of a typical segmentation and

resampling of SPECT onto the MRI grid.

Following these steps, texture/shape analysis methods

of Sec. III were applied. For the normalization framework

(Sec. II), the following steps were also needed:

3. Use the LDDMM method to obtain templates, one

each for the caudate and putamen, and find the

optimal mappings from the template to each subjects.

4. Map the SPECT intensities for each subject to the

template coordinates using the mapping from 3.

Following this, we performed PCA on the mapped

SPECT intensities to obtain the regions with maximum

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variation of the signal across subjects

replaced by more sophisticated non-linear

reduction techniques such as manifold lea

direction we intend to explore in the future

Figure 1: Same slice of the MRI (left) and SPECT

the cross-sections of the segmented caudate and

shown by curves.

V. RESULTS

We first demonstrate results for classvs. PD in Sec. V-A. However, the major

the present research effort is to provide

track progression of disease, as pursued in

A. Classification LDDMM normalization framework (S

shows the two-dimensional spaces obtai

which best distinguish the diseased fro

patients. In the case of the putamen, the fir

components best separated PDs from HCs

was not successful utilizing the caudate,

routine observations.

We note that in this analysis, we

affected side, i.e. with lower uptake, fo

This is consistent with the asymmetry of

the putamen, and formation of a gradient

the putamen. In fact, an approach in past w

subdivide the putamen into 2 or 3 sub-re

intermediate and posterior). In any case

moves beyond this conventional approa

PCA images that explain the variability b

populations (Figure 3).

Figure 2: A scatter plot of the putamen in the 2D

components (PC1 and PC3) showing HC (x and PD

s. PCA can be

r dimensionality

arning. This is a

e.

T image (right) with

d putamen surfaces

sification of HC

goal pursued in

methodology to

n Sec. V-B.

ec. II): Figure 2

ned from PCA,

om the healthy

rst two principal

s. The separation

consistent with

used the more

or each subject.

f uptake loss in

especially along

work has been to

egions (anterior,

e, our technique

ach by seeking

between subject

D space of principal

D (o).

Figure 3: (Left vs. Right) PCA loa

principal components (PC1 and PC

putamen. Darker regions show higher

therefore signify regions that correspo

the corresponding principal component

Shape/texture analysis (

application of two histogram

skewness and kurtosis, also sh

HC and PD, as seen in Fig. 4.

Figure 4: A scatter plot of two h

putamen (skewness and kurtosis) for PD

B. Progression It is likely that while utiliz

(of putman) is preferred for cl

using the less affected side

enhanced tracking of progr

following initial asymmetry in

side can more severely progre

such can provide a higher dyna

seen in Fig. 2-4 of [28]). In th

we did not detect a significant

when utilizing more affected

(thus results are only shown for

In our studies, we correl

against 7 different clinical mea

1) The unified Parkinson

(UPDRS)

2) Gait

3) Tremor

4) Bradykinesia

5) Rigidity

6) Disease duration (DD) –

adings corresponding to the two

3) plotted in Figure 2 for the

r loadings in absolute value, and

ond to the variance explained by

t.

Sec. III): An example

m-based metrics, namely

howed separation between

histogram-based features for the

D vs. HC.

zing the more affected side

lassification/diagnosis, that

may be the choice for

ression. This is because

n uptake, the less affected

ess over the years, and as

amic range (e.g. as we can

he present work, however,

t difference in performance

side vs. less affected side

r one case).

ated image-based metrics

sures::

n's disease rating scale

– from diagnosis

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7) DD – from symptoms

Items (2-5) are motor sub-scores within the UPDRS

motor score.

We found that 16, of the various metrics considered,

depicted more substantial correlations against clinical

scores than other metrics did, thus we limit our summary

to these in what follows. The 16 metrics are as follows:

1) Conventional mean (normalized to mean reference

uptake in the occipital cortex).

2) 3D-MIs: B3.

3,4,5) Histogram-based: standard deviation; skewness;

Kurtosis.

6,7,8) NGTD: Coarseness; Busyness; Strength.

9,10,11,12) GLSD (haralick): Energy; Homogenuity;

Cluster-Shade; Agreement.

13,14,15,16) PCA analysis: First four PC components.

Fig. 5 depicts, for n=128 subjects, the performance of

each of the 16 abovementioned metrics in relation to the

UPDRS score. On top of each plot three correlation

indices are reported: i) Pearson (p), ii) Kendall (k), and

Spearman (s). The first one assumes linear correlation,

while the latter two are non-parametric.

It is seen that conventional mean uptake (normalized

to reference region) results in high correlation (e.g. p=-

0.65), and the two non-conventional metrics comparable

to it are skewness (p=0.52) and Cluster-Shade (p=0.59).

Fig. 6 depicts a summary of correlation values (p) for

a figure such as Fig. 5, in the case of all seven clinical

measures. It is seen that along with conventional mean

uptake metric (#1), other texture/shape metrics also show

considerable correlations, including metrics #4 and #12,

namely skewness and Cluster-Shade.

Fig. 7 is very similar to Fig. 5, except that PD-only

subjects (n=80) are only considered. It is seen that the

correlations with UPDRS score are substantially

dampened. This means that HC vs. PD discrimination

plays a considerable role in introducing correlations, and

sheds light on the fact that prognosis of PD-only subjects

is a difficult task. Similar plots as Fig. 7 for PD-only

subjects are shown in Fig. 8, this time correlating imaging

to DD (from symptoms), instead of UPDRS. Finally, Fig.

9 summarizes correlation values (p) in the PD-only case

for all seven clinical measures.

It is seen that some metrics may outperform

conventional mean uptake metric when applied to PD-

only subjects in terms of correlation with clinical

measures. In any case, the correlations are seen to be

weak in this case. We believe this may be enhanced (as

we pursue in ongoing work) by performing more

effective SPECT to MRI mappings. This is a challenging

problem, given the blurred nature of SPECT and the

concentrated nature of uptake in DAT imaging.

Another logical step is to consider multi-metric

analysis. We performed preliminary analysis in this

context, using the multiple correlation coefficient R, in

the case of conventional mean vs. feature-of-interest (e.g.

cluster-shade) vs. their joint combination. Here are some

results:

PD+HC subjects - DD (symptoms): Conventional -

0.65; Cluster-Shade - 0.59; Combined: 0.70.

PD only - DD (diagnosis): Conventional - 0.04;

Strength - 0.29; Combined - 0.32.

PD only - DD (symptoms): Conventional - 0.13;

Strength - 0.09; Combined - 0.25.

PD only - UPDRS: Conventional - 0.05; Strength -

0.23; Combined - 0.27.

Thus, it is seen that use of multi-metric analysis,

incorporating conventional mean uptake in addition to

novel shape/texture analysis can enhance the ability to

track disease via imaging.

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Figure 5: Plots of metric values vs. UPDRS score, for 16 different metrics as described in the text. The first 12 are shape/texture metrics applied to the

original patient-space, while the last 4 are PCA components as generated in the normalized/template space. HC+PD subjects were considered.

Figure 6: Plots of correlation (y-axis) vs. metric of interest (16 different metrics on the x-axis) for each of the 7 clinical measures as mentioned in the

text. HC+PD subjects were considered.

Page 6: Application of Pattern Recognition Framework for ... of Pattern Recognition Framework for Quantification of Parkinson’s Disease in DAT SPECT Imaging Saurabh Jain, Yousef Salimpour,

Figure 7: Plots of metric values vs. UPDRS score, for 16 different metrics as described in the text. Here, PD-only subjects were considered.

Figure 8: Plots of metric values vs. DD (from symptoms), for 16 different metrics as described in the text. Here, PD-only subjects were considered.

Page 7: Application of Pattern Recognition Framework for ... of Pattern Recognition Framework for Quantification of Parkinson’s Disease in DAT SPECT Imaging Saurabh Jain, Yousef Salimpour,

Figure 9: Plots of correlation (y-axis) vs. metric of interest (16 different metrics on the x-axis) for each of the 7 clinical measures as mentioned in the

text. PD-only subjects were considered.

VI. MORE DISCUSSION AND CONCLUSION

We also note that the normalization framework

enables the performance of an additional kind of analysis.

This work is in progress, but we show a sample analysis:

since all structures are registered to a common template,

it is now possible to ask which voxels correlate most with

a given clinical score. An example of this is shown in Fig.

10 for the particular case of bradykinesia (motor sub-

score within the UPDRS). This approach can be

performed for different clinical scores, and also for the

caudate, in addition to the putamen, and has the potential

to open-up new understandings about sub-regional

involvements with clinical manifestations in PD.

Overall, the presented approach of utilizing novel

pattern recognition as applied to imaging of PD could

have important clinical implications. Potentially enhanced

sensitivity to subtle neuroanatomical changes is expected

to provide novel insights into the relationship between

dopaminergic alterations and PD manifestations, while

extending the clinical usefulness of this imaging

technique. This approach can also be applied to images

from subjects at increased risk of PD (e.g. mutation

carriers or subjects with rapid-eye-movement sleep

behavior disorder) in an attempt to discern dopaminergic

patterns that might be involved in pathogenesis and to

assess the impact of novel disease modifying therapies.

Figure 10: Plots of correlation vs. Bradykineasia (motor-subscore of the

UPDRS) in the normalized template for n=128.

ACKNOWLEDGEMENTS

The authors wish to gratefully acknowledge grant support

from the Michael J. Fox Foundation (RRIA2013; PIs: A.

Rahmim, V. Sossi).

REFERENCES

1. Kupsch, A.R., et al., Impact of DaTscan SPECT imaging on

clinical management, diagnosis, confidence of diagnosis, quality

of life, health resource use and safety in patients with clinically

uncertain parkinsonian syndromes: a prospective 1-year follow-up

Page 8: Application of Pattern Recognition Framework for ... of Pattern Recognition Framework for Quantification of Parkinson’s Disease in DAT SPECT Imaging Saurabh Jain, Yousef Salimpour,

of an open-label controlled study. Journal of Neurology

Neurosurgery and Psychiatry, 2012. 83(6): p. 620-628.

2. Grachev, I., et al., Impact of DaTscan (TM) SPECT Imaging on

Clinical Management, Diagnosis, and Confidence of Diagnosis in

Patients with Clinically Uncertain Parkinsonian Syndromes: A

Prospective 1-Year Follow-Up Study. Neurology, 2012. 78.

3. Catafau, A.M. and E. Tolosa, Impact of dopamine transporter

SPECT using I-123-ioflupane on diagnosis and management of

patients with clinically uncertain Parkinsonian syndromes.

Movement Disorders, 2004. 19(10): p. 1175-1182.

4. Djang, D.S.W., et al., SNM Practice Guideline for Dopamine

Transporter Imaging with I-123-Ioflupane SPECT 1.0. Journal of

Nuclear Medicine, 2012. 53(1): p. 154-163.

5. Badiavas, K., et al., SPECT imaging evaluation in movement

disorders: far beyond visual assessment. European journal of

nuclear medicine and molecular imaging, 2011. 38(4): p. 764-773.

6. Koch, W., et al., Clinical testing of an optimized software solution

for an automated, observer-independent evaluation of dopamine

transporter SPECT studies. Journal of Nuclear Medicine, 2005.

46(7): p. 1109-1118.

7. Nandhagopal, R., et al., Progression of dopaminergic dysfunction

in a LRRK2 kindred A multitracer PET study. Neurology, 2008.

71(22): p. 1790-1795.

8. Sossi, V., et al., Dopamine Turnover Increases in Asymptomatic

LRRK2 Mutations Carriers. Movement Disorders, 2010. 25(16): p.

2717-2723.

9. Martinez-Murcia, F.J., et al., Parametrization of textural patterns in

I-123-ioflupane imaging for the automatic detection of

Parkinsonism. Medical Physics, 2014. 41(1).

10. Parkinson's Progression Markers Initiative. Available from:

http://www.ppmi-info.org/.

11. Sossi, V., et al., New insights into levodpoa induced dopamine

release in Parkinson's disease. J. Cereb. Blood Flow & Metab.,

2012. 32 (suppl. 1): p. P114.

12. Gonzalez, M., et al., Novel spatial analysis method for PET data

using 3D moment invariants: applications to Parkinson's disease. J.

Cereb. Blood Flow & Metab., 2012. 32 (suppl. 1): p. P117.

13. Gonzalez, M.E., et al., Novel spatial analysis method for PET

images using 3D moment invariants: Applications to Parkinson's

disease. Neuroimage, 2013. 68: p. 11-21.

14. Rahmim, A., et al., Novel parametric PET image quantification

using texture and shape analysis. Proc. IEEE Nucl. Sci. Symp.

Conf. , 2012: p. 2227-2230.

15. Blinder, S., et al., Texture and Shape Analysis on High and Low

Spatial Resolution Emission Images. Proc. IEEE Nucl. Sci. Symp.

Conf. , 2014.

16. Vaillant, M. and J. Glaunès, Surface Matching via Currents, in

Information Processing in Medical Imaging, G. Christensen and

M. Sonka, Editors. 2005, Springer Berlin Heidelberg. p. 381-392.

17. Ma, J., M.I. Miller, and L. Younes, A Bayesian Generative Model

for Surface Template Estimation. International Journal of

Biomedical Imaging, 2010. 2010: p. 14.

18. El Naqa, I., et al., Exploring feature-based approaches in PET

images for predicting cancer treatment outcomes. Pattern

Recognition, 2009. 42(6): p. 1162-1171.

19. Amadasun, M. and R. King, Textural Features Corresponding to

Textural Properties. Ieee Transactions on Systems Man and

Cybernetics, 1989. 19(5): p. 1264-1274.

20. Haralick, R.M., Shanmuga.K, and I. Dinstein, Textural Features

for Image Classification. Ieee Transactions on Systems Man and

Cybernetics, 1973. Smc3(6): p. 610-621.

21. Conners, R.W., M.M. Trivedi, and C.A. Harlow, Segmentation of

a High-Resolution Urban Scene Using Texture Operators.

Computer Vision Graphics and Image Processing, 1984. 25(3): p.

273-310.

22. Conners, R.W. and C.A. Harlow, Toward a Structural Textural

Analyzer Based on Statistical-Methods. Computer Graphics and

Image Processing, 1980. 12(3): p. 224-256.

23. Oh, G., S. Lee, and S.Y. Shin, Fast determination of textural

periodicity using distance matching function. Pattern Recognition

Letters, 1999. 20(2): p. 191-197.

24. Parkkinen, J., K. Selkainaho, and E. Oja, Detecting Texture

Periodicity from the Cooccurrence Matrix. Pattern Recognition

Letters, 1990. 11(1): p. 43-50.

25. Cohen, J., A Coefficient of Agreement for Nominal Scales.

Educational and Psychological Measurement, 1960. 20(1): p. 37-

46.

26. Tang, X.Y., et al., Bayesian Parameter Estimation and

Segmentation in the Multi-Atlas Random Orbit Model. Plos One,

2013. 8(6).

27. Jenkinson, M. and S. Smith, A global optimisation method for

robust affine registration of brain images. Medical Image Analysis,

2001. 5(2): p. 143-156.

28. Nandhagopal, R., et al., Longitudinal progression of sporadic

Parkinson&#039;s disease: a multi-tracer positron emission

tomography study. Brain, 2009. 132(11): p. 2970-2979.