The automatic analysis of medical images has played a key role in many discoveries in neuroscience over the past two decades. Magnetic resonance imaging (MRI) maintains a central role in this scientific process as well as in clinical neuroimaging because of its ability to use different pulse sequences that can provide alternate contrasts capable of revealing subtle tissue differences in both normal and diseased tissues. Yet there are three widely recognized problems in the reliable and consistent application of automatic image processing algorithms to MR data. First, the lack of a standardized scale in the image measurements means that results obtained on different scanners or at different times are not necessarily comparably quantified for individual studies or reliably pooled for population studies. For example, T1-weighted images are routinely acquired, but differences in the pulse sequences can cause significant differences in the brain tissue contrasts. Second, tissue contrasts that are ideal for certain steps in automatic processing are not always acquired in a given study or at a given imaging center. For example, although double-echo PD/T2-weighted images are routinely acquired, FLAIR images are often omitted for time considerations unless white matter lesions are expected or directly under study. Third, images often have intensity shading artifacts caused by spatially varying coil sensitivity patterns. These problems are worse at higher field strengths, preventing consistent analysis of these data without correction. All three of these problems will be addressed in this research project by investigation and further development of the method called Magnetic Resonance Image Example-based Contrast Synthesis (MIMECS). MIMECS is a post processing method that uses a standardized atlas with multiple images in order to synthesize contrasts that are consistent with the atlas given one or more subject images. The strategy is quite different than past approaches, which have focused on rich data acquisition, nonlinear atlas registration, or histogram modification techniques. MIMECS focuses on image synthesis using patches that index into an atlas thousands of times in order to learn an optimal synthesis formula at each voxel. It uses anatomical information from the atlas while avoiding the time-consuming process that would be required of a multi-atlas nonlinear registration approach. The research plan comprises three specific aims: 1) The theory of example-based image synthesis will be studied in order to optimize MIMECS;2) The computational approach will be refined and optimized for different applications;3) The use of MIMECS in synthesizing both FLAIR images for white matter lesion detection and optimized T1-weighted images for cortical surface extraction will be thoroughly evaluated on large existing data sets. The software will be thoroughly tested and then released as open source software within the Java Image Science Toolkit (JIST) for widespread availability to the neuroscience community.

Public Health Relevance

Automated image analysis of magnetic resonance images plays a central role in neuroscience, yet it is very challenging to obtain consistent results when data is acquired from different scanners or at significantly different times. This exploratory research project will develop, validate, and make freely available as an open source software tool a post processing method called Magnetic Resonance Image Example-based Contrast Synthesis (MIMECS), which addresses these standardization issues using a novel atlas-based strategy.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21EB012765-01A1
Application #
8191836
Study Section
Special Emphasis Panel (ZRG1-NT-B (08))
Program Officer
Pai, Vinay Manjunath
Project Start
2011-08-01
Project End
2013-07-31
Budget Start
2011-08-01
Budget End
2012-07-31
Support Year
1
Fiscal Year
2011
Total Cost
$197,456
Indirect Cost
Name
Johns Hopkins University
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Jog, Amod; Carass, Aaron; Roy, Snehashis et al. (2017) Random forest regression for magnetic resonance image synthesis. Med Image Anal 35:475-488
Chen, Min; Carass, Aaron; Jog, Amod et al. (2017) Cross contrast multi-channel image registration using image synthesis for MR brain images. Med Image Anal 36:2-14
Roy, Snehashis; Carass, Aaron; Pacheco, Jennifer et al. (2016) Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation. Neuroimage Clin 11:264-275
Jog, Amod; Carass, Aaron; Pham, Dzung L et al. (2015) Multi-Output Decision Trees for Lesion Segmentation in Multiple Sclerosis. Proc SPIE Int Soc Opt Eng 9413:
Jog, Amod; Carass, Aaron; Roy, Snehashis et al. (2015) MR image synthesis by contrast learning on neighborhood ensembles. Med Image Anal 24:63-76
Chen, Min; Jog, Amod; Carass, Aaron et al. (2015) Using image synthesis for multi-channel registration of different image modalities. Proc SPIE Int Soc Opt Eng 9413:
Roy, Snehashis; Carass, Aaron; Prince, Jerry L et al. (2015) Longitudinal Patch-Based Segmentation of Multiple Sclerosis White Matter Lesions. Mach Learn Med Imaging 9352:194-202
Roy, Snehashis; He, Qing; Sweeney, Elizabeth et al. (2015) Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation. IEEE J Biomed Health Inform 19:1598-609
Nyquist, Paul A; Bilgel, Murat; Gottesman, Rebecca et al. (2015) Age differences in periventricular and deep white matter lesions. Neurobiol Aging 36:1653-1658
Roy, Snehashis; He, Qing; Carass, Aaron et al. (2014) Example Based Lesion Segmentation. Proc SPIE Int Soc Opt Eng 9034:

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