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Longitudinal Modeling of Appearance and Shape and its Potential for Clinical Use

Institution:
Tandon School of Engineering, Department of Computer Science and Engineering, NYU, Brooklyn, NY, USA. Electronic address: gerig@nyu.edu.
Publication Date:
Oct-2016
Journal:
Med Image Anal
Volume Number:
33
Pages:
114-21
Citation:
Med Image Anal. 2016 Oct;33:114-21.
PubMed ID:
27344938
PMCID:
PMC5381523
Keywords:
Longitudinal imaging, Mixed-effects modeling, Shape analysis, Shape regression
Appears in Collections:
NA-MIC
Sponsors:
U54 EB005149/EB/NIBIB NIH HHS/United States
U01 NS082086/NS/NINDS NIH HHS/United States
P50 MH064065/MH/NIMH NIH HHS/United States
R01 MH070890/MH/NIMH NIH HHS/United States
R01 NS050568/NS/NINDS NIH HHS/United States
R01 HD055741/HD/NICHD NIH HHS/United States
Generated Citation:
Gerig G., Fishbaugh J., Sadeghi N. Longitudinal Modeling of Appearance and Shape and its Potential for Clinical Use. Med Image Anal. 2016 Oct;33:114-21. PMID: 27344938. PMCID: PMC5381523.
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Clinical assessment routinely uses terms such as development, growth trajectory, degeneration, disease progression, recovery or prediction. This terminology inherently carries the aspect of dynamic processes, suggesting that single measurements in time and cross-sectional comparison may not sufficiently describe spatiotemporal changes. In view of medical imaging, such tasks encourage subject-specific longitudinal imaging. Whereas follow-up, monitoring and prediction are natural tasks in clinical diagnosis of disease progression and of assessment of therapeutic intervention, translation of methodologies for calculation of temporal profiles from longitudinal data to clinical routine still requires significant research and development efforts. Rapid advances in image acquisition technology with significantly reduced acquisition times and with increase of patient comfort favor repeated imaging over the observation period. In view of serial imaging ranging over multiple years, image acquisition faces the challenging issue of scanner standardization and calibration which is crucial for successful spatiotemporal analysis. Longitudinal 3D data, represented as 4D images, capture time-varying anatomy and function. Such data benefits from dedicated analysis methods and tools that make use of the inherent correlation and causality of repeated acquisitions of the same subject. Availability of such data spawned progress in the development of advanced 4D image analysis methodologies that carry the notion of linear and nonlinear regression, now applied to complex, high-dimensional data such as images, image-derived shapes and structures, or a combination thereof. This paper provides examples of recently developed analysis methodologies for 4D image data, primarily focusing on progress in areas of core expertise of the authors. These include spatiotemporal shape modeling and growth trajectories of white matter fiber tracts demonstrated with examples from ongoing longitudinal clinical neuroimaging studies such as analysis of early brain growth in subjects at risk for mental illness and neurodegeneration in Huntington's disease (HD). We will discuss broader aspects of current limitations and need for future research in view of data consistency and analysis methodologies.

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