Faculty Bibliography

Purpose : We have used hyperspectral imaging to extract candidate spectra of fluorophores ex-vivo in normal and diseased RPE (PMID 25574430; ARVO 15' EAbstracts 3956 & 4369). Herein we characterize a distinct AF signal of melanosomesmelanolipofuscin (M/ML) granules in the RPE of donors with geographic atrophy (GA) and neovascular AMD. Methods : Hyperspectral AF images were captured at 18 locations from 6 RPE/Bruch's-membrane flat-mounts of donor eyes with late AMD. Imaging was performed at 2 excitation bands, 436-460 and 480-510 nm; emission was captured between 420-720 nm in 10 nm intervals. Mathematical factorization was applied to extract abundant emission spectra and their spatial abundance images from RPE organelles (labeled S1-S3) and drusen/sub-RPE deposits (labeled SDr). To correlate AF with M/ML, images were compared with those captured by bright-field (BF) microscopy at the same locations. Results : At 436 nm excitation, a broad AF signal S3 (Fig.1: Spectra & S3) was extracted at all 18 locations, with mean emission maximum of 631+/-25 nm, range 580-650 nm. The S3 spatial abundance image demonstrated correlation with areas containing M/ML granules in 14/18 locations on BF images (77.8%), and in 12/18 locations on AF composite images (66.7%) (Fig.1: BF & AF). Spectra S1 and S2 showed general correlation with lipofuscin/ML (LF/ML) (Fig.1: S2), not with M/ML granules. Conclusions : Hyperspectral imaging of the RPE in AMD extracted a consistent AF signal in the red wavelengths that strongly correlated to areas dense with M/ML granules. A similar signal was more diffusely distributed in normal RPE, suggesting a progressive pathophysiology of interest in AMD. Further investigation of melanin distribution in AMD using super-resolution structured illumination and imaging mass spectrometry is warranted

PURPOSE: To elucidate the molecular pathogenesis of age-related macular degeneration (AMD) and interpretation of fundus autofluorescence imaging, the authors identified spectral autofluorescence characteristics of drusen and retinal pigment epithelium (RPE) in donor eyes with AMD. METHODS: Macular RPE/Bruch membrane flat mounts were prepared from 5 donor eyes with AMD. In 12 locations (1-3 per eye), hyperspectral autofluorescence images in 10-nm-wavelength steps were acquired at 2 excitation wavelengths (lambdaex 436, 480 nm). A nonnegative tensor factorization algorithm was used to recover 5 abundant emission spectra and their corresponding spatial localizations. RESULTS: At lambdaex 436 nm, the authors consistently localized a novel spectrum (SDr) with a peak emission near 510 nm in drusen and sub-RPE deposits. Abundant emission spectra seen previously (S0 in Bruch membrane and S1, S2, and S3 in RPE lipofuscin/melanolipofuscin, respectively) also appeared in AMD eyes, with the same shapes and peak wavelengths as in normal tissue. Lipofuscin/melanolipofuscin spectra localizations in AMD eyes varied widely in their overlap with drusen, ranging from none to complete. CONCLUSION: An emission spectrum peaking at approximately 510 nm (lambdaex 436 nm) appears to be sensitive and specific for drusen and sub-RPE deposits. One or more abundant spectra from RPE organelles exhibit characteristic relationships with drusen.

With the increasing use of efficient multimodal 3D imaging, clinicians are able to access longitudinal imaging to stage pathological diseases, to monitor the efficacy of therapeutic interventions, or to assess and quantify rehabilitation efforts. Analysis of such four-dimensional (4D) image data presenting pathologies, including disappearing and newly appearing lesions, represents a significant challenge due to the presence of complex spatio-temporal changes. Image analysis methods for such 4D image data have to include not only a concept for joint segmentation of 3D datasets to account for inherent correlations of subject-specific repeated scans but also a mechanism to account for large deformations and the destruction and formation of lesions (e.g., edema, bleeding) due to underlying physiological processes associated with damage, intervention, and recovery. In this paper, we propose a novel framework that provides a joint segmentation-registration framework to tackle the inherent problem of image registration in the presence of objects not present in all images of the time series. Our methodology models 4D changes in pathological anatomy across time and and also provides an explicit mapping of a healthy normative template to a subject's image data with pathologies. Since atlas-moderated segmentation methods cannot explain appearance and locality pathological structures that are not represented in the template atlas, the new framework provides different options for initialization via a supervised learning approach, iterative semisupervised active learning, and also transfer learning, which results in a fully automatic 4D segmentation method. We demonstrate the effectiveness of our novel approach with synthetic experiments and a 4D multimodal MRI dataset of severe traumatic brain injury (TBI), including validation via comparison to expert segmentations. However, the proposed methodology is generic in regard to different clinical applications requiring quantitative analysis of 4D imaging representing spatio-temporal changes of pathologies.

With increasing use of subject-specific longitudinal imaging for assessment of development, degeneration and disease progression, there is a clear need for image analysis segmentation/registration tools dedicated to 4D image time series. Previous work has mostly focused on temporal modeling of geometric deformations and shape changes, assuming that image intensity changes can be normalized. However, in studies of early infant development or aging, e.g., we encounter low contrast and appearance alterations due to tissue property changes which pose challenges to temporal registration and 4D segmentation. The two problems are linked since registration can be solved if appearance changes are accounted for, but 4D segmentation requires registration of image time series. In this paper, we propose to integrate a temporal appearance change model into diffeomorphic registration thus accounting for such variations, where voxel-wise intensity model parameters are calculated jointly with temporal image coregistration. Moreover, we demonstrate novel 4D segmentation of co-registered images that uses local intensity change rather than intensity itself via Gaussian mixture model. Both methods can be seen as two stages of an integrated registration/segmentation framework for 4D time-discrete image data making use of the same underlying model of longitudinal appearance changes. We demonstrate feasibility of the new approach with verification on longitudinal, multimodal pediatric MRI of infants in the age range neonates to 24 months

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.

The quantification of local surface morphology in the human cortex is important for examining population differences as well as developmental changes in neurodegenerative or neurodevelopmental disorders. We propose a novel cortical shape measure, referred to as the 'shape complexity index' (SCI), that represents localized shape complexity as the difference between the observed distributions of local surface topology, as quantified by the shape index (SI) measure, to its best fitting simple topological model within a given neighborhood. We apply a relatively small, adaptive geodesic kernel to calculate the SCI. Due to the small size of the kernel, the proposed SCI measure captures fine differences of cortical shape. With this novel cortical feature, we aim to capture comparatively small local surface changes that capture a) the widening versus deepening of sulcal and gyral regions, as well as b) the emergence and development of secondary and tertiary sulci. Current cortical shape measures, such as the gyrification index (GI) or intrinsic curvature measures, investigate the cortical surface at a different scale and are less well suited to capture these particular cortical surface changes. In our experiments, the proposed SCI demonstrates higher complexity in the gyral/sulcal wall regions, lower complexity in wider gyral ridges and lowest complexity in wider sulcal fundus regions. In early postnatal brain development, our experiments show that SCI reveals a pattern of increased cortical shape complexity with age, as well as sexual dimorphisms in the insula, middle cingulate, parieto-occipital sulcal and Broca's regions. Overall, sex differences were greatest at 6months of age and were reduced at 24months, with the difference pattern switching from higher complexity in males at 6months to higher complexity in females at 24months. This is the first study of longitudinal, cortical complexity maturation and sex differences, in the early postnatal period from 6 to 24months of age with fine scale, cortical shape measures. These results provide information that complement previous studies of gyrification index in early brain development.

PURPOSE: To evaluate the performance of an edge-based registration technique in correcting for respiratory motion artifacts in magnetic resonance renographic (MRR) data and to examine the efficiency of a semiautomatic software package in processing renographic data from a cohort of clinical patients. MATERIALS AND METHODS: The developed software incorporates an image-registration algorithm based on the generalized Hough transform of edge maps. It was used to estimate glomerular filtration rate (GFR), renal plasma flow (RPF), and mean transit time (MTT) from 36 patients who underwent free-breathing MRR at 3T using saturation-recovery turbo-FLASH. The processing time required for each patient was recorded. Renal parameter estimates and model-fitting residues from the software were compared to those from a previously reported technique. Interreader variability in the software was quantified by the standard deviation of parameter estimates among three readers. GFR estimates from our software were also compared to a reference standard from nuclear medicine. RESULTS: The time taken to process one patient's data with the software averaged 12 +/- 4 minutes. The applied image registration effectively reduced motion artifacts in dynamic images by providing renal tracer-retention curves with significantly smaller fitting residues (P < 0.01) than unregistered data or data registered by the previously reported technique. Interreader variability was less than 10% for all parameters. GFR estimates from the proposed method showed greater concordance with reference values (P < 0.05). CONCLUSION: These results suggest that the proposed software can process MRR data efficiently and accurately. Its incorporated registration technique based on the generalized Hough transform effectively reduces respiratory motion artifacts in free-breathing renographic acquisitions. J. Magn. Reson. Imaging 2015.