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

Obtaining quantitative measures from biomedical images often requires segmentation, i.e., finding and outlining the structures of interest. Multi-modality imaging datasets, in which multiple imaging measures are available at each spatial location, are increasingly common, particularly in MRI. In applications where fully automatic segmentation algorithms are unavailable or fail to perform at desired levels of accuracy, semi-automatic segmentation can be a time-saving alternative to manual segmentation, allowing the human expert to guide segmentation, while minimizing the effort expended by the expert on repetitive tasks that can be automated. However, few existing 3D image analysis tools support semi-automatic segmentation of multi-modality imaging data. This paper describes new extensions to the ITK-SNAP interactive image visualization and segmentation tool that support semi-automatic segmentation of multi-modality imaging datasets in a way that utilizes information from all available modalities simultaneously. The approach combines Random Forest classifiers, trained by the user by placing several brushstrokes in the image, with the active contour segmentation algorithm. The new multi-modality semi-automatic segmentation approach is evaluated in the context of high-grade glioblastoma segmentation.

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.

Diffusion-weighted (DW) MRI has become a widely adopted imaging modality to reveal the underlying brain connectivity. Long acquisition times and/or non-cooperative patients increase the chances of motion-related artifacts. Whereasslow bulkmotion results in inter-gradient misalignment which can be handled via retrospective motion correction algorithms,fast bulkmotion usually affects data during the application of a single diffusion gradient causing signal dropout artifacts. Common practices opt to discard gradients bearing signal attenuation due to the difficulty of their retrospective correction, with the disadvantage to lose full gradients for further processing. Nonetheless, such attenuation might only affect limited number of slices within a gradient volume. Q-space resampling has recently been proposed to recover corrupted slices while saving gradients for subsequent reconstruction. However, few corrupted gradients are implicitly assumed which might not hold in case of scanning unsedated infants or patients in pain. In this paper, we propose to adopt recent advances in compressive sensing based reconstruction of the diffusion orientation distribution functions (ODF) with under sampled measurements to resample corrupted slices. We make use of Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) basis functions which can analytically model ODF from arbitrary sampled signals. We demonstrate the impact of the proposed resampling strategy compared to state-of-art resampling and gradient exclusion on simulated intra-gradient motion as well as samples from real DWI data.

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

BACKGROUND AND PURPOSE: Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography-derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop. METHODS: Eight international teams from leading institutions reconstructed the pyramidal tract in four neurosurgical cases presenting with a glioma near the motor cortex. Tractography methods included deterministic, probabilistic, filtered, and global approaches. Standardized evaluation of the tracts consisted in the qualitative review of the pyramidal pathways by a panel of neurosurgeons and DTI experts and the quantitative evaluation of the degree of agreement among methods. RESULTS: The evaluation of tractography reconstructions showed a great interalgorithm variability. Although most methods found projections of the pyramidal tract from the medial portion of the motor strip, only a few algorithms could trace the lateral projections from the hand, face, and tongue area. In addition, the structure of disagreement among methods was similar across hemispheres despite the anatomical distortions caused by pathological tissues. CONCLUSIONS: The DTI Challenge provides a benchmark for the standardized evaluation of tractography methods on neurosurgical data. This study suggests that there are still limitations to the clinical use of tractography for neurosurgical decision making.

Substance use during pregnancy and the postpartum period may have significant implications for both mother and the developing child. However, the neurobiological basis of the impact of substance use on parenting is less well understood. Here, we examined the impact of maternal substance use on cortical gray matter (GM) and white matter (WM) volumes and whether this was associated with individual differences in motivational systems of behavioral activation and inhibition. Mothers were included in the substance-using group if any addictive substance was used during pregnancy and/or in the immediate postpartum period (within 3 months of delivery). GM volume was reduced in substance-using mothers compared to non-substance-using mothers, particularly in frontal brain regions. In substance-using mothers, we also found that frontal GM was negatively correlated with levels of behavioral activation (i.e., the motivation to approach rewarding stimuli). This effect was absent in non-substance-using mothers. Taken together, these findings indicate a reduction in GM volume is associated with substance use and that frontal GM volumetric differences may be related to approach motivation in substance-using mothers.

Numerous brain imaging studies indicate that the corpus callosum is smaller in older children and adults with autism spectrum disorder. However, there are no published studies examining the morphological development of this connective pathway in infants at-risk for the disorder. Magnetic resonance imaging data were collected from 270 infants at high familial risk for autism spectrum disorder and 108 low-risk controls at 6, 12 and 24 months of age, with 83% of infants contributing two or more data points. Fifty-seven children met criteria for ASD based on clinical-best estimate diagnosis at age 2 years. Corpora callosa were measured for area, length and thickness by automated segmentation. We found significantly increased corpus callosum area and thickness in children with autism spectrum disorder starting at 6 months of age. These differences were particularly robust in the anterior corpus callosum at the 6 and 12 month time points. Regression analysis indicated that radial diffusivity in this region, measured by diffusion tensor imaging, inversely predicted thickness. Measures of area and thickness in the first year of life were correlated with repetitive behaviours at age 2 years. In contrast to work from older children and adults, our findings suggest that the corpus callosum may be larger in infants who go on to develop autism spectrum disorder. This result was apparent with or without adjustment for total brain volume. Although we did not see a significant interaction between group and age, cross-sectional data indicated that area and thickness differences diminish by age 2 years. Regression data incorporating diffusion tensor imaging suggest that microstructural properties of callosal white matter, which includes myelination and axon composition, may explain group differences in morphology.