MR2-Net: Retinal OCTA Image Stitching via Multi-Scale Representation Learning and Dynamic Location Guidance.

Optical coherence tomography angiography (OCTA) plays a crucial role in quantifying and analyzing retinal vascular diseases. However, the limited field of view (FOV) inherent in most commercial OCTA imaging systems poses a significant challenge for clinicians, restricting the possibility to analyze larger retinal regions of high resolution. Automatic stitching of OCTA scans in adjacent regions may provide a promising solution to extend the region of interest. However, commonly-used stitching algorithms face difficulties in achieving effective alignment due to noise, artifacts and dense vasculature present in OCTA images. To address these challenges, we propose a novel retinal OCTA image stitching network, named MR2-Net, which integrates multi-scale representation learning and dynamic location guidance. In the first stage, an image registration network with a progressive multi-resolution feature fusion is proposed to derive deep semantic information effectively. Additionally, we introduce a dynamic guidance strategy to locate the foveal avascular zone (FAZ) and constrain registration errors in overlapping vascular regions. In the second stage, an image fusion network based on multiple mask constraints and adjacent image aggregation (AIA) strategies is developed to further eliminate the artifacts in the overlapping areas of stitched images, thereby achieving precise vessel alignment. To validate the effectiveness of our method, we conduct a series of experiments on two delicately constructed datasets, i.e., OPTOVUE-OCTA and SVision-OCTA. Experimental results demonstrate that our method outperforms other image stitching methods and effectively generates high-quality wide-field OCTA images, achieving a structural similarity index (SSIM) score of 0.8264 and 0.8014 on the two datasets, respectively.

Improving Alzheimer Diagnoses With An Interpretable Deep Learning Framework: Including Neuropsychiatric Symptoms.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by the progressive cognitive decline. Among the various clinical symptoms, neuropsychiatric symptoms (NPS) commonly occur during the course of AD. Previous researches have demonstrated a strong association between NPS and severity of AD, while the research methods are not sufficiently intuitive. Here, we report a hybrid deep learning framework for AD diagnosis using multimodal inputs such as structural MRI, behavioral scores, age, and gender information. The framework uses a 3D convolutional neural network to automatically extract features from MRI. The imaging features are passed to the Principal Component Analysis for dimensionality reduction, which fuse with non-imaging information to improve the diagnosis of AD. According to the experimental results, our model achieves an accuracy of 0.91 and an area under the curve of 0.97 in the task of classifying AD and cognitively normal individuals. SHapley Additive exPlanations are used to visually exhibit the contribution of specific NPS in the proposed model. Among all behavioral symptoms, apathy plays a particularly important role in the diagnosis of AD, which can be considered a valuable factor in further studies, as well as clinical trials.

Retinotopic degeneration of the retina and optic tracts in autosomal dominant Alzheimer's disease.

INTRODUCTION: We investigated the correlation between retinal thickness and optic tract integrity in subjects with autosomal dominant Alzheimer's disease (ADAD) causing mutations. METHODS: Retinal thicknesses and diffusion tensor images (DTI) were obtained using optical coherence tomography and magnetic resonance imaging, respectively. The association between retinal thickness and DTI measures was adjusted for age, sex, retinotopy, and correlation between eyes. RESULTS: Optic tract mean diffusivity and axial diffusivity were negatively correlated with retinotopically defined ganglion cell inner plexiform thickness (GCIPL). Fractional anisotropy was negatively correlated with retinotopically defined retinal nerve fiber layer thickness. There was no correlation between outer nuclear layer (ONL) thickness and any DTI measure. DISCUSSION: In ADAD, GCIPL thickness is significantly associated with retinotopic optic tract DTI measures even in minimally symptomatic subjects. Similar associations were not present with ONL thickness or when ignoring retinotopy. We provide in vivo evidence for optic tract changes resulting from ganglion cell pathology in ADAD.

Characterization of spastic paraplegia in a family with a novel PSEN1 mutation.

Spastic paraparesis has been described to occur in 13.7% of PSEN1 mutations and can be the presenting feature in 7.5%. In this paper, we describe a family with a particularly young onset of spastic paraparesis due to a novel mutation in PSEN1 (F388S). Three affected brothers underwent comprehensive imaging protocols, two underwent ophthalmological evaluations and one underwent neuropathological examination after his death at age 29. Age of onset was consistently at age 23 with spastic paraparesis, dysarthria and bradyphrenia. Pseudobulbar affect followed with progressive gait problems leading to loss of ambulation in the late 20s. Cerebrospinal fluid levels of amyloid-ß, tau and phosphorylated tau and florbetaben PET were consistent with Alzheimer's disease. Flortaucipir PET showed an uptake pattern atypical for Alzheimer's disease, with disproportionate signal in posterior brain areas. Diffusion tensor imaging showed decreased mean diffusivity in widespread areas of white matter but particularly in areas underlying the peri-Rolandic cortex and in the corticospinal tracts. These changes were more severe than those found in carriers of another PSEN1 mutation, which can cause spastic paraparesis at a later age (A431E), which were in turn more severe than among persons carrying autosomal dominant Alzheimer's disease mutations not causing spastic paraparesis. Neuropathological examination confirmed the presence of cotton wool plaques previously described in association with spastic parapresis and pallor and microgliosis in the corticospinal tract with severe amyloid-ß pathology in motor cortex but without unequivocal disproportionate neuronal loss or tau pathology. In vitro modelling of the effects of the mutation demonstrated increased production of longer length amyloid-ß peptides relative to shorter that predicted the young age of onset. In this paper, we provide imaging and neuropathological characterization of an extreme form of spastic paraparesis occurring in association with autosomal dominant Alzheimer's disease, demonstrating robust diffusion and pathological abnormalities in white matter. That the amyloid-ß profiles produced predicted the young age of onset suggests an amyloid-driven aetiology though the link between this and the white matter pathology remains undefined.

Reciprocal assistance of intravascular imaging in three-dimensional stent reconstruction: Using cross-modal translation based on disentanglement representation.

BACKGROUND: Accurate and efficient 3-dimension (3D) reconstruction of coronary stents in intravascular imaging of optical coherence tomography (OCT) or intravascular ultrasound (IVUS) is important for optimization of complex percutaneous coronary interventions (PCI). Deep learning has been used to address this technical challenge. However, manual annotation of stent is strenuous, especially for IVUS images. To this end, we aim to explore whether the OCT and IVUS images can assist each other in stent 3D reconstruction when one of them is lack of labeled dataset. METHODS: We firstly performed cross-modal translation between OCT and IVUS images, where disentangled representation was employed to generate synthetic images with good stent consistency. The reciprocal assistance of OCT and IVUS in stent 3D reconstruction was then conducted by applying unsupervised and semi-supervised learning with the aid of synthetic images. Stent consistency in synthetic images and reciprocal effectiveness in stent 3D reconstruction were quantitatively assessed by F1-Score (FS) on two datasets: OCT-High Definition IVUS (HD IVUS) and OCT-Conventional IVUS (IVUS). RESULTS: The employment of disentangled representation achieved higher stent consistency in synthetic images (OCT to HD IVUS: FS=0.789 vs 0.684; HD IVUS to OCT: FS=0.766 vs 0.682; OCT to IVUS: FS=0.806 vs 0.664; IVUS to OCT: FS=0.724 vs 0.673). For stent 3D reconstruction, the assistance from synthetic images significantly promoted unsupervised adaptation across modalities (OCT to HD IVUS: FS=0.776 vs 0.109; HD IVUS to OCT: FS=0.826 vs 0.125; OCT to IVUS: FS=0.782 vs 0.068; IVUS to OCT: FS=0.815 vs 0.123), and improved performance in semi-supervised learning, especially when only limited labeled data was available. CONCLUSION: The intravascular images of OCT and IVUS can provide reciprocal assistance to each other in stent 3D reconstruction by cross-modal translation, where the stent consistency in synthetic images was maintained by disentangled representation.

Retinal ganglion cell endowment is correlated with optic tract fiber cross section, not density.

There is substantial variation between healthy individuals in the number of retinal ganglion cells (RGC) in the eye, with commensurate variation in the number of axons in the optic tracts. Fixel-based analysis of diffusion MR produces estimates of fiber density (FD) and cross section (FC). Using these fixel measurements along with retinal imaging, we asked if individual differences in RGC tissue volume are correlated with individual differences in FD and FC measurements obtained from the optic tracts, and subsequent structures along the cortical visual pathway. We find that RGC endowment is correlated with optic tract FC, but not with FD. RGC volume had a decreasing relationship with measurements from subsequent regions of the visual system (LGN volume, optic radiation FC/FD, and V1 surface area). However, we also found that the variations in each visual area were correlated with the variations in its immediately adjacent visual structure. We only observed these serial correlations when FC is used as the measure of interest for the optic tract and radiations, but no significant relationship was found when FD represented these white matter structures. From these results, we conclude that the variations in RGC endowment, LGN volume, and V1 surface area are better predicted by the overall cross section of the optic tract and optic radiations as compared to the intra-axonal restricted signal component of these white matter pathways. Additionally, the presence of significant correlations between adjacent, but not distant, anatomical structures suggests that there are multiple, local sources of anatomical variation along the visual pathway.

Enhanced Association of Tau Pathology and Cognitive Impairment in Mild Cognitive Impairment Subjects with Behavior Symptoms.

BACKGROUND: Mild cognitive impairment (MCI) individuals with neuropsychiatric symptoms (NPS) are more likely to develop dementia. OBJECTIVE: We sought to understand the relationship between neuroimaging markers such as tau pathology and cognitive symptoms both with and without the presence of NPS during the prodromal period of Alzheimer's disease. METHODS: A total of 151 MCI subjects with tau positron emission tomographic (PET) scanning with 18F AV-1451, amyloid-ß (Aß) PET scanning with florbetapir or florbetaben, magnetic resonance imaging, and cognitive and behavioral evaluations were selected from the Alzheimer's Disease Neuroimaging Initiative. A 4-group division approach was proposed using amyloid (A-/A+) and behavior (B-/B+) status: A-B-, A-B+, A+B-, and A+B+. Pearson's correlation test was conducted for each group to examine the association between tau deposition and cognitive performance. RESULTS: No statistically significant association between tau deposition and cognitive impairment was found for subjects without behavior symptoms in either the A-B-or A+B-groups after correction for false discovery rate. In contrast, tau deposition was found to be significantly associated with cognitive impairment in entorhinal cortex and temporal pole for the A-B+ group and nearly the whole cerebrum for the A+B+ group. CONCLUSION: Enhanced associations between tauopathy and cognitive impairment are present in MCI subjects with behavior symptoms, which is more prominent in the presence of elevated amyloid pathology. MCI individuals with NPS may thus be at greater risk for further cognitive decline with the increase of tau deposition in comparison to those without NPS.

Locus coeruleus integrity is related to tau burden and memory loss in autosomal-dominant Alzheimer's disease.

Abnormally phosphorylated tau, an indicator of Alzheimer's disease, accumulates in the first decades of life in the locus coeruleus (LC), the brain's main noradrenaline supply. However, technical challenges in in-vivo assessments have impeded research into the role of the LC in Alzheimer's disease. We studied participants with or known to be at-risk for mutations in genes causing autosomal-dominant Alzheimer's disease (ADAD) with early onset, providing a unique window into the pathogenesis of Alzheimer's largely disentangled from age-related factors. Using high-resolution MRI and tau PET, we found lower rostral LC integrity in symptomatic participants. LC integrity was associated with individual differences in tau burden and memory decline. Post-mortem analyses in a separate set of carriers of the same mutation confirmed substantial neuronal loss in the LC. Our findings link LC degeneration to tau burden and memory in Alzheimer's, and highlight a role of the noradrenergic system in this neurodegenerative disease.

Unsupervised Deep Learning for FOD-Based Susceptibility Distortion Correction in Diffusion MRI.

Susceptibility induced distortion is a major artifact that affects the diffusion MRI (dMRI) data analysis. In the Human Connectome Project (HCP), the state-of-the-art method adopted to correct this kind of distortion is to exploit the displacement field from the B0 image in the reversed phase encoding images. However, both the traditional and learning-based approaches have limitations in achieving high correction accuracy in certain brain regions, such as brainstem. By utilizing the fiber orientation distribution (FOD) computed from the dMRI, we propose a novel deep learning framework named DistoRtion Correction Net (DrC-Net), which consists of the U-Net to capture the latent information from the 4D FOD images and the spatial transformer network to propagate the displacement field and back propagate the losses between the deformed FOD images. The experiments are performed on two datasets acquired with different phase encoding (PE) directions including the HCP and the Human Connectome Low Vision (HCLV) dataset. Compared to two traditional methods topup and FODReg and two deep learning methods S-Net and flow-net, the proposed method achieves significant improvements in terms of the mean squared difference (MSD) of fractional anisotropy (FA) images and minimum angular difference between two PEs in white matter and also brainstem regions. In the meantime, the proposed DrC-Net takes only several seconds to predict a displacement field, which is much faster than the FODReg method.

Hippocampal asymmetry of regional development and structural covariance in preterm neonates.

Premature birth is associated with a high prevalence of neurodevelopmental impairments in surviving infants. The hippocampus is known to be critical for learning and memory, yet the putative effects of hippocampal dysfunction remain poorly understood in preterm neonates. In particular, while asymmetry of the hippocampus has been well noted both structurally and functionally, how preterm birth impairs hippocampal development and to what extent the hippocampus is asymmetrically impaired by preterm birth have not been well delineated. In this study, we compared volumetric growth and shape development in the hippocampal hemispheres and structural covariance (SC) between hippocampal vertices and cortical thickness in cerebral cortex regions between two groups. We found that premature infants had smaller volumes of the right hippocampi only. Lower thickness was observed in the hippocampal head in both hemispheres for preterm neonates compared with full-term peers, though preterm neonates exhibited an accelerated age-related change of hippocampal thickness in the left hippocampi. The SC between the left hippocampi and the limbic lobe of the premature infants was severely impaired compared with the term-born neonates. These findings suggested that the development of the hippocampus during the third trimester may be altered following early extrauterine exposure with a high degree of asymmetry.

Association of Tau Pathology With Clinical Symptoms in the Subfields of Hippocampal Formation.

Objective: To delineate the relationship between clinical symptoms and tauopathy of the hippocampal subfields under different amyloid statuses. Methods: One hundred and forty-three subjects were obtained from the ADNI project, including 87 individuals with normal cognition, 46 with mild cognitive impairment, and 10 with Alzheimer's disease (AD). All subjects underwent the tau PET, amyloid PET, T1W, and high-resolution T2W scans. Clinical symptoms were assessed by the Neuropsychiatric Inventory (NPI) total score and Alzheimer's Disease Assessment Scale cognition 13 (ADAS-cog-13) total score, comprising memory and executive function scores. The hippocampal subfields including Cornu Ammonis (CA1-3), subiculum (Sub), and dentate gyrus (DG), as well as the adjacent para-hippocampus (PHC) and entorhinal cortex (ERC), were segmented automatically using the Automatic Segmentation of Hippocampal Subfields (ASHS) software. The relationship between tauopathy/volume of the hippocampal subfields and assessment scores was calculated using partial correlation analysis under different amyloid status, by controlling age, gender, education, apolipoprotein E (APOE) allele ɛ4 carrier status, and, time interval between the acquisition time of tau PET and amyloid PET scans. Results: Compared with amyloid negative (A-) group, individuals from amyloid positive (A+) group are more impaired based on the Mini-mental State Examination (MMSE; p = 3.82e-05), memory (p = 6.30e-04), executive function (p = 0.0016), and ADAS-cog-13 scores (p = 5.11e-04). Significant decrease of volume (CA1, DG, and Sub) and increase of tau deposition (CA1, Sub, ERC, and PHC) of the hippocampal subfields of both hemispheres were observed for the A+ group compared to the A- group. Tauopathy of ERC is significantly associated with memory score for the A- group, and the associated regions spread into Sub and PHC for the A+ group. The relationship between the impairment of behavior or executive function and tauopathy of the hippocampal subfield was discovered within the A+ group. Leftward asymmetry was observed with the association between assessment scores and tauopathy of the hippocampal subfield, which is more prominent for the NPI score for the A+ group. Conclusion: The associations of tauopathy/volume of the hippocampal subfields with clinical symptoms provide additional insight into the understanding of local changes of the human HF during the AD continuum and can be used as a reference for future studies.

Combination therapy of tanshinone IIA and puerarin for pulmonary fibrosis via targeting IL6-JAK2-STAT3/STAT1 signaling pathways.

Efficient therapy of idiopathic pulmonary fibrosis (IPF) is still a major challenge. The current studies with single-target drug therapy are the pessimistic approaches due to the complex characteristics of IPF. Here, a combination therapy of Tanshinone IIA and Puerarin for IPF was proposed to alleviate IPF due to their antiinflammatory and anti-fibrotic effects. In vivo, the combination therapy could significantly attenuate the area of ground glass opacification that was presented by 85% percentile density score of the micro-CT images when compared to single conditions. In addition, the combination therapy enormously improved the survival rate and alleviated pathological changes in bleomycin (BLM)-induced IPF mice. By using a wide spectrum of infiltration biomarkers in immunofluorescence assay in pathological sections, we demonstrate that fewer IL6 related macrophage infiltration and fibrosis area after this combination therapy, and further proved that IL6-JAK2-STAT3/STAT1 is the key mechanism of the combination therapy. In vitro, combination therapy markedly inhibited the fibroblasts activation and migration which was induced by TGF-ß1 or/and IL6 through JAK2-STAT3/STAT1 signaling pathway. This study demonstrated that combination therapeutic effect of TanIIA and Pue on IPF may be related to the reduced inflammatory response targeting IL6, which could be an optimistic and effective approach for IPF.

3D Retinal Vessel Density Mapping With OCT-Angiography.

Optical Coherence Tomography Angiography (OCTA) is a novel, non-invasive imaging modality of retinal capillaries at micron resolution. Recent studies have correlated macular OCTA vascular measures with retinal disease severity and supported their use as a diagnostic tool. However, these measurements mostly rely on a few summary statistics in retinal layers or regions of interest in the two-dimensional (2D) en face projection images. To enable 3D and localized comparisons of retinal vasculature between longitudinal scans and across populations, we develop a novel approach for mapping retinal vessel density from OCTA images. We first obtain a high-quality 3D representation of OCTA-based vessel networks via curvelet-based denoising and optimally oriented flux (OOF). Then, an effective 3D retinal vessel density mapping method is proposed. In this framework, a vessel density image (VDI) is constructed by diffusing the vessel mask derived from OOF-based analysis to the entire image volume. Subsequently, we utilize a non-linear, 3D OCT image registration method to provide localized comparisons of retinal vasculature across subjects. In our experimental results, we demonstrate an application of our method for longitudinal qualitative analysis of two pathological subjects with edema during the course of clinical care. Additionally, we quantitatively validate our method on synthetic data with simulated capillary dropout, a dataset obtained from a normal control (NC) population divided into two age groups and a dataset obtained from patients with diabetic retinopathy (DR). Our results show that we can successfully detect localized vascular changes caused by simulated capillary loss, normal aging, and DR pathology even in presence of edema. These results demonstrate the potential of the proposed framework in localized detection of microvascular changes and monitoring retinal disease progression.

Emotional detachment, gait ataxia, and cerebellar dysconnectivity associated with compound heterozygous mutations in the SPG7 gene.

We report a patient with autism-like deficits in emotional connectedness, executive dysfunction, and ataxia beginning at age 39. He had compound heterozygous variants in SPG7 (A510V and 1552+1 G>T substitutions), mutation of which is classically associated with spastic paraparesis. Diffusion MRI demonstrated abnormalities in the cerebellar outflow tracts. Transcranial magnetic stimulation showed a prolonged cortical silent period representing exaggerated cortical inhibition, as previously described with pure cerebellar degeneration. The acquired cerebellar cognitive affective syndrome in association with specific anatomic and neurophysiological abnormalities in the cerebellum expand the spectrum of SPG7-related neurodegeneration and support a role for cerebellar output in socio-emotional behavior.

A probabilistic atlas of locus coeruleus pathways to transentorhinal cortex for connectome imaging in Alzheimer's disease.

According to the latest Braak staging of Alzheimer's disease (AD), tau pathology occurs earliest in the brain in the locus coeruleus (LC) of the brainstem, then propagates to the transentorhinal cortex (TEC), and later to other neocortical regions. Recent animal and in vivo human brain imaging research also support the trans-axonal propagation of tau pathology. In addition, neurochemical studies link norepinephrine to behavioral symptoms in AD. It is thus critical to examine the integrity of the LC-TEC pathway in studying the early development of the disease, but there has been limited work in this direction. By leveraging the high-resolution and multi-shell diffusion MRI data from the Human Connectome Project (HCP), in this work we develop a novel method for the reconstruction of the LC-TEC pathway in a cohort of 40 HCP subjects carefully selected based on rigorous quality control of the residual distortion artifacts in the brainstem. A probabilistic atlas of the LC-TEC pathway of both hemispheres is then developed in the MNI152 space and distributed publicly on the NITRC website. To apply our atlas on clinical imaging data, we develop an automated approach to calculate the medial core of the LC-TEC pathway for localized analysis of connectivity changes. In a cohort of 138 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we demonstrate the detection of the decreased fiber integrity in the LC-TEC pathways with increasing disease severity.

3D Shape Modeling and Analysis of Retinal Microvasculature in OCT-Angiography Images.

3D optical coherence tomography angiography (OCT-A) is a novel and non-invasive imaging modality for analyzing retinal diseases. The studies of microvasculature in 2D en face projection images have been widely implemented, but comprehensive 3D analysis of OCT-A images with rich depth-resolved microvascular information is rarely considered. In this paper, we propose a robust, effective, and automatic 3D shape modeling framework to provide a high-quality 3D vessel representation and to preserve valuable 3D geometric and topological information for vessel analysis. Effective vessel enhancement and extraction steps by means of curvelet denoising and optimally oriented flux (OOF) filtering are first designed to produce 3D microvascular networks. Afterwards, a novel 3D data representation of OCT-A microvasculature is reconstructed via advanced mesh reconstruction techniques. Based on the 3D surfaces, shape analysis is established to extract novel shape-based microvascular area distortion via the Laplace-Beltrami eigen-projection. The extracted feature is integrated into a graph-cut segmentation system to categorize large vessels and small capillaries for more precise shape analysis. The proposed framework is validated on a dedicated repeated scan dataset including 260 volume images and shows high repeatability. Statistical analysis using the surface area biomarker is performed on small capillaries to avoid the effect of tailing artifact from large vessels. It shows significant differences ( ) between DR stages on 100 subjects in a OCTA-DR dataset. The proposed shape modeling and analysis framework opens the possibility for further investigating OCT-A microvasculature in a new perspective.

Automated Deformation-Based Analysis of 3D Optical Coherence Tomography in Diabetic Retinopathy.

Diabetic retinopathy (DR) is a significant microvascular complication of diabetes mellitus and a leading cause of vision impairment in working age adults. Optical coherence tomography (OCT) is a routinely used clinical tool to observe retinal structural and thickness alterations in DR. Pathological changes that alter the normal anatomy of the retina, such as intraretinal edema, pose great challenges for conventional layer-based analysis of OCT images. We present an alternative approach for the automated analysis of OCT volumes in DR research based on nonlinear registration. In this paper, we first obtain an anatomically consistent volume of interest (VOI) in different OCT images via carefully designed masking and affine registration. After that, efficient B-spline transformations are computed using stochastic gradient descent optimization. Using the OCT volumes of normal controls, for which layer-based segmentation works well, we demonstrate the accuracy of our registration-based analysis in aligning layer boundaries. By nonlinearly registering the OCT volumes of DR subjects to an atlas constructed from normal controls and measuring the Jacobian determinant of the deformation, we can simultaneously visualize tissue contraction and expansion due to DR pathology. Tensor-based morphometry (TBM) can also be performed for quantitative analysis of local structural changes. In our experimental results, we apply our method to a dataset of 105 subjects and demonstrate that volumetric OCT registration and TBM analysis can successfully detect local retinal structural alterations due to DR.

Unsupervised Deep Learning for Susceptibility Distortion Correction in Connectome Imaging.

To reduce the residual distortion in high resolution diffusion MRI (dMRI) data preprocessed by the HCP-Pipeline, we propose an unsupervised deep learning based method to correct the residual susceptibility induced distortion. Instead of using B0 images from two phase encoding (PE), fiber orientation distribution (FOD) images computed from dMRI data, which provide more reliable contrast information, are used in our method. Our deep learning framework named DistoRtion Correction Net (DrC-Net) uses an U-Net to capture the latent features from FOD images and estimates a deformation field along the phase encoding direction. With the help of a transformer network, we can propagate the deformation feature to the FOD images and back propagate the losses between the deformed images and true undistorted images. The proposed DrC-Net is trained on 60 subjects randomly selected from 100 subjects in the Human Connectome Project (HCP) dataset. We evaluated the DrC-Net on the rest 40 subjects and the results show a similar performance compared to the training dataset. Our evaluation method used mean squared difference (MSD) of fractional anisotropy (FA) and minimum angular difference between two PEs. We compared the DrC-Net to topup method used in the HCP-Pipeline, and the results show a significant improvement to correct the susceptibility induced distortions in both evaluation methods.

Evaluation of an Open Source Registration Package for Automatic Contour Propagation in Online Adaptive Intensity-Modulated Proton Therapy of Prostate Cancer.

Objective: Our goal was to investigate the performance of an open source deformable image registration package, elastix, for fast and robust contour propagation in the context of online-adaptive intensity-modulated proton therapy (IMPT) for prostate cancer. Methods: A planning and 7-10 repeat CT scans were available of 18 prostate cancer patients. Automatic contour propagation of repeat CT scans was performed using elastix and compared with manual delineations in terms of geometric accuracy and runtime. Dosimetric accuracy was quantified by generating IMPT plans using the propagated contours expanded with a 2 mm (prostate) and 3.5 mm margin (seminal vesicles and lymph nodes) and calculating dosimetric coverage based on the manual delineation. A coverage of V 95% ≥ 98% (at least 98% of the target volumes receive at least 95% of the prescribed dose) was considered clinically acceptable. Results: Contour propagation runtime varied between 3 and 30 s for different registration settings. For the fastest setting, 83 in 93 (89.2%), 73 in 93 (78.5%), and 91 in 93 (97.9%) registrations yielded clinically acceptable dosimetric coverage of the prostate, seminal vesicles, and lymph nodes, respectively. For the prostate, seminal vesicles, and lymph nodes the Dice Similarity Coefficient (DSC) was 0.87 ± 0.05, 0.63 ± 0.18, and 0.89 ± 0.03 and the mean surface distance (MSD) was 1.4 ± 0.5 mm, 2.0 ± 1.2 mm, and 1.5 ± 0.4 mm, respectively. Conclusion: With a dosimetric success rate of 78.5-97.9%, this software may facilitate online adaptive IMPT of prostate cancer using a fast, free and open implementation.

FOD-based registration for susceptibility distortion correction in brainstem connectome imaging.

The high resolution, multi-shell diffusion MRI (dMRI) data from the Human Connectome Project (HCP) provides a great opportunity to map fine-grained fiber pathways in human brainstem, but the severe susceptibility-induced distortion around the brainstem poses a significant challenge. While the correction tools used in the HCP Pipeline greatly reduce the distortion artifacts in the preprocessed data, significant residual distortions are still widely present, especially in the brainstem region. One fundamental reason is that the topup tool used in the HCP Pipeline only relies on the B0 images, which lack sufficient contrast about white matter pathways, to estimate the distortion displacement between opposite phase encodings (PEs). To fully utilize the rich information of HCP data that includes dMRI data from two opposite PEs, we compute the fiber orientation distributions (FODs) from the data of each PE and propose a novel method to estimate and correct the residual distortion using FOD-based registration. Using the dMRI data of 94 HCP subjects, we show quantitatively that our method can reduce the misalignment of main fiber direction in the brainstem by 21% as compared to the topup tool used in the HCP Pipeline. Our method is fully compatible with the HCP Pipeline and thus can be readily integrated with it to enhance distortion correction in connectome imaging research.