Objective: Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterogeneity. We hypothesized that the profiles of multiple DBSI metrics can identify lesion-defining patterns. Here we test this hypothesis by combining a deep learning algorithm using deep neural network (DNN) with DBSI and other imaging methods. Methods: Thirty-eight MS patients were scanned with diffusion-weighted imaging, magnetization transfer imaging, and standard conventional MRI sequences (cMRI). A total of 499 regions of interest were identified on standard MRI and labeled as persistent black holes (PBH), persistent gray holes (PGH), acute black holes (ABH), acute gray holes (AGH), nonblack or gray holes (NBH), and normal appearing white matter (NAWM). DBSI, diffusion tensor imaging (DTI), and magnetization transfer ratio (MTR) were applied to the 43,261 imaging voxels extracted from these ROIs. The optimized DNN with 10 fully connected hidden layers was trained using the imaging metrics of the lesion subtypes and NAWM. Results: Concordance, sensitivity, specificity, and accuracy were determined for the different imaging methods. DBSI-DNN derived lesion classification achieved 93.4% overall concordance with predetermined lesion types, compared with 80.2% for DTI-DNN model, 78.3% for MTR-DNN model, and 74.2% for cMRI-DNN model. DBSI-DNN also produced the highest specificity, sensitivity, and accuracy. Conclusions: DBSI-DNN improves the classification of different MS lesion subtypes, which could aid clinical decision making. The efficacy and efficiency of DBSI-DNN shows great promise for clinical applications in automatic MS lesion detection and classification.

Purpose: Glioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted to examine GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation remains an unmet need in the clinical management of GBMs.Experimental Design: We employ a novel diffusion histology imaging (DHI) approach, combining diffusion basis spectrum imaging (DBSI) and machine learning, to detect, differentiate, and quantify areas of high cellularity, tumor necrosis, and tumor infiltration in GBM.Results: Gadolinium-enhanced T1-weighted or hyperintense fluid-attenuated inversion recovery failed to reflect the morphologic complexity underlying tumor in patients with GBM. Contrary to the conventional wisdom that apparent diffusion coefficient (ADC) negatively correlates with increased tumor cellularity, we demonstrate disagreement between ADC and histologically confirmed tumor cellularity in GBM specimens, whereas DBSI-derived restricted isotropic diffusion fraction positively correlated with tumor cellularity in the same specimens. By incorporating DBSI metrics as classifiers for a supervised machine learning algorithm, we accurately predicted high tumor cellularity, tumor necrosis, and tumor infiltration with 87.5%, 89.0%, and 93.4% accuracy, respectively.Conclusions: Our results suggest that DHI could serve as a favorable alternative to current neuroimaging techniques in guiding biopsy or surgery as well as monitoring therapeutic response in the treatment of GBM.

PurposeGlioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted for examining GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in the clinical management of GBMs.Experimental DesignWe employ a novel Diffusion Histology Imaging (DHI) approach, combining diffusion basis spectrum imaging (DBSI) and machine learning, to detect, differentiate, and quantify areas of high cellularity, tumor necrosis, and tumor infiltration in GBM.ResultsGd-enhanced T1W or hyper-intense FLAIR failed to reflect the morphological complexity underlying tumor in GBM patients. Contrary to the conventional wisdom that apparent diffusion coefficient (ADC) negatively correlates with increased tumor cellularity, we demonstrate disagreement between ADC and histologically confirmed tumor cellularity in glioblastoma specimens, whereas DBSI-derived restricted isotropic diffusion fraction positively correlated with tumor cellularity in the same specimens. By incorporating DBSI metrics as classifiers for a supervised machine learning algorithm, we accurately predicted high tumor cellularity, tumor necrosis, and tumor infiltration with 87.5%, 89.0% and 93.4% accuracy, respectively.ConclusionOur results suggest that DHI could serve as a favorable alternative to current neuroimaging techniques for guiding biopsy or surgery as well as monitoring therapeutic response in the treatment of glioblastoma.Translational RelevanceCurrent clinical diagnosis, surgical planning, and assessment of treatment response for GBM patients relies heavily on gadolinium-enhanced T1-weighted MRI, which is non-specific for tumor growth and merely reflects a disrupted blood-brain barrier. The complex tumor microenvironment and spatial heterogeneity make GBM difficult to characterize using current clinical imaging modalities. In this study, we developed a novel imaging technique to characterize and accurately predict key histological features of GBM - high tumor cellularity, tumor necrosis, and tumor infiltration. While further validation in a larger cohort of patients is needed, the current proof-of-concept approach could provide a solution to resolve important clinical questions such as the identification of true tumor progression vs. pseudoprogression or radiation necrosis.

High-grade pediatric brain tumors exhibit the highest cancer mortality rates in children. While conventional MRI has been widely adopted for examining pediatric high-grade brain tumors clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in their clinical management. We employed a novel Diffusion Histology Imaging (DHI) approach employing diffusion basis spectrum imaging (DBSI) derived metrics as the input classifiers for deep neural network analysis. DHI aims to detect, differentiate, and quantify heterogeneous areas in pediatric high-grade brain tumors, which include normal white matter (WM), densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis, and hemorrhage. Distinct diffusion metric combination would thus indicate the unique distributions of each distinct tumor histology features. DHI, by incorporating DBSI metrics and the deep neural network algorithm, classified pediatric tumor histology with an overall accuracy of 85.8%. Receiver operating analysis (ROC) analysis suggested DHI’s great capability in distinguishing individual tumor histology with AUC values (95% CI) of 0.984 (0.982–0.986), 0.960 (0.956–0.963), 0.991 (0.990–0.993), 0.950 (0.944–0.956), 0.977 (0.973–0.981) and 0.976 (0.972–0.979) for normal WM, densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis and hemorrhage, respectively. Our results suggest that DBSI-DNN, or DHI, accurately characterized and classified multiple tumor histologic features in pediatric high-grade brain tumors. If these results could be further validated in patients, the novel DHI might emerge as a favorable alternative to the current neuroimaging techniques to better guide biopsy and resection as well as monitor therapeutic response in patients with high-grade brain tumors.

High-grade pediatric brain tumors constitute the highest mortality of cancer-death in children.While conventional MRI has been widely adopted for examining pediatric high-grade brain tumor clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in the clinical management of pediatric brain tumor. We employed a novel Diffusion Histology Imaging (DHI) approach that incorporates diffusion basis spectrum imaging (DBSI) and deep neural network. DHI aims to detect, differentiate, and quantify heterogenous areas in pediatric highgrade brain tumors, which include normal white matter (WM), densely cellular tumor (DC tumor), less densely cellular tumor (LDC tumor), infiltrating edge, necrosis, and hemorrhage. Distinct diffusion metric combination would thus indicate the unique distributions of each distinct tumor histology features. DHI, by incorporating DBSI metrics and the deep neural network algorithm, classified pediatric tumor histology with an overall accuracy of 83.3%. Receiver operating analysis (ROC) analysis suggested DHI's great capability in distinguishing individual tumor histology with AUC values (95%CI) of 0.983 (0.985-0.989), 0.961 (0.957-0.964), 0.993 (0.992-0.994), 0.953 (0.947-0.958), 0.974 (0.970-0.978) and 0.980 (0.977-0.983) for normal WM, DC tumor, LDCtumor, infiltrating edge, necrosis and hemorrhage, respectively. Our results suggest that DBSI-DNN, or DHI, accurately characterized and classified multiple tumor histologic features in pediatric high-grade brain tumors. If further validated in patients, the novel DHI might emerge as a favorable alternative to the current neuroimaging techniques to better guide biopsy and resection as well as monitor therapeutic response in patients with high-grade brain tumors.

Background: Diagnosing MS through magnetic resonance imaging (MRI) requires extensive clinical experience and tedious work. Furthermore, MRI-indicated MS lesion locations rarely align with the patients' symptoms and often contradict with pathology studies. Our lab has developed and modified a novel diffusion basis spectrum imaging (DBSI) technique to address the shortcomings of MRI-based MS diagnoses. Although primary DBSI metrics have been demonstrated to be associated with axonal injury/loss, demyelination and inflammation, a more detailed analysis using multiple DBSI-structural metrics to improve the accuracy of MS lesion detection and differentiation is still needed. Here we report that Diffusion Histology Imaging (DHI), an improved approach that combines a deep neural network (DNN) algorithm with improved DBSI analyses, accurately detected and classified various MS lesion types. Methods: Thirty-eight multiple sclerosis patients were scanned with T2-weighted imaging (T2WI) using fluid attenuated inversion recovery (FLAIR), T1-weighted imaging (T1WI) using magnetization-prepared rapid acquisition with gradient echo (MPRAGE), magnetization transfer contrast (MTR) imaging and diffusion-weighted imaging. The imaging results identified 43,261 voxels from 91 persistent black hole (PBH) lesions, 89 persistent gray hole (PGH) lesions, 16 acute gray hole (AGH) lesions, 189 non-black hole (NBH) lesions and 113 normal-appearing white matter (NAWM) areas. Data extracted from these lesions were randomly split into training, validation, and testing groups with an 8:1:1 ratio. The DNN was constructed with 10 fully-connected hidden layers using TensorFlow 2.0 in Python. Batch normalization and dropout regularization were used for model optimization. Results: Each MS lesion type had unique DBSI derived diffusion metric profiles. Based on these DBSI diffusion metric profiles, DHI achieved a 93.6% overall concordance with neurologist determinations of all five MS lesions, compared with 74.3% from conventional MRI (cMRI)-DNN model, 78.2% from MTR-DNN model, and 80% from DTI-DNN model. DHI also achieved greater performances on detecting individual MS lesion types compared to other models. Specifically, DHI showed great performances on prediction of PBH (AUC: 0.991; F1-score: 0.923), PGH (AUC: 0.977; F1-score: 0.823) and AGH (AUC: 0.987; F1-score: 0.887), which significantly outperformed other models. Conclusions: DHI significantly improves the detection and classification accuracy for various MS lesion types, which could greatly aid the clinical decisions of neurologists and neuroradiologists. The efficacy and efficiency of this DNN model shows great promise for clinical application.

Background: Multiple sclerosis (MS) is a prototype neuroinflammatory disorder with increasingly recognized role for neurodegeneration. Most first-line treatments cannot prevent the progression of neurodegeneration and the resultant disability. Interventions can improve symptoms of MS and might provide insights into the underlying pathology. Objective: To investigate the effect of intermittent caloric restriction on neuroimaging markers of MS. Methods: We randomized ten participants with relapsing remitting MS to either a 12-week intermittent calorie restriction (iCR) diet (n = 5) or control (n = 5). Cortical thickness and volumes were measured through FreeSurfer, cortical perfusion was measured by arterial spin labeling and neuroinflammation through diffusion basis spectrum imaging. Results: After 12 weeks of iCR, brain volume increased in the left superior and inferior parietal gyri (p: 0.050 and 0.049, respectively) and the banks of the superior temporal sulcus (p: 0.01). Similarly in the iCR group, cortical thickness improved in the bilateral medial orbitofrontal gyri (p: 0.04 and 0.05 in right and left, respectively), the left superior temporal gyrus (p: 0.03), and the frontal pole (p: 0.008) among others. Cerebral perfusion decreased in the bilateral fusiform gyri (p: 0.047 and 0.02 in right and left, respectively) and increased in the bilateral deep anterior white matter (p: 0.03 and 0.013 in right and left, respectively). Neuroinflammation, demonstrated through hindered and restricted water fractions (HF and RF), decreased in the left optic tract (HF p: 0.02), and the right extreme capsule (RF p: 0.007 and HF p: 0.003). Conclusion: These pilot data suggest therapeutic effects of iCR in improving cortical volume and thickness and mitigating neuroinflammation in midlife adults with MS.

<p>Supplementary methods, figures and tables</p>