Gaurav Verma

Bio: Gaurav Verma is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: Medicine & Neuroscience. The author has an hindex of 14, co-authored 36 publications receiving 653 citations. Previous affiliations of Gaurav Verma include Hospital of the University of Pennsylvania & University of Pennsylvania.

Imaging of glutamate neurotransmitter alterations in Alzheimer's disease.

Abstract: Glutamate (Glu) is a major excitatory neurotransmitter in the brain and has been shown to decrease in the early stages of Alzheimer’s disease (AD). Using a glutamate chemical (amine) exchange saturation transfer (GluCEST) method, we imaged the change in [Glu] in the APP-PS1 transgenic mouse model of AD at high spatial resolution. Compared with wild-type controls, AD mice exhibited a notable reduction in GluCEST contrast (~30%) in all areas of the brain. The change in [Glu] was further validated through 1 H MRS. A positive correlation was observed between GluCEST contrast and 1 H MRS-measured Glu/total creatine ratio. This method potentially provides a novel noninvasive biomarker for the diagnosis of the disease in preclinical stages and enables the development of disease-modifying therapies for AD. Copyright © 2012 John Wiley & Sons, Ltd.

A convolutional neural network to filter artifacts in spectroscopic MRI.

Abstract: Purpose Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. Methods A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. Results When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. Conclusion The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning.

MR spectroscopic imaging and diffusion‐weighted imaging of prostate cancer with Gleason scores

Abstract: Purpose: To investigate functional changes in prostate cancer patients with three pathologically proven different Gleason scores (GS) (3+3, 3+4, and 4+3) using magnetic resonance spectroscopic imaging (MRSI) and diffusion-weighted imaging (DWI). Materials and Methods: In this study MRSI and DWI data were acquired in 41 prostate cancer patients using a 1.5T MRI scanner with a body matrix combined with an endorectal coil. The metabolite ratios of (Cho+Cr)/Cit were calculated from the peak integrals of total choline (Cho), creatine (Cr), and citrate (Cit) in MRSI. Apparent diffusion coefficient (ADC) values were derived from DWI for three groups of Gleason scores. The sensitivity and specificity of MRSI and DWI in patients were calculated using receiver operating characteristic curve (ROC) analysis. Results: The mean and standard deviation of (Cho+Cr)/Cit ratios of GS 3+3, GS 3+4, and GS 4+3 were: 0.44 ± 0.02, 0.56 ± 0.06, and 0.88 ± 0.11, respectively. For the DWI, the mean and standard deviation of ADC values in GS 3+3, GS 3+4, and GS 4+3 were: 1.13 ± 0.11, 0.97 ± 0.10, and 0.83 ± 0.08 mm2/sec, respectively. Statistical significances were observed between the GS and metabolite ratio as well as ADC values and GS. Conclusion: Combined MRSI and DWI helps identify the presence and the proportion of aggressive cancer (ie, Gleason grade 4) that might not be apparent on biopsy sampling. This information can guide subsequent rebiopsy management, especially for active surveillance programs. J. Magn. Reson. Imaging 2012;36:697–703. © 2012 Wiley Periodicals, Inc.

Whole-Brain Analysis of Amyotrophic Lateral Sclerosis by Using Echo-Planar Spectroscopic Imaging

Abstract: Significantly lower N-acetylaspartate (NAA)/creatinine and NAA/choline ratios in the precentral gyrus, midfrontal region, left caudate, occipital lobe, and other regions confirm that amyotrophic lateral sclerosis is a multisystem disorder of the entire brain and is not restricted to the motor cortex.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Regression of Glioblastoma after Chimeric Antigen Receptor T-Cell Therapy

Abstract: A patient with recurrent multifocal glioblastoma received chimeric antigen receptor (CAR)-engineered T cells targeting the tumor-associated antigen interleukin-13 receptor alpha 2 (IL13Rα2). Multiple infusions of CAR T cells were administered over 220 days through two intracranial delivery routes - infusions into the resected tumor cavity followed by infusions into the ventricular system. Intracranial infusions of IL13Rα2-targeted CAR T cells were not associated with any toxic effects of grade 3 or higher. After CAR T-cell treatment, regression of all intracranial and spinal tumors was observed, along with corresponding increases in levels of cytokines and immune cells in the cerebrospinal fluid. This clinical response continued for 7.5 months after the initiation of CAR T-cell therapy. (Funded by Gateway for Cancer Research and others; ClinicalTrials.gov number, NCT02208362 .).

An overview of deep learning in medical imaging focusing on MRI

Abstract: What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

Engineered T cells: the promise and challenges of cancer immunotherapy

Abstract: The immune system evolved to distinguish non-self from self to protect the organism. As cancer is derived from our own cells, immune responses to dysregulated cell growth present a unique challenge. This is compounded by mechanisms of immune evasion and immunosuppression that develop in the tumour microenvironment. The modern genetic toolbox enables the adoptive transfer of engineered T cells to create enhanced anticancer immune functions where natural cancer-specific immune responses have failed. Genetically engineered T cells, so-called 'living drugs', represent a new paradigm in anticancer therapy. Recent clinical trials using T cells engineered to express chimeric antigen receptors (CARs) or engineered T cell receptors (TCRs) have produced stunning results in patients with relapsed or refractory haematological malignancies. In this Review we describe some of the most recent and promising advances in engineered T cell therapy with a particular emphasis on what the next generation of T cell therapy is likely to entail.