S. Jambawalikar

发表

Diffusion tensor imaging of peripheral nerves

Haifang Li, S. Jambawalikar, Jeremy Baum, 2010, Skeletal Radiology.

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials

N. Obuchowski, L. Schwartz, Wei Huang, 2018, Journal of magnetic resonance imaging : JMRI.

SU‐E‐I‐04: Texture Feature Based CAD for Breast Cancer Detection

Haifang Li, S. Jambawalikar, T. Button, 2011 .

Convolutional Neural Network Detection of Axillary Lymph Node Metastasis Using Standard Clinical Breast MRI.

R. Vanguri, Haifang Li, S. Jambawalikar, 2019, Clinical breast cancer.

Repeatability of Quantitative Diffusion-Weighted Imaging Metrics in Phantoms, Head-and-Neck and Thyroid Cancers: Preliminary Findings

N. Obuchowski, L. Schwartz, T. Chenevert, 2019, Tomography.

COVID-19 neuropathology at Columbia University Irving Medical Center/New York Presbyterian Hospital

A. Khandji, J. Claassen, P. Canoll, 2021, medRxiv.

Advanced MR Imaging of the Temporal Bone.

S. Jambawalikar, G. Moonis, Michael Z. Liu, 2019, Neuroimaging clinics of North America.

Deep semantic lung segmentation for tracking potential pulmonary perfusion biomarkers in chronic obstructive pulmonary disease (COPD): The multi‐ethnic study of atherosclerosis COPD study

B. Schmidt, F. Wacker, S. Jambawalikar, 2020, Journal of magnetic resonance imaging : JMRI.

Deep Learning for Functional Brain Connectivity: Are We There Yet?

Ulas Bagci, Sachin Jambawalikar, Harish RaviPrakash, 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

A multi-institute automated segmentation evaluation on a standard dataset: Findings from the international workshop on osteoarthritis imaging segmentation challenge

Mehmet Akcakaya, Drew A. Torigian, Akshay Pai, 2020 .

Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis

Concetto Spampinato, Ulas Bagci, Baris Turkbey, 2021, MLMI@MICCAI.

Deep Learning of Computed Tomography Virtual Wedge Resection for Prediction of Histologic Usual Interstitial Pneumonitis

A. Saqi, S. Jambawalikar, S. Mutasa, 2020 .

Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image Data.

S. Jambawalikar, S. Mutasa, R. Ha, 2019, AJR. American journal of roentgenology.

High‐resolution simulation of B0 field conditions in the human heart from segmented computed tomography images

S. Jambawalikar, C. Juchem, L. Schreiber, 2022, NMR in biomedicine.

Insulin Hexamer-Caged Gadolinium Ion as MRI Contrast-o-phore.

M. Stojanović, S. Jambawalikar, T. Tran, 2018, Chemistry.

Tailored Magnetic Resonance Fingerprinting

S. Jambawalikar, A. Lignelli, M. Fung, 2022, medRxiv.

Chronic convection-enhanced delivery of topotecan for patients with recurrent glioblastoma: a first-in-patient, single-centre, single-arm, phase 1b trial.

J. Grinband, P. Canoll, S. Jambawalikar, 2022, The Lancet. Oncology.

Pharmacokinetic analysis and drug delivery efficiency of the focused ultrasound-induced blood-brain barrier opening in non-human primates.

E. Konofagou, S. Jambawalikar, Shih-Ying Wu, 2017, Magnetic resonance imaging.

Predicting Post Neoadjuvant Axillary Response Using a Novel Convolutional Neural Network Algorithm

S. Jambawalikar, P. Chang, S. Mutasa, 2018, Annals of Surgical Oncology.

Treatment of Recurrent Glioblastoma by Chronic Convection-Enhanced Delivery of Topotecan

J. Grinband, P. Canoll, S. Jambawalikar, 2021, medRxiv.

The role of initial chest X-ray in triaging patients with suspected COVID-19 during the pandemic

J. Austin, S. Jambawalikar, M. Salvatore, 2020, Emergency Radiology.

Deep learning prediction of axillary lymph node status using ultrasound images

Shawn H. Sun, S. Jambawalikar, S. Mutasa, 2022, Comput. Biol. Medicine.

3D Isotropic Super-resolution Prostate MRI Using Generative Adversarial Networks and Unpaired Multiplane Slices

Hao-Yun Hsu, Sachin Jambawalikar, Richard Ha, 2021, Journal of Digital Imaging.

Surface Point Cloud Ultrasound with Transcranial Doppler: Coregistration of Surface Point Cloud Ultrasound with Magnetic Resonance Angiography for Improved Reproducibility, Visualization, and Navigation in Transcranial Doppler Ultrasound

J. N. Stember, K. L. Terilli, E. Perez, 2020, Journal of Digital Imaging.

Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks

Ulas Bagci, Bradford J. Wood, Elizabeth A. Krupinski, 2019, Journal of Digital Imaging.

Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study

Jasnit S. Makkar, L. Schwartz, R. Vanguri, 2020, European Radiology.

Cardiac-Specific Conversion Factors to Estimate Radiation Effective Dose From Dose-Length Product in Computed Tomography.

S. Halliburton, A. Einstein, S. Jambawalikar, 2018, JACC. Cardiovascular imaging.

Can diffusion‐weighted imaging serve as a biomarker of fibrosis in pancreatic adenocarcinoma?

E. Hecht, S. Jambawalikar, J. Chabot, 2017, Journal of magnetic resonance imaging : JMRI.

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset pdf

Mehmet Akcakaya, Akshay Pai, Ulas Bagci, 2020, Radiology. Artificial intelligence.

Investigating the mechanical function of the cervix during pregnancy using finite element models derived from high-resolution 3D MRI

S Jambawalikar, S. Jambawalikar, R. Wapner, 2016, Computer methods in biomechanics and biomedical engineering.

Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm

Richard Ha, Peter D. Chang, Simukayi Mutasa, 2019, Journal of Digital Imaging.

A Pilot Study on Feasibility of Ultrashort Echo Time T2* Cartilage Mapping in the Sacroiliac Joints

Phuong-Anh T. Duong, S. Jambawalikar, P. Quarterman, 2021, Journal of computer assisted tomography.

The BBB surrounds cerebral microvessels and tightly regulates the influx and efflux of molecules circulating in the bloodstream, hindering the delivery of pharmacological agents

S. Jambawalikar, A. Pouliopoulos, M. Karakatsani, 2021 .

Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset

Simukayi Mutasa, Jenika Karcich, Ralph T. Wynn, 2018, Journal of Digital Imaging.

Weakly Supervised Deep Learning Approach to Breast MRI Assessment.

Shawn H. Sun, S. Jambawalikar, R. Ha, 2021, Academic radiology.

A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database.

Simukayi Mutasa, Sachin Jambawalikar, Peter Chang, 2020, Magnetic resonance imaging.

Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score

Simukayi Mutasa, Jenika Karcich, Kevin Kalinsky, 2018, Journal of magnetic resonance imaging : JMRI.

Channel width optimized neural networks for liver and vessel segmentation in liver iron quantification

Yucheng Liu, Simukayi Mutasa, Sachin Jambawalikar, 2020, Comput. Biol. Medicine.

Segmentation of Brain Tumors Using DeepLabv3+

Rami Vanguri, Piyush Kumar, Ahana Roy Choudhury, 2018, BrainLes@MICCAI.

Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset.

Simukayi Mutasa, Jenika Karcich, Sachin Jambawalikar, 2019, Academic radiology.

Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset

Richard Ha, Peter D. Chang, Simukayi Mutasa, 2018, Journal of Digital Imaging.

Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement

Richard Ha, Peter D. Chang, Eralda Mema, 2018, Journal of Digital Imaging.

Dynamic infrared imaging for the detection of malignancy.

Haifang Li, Sachin Jambawalikar, Haifang Li, 2004, Physics in medicine and biology.

Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom

L. Schwartz, W. Reddick, T. Chenevert, 2021, PloS one.

Dosimetric assessment of patient dose calculation on a deep learning‐based synthesized computed tomography image for adaptive radiotherapy

S. Jambawalikar, C. Wuu, D. Horowitz, 2022, Journal of applied clinical medical physics.

Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle.

Ulas Bagci, Sachin Jambawalikar, David Gutman, 2020, Radiology. Artificial intelligence.

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review

Phuong-Anh T. Duong, S. Jambawalikar, Michael Z. Liu, 2023, Tomography.

3D Printing and Heart Failure: The Present and the Future.

Kanwal M. Farooqi, Hod Lipson, E. Bacha, 2019, JACC. Heart failure.

A Note on Approximate Minimum Volume Enclosing Ellipsoid of Ellipsoids

Sachin Jambawalikar, Piyush Kumar, Piyush Kumar, 2008, 2008 International Conference on Computational Sciences and Its Applications.

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Syed Muhammad Anwar, Mehmet Akcakaya, Drew A. Torigian, 2020, Journal of Signal Processing Systems.

Multicenter Repeatability Study of a Novel Quantitative Diffusion Kurtosis Imaging Phantom

L. Schwartz, T. Chenevert, S. Jambawalikar, 2019, Tomography.

Convolutional Neural Networks for the Detection and Measurement of Cerebral Aneurysms on Magnetic Resonance Angiography

Joseph N Stember, Peter Chang, Danielle M Stember, 2018, Journal of Digital Imaging.

Estimating Effective Dose of Radiation From Pediatric Cardiac CT Angiography Using a 64-MDCT Scanner: New Conversion Factors Relating Dose-Length Product to Effective Dose.

A. Einstein, S. Jambawalikar, Yanping Xu, 2017, AJR. American journal of roentgenology.

Contrast-Free Detection of Focused Ultrasound-Induced Blood-Brain Barrier Opening Using Diffusion Tensor Imaging

Elisa E. Konofagou, Sachin R. Jambawalikar, Maria Eleni Karakatsani, 2020, IEEE Transactions on Biomedical Engineering.

Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset

Ralph T. Wynn, S. Jambawalikar, P. Chang, 2018, Journal of Digital Imaging.

Potential Role of Convolutional Neural Network Based Algorithm in Patient Selection for DCIS Observation Trials Using a Mammogram Dataset.

Simukayi Mutasa, Jenika Karcich, Peter Chang, 2020, Academic radiology.

An image processing approach to compensate for the bladder wall motion and deformation in MR cystography

Z. Liang, H. Li, D. Harrington, 2011, 2011 IEEE Nuclear Science Symposium Conference Record.

Fusion of aerial lidar and images for road segmentation with deep CNN

Tathagata Mukherjee, Eduardo L. Pasiliao, Sachin Jambawalikar, 2018, SIGSPATIAL/GIS.

Insulin Hexamer-Caged Gadolinium Ion as MRI Contrast-o-phore.

M. Stojanović, S. Jambawalikar, T. Tran, 2018, Chemistry.

Feasibility of ultrashort echo time (UTE) T2* cartilage mapping in the hip: a pilot study

S. Jambawalikar, D. Jaramillo, P. Quarterman, 2021, Acta radiologica.