J. Son

发表

American College of Radiology-Compliant Short Protocol Breast MRI for High-Risk Breast Cancer Screening: A Prospective Feasibility Study.

M. Scoggins, W. Yang, Jingfei Ma, 2018, AJR. American journal of roentgenology.

Should abbreviated breast MRI be compliant with American College of Radiology requirements for MRI accreditation?

M. Scoggins, Jingfei Ma, B. Arun, 2020, Magnetic resonance imaging.

Original contributions A fast spin echo two-point Dixon technique and its combination with sensitivity encoding for efficient T2-weighted imaging B

R. Stafford, Jingfei Ma, Haesun Choi, 2005 .

MetNet: computer-aided segmentation of brain metastases in post-contrast T1-weighted magnetic resonance imaging.

Jason M. Johnson, Jingfei Ma, T. Briere, 2020, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

Quantitative Apparent Diffusion Coefficients From Peritumoral Regions as Early Predictors of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer.

Jason B. White, Tanya W Moseley, V. Valero, 2022, Journal of magnetic resonance imaging : JMRI.

Assessment of Early Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer Using Amide Proton Transfer-weighted Chemical Exchange Saturation Transfer MRI: A Pilot Study.

Jason B. White, K. Hwang, G. Rauch, 2021, Radiology. Imaging cancer.

Tumor necrosis by pretreatment breast MRI: association with neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC)

Tanya W. Moseley, Jason B. White, Tanya W Moseley, 2020, Breast Cancer Research and Treatment.

Evaluation of feasibility and image quality of a new radial quantitative T2 weighted imaging sequence for liver MRI.

P. Bhosale, Jingfei Ma, J. Szklaruk, 2020, Clinical imaging.

Multimodal Imaging of Pathologic Response to Chemoradiation in Esophageal Cancer.

O. Mawlawi, Steven H. Lin, B. Hobbs, 2018, International journal of radiation oncology, biology, physics.

Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI

Tanya W. Moseley, Miral M Patel, Jason B. White, 2023, Scientific Reports.

Computer-aided detection of mantle cell lymphoma on 18F-FDG PET/CT using a deep learning convolutional neural network.

Michael L. Wang, Jingfei Ma, H. Macapinlac, 2021, American journal of nuclear medicine and molecular imaging.

Fat‐suppressed three‐dimensional dual echo dixon technique for contrast agent enhanced MRI

Jingfei Ma, Jong Bum Son, John D Hazle, 2006, Journal of magnetic resonance imaging : JMRI.

Computer-aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single-Shot Detectors.

Jason M. Johnson, Jingfei Ma, T. Briere, 2020, Radiology.

MO-FG-CAMPUS-IeP3-03: Optimal Parameters to Determine the Apparent Diffusion Coefficient in Diffusion Weighted Imaging Via Simulation.

J. Son, D. Perera, 2016, Medical physics.

Fast spin‐echo triple‐echo dixon (fTED) technique for efficient T2‐weighted water and fat imaging

Jingfei Ma, Jong Bum Son, Yuxiang Zhou, 2007, Magnetic resonance in medicine.

A fast spin echo two-point Dixon technique and its combination with sensitivity encoding for efficient T2-weighted imaging.

Jingfei Ma, James A Bankson, Jong Bum Son, 2005, Magnetic resonance imaging.

An improved region growing algorithm for phase correction in MRI

Jingfei Ma, Jong Bum Son, John D Hazle, 2016, Magnetic resonance in medicine.

A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer.

K. Hwang, G. Rauch, B. Adrada, 2023, Radiology. Imaging cancer.

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

B. Adrada, Jingfei Ma, R. Candelaria, 2023, Cancers.

A flexible fast spin echo triple‐echo Dixon technique

Jingfei Ma, Ersin Bayram, Ken-Pin Hwang, 2017, Magnetic resonance in medicine.

Single-point Dixon water-fat imaging using 64-channel single-echo acquisition MRI.

S. Wright, J. Ji, J. Son, 2008, Concepts in magnetic resonance. Part B, Magnetic resonance engineering.

Comparison of free breathing and respiratory triggered diffusion-weighted imaging sequences for liver imaging

P. Bhosale, Jingfei Ma, Wei Wei, 2019, World journal of radiology.

Multimodal Imaging of Pathologic Response to Chemoradiation in Esophageal Cancer.

O. Mawlawi, Steven H. Lin, B. Hobbs, 2018, International journal of radiation oncology, biology, physics.

MRI-based digital models forecast patient-specific treatment responses to neoadjuvant chemotherapy in triple-negative breast cancer.

T. Yankeelov, V. Valero, G. Rauch, 2022, Cancer research.

Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs

Tanya W. Moseley, G. Rauch, M. Scoggins, 2023, 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Assessment of Response to Neoadjuvant Systemic Treatment in Triple-Negative Breast Cancer Using Functional Tumor Volumes from Longitudinal Dynamic Contrast-Enhanced MRI

K. Hunt, L. Pinsky, V. Valero, 2023, Cancers.

Functional Tumor Volume by Fast Dynamic Contrast‐Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple‐Negative Breast Cancer

K. Hwang, G. Rauch, B. Adrada, 2021, Journal of magnetic resonance imaging : JMRI.

Multiparametric MRI–based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer

G. Rauch, M. Scoggins, B. Adrada, 2024, Scientific reports.

Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer

K. Hunt, G. Rauch, B. Adrada, 2023, Frontiers in oncology.