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

Radiology, 2020-05, Vol.295 (2), p.407-415

2020

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

Scientific reports, 2024-07, Vol.14 (1), p.16073-10, Article 16073

2024

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

Scientific reports, 2023-01, Vol.13 (1), p.1171-1171, Article 1171

2023

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Fast spin‐echo triple‐echo dixon (fTED) technique for efficient T 2 ‐weighted water and fat imaging

Magnetic resonance in medicine, 2007-07, Vol.58 (1), p.103-109

2007

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PULSAR: A Matlab toolbox for parallel magnetic resonance imaging using array coils and multiple channel receivers

Concepts in magnetic resonance. Part B, Magnetic resonance engineering, 2007-02, Vol.31B (1), p.24-36

2007

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

Journal of magnetic resonance imaging, 2006-01, Vol.23 (1), p.36-41

2006

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

Radiotherapy and oncology, 2020-12, Vol.153, p.189-196

2020

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Tumor necrosis by pretreatment breast MRI: association with neoadjuvant systemic therapy

Breast cancer research and treatment, 2021-01, Vol.185 (1), p.1

2021

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MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

Cancer research (Chicago, Ill.), 2022-09, Vol.82 (18), p.3394-3404

2022

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An improved region growing algorithm for phase correction in MRI: Region Growing Algorithm for Phase Correction

Magnetic resonance in medicine, 2016-08, Vol.76 (2), p.519-529

2016

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Multimodal Imaging of Pathologic Response to Chemoradiation in Esophageal Cancer

International journal of radiation oncology, biology, physics, 2018-11, Vol.102 (4), p.996-1001

2018

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

Concepts in magnetic resonance. Part B, Magnetic resonance engineering, 2008-08, Vol.33B (3), p.152-162

2008

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Abstract P1-08-08: Forecasting treatment response to neoadjuvant systemic therapy in triple negative breast cancer via mathematical modeling and quantitative MRI

Cancer research (Chicago, Ill.), 2022-02, Vol.82 (4_Supplement), p.P1-P1-08-08

2022

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

Magnetic resonance imaging, 2020-10, Vol.72, p.87-94

2020

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Abstract P1-08-03: Deep learning for early prediction of neoadjuvant chemotherapy response in triple negative breast cancers

Cancer research (Chicago, Ill.), 2022-02, Vol.82 (4_Supplement), p.P1-P1-08-03

2022

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

World journal of radiology, 2019-11, Vol.11 (11), p.134-143

2019

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Abstract PD11-06: Radiomics model based on magnetic resonance image compilation (MagIC) as early predictor of pathologic complete response to neoadjuvant systemic therapy in triple-negative breast cancer

Cancer research (Chicago, Ill.), 2022-02, Vol.82 (4_Supplement), p.PD11-06-PD11-06

2022

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Abstract PD6-07: Volumetric changes on longitudinal dynamic contrast enhanced MR imaging (DCE-MRI) as an early treatment response predictor to neoadjuvant systemic therapy (NAST) in triple negative breast cancer (TNBC) patients

Cancer research (Chicago, Ill.), 2021-02, Vol.81 (4_Supplement), p.PD6-07-PD6-07

2021

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A flexible fast spin echo triple‐echo Dixon technique

Magnetic resonance in medicine, 2017-03, Vol.77 (3), p.1049-1057

2017

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

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

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

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

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

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

Fast spin‐echo triple‐echo dixon (fTED) technique for efficient T 2 ‐weighted water and fat imaging

PULSAR: A Matlab toolbox for parallel magnetic resonance imaging using array coils and multiple channel receivers

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

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

Tumor necrosis by pretreatment breast MRI: association with neoadjuvant systemic therapy

MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

An improved region growing algorithm for phase correction in MRI: Region Growing Algorithm for Phase Correction

Multimodal Imaging of Pathologic Response to Chemoradiation in Esophageal Cancer

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

Abstract P1-08-08: Forecasting treatment response to neoadjuvant systemic therapy in triple negative breast cancer via mathematical modeling and quantitative MRI

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

Abstract P1-08-03: Deep learning for early prediction of neoadjuvant chemotherapy response in triple negative breast cancers

Diffusion Tensor Imaging for Characterizing Changes in Triple-Negative Breast Cancer During Neoadjuvant Systemic Therapy

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

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

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

Abstract PD11-06: Radiomics model based on magnetic resonance image compilation (MagIC) as early predictor of pathologic complete response to neoadjuvant systemic therapy in triple-negative breast cancer

Abstract PD6-07: Volumetric changes on longitudinal dynamic contrast enhanced MR imaging (DCE-MRI) as an early treatment response predictor to neoadjuvant systemic therapy (NAST) in triple negative breast cancer (TNBC) patients

A flexible fast spin echo triple‐echo Dixon technique

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