Editorial for “A Deep Learning‐Based Fully Automated Cardiac <scp>MRI</scp> Segmentation Approach for Tetralogy of Fallot Patients”

The authors declare no conflicts of interest. No data were generated or analyzed during the study.

Authors

Prashant Nagpal (ORCID: https://orcid.org/0000-0003-2347-034X)
Sarv Priya (ORCID: https://orcid.org/0000-0003-2442-1902)

Institutions

University of Wisconsin–Madison (US)

Publication Details

Journal: Journal of Magnetic Resonance Imaging
Published: 2025-09-10
DOI: https://doi.org/10.1002/jmri.70114
Primary Topic: Congenital Heart Disease Studies
Type: article
Field-Weighted Citation Impact: 0.00

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article

Editorial for “A Deep Learning‐Based Fully Automated Cardiac MRI Segmentation Approach for Tetralogy of Fallot Patients”

Prashant Nagpal, Sarv Priya

Sep 10, 2025

Journal of Magnetic Resonance Imaging

Congenital Heart Disease Studies

article

Editorial for “A Deep Learning‐Based Fully Automated Cardiac MRI Segmentation Approach for Tetralogy of Fallot Patients”

Prashant Nagpal, Sarv Priya

September 10, 2025article en

Abstract

The authors declare no conflicts of interest. No data were generated or analyzed during the study.

Journal of Magnetic Resonance ImagingVol. 63(1)

University of Wisconsin–Madison (US)

Openalex Percentile: Top 67%

Congenital Heart Disease Studies

DOI Source OpenAlex

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Editorial for “A Deep Learning‐Based Fully Automated Cardiac <scp>MRI</scp> Segmentation Approach for Tetralogy of Fallot Patients” — Prashant Nagpal, Sarv Priya · Journal of Magnetic Resonance Imaging (2025) | TGRS Research Map | TGRS