Deep learning-based prescription of cardiac MRI planes

47Citations
Citations of this article
56Readers
Mendeley users who have this article in their library.

Abstract

Purpose: To develop and evaluate a system to prescribe imaging planes for cardiac MRI based on deep learning (DL)−based localization of key anatomic landmarks. Materials and Methods: Annotated landmarks on 892 long-axis (LAX) and 493 short-axis (SAX) cine steady-state free precession series from cardiac MR images were retrospectively collected between February 2012 and June 2017. U-Net-based heatmap regression was used for localization of cardiac landmarks, which were used to compute cardiac MRI planes. Performance was evaluated by comparing localization distances and plane angle differences between DL predictions and ground truth. The plane angulations from DL were compared with those prescribed by the technologist at the original time of acquisition. Data were split into 80% for training and 20% for testing, and results confirmed with fivefold cross-validation. Results: On LAX images, DL localized the apex within mean 12.56 mm ± 19.11 (standard deviation) and the mitral valve (MV) within 7.68 mm ± 6.91. On SAX images, DL localized the aortic valve within 5.78 mm ± 5.68, MV within 5.90 mm ± 5.24, pulmonary valve within 6.55 mm ± 6.39, and tricuspid valve within 6.39 mm ± 5.89. On the basis of these localizations, average angle bias and mean error of DL-predicted imaging planes relative to ground truth annotations were as follows: SAX, −1.27° ± 6.81 and 4.93° ± 4.86; four chambers, 0.38° ± 6.45 and 5.16° ± 3.80; three chambers, 0.13° ± 12.70 and 9.02° ± 8.83; and two chamber, 0.25° ± 9.08 and 6.53° ± 6.28, respectively. Conclusion: DL-based anatomic localization is a feasible strategy for planning cardiac MRI planes. This approach can produce imaging planes comparable to those defined by ground truth landmarks.

References Powered by Scopus

Long Short-Term Memory

76938Citations
N/AReaders
Get full text

Learning from imbalanced data

7282Citations
N/AReaders
Get full text

Deep learning: A primer for radiologists

856Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Deep learning single-frame and multiframe super-resolution for cardiac MRI

91Citations
N/AReaders
Get full text

Cardiac MRI: State of the Art

60Citations
N/AReaders
Get full text

Automated ct staging of chronic obstructive pulmonary disease severity for predicting disease progression and mortality with a deep learning convolutional neural network

42Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Blansit, K., Retson, T., Masutani, E., Bahrami, N., & Hsiao, A. (2019). Deep learning-based prescription of cardiac MRI planes. Radiology: Artificial Intelligence, 1(6). https://doi.org/10.1148/ryai.2019180069

Readers over time

‘19‘20‘21‘22‘23‘2406121824

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 14

47%

Researcher 14

47%

Professor / Associate Prof. 1

3%

Lecturer / Post doc 1

3%

Readers' Discipline

Tooltip

Medicine and Dentistry 12

48%

Engineering 10

40%

Computer Science 2

8%

Nursing and Health Professions 1

4%

Save time finding and organizing research with Mendeley

Sign up for free
0