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Retrospective Detection and Suppression of Dark-Rim Artifacts in First-Pass Perfusion Cardiac MRI Enabled by Deep
Abstract:
The dark-rim artifact (DRA) remains an important challenge in the routine clinical use of first-pass perfusion (FPP) cardiac magnetic resonance imaging (cMRI). The DRA mimics the appearance of perfusion defects in the subendocardial wall and reduces the accuracy of diagnosis in patients with suspected ischemic heart disease. The main causes for DRA are known to be Gibbs ringing and bulk motion of the heart. The goal of this work is to propose a deep-learning-enabled automatic approach for the detection of motion-induced DRAs in FPP cMRI datasets. To this end, we propose a new algorithm that can detect the DRA in individual time frames by analyzing multiple reconstructions of the same time frame (k-space data) with varying temporal windows. In addition to DRA detection, our approach is also capable of suppressing the extent and severity of DRAs as a byproduct of the same reconstruction-analysis process. In this proof-of-concept study, our proposed method showed a good performance for automatic detection of subendocardial DRAs in stress perfusion cMRI studies of patients with suspected ischemic heart disease. To the best of our knowledge, this is the first approach that performs deep-learning-enabled detection and suppression of DRAs in cMRI.Clinical Relevance- Our approach enables clinicians to provide a more accurate diagnosis of ischemic heart disease by detecting and suppressing subendocardial dark-rim artifacts in first-pass perfusion cMRI datasets.
Insights
A new deep learning method automatically detects and reduces dark-rim artifacts in cardiac MRI scans. This improves diagnostic accuracy for ischemic heart disease by enhancing image clarity.
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Medical Image Analysis
Background:
- Dark-rim artifacts (DRAs) in first-pass perfusion (FPP) cardiac MRI (cMRI) mimic perfusion defects, hindering diagnosis of ischemic heart disease.
- Gibbs ringing and cardiac motion are primary causes of DRAs, reducing diagnostic accuracy.
- Accurate detection and mitigation of DRAs are crucial for reliable FPP cMRI interpretation.
Purpose of the Study:
- To develop a deep learning-based automatic approach for detecting motion-induced DRAs in FPP cMRI.
- To simultaneously suppress the extent and severity of DRAs using the same reconstruction-analysis process.
- To enhance the diagnostic accuracy of FPP cMRI for suspected ischemic heart disease.
Main Methods:
- A novel algorithm analyzes multiple reconstructions of k-space data from individual time frames with varying temporal windows.
- Deep learning techniques are employed for the automatic detection of DRAs.
- The method integrates DRA detection with artifact suppression within a unified reconstruction-analysis framework.
Main Results:
- The proposed method demonstrated good performance in automatically detecting subendocardial DRAs in stress perfusion cMRI studies.
- The approach successfully identified DRAs in patients with suspected ischemic heart disease.
- This study represents the first deep learning-enabled method for DRA detection and suppression in cMRI.
Conclusions:
- The developed deep learning approach effectively detects and suppresses dark-rim artifacts in FPP cMRI.
- This method has the potential to significantly improve the accuracy of diagnosing ischemic heart disease.
- Clinical implementation of this technique can lead to more reliable diagnostic outcomes in cardiac MRI.
