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Published on: September 16, 2017
Automatic cardiac T2* relaxation time estimation from magnetic resonance images using region growing method with
Kittichai Wantanajittikul1, Nipon Theera-Umpon2, Suwit Saekho3
1Biomedical Engineering Program, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand; Biomedical Engineering Center, Chiang Mai University, Chiang Mai, Thailand.
Insights
This study introduces an automated method using region growing algorithms to segment regions of interest in cardiac MRI scans for accurate T2* (T2-star) value estimation, improving diagnosis of iron-overload cardiomyopathy.
Area of Science:
- Cardiovascular Imaging
- Medical Image Analysis
- Computational Medicine
Background:
- Iron-overload cardiomyopathy is a significant cause of mortality, often diagnosed late due to unpredictable iron deposition.
- Early detection and treatment are crucial as the condition is reversible with timely iron chelation therapy.
- Current T2* (T2-star) MRI assessment relies on manual region of interest (ROI) selection, which is time-consuming and requires expertise.
Purpose of the Study:
- To develop an automated cardiac T2* (T2-star) measurement method using a region growing algorithm for ROI segmentation in cardiac MRI.
- To improve the efficiency and accuracy of diagnosing iron-overload cardiomyopathy.
Main Methods:
- Utilized a region growing algorithm for automatic segmentation of the region of interest (ROI) in cardiac MR images.
- Applied mathematical morphologies to minimize segmentation errors.
- Evaluated the algorithm on 30 free-breathing, respiratory-triggered MR images, comparing results with manual segmentation by two experts.
Main Results:
- The automated segmentation achieved high performance metrics: 0.76 positive predictive value, 0.84 sensitivity, 7.78 pixels Hausdorff distance, and 0.80 Dice similarity coefficient against expert opinions.
- The T2* (T2-star) values derived from automated ROIs showed a mean difference of only 1.40ms compared to expert estimations.
- The proposed method demonstrated superior segmentation and T2* (T2-star) value estimation compared to previous techniques.
Conclusions:
- The developed region growing algorithm provides an accurate and efficient method for cardiac T2* (T2-star) estimation.
- Automated ROI segmentation significantly enhances the diagnostic process for iron-overload cardiomyopathy.
- This technique holds promise for earlier and more reliable detection of cardiac iron overload.
Background And Objective:
Heart failure due to iron-overload cardiomyopathy is one of the main causes of mortality. The cardiomyopathy is reversible if intensive iron chelation treatment is done in time, but the diagnosis is often delayed because the cardiac iron deposition is unpredictable and the symptoms are lately detected. There are many ways to assess iron-overload. However, the widely used and approved method is by using MRI which is performed by calculating the T2* (T2-star). In order to compute the T2* value, the region of interest (ROI) is manually selected by an expert which may require considerable time and skills. The aim of this work is hence to develop the cardiac T2* measurement by using region growing algorithm for automatically segmenting the ROI in cardiac MR images. Mathematical morphologies are also used to reduce some errors.
Methods:
Thirty MR images with free-breathing and respiratory-trigger technique were used in this work. The segmentation algorithm yields good results when compared with the manual segmentation performed by two experts.
Results:
The averages of positive predictive value, the sensitivity, the Hausdorff distance, and the Dice similarity coefficient are 0.76, 0.84, 7.78 pixels, and 0.80 when compared with the two experts' opinions. The T2* values were carried out based on the automatically segmented ROI's. The mean difference of T2* values between the proposed technique and the experts' opinion is about 1.40ms.
Conclusions:
The results demonstrate the accuracy of the proposed method in T2* value estimation. Some previous methods were implemented for comparisons. The results show that the proposed method yields better segmentation and T2* value estimation performances.
