Automatic cardiac T2* relaxation time estimation from magnetic resonance images using region growing method with automatically initialized seed points

• Novel automatic cardiac T2* relaxation time estimation algorithm from MR images.
• Novel automatic ROI segmentation algorithm in cardiac MR images.
• Segmentation results from our technique are very close to the experts’ opinions.
• Cardiac T2* values from our technique are very close to that calculated by experts.
• Our method does not need manual processes in ROI segmentation and T2* calculation.

Background and objectiveHeart 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.MethodsThirty 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.ResultsThe 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 1.40 ms.ConclusionsThe 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.

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