Machine learning based prediction of image quality in prostate MRI using rapid localizer images

Abdullah Al-Hayali; Amin Komeili; Azar Azad; Paul Sathiadoss; Nicola Schieda; Eranga Ukwatta

doi:10.1117/1.JMI.11.2.026001

1 March 2024 Machine learning based prediction of image quality in prostate MRI using rapid localizer images

Abdullah Al-Hayali, Amin Komeili, Azar Azad, Paul Sathiadoss, Nicola Schieda, Eranga Ukwatta

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 2, 026001 (March 2024). https://doi.org/10.1117/1.JMI.11.2.026001

Abstract

Purpose

Diagnostic performance of prostate MRI depends on high-quality imaging. Prostate MRI quality is inversely proportional to the amount of rectal gas and distention. Early detection of poor-quality MRI may enable intervention to remove gas or exam rescheduling, saving time. We developed a machine learning based quality prediction of yet-to-be acquired MRI images solely based on MRI rapid localizer sequence, which can be acquired in a few seconds.

Approach

The dataset consists of 213 (147 for training and 64 for testing) prostate sagittal T2-weighted (T2W) MRI localizer images and rectal content, manually labeled by an expert radiologist. Each MRI localizer contains seven two-dimensional (2D) slices of the patient, accompanied by manual segmentations of rectum for each slice. Cascaded and end-to-end deep learning models were used to predict the quality of yet-to-be T2W, DWI, and apparent diffusion coefficient (ADC) MRI images. Predictions were compared to quality scores determined by the experts using area under the receiver operator characteristic curve and intra-class correlation coefficient.

Results

In the test set of 64 patients, optimal versus suboptimal exams occurred in 95.3% (61/64) versus 4.7% (3/64) for T2W, 90.6% (58/64) versus 9.4% (6/64) for DWI, and 89.1% (57/64) versus 10.9% (7/64) for ADC. The best performing segmentation model was 2D U-Net with ResNet-34 encoder and ImageNet weights. The best performing classifier was the radiomics based classifier.

Conclusions

A radiomics based classifier applied to localizer images achieves accurate diagnosis of subsequent image quality for T2W, DWI, and ADC prostate MRI sequences.

Citation Download Citation

Abdullah Al-Hayali, Amin Komeili, Azar Azad, Paul Sathiadoss, Nicola Schieda, and Eranga Ukwatta "Machine learning based prediction of image quality in prostate MRI using rapid localizer images," Journal of Medical Imaging 11(2), 026001 (1 March 2024). https://doi.org/10.1117/1.JMI.11.2.026001

Received: 13 January 2023; Accepted: 29 January 2024; Published: 1 March 2024

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KEYWORDS

Image quality

Magnetic resonance imaging

Image segmentation

Diffusion weighted imaging

Prostate

Analog to digital converters

Radiomics

Purpose

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