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A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

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Deep learning for assessing image quality in bi-parametric prostate MRI: A feasibility study.

Deniz Alis¹, Mustafa Said Kartal², Mustafa Ege Seker³

¹Acibadem Mehmet Ali Aydinlar University, School of Medicine, Department of Radiology, Istanbul, 34457, Turkey.

European Journal of Radiology

|June 24, 2023

Summary

Deep learning (DL) shows promise for automated bi-parametric MRI quality assessment, matching less-experienced readers. This technology could assist or replace human visual evaluations for improved consistency.

Keywords:

Deep Learning Image quality MRI Prostate

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Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Human readers exhibit inconsistent image quality assessments in bi-parametric MRI, impacting diagnostic reliability.
Existing quality assessment systems like PI-QUAL have limitations in reader consistency.

Purpose of the Study:

To evaluate the feasibility of deep learning (DL) for automated image quality assessment in bi-parametric prostate MRI.
To compare the performance of a DL model against less-experienced human readers.

Main Methods:

A 3D deep learning model was trained on 500 bi-parametric prostate MRI scans from the PI-CAI dataset.
Image quality was assessed using a 3-point Likert scale (poor, moderate, excellent).
The DL model's performance was compared to four less-experienced readers evaluating 100 test scans.

Main Results:

The DL model achieved moderate (κ=0.42) and good (κ=0.61) agreement with expert consensus for T2W images and ADC maps, respectively.
Less-experienced readers showed fair to moderate (κ=0.39–0.56) and fair to good (κ=0.39–0.62) agreement for T2W images and ADC maps.

Conclusions:

Deep learning models can achieve performance comparable to less-experienced readers in assessing bi-parametric prostate MRI quality.
Automated DL assessment offers a viable tool to enhance consistency and potentially assist human readers.
Future DL models trained on larger, expert-annotated datasets may provide reliable automated image quality evaluation.