人工知能ツールが医療画像の使いやすさを向上させる可能性(Artificial intelligence tool may enhance usability of medical images)

ad

2024-06-12 ワシントン大学セントルイス校

ワシントン大学セントルイス校のバイオメディカルエンジニア、アビナブ・ジャは、医療画像処理に使用されるAIツールは、視覚的な美しさではなく臨床タスクに基づいて評価されるべきだと主張しています。IEEE Transactions on Radiation and Plasma Medical Sciencesに発表された研究で、ジャとその協力者は、臨床タスクのパフォーマンスを向上させる可能性のあるツールを開発しました。このツールは、心筋灌流イメージング(MPI)単一光子放射断層撮影(SPECT)画像のノイズ除去に関するものです。新しいツール「DEMIST」は、深層学習フレームワークを活用し、検出タスクに影響を与える特徴を保持しながら画像を選択的にクリーンアップします。

<関連情報>

DEMIST:心筋灌流SPECTのためのディープラーニングベースの検出タスク特異的ノイズ除去アプローチ DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT

Md Ashequr Rahman; Zitong Yu; Richard Laforest; …
IEEE Transactions on Radiation and Plasma Medical Sciences  Published:25 March 2024
DOI:https://doi.org/10.1109/TRPMS.2024.3379215

Abstract

There is an important need for methods to process myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images acquired at lower-radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects compared to low-dose images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners ( N= 338). The evaluation was performed at low-dose levels of 6.25%, 12.5%, and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic deep learning-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT.

医療・健康
ad
ad
Follow
ad
タイトルとURLをコピーしました