MEMORY-EFFICIENT DEEP END-TO-END POSTERIOR NETWORK (DEEPEN) INVERSE PROBLEMS (反転問題のための深層の末端から末端までの後端のネットワーク) について

Jyothi Rikhab Chand¹, Mathews Jacob¹

¹Department of Electrical and Computer Engineering, University of Iowa, IA, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|August 29, 2025

まとめ

磁気共鳴画像の再構築のための記憶効率の良いディープラーニング方法を開発しましたこのアプローチは,後部分布を学習し,画像の回復を改善し,不確実性マップを提供します.

科学分野:

医療用イメージング
計算神経科学
機械学習

背景:

エンドツーエンド (E2E) のアンロールされた最適化フレームワークは,磁気共鳴 (MR) 画像回復に有望である.
これらの決定論的方法は,トレーニング中にメモリ使用量が高く,後部分布サンプリング機能が不足しているため,課題に直面しています.

研究の目的:

MR画像再構築における後部分布のE2E学習のためのメモリ効率的なアプローチを導入する.
画像の復元とともに不確実性の定量化が可能になります.

主な方法:

データの一貫性確率と CNNパラメータ化された前エネルギーモデルを組み合わせた新しいフレームワークです.
最大確率の最適化によるCNNのE2E学習
低サンプル MR データからの画像回収のための最大A Posteriori (MAP) の最適化.

主要な成果:

提案された方法は,メモリ密度の高いE2Eアンロールアルゴリズムと同等の性能を達成します.
MR画像再構築において既存の記憶効率の良い同類を上回ります
フレームワークは,後部分布サンプリングから得られた不確実性マップを成功裏に生成します.

結論:

このメモリ効率の良い E2E 学習フレームワークは MR 画像再構築を進めている.
高次元 (3D+) MRIイメージングに有効なソリューションを提供します.
後部分布をサンプリングする能力は,貴重な不確実性情報を提供します.

キーワード:

エネルギーモデル MAPの見積もりパラレルMRI再構築不確実性の推定

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