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リストモードのTOF-PET3D画像再構築は,ストキャスティックなプライマル-デュアルネットワークを用いて行われます

Kun Tian¹, Rui Hu¹, Yiming Wan¹

¹State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, People's Republic of China.

Medical physics

|February 15, 2026

まとめ

新しいディープラーニング法であるLM-SPD-Netは,リストモードデータから直接ポジトロン放出トモグラフィー (PET) 画像を再構築します. このアプローチは画像の質を向上させ,タイム・オブ・フライト (TOF) PET再構築におけるコンピューティング上の課題を克服します.

科学分野:

核医学画像検査について
医療画像再構築医療画像再構築
ディープラーニングアプリケーション

背景:

ポジトロン放出トモグラフィ (PET) は,生物学的活動を視覚化するために高い感度を提供します.
従来のPETイメージングは,空間解像度と信号対ノイズ比 (SNR) の制限に直面しています.
Time-of-flight (TOF) データ統合により,PET画像の品質は向上するが,計算上の要求は増大する.

研究の目的:

リストモードのPET画像の直接再構築のための新しいディープラーニングフレームワークを導入する.
TOF-PETデータに関連するコンピューティングとメモリ上の課題に対処するために.
再構築されたPET画像の品質を高めるために.

主な方法:

LM-SPD-Net,リストモードのTOF-PET再構築フレームワークで,ストキャスティックなプライマル-デュアルネットワークアーキテクチャを使用しています.
イメージドメインの処理のためのプライマルモジュール (CNN) と,データドメインの機能のためのデュアルモジュール (FCNN).
効率的な3D再構築のために,物理情報に基づくプロジェクションモデルとサブセットパーティションの統合.

主要な成果:

LM-SPD-Netは,LM-OSEM,LM-SPDHG,およびFast-PETと比較して優れたパフォーマンスを示しました.
ピークシグナル・ノイズ比 (PSNR) と構造相似度指数 (SSIM) の指標で5%~20%の改善を達成しました.
シミュレートされたデータと半現実の臨床データの両方で,視覚的に画像の質が向上しました.

結論:

提案されたLM-SPD-Netメソッドは,全体的な主体構造を高精度で効果的に再構築します.
腫瘍やタラムスなどの臨床的に重要な領域で優れたパフォーマンスを維持しました.
堅実なパフォーマンスを示し,特に低カウント条件下では.

キーワード:

画像の復元,再構築,再構築リスト・モード・データポジトロン放出トモグラフィー (PET)

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