IMPORTANT-Net: Integrated MRI Multi-Parameter Reinforcement Fusion Generator with Attention Network for Synthesizing Absent Data

要約

磁気共鳴画像（MRI）は、乳房の病変検出において高い感度を有する。異なる設定で得られたシーケンスは、病変の特異的な特性を捉えることができる。このようなマルチパラメータMRI情報は、病変分類における放射線科医のパフォーマンスを向上させ、また、様々なタスクにおける人工知能モデルのパフォーマンスを向上させることが示されている。しかし、マルチパラメータMRIの取得は、経済的・時間的な観点から検査コストが高くなり、また、特殊な集団に対する安全性の懸念もあるため、フルスペクトルMRIシーケンスの取得は耐久性に欠けるものとなっている。本研究では、既存のMRIパラメータからの素朴な入力融合や特徴量連結とは異なり、MRIパラメータから特徴量を抽出し、その特徴量からMRIパラメータを生成する。a new $ntegrated MRI $ulti-$textbf{M}$ulti$textbf{P}$arameter reinf$textbf{O}$rcement fusion generato$textbf{R}$ wi$textbf{T}$h $tte´intex要約(オリジナル)

Magnetic resonance imaging (MRI) is highly sensitive for lesion detection in the breasts. Sequences obtained with different settings can capture the specific characteristics of lesions. Such multi-parameter MRI information has been shown to improve radiologist performance in lesion classification, as well as improving the performance of artificial intelligence models in various tasks. However, obtaining multi-parameter MRI makes the examination costly in both financial and time perspectives, and there may be safety concerns for special populations, thus making acquisition of the full spectrum of MRI sequences less durable. In this study, different than naive input fusion or feature concatenation from existing MRI parameters, a novel $\textbf{I}$ntegrated MRI $\textbf{M}$ulti-$\textbf{P}$arameter reinf$\textbf{O}$rcement fusion generato$\textbf{R}$ wi$\textbf{T}$h $\textbf{A}$tte$\textbf{NT}$ion Network (IMPORTANT-Net) is developed to generate missing parameters. First, the parameter reconstruction module is used to encode and restore the existing MRI parameters to obtain the corresponding latent representation information at any scale level. Then the multi-parameter fusion with attention module enables the interaction of the encoded information from different parameters through a set of algorithmic strategies, and applies different weights to the information through the attention mechanism after information fusion to obtain refined representation information. Finally, a reinforcement fusion scheme embedded in a $V^{-}$-shape generation module is used to combine the hierarchical representations to generate the missing MRI parameter. Results showed that our IMPORTANT-Net is capable of generating missing MRI parameters and outperforms comparable state-of-the-art networks. Our code is available at https://github.com/Netherlands-Cancer-Institute/MRI_IMPORTANT_NET.

arxiv情報

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arxiv.jp, DeepL