二維圖像評估快速動態磁共振影像重建演算法

Abstract

鑑於當今磁振造影影像加速與重建方法的種類與複雜度持續增加，若能有一可靠且客觀評估各種演算法特性的指標對於影像加速的發展將會有相當大的助益。本論文中將提出一分析方法用以解析影像加速演算法的各項特徵。在微擾模式的協助下，僅需透過對造影與影像重建過程中的瞭解，如取樣函數與各線圈對於空間的訊號的靈敏度，便能夠計算重建演算法的特徵資訊。這些特徵訊息包含調變轉換函數(MTF)，訊號假影比，以及雜訊擴增比，將以頻譜空間的二維圖像顯示，藉此使各項加速演算法的特徵能夠藉由圖像灰階提供簡而易懂的訊息。調變轉換函數為一提供演算法對訊息擷取能力的指標，訊號假影比可突顯出演算法所產生的假影對於主要訊號的影響，雜訊擴增比則是用以瞭解演算法是否能夠有效抑制雜訊。論文中，以一組仿體實驗與三組人體心臟影像造影作為基礎，以四倍與八倍模擬加速重建後的資料測試我們所提出的分析方法。經由分析結果瞭解受測演算法的特徵後，我們提出了一個結合被測試的兩種影像加速重建法優點的新演算法，同時在此新算法中加入GRAPPA, GEYSER 和SPACE-RIP以期能夠達到更高品質的重建效果。我們利用了此新演算法重建3.5倍與6.5倍加速倍率所擷取的心臟動態磁振影像資料。重建結果顯示，此方法能夠有效重建品質良好且高加速倍率的心臟動態影像，同時擁有計算快速的優點。

As the number and complexity of partially sampled dynamic imaging methods continue to increase, reliable strategies to evaluate performance may prove most useful. In the present work, an analytical framework to evaluate given reconstruction methods is presented. A perturbation algorithm allows the proposed evaluation scheme to perform robustly without requiring knowledge about the inner workings of the method being evaluated. A main output of the evaluation process consists of a 2D modulation transfer function (MTF), an easy-to-interpret visual rendering of a method’s ability to capture all combinations of spatial and temporal frequencies. Approaches to evaluate noise properties and artifact content at all spatial and temporal frequencies are also proposed. One fully sampled phantom and three fully sampled cardiac cine datasets were subsampled (R=4 and 8), and reconstructed with the different methods tested here. A hybrid method, which combines the main advantageous features observed in our assessments, was proposed and tested in a cardiac cine application, with acceleration factors of 3.5 and 6.3 (skip factor of 4 and 8, respectively). This approach combines features from methods such as k-t sensitivity-encoding (k-t SENSE), unaliasing by Fourier encoding the overlaps in the temporal dimension-SENSE (UNFOLD-SENSE), generalized autocalibrating partially parallel acquisition (GRAPPA), sensitivity profiles from an array of coils for encoding and reconstruction in parallel (SPACE-RIP), self, hybrid referencing with UNFOLD and GRAPPA (SHRUG) and GRAPPA-enhanced sensitivity maps for SENSE reconstructions (GEYSER).