Vasylechko Serge Didenko, Tsai Andy, Afacan Onur, Kurugol Sila
Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA.
Harvard Medical School, Boston, Massachusetts, USA.
Magn Reson Med. 2025 Sep;94(3):1284-1300. doi: 10.1002/mrm.30536. Epub 2025 May 1.
To improve the quality of abdominal diffusion-weighted MR images (DW-MRI) when acquired using single-repetition (NEX = 1) protocols, and thereby increase apparent diffusion coefficient (ADC) map accuracy and lesion conspicuity at high b-values. We aim to reduce the effect of blurring due to motion that obscures small lesions when averaging multiple repetition images at each b-value, which is the current clinical standard.
We propose a self-supervised denoising diffusion probabilistic model (ssDDPM) to improve DW-MRI quality given noisy single-repetition acquisitions in pediatric abdominal scans. The ssDDPM is designed for multi-b-value DW-MRI and incorporates diffusion signal decay model (i.e., ADC model) constraints into its loss term. The model is trained to denoise single-repetition images from multiple b-values while ensuring that the output adheres to the signal decay model. Training was performed on a dataset of 120 pediatric subjects with liver tumors. The performance of ssDDPM was compared with non-local means (NLM) filtering and deep image prior (DIP) denoising techniques. These techniques have the capability to denoise single repetition images unlike the other techniques in literature that requires multiple direction or repetition images. Evaluation included qualitative radiologist's image quality assessment, receiver operating characteristic (ROC) analysis for lesion detection, and ADC fitting accuracy compared with motion-free, breath-hold reference data.
The ssDDPM demonstrated superior performance over comparison methods in terms of image quality, lesion conspicuity, and ADC map accuracy in NEX = 1 images. It received higher scores in radiologist assessments and showed better lesion discrimination in ROC analysis. Additionally, ssDDPM provided more precise and accurate ADC estimates when compared with the motion-free, breath-hold reference data.
The ssDDPM effectively reduces motion related deblurring and enhances the quality of DW-MRI images by directly denoising single-repetition (NEX = 1) images while respecting signal decay model constraints. This method improves the assessment of pediatric liver lesions, offering a more accurate and efficient diagnostic tool with reduced scan times, when compared with current clinical practice and other denoising techniques.
在使用单次重复（NEX = 1）协议采集腹部扩散加权磁共振图像（DW-MRI）时提高图像质量，从而提高高b值下的表观扩散系数（ADC）图准确性和病变清晰度。我们旨在减少由于运动导致的模糊效应，这种模糊效应在对每个b值的多个重复图像进行平均时会掩盖小病变，而这是当前的临床标准。
我们提出一种自监督去噪扩散概率模型（ssDDPM），以在儿科腹部扫描中给定噪声单次重复采集的情况下提高DW-MRI质量。ssDDPM专为多b值DW-MRI设计，并将扩散信号衰减模型（即ADC模型）约束纳入其损失项。该模型经过训练，对来自多个b值的单次重复图像进行去噪，同时确保输出符合信号衰减模型。在一个包含120名患有肝肿瘤的儿科受试者的数据集上进行训练。将ssDDPM的性能与非局部均值（NLM）滤波和深度图像先验（DIP）去噪技术进行比较。与文献中其他需要多个方向或重复图像的技术不同，这些技术具有对单次重复图像进行去噪的能力。评估包括放射科医生的定性图像质量评估、用于病变检测的受试者操作特征（ROC）分析，以及与无运动、屏气参考数据相比的ADC拟合准确性。
在NEX = 1图像的图像质量、病变清晰度和ADC图准确性方面，ssDDPM表现出优于比较方法的性能。它在放射科医生评估中获得更高分数，并且在ROC分析中显示出更好的病变辨别能力。此外，与无运动、屏气参考数据相比，ssDDPM提供了更精确和准确的ADC估计。
ssDDPM通过在尊重信号衰减模型约束的同时直接对单次重复（NEX = 1）图像进行去噪，有效减少了与运动相关的模糊并提高了DW-MRI图像质量。与当前临床实践和其他去噪技术相比，该方法改善了对儿科肝脏病变的评估，提供了一种更准确、高效的诊断工具，同时减少了扫描时间。
