A deep learning framework for reconstructing Breast Amide Proton Transfer weighted imaging sequences from sparse frequency offsets to dense frequency offsets

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Qiuhui Yang , Shu Su , Tianyu Zhang , Meng Wang , Weiqiang Dou , Kefeng Li , Ya Ren , Yijia Zheng , Mingwei Wang , Yi Xu , Yue Sun , Zhou Liu , Tao Tan
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Abstract

Amide Proton Transfer (APT) technique is a novel functional MRI technique that enables quantification of protein metabolism, but its wide application is largely limited in clinical settings by its long acquisition time. One way to reduce the scanning time is to obtain fewer frequency offset images during image acquisition. However, sparse frequency offset images are not inadequate to fit the z-spectral, a curve essential to quantifying the APT effect, which might compromise its quantification. In our study, we develop a deep learning-based model that allows for reconstructing dense frequency offsets from sparse ones, potentially reducing scanning time. We propose to leverage time-series convolution to extract both short and long-range spatial and frequency features of the APT imaging sequence. Our proposed model outperforms other seq2seq models, achieving superior reconstruction with a peak signal-to-noise ratio of 45.8 (95% confidence interval (CI): [44.9 46.7]), and a structural similarity index of 0.989 (95% CI:[0.987 0.993]) for the tumor region. We have integrated a weighted layer into our model to evaluate the impact of individual frequency offset on the reconstruction process. The weights assigned to the frequency offset at ±6.5 ppm, 0 ppm, and 3.5 ppm demonstrate higher significance as learned by the model. Experimental results demonstrate that our proposed model effectively reconstructs dense frequency offsets (n = 29, from 7 to -7 with 0.5 ppm as an interval) from data with 21 frequency offsets, reducing scanning time by 25%. This work presents a method for shortening the APT imaging acquisition time, offering potential guidance for parameter settings in APT imaging and serving as a valuable reference for clinicians.
基于深度学习框架的乳腺酰胺质子转移加权成像序列稀疏频偏到密集频偏重建
酰胺质子转移(Amide Proton Transfer, APT)技术是一种新型的功能磁共振成像技术,可以定量分析蛋白质代谢,但由于其获取时间长,在临床上的广泛应用受到很大限制。减少扫描时间的一种方法是在图像采集过程中获得较少的频率偏移图像。然而,稀疏的频率偏移图像并不足以拟合z谱,z谱是量化APT效应所必需的曲线,这可能会影响其量化。在我们的研究中,我们开发了一种基于深度学习的模型,允许从稀疏的频率偏移重建密集的频率偏移,潜在地减少扫描时间。我们建议利用时间序列卷积来提取APT成像序列的短期和长期空间和频率特征。我们提出的模型优于其他seq2seq模型,实现了更好的重建,峰值信噪比为45.8(95%置信区间(CI):[44.9 46.7]),肿瘤区域的结构相似性指数为0.989 (95% CI:[0.987 0.993])。我们将一个加权层集成到我们的模型中,以评估单个频率偏移对重建过程的影响。在±6.5 ppm、0 ppm和3.5 ppm分配给频率偏移的权重显示出模型学习到的更高的重要性。实验结果表明,该模型有效地从21个频偏数据中重建密集频偏(n = 29,从7到-7,间隔为0.5 ppm),将扫描时间缩短了25%。本工作提出了一种缩短APT成像采集时间的方法,为APT成像的参数设置提供了潜在的指导,对临床医生有价值的参考价值。
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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
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