paper_authors: Yuyang Hu, Mauricio Delbracio, Peyman Milanfar, Ulugbek S. Kamilov
for: 这个论文是为了解决图像恢复问题而写的。
methods: 这个论文使用的方法是使用先行训练的深度神经网络作为恢复运算的假设。
results: numerical results show that the method using a super-resolution prior achieves state-of-the-art performance both quantitatively and qualitatively。Here’s the full translation of the abstract in Simplified Chinese:
for: 这个论文是为了解决图像恢复问题而写的。
methods: 这个论文使用的方法是使用先行训练的深度神经网络作为恢复运算的假设。
results: 数值结果表明，使用超解像前提可以达到状态体系数量和质量上的最佳性能。I hope that helps!Abstract
Image denoisers have been shown to be powerful priors for solving inverse problems in imaging. In this work, we introduce a generalization of these methods that allows any image restoration network to be used as an implicit prior. The proposed method uses priors specified by deep neural networks pre-trained as general restoration operators. The method provides a principled approach for adapting state-of-the-art restoration models for other inverse problems. Our theoretical result analyzes its convergence to a stationary point of a global functional associated with the restoration operator. Numerical results show that the method using a super-resolution prior achieves state-of-the-art performance both quantitatively and qualitatively. Overall, this work offers a step forward for solving inverse problems by enabling the use of powerful pre-trained restoration models as priors.
摘要
图像去噪器已经被证明是解析问题的强大先验。在这项工作中，我们介绍了一种扩展这些方法，允许任何图像恢复网络被用作隐藏先验。我们的方法使用由深度神经网络预训练为普通恢复操作器的先验。我们的理论结果分析其趋向于一个全局函数相关的恢复运算的站点点。数值结果表明，使用超解像先验可以达到现代水平的性能 both quantitatively and qualitatively。总之，这项工作为解析问题提供了一个前进，允许使用强大的预训练恢复模型作为先验。
Predicting Lung Cancer’s Metastats’ Locations Using Bioclinical Model
results: 实验 validate the bioclinical model on 10 patient data, with 74% accuracy in metastasis location prediction.Abstract
Lung cancer is a leading cause of cancer-related deaths worldwide. The spread of the disease from its primary site to other parts of the lungs, known as metastasis, significantly impacts the course of treatment. Early identification of metastatic lesions is crucial for prompt and effective treatment, but conventional imaging techniques have limitations in detecting small metastases. In this study, we develop a bioclinical model for predicting the spatial spread of lung cancer's metastasis using a three-dimensional computed tomography (CT) scan. We used a three-layer biological model of cancer spread to predict locations with a high probability of metastasis colonization. We validated the bioclinical model on real-world data from 10 patients, showing promising 74% accuracy in the metastasis location prediction. Our study highlights the potential of the combination of biophysical and ML models to advance the way that lung cancer is diagnosed and treated, by providing a more comprehensive understanding of the spread of the disease and informing treatment decisions.
摘要
肺癌是全球最主要的肿瘤相关死亡原因之一。肿瘤从主要位点至其他肺部的传播，即肿瘤转移，对治疗诊断产生重要影响。早期识别转移 lesions 非常重要，但传统的成像技术有限制可以探测小转移。在这项研究中，我们开发了一种生物клиниче模型，用于预测肺癌转移的空间扩散。我们使用了三层生物模型来预测可能受感染的位置。我们验证了生物клиниче模型使用实际数据，从10名患者中提取了数据，并达到了74%的准确率。我们的研究表明，将生物物理和机器学习模型相结合，可以推动肺癌的诊断和治疗方法的进步，提供更全面的疾病扩散的理解，以及更加准确的诊断和治疗决策。
Fourier PD and PDUNet: Complex-valued networks to speed-up MR Thermometry during Hypterthermia
paper_authors: Rupali Khatun, Soumick Chatterjee, Christoph Bert, Martin Wadepohl, Manfred Schmidt, Oliver J. Ott, Rainer Fietkau, Andreas Nürnberger, Udo S. Gaipl, Benjamin Frey for: 这个研究的目的是提高下采样的MR温度测量数据重建的解决方案，以提高解决速度和减少artefacts。methods: 这个研究使用了深度学习技术来重建高度下采样的MR温度测量数据，并使用了两种不同的深度学习模型：Fourier Primal-Dual网络和Fourier Primal-Dual UNet。results: 研究发现，使用深度学习模型可以减少下采样MR温度测量数据与完全采样MR温度测量数据之间的温度差距，从1.5 $\degree$C降至0.5 $\degree$C。Abstract
Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tumour tissue is exogenously heated to temperatures of 39 to 43 $\degree$C for 60 minutes. Temperature monitoring can be performed noninvasively using dynamic magnetic resonance imaging (MRI). However, the slow nature of MRI leads to motion artefacts in the images due to the movements of patients during image acquisition time. By discarding parts of the data, the speed of the acquisition can be increased - known as Undersampling. However, due to the invalidation of the Nyquist criterion, the acquired images have lower resolution and can also produce artefacts. The aim of this work was, therefore, to reconstruct highly undersampled MR thermometry acquisitions with better resolution and with less artefacts compared to conventional techniques like compressed sensing. The use of deep learning in the medical field has emerged in recent times, and various studies have shown that deep learning has the potential to solve inverse problems such as MR image reconstruction. However, most of the published work only focusses on the magnitude images, while the phase images are ignored, which are fundamental requirements for MR thermometry. This work, for the first time ever, presents deep learning based solutions for reconstructing undersampled MR thermometry data. Two different deep learning models have been employed here, the Fourier Primal-Dual network and Fourier Primal-Dual UNet, to reconstruct highly undersampled complex images of MR thermometry. It was observed that the method was able to reduce the temperature difference between the undersampled MRIs and the fully sampled MRIs from 1.5 $\degree$C to 0.5 $\degree$C.
摘要
高级热辐射（HT）在结合放射线和/或化学疗法的抗癌治疗中得到了承认。在HT中，肿瘤组织被外源性加热到39-43℃的温度，持续60分钟。肿瘤组织的温度可以非侵入性地监测使用动力磁共振成像（MRI）。然而，由于患者在获取图像时的运动，MRI图像会受到运动artefacts的影响。通过抛弃一部分数据，可以快速化图像获取过程 - bekannt als Undersampling。然而，由于遵循 Nyquist критериion的无效化，获取的图像具有更低的分辨率，并且可能会产生artefacts。因此，本研究的目标是使用深度学习来重建高度受抽象的 MR 热图像数据，以提高分辨率和减少artefacts，并且不同于传统的压缩感知技术。深度学习在医学领域的应用已经在最近几年得到了广泛的关注，而且许多研究表明，深度学习有可能解决 inverse 问题，如 MR 图像重建。然而，大多数已发表的研究仅关注了 magnitude 图像，而忽略了阶跃图像，这些图像是 MR 热测量的基本需求。本研究是首次使用深度学习来重建高度受抽象的 MR 热图像数据。我们在这里采用了两种深度学习模型：Fourier Primal-Dual 网络和 Fourier Primal-Dual UNet，来重建高度受抽象的 MR 热图像。我们发现，该方法可以将高级受抽象 MR 热图像和完全样本 MR 热图像之间的温度差降低至0.5℃。