CT和MRI是两种广泛使用的临床成像方式，用于非侵入性诊断。然而，这两种方式都有一定的问题。 CT使用有害电离辐射，MRI患有缓慢的采集速度。欠采样可以解决这两个问题，例如稀疏抽样。然而，这种向下采样的数据导致降低分辨率并引入人工制品。已经提出了几种技术，包括基于深度的学习方法，以重建此类数据。然而，这两个方式的欠采样重建问题总是被认为是两个不同的问题，并通过不同的研究工作分开解决。本文通过在径向MRI上应用傅立叶变换的预处理来实现稀疏CT和缺口MRI重建的统一解决方案，然后使用SCOMAGE ups采样与滤波后投影结合使用SCOMAGE Cups采样来实现的基于傅里叶变换的预处理。原始网络是一种基于深度学习的方法，用于重建稀疏采样的CT数据。本文介绍了原始 - 双工UNET，从精度和重建速度方面提高了原始双网络。所提出的方法导致平均SSSIM为0.932，同时对风扇束几何进行稀疏CT重建，其稀疏水平为16，实现了对先前模型的统计上显着的改进，这导致0.919。此外，所提出的模型导致0.903和0.957平均SSIM，同时重建具有16-统计上显着改善的加速因子，在原始模型上重建了缺乏采样的脑和腹部MRI数据，这导致0.867和0.949。最后，本文表明，所提出的网络不仅提高了整体图像质量，而且还提高了兴趣区域的图像质量;以及在针的存在下更好地推广。

柱生成（CG）是解决大规模优化问题的有效方法。CG通过求解列（即变量）的子集并逐渐包括可以改善当前子问题的解决方案的新列。通过反复解决定价问题，根据需要产生新列，这通常是NP - 硬的并且是CG方法的瓶颈。为了解决这个问题，我们提出了一种基于机器学习的定价启发式（MLPH），可以有效地产生许多高质量的柱。在CG的每次迭代中，我们的MLPH利用ML模型来预测定价问题的最佳解决方案，然后用于引导采样方法以有效地产生多个高质量柱。使用图形着色问题，我们经验证明，与六种最先进的方法相比，MLPH显着增强，并且CG的改善可能导致分支和价格精确方法的显着更好的性能。

本文介绍了一种增强的元启发式（ML-ACO），将机器学习（ML）和蚁群优化（ACO）结合起来解决组合优化问题。为了说明我们ML-ACO算法的底层机制，我们首先描述测试问题，定向问题。在这个问题中，目的是找到一个路线，该路线在时间预算中在图中访问顶点的子集，以最大化收集的分数。在我们ML-ACO算法的第一阶段，使用一组小问题实例训练ML模型，其中已知最佳解决方案。具体地，分类模型用于将边缘分类为最佳路由的一部分，或不使用特定于问题的特征和统计测量。然后，训练模型用于预测测试问题实例图表中的边缘所属的概率属于相应的最优路由。在第二阶段，我们将预测的概率纳入我们算法的ACO组件，即，使用概率值作为启发式权重或者热启动信息素矩阵。这里，在构建可行的路线时偏向有利于这些预测的高质量边缘的概率值。我们已经测试了多种分类模型，包括图形神经网络，逻辑回归和支持向量机，实验结果表明，我们的解决方案预测方法一直促进ACO的性能。此外，我们经验证明我们在小型合成实例上培训的ML模型概括为大型合成和现实世界的情况。我们将ML与META-HEURISTIC集成的方法是通用的，可以应用于各种优化问题。

Coronary Computed Tomography Angiography (CCTA) provides information on the presence, extent, and severity of obstructive coronary artery disease. Large-scale clinical studies analyzing CCTA-derived metrics typically require ground-truth validation in the form of high-fidelity 3D intravascular imaging. However, manual rigid alignment of intravascular images to corresponding CCTA images is both time consuming and user-dependent. Moreover, intravascular modalities suffer from several non-rigid motion-induced distortions arising from distortions in the imaging catheter path. To address these issues, we here present a semi-automatic segmentation-based framework for both rigid and non-rigid matching of intravascular images to CCTA images. We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image. We validate our co-registration framework on a cohort of $n=40$ patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our results indicate that our non-rigid registration significantly outperforms other co-registration approaches for luminal bifurcation alignment in both longitudinal (mean mismatch: 3.3 frames) and rotational directions (mean mismatch: 28.6 degrees). By providing a differentiable framework for automatic multi-modal intravascular data fusion, our developed co-registration modules significantly reduces the manual effort required to conduct large-scale multi-modal clinical studies while also providing a solid foundation for the development of machine learning-based co-registration approaches.

The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.

Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk associated with the actions taken, which may be critical in certain applications. To address that issue, the present research work introduces a novel methodology based on distributional RL to derive sequential decision-making policies that are sensitive to the risk, the latter being modelled by the tail of the return probability distribution. The core idea is to replace the $Q$ function generally standing at the core of learning schemes in RL by another function taking into account both the expected return and the risk. Named the risk-based utility function $U$, it can be extracted from the random return distribution $Z$ naturally learnt by any distributional RL algorithm. This enables to span the complete potential trade-off between risk minimisation and expected return maximisation, in contrast to fully risk-averse methodologies. Fundamentally, this research yields a truly practical and accessible solution for learning risk-sensitive policies with minimal modification to the distributional RL algorithm, and with an emphasis on the interpretability of the resulting decision-making process.

Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist readers required to cover this increase will continue to expand, consequently introducing a demand for AI-based tools that improve the efficiency with which radiologists can comfortably interpret these exams. AI has been shown to improve efficiency in medical-image generation, processing, and interpretation, and a variety of such AI models have been developed across research labs worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation. This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium. We identify barriers between AI-model development in research labs and subsequent clinical deployment and propose solutions. Our report provides guidance on processes which take an imaging AI model from development to clinical implementation in a healthcare institution. We discuss various AI integration points in a clinical Radiology workflow. We also present a taxonomy of Radiology AI use-cases. Through this report, we intend to educate the stakeholders in healthcare and AI (AI researchers, radiologists, imaging informaticists, and regulators) about cross-disciplinary challenges and possible solutions.

The future of population-based breast cancer screening is likely personalized strategies based on clinically relevant risk models. Mammography-based risk models should remain robust to domain shifts caused by different populations and mammographic devices. Modern risk models do not ensure adaptation across vendor-domains and are often conflated to unintentionally rely on both precursors of cancer and systemic/global mammographic information associated with short- and long-term risk, respectively, which might limit performance. We developed a robust, cross-vendor model for long-term risk assessment. An augmentation-based domain adaption technique, based on flavorization of mammographic views, ensured generalization to an unseen vendor-domain. We trained on samples without diagnosed/potential malignant findings to learn systemic/global breast tissue features, called mammographic texture, indicative of future breast cancer. However, training so may cause erratic convergence. By excluding noise-inducing samples and designing a case-control dataset, a robust ensemble texture model was trained. This model was validated in two independent datasets. In 66,607 Danish women with flavorized Siemens views, the AUC was 0.71 and 0.65 for prediction of interval cancers within two years (ICs) and from two years after screening (LTCs), respectively. In a combination with established risk factors, the model's AUC increased to 0.68 for LTCs. In 25,706 Dutch women with Hologic-processed views, the AUCs were not different from the AUCs in Danish women with flavorized views. The results suggested that the model robustly estimated long-term risk while adapting to an unseen processed vendor-domain. The model identified 8.1% of Danish women accounting for 20.9% of ICs and 14.2% of LTCs.

Quaternion valued neural networks experienced rising popularity and interest from researchers in the last years, whereby the derivatives with respect to quaternions needed for optimization are calculated as the sum of the partial derivatives with respect to the real and imaginary parts. However, we can show that product- and chain-rule does not hold with this approach. We solve this by employing the GHRCalculus and derive quaternion backpropagation based on this. Furthermore, we experimentally prove the functionality of the derived quaternion backpropagation.

In this work, a method for obtaining pixel-wise error bounds in Bayesian regularization of inverse imaging problems is introduced. The proposed method employs estimates of the posterior variance together with techniques from conformal prediction in order to obtain coverage guarantees for the error bounds, without making any assumption on the underlying data distribution. It is generally applicable to Bayesian regularization approaches, independent, e.g., of the concrete choice of the prior. Furthermore, the coverage guarantees can also be obtained in case only approximate sampling from the posterior is possible. With this in particular, the proposed framework is able to incorporate any learned prior in a black-box manner. Guaranteed coverage without assumptions on the underlying distributions is only achievable since the magnitude of the error bounds is, in general, unknown in advance. Nevertheless, experiments with multiple regularization approaches presented in the paper confirm that in practice, the obtained error bounds are rather tight. For realizing the numerical experiments, also a novel primal-dual Langevin algorithm for sampling from non-smooth distributions is introduced in this work.