Functional magnetic resonance imaging (fMRI) has opened new frontiers in neuroscience by instrumentally driving our understanding of brain function and development. Despite its substantial successes, fMRI studies persistently encounter obstacles stemming from inherent, unavoidable physiological confounds. The adverse effects of these confounds are especially noticeable with higher magnetic fields, which have been gaining momentum in fMRI experiments. This review focuses on the four major physiological confounds impacting fMRI studies: low-frequency fluctuations in both breathing depth and rate, low-frequency fluctuations in the heart rate, thoracic movements, and cardiac pulsatility. Over the past three decades, numerous correction techniques have emerged to address these challenges. Correction methods have effectively enhanced the detection of task-activated voxels and minimized the occurrence of false positives and false negatives in functional connectivity studies. While confound correction methods have merit, they also have certain limitations. For instance, model-based approaches require externally recorded physiological data that is often unavailable in fMRI studies. Methods reliant on independent component analysis, on the other hand, need prior knowledge about the number of components. Machine learning techniques, although showing potential, are still in the early stages of development and require additional validation. This article reviews the mechanics of physiological confound correction methods, scrutinizes their performance and limitations, and discusses their impact on fMRI studies.

Traditional medical image generation often lacks patient-specific clinical information, limiting its clinical utility despite enhancing downstream task performance. In contrast, medical image translation precisely converts images from one modality to another, preserving both anatomical structures and cross-modal features, thus enabling efficient and accurate modality transfer and offering unique advantages for model development and clinical practice. This paper reviews the latest advancements in deep learning(DL)-based medical image translation. Initially, it elaborates on the diverse tasks and practical applications of medical image translation. Subsequently, it provides an overview of fundamental models, including convolutional neural networks (CNNs), transformers, and state space models (SSMs). Additionally, it delves into generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Autoregressive Models (ARs), diffusion Models, and flow Models. Evaluation metrics for assessing translation quality are discussed, emphasizing their importance. Commonly used datasets in this field are also analyzed, highlighting their unique characteristics and applications. Looking ahead, the paper identifies future trends, challenges, and proposes research directions and solutions in medical image translation. It aims to serve as a valuable reference and inspiration for researchers, driving continued progress and innovation in this area.

To review the current risk and prognostic stratification systems in localised prostate cancer. To explore some of the most promising adjuncts to clinical models and what the evidence has shown regarding their value. There are many new biomarker-based models seeking to improve, optimise or replace clinical models. There are promising data on the value of MRI, radiomics, genomic classifiers and most recently artificial intelligence tools in refining stratification. Despite the extensive literature however, there remains uncertainty on where in pathways they can provide the most benefit and whether a biomarker is most useful for prognosis or predictive use. Comparisons studies have also often overlooked the fact that clinical models have themselves evolved and the context of the baseline used in biomarker studies that have shown superiority have to be considered. For new biomarkers to be included in stratification models, well designed prospective clinical trials are needed. Until then, there needs to be caution in interpretation of their use for day-to-day decision making. It is critical that users balance any purported incremental value against the performance of the latest clinical classification and multivariate models especially as the latter are cost free and widely available.

Although there are relatively few diverse, high-quality medical imaging datasets on which to train computer vision artificial intelligence models, even fewer datasets contain expertly classified observations that can be repurposed to train or test such models. The traditional annotation process is laborious and time-consuming. Repurposing annotations and consolidating similar types of annotations from disparate sources has never been practical. Until recently, the use of natural language processing to convert a clinical radiology report into labels required custom training of a language model for each use case. Newer technologies such as large language models have made it possible to generate accurate and normalized labels at scale, using only clinical reports and specific prompt engineering. The combination of automatically generated labels extracted and normalized from reports in conjunction with foundational image models provides a means to create labels for model training. This article provides a short history and review of the annotation and labeling process of medical images, from the traditional manual methods to the newest semiautomated methods that provide a more scalable solution for creating useful models more efficiently. <b>Keywords:</b> Feature Detection, Diagnosis, Semi-supervised Learning © RSNA, 2025.

Brain lesion segmentation is challenging in medical image analysis, aiming to delineate lesion regions precisely. Deep learning (DL) techniques have recently demonstrated promising results across various computer vision tasks, including semantic segmentation, object detection, and image classification. This paper offers an overview of recent DL algorithms for brain tumor and stroke segmentation, drawing on literature from 2021 to 2024. It highlights the strengths, limitations, current research challenges, and unexplored areas in imaging-based brain lesion classification based on insights from over 250 recent review papers. Techniques addressing difficulties like class imbalance and multi-modalities are presented. Optimization methods for improving performance regarding computational and structural complexity and processing speed are discussed. These include lightweight neural networks, multilayer architectures, and computationally efficient, highly accurate network designs. The paper also reviews generic and latest frameworks of different brain lesion detection techniques and highlights publicly available benchmark datasets and their issues. Furthermore, open research areas, application prospects, and future directions for DL-based brain lesion classification are discussed. Future directions include integrating neural architecture search methods with domain knowledge, predicting patient survival levels, and learning to separate brain lesions using patient statistics. To ensure patient privacy, future research is anticipated to explore privacy-preserving learning frameworks. Overall, the presented suggestions serve as a guideline for researchers and system designers involved in brain lesion detection and stroke segmentation tasks.

Federated Learning (FL) has emerged as a promising approach for collaborative medical image analysis while preserving data privacy, making it particularly suitable for radiomics tasks. This paper presents a systematic meta-analysis of recent surveys on Federated Learning in Medical Imaging (FL-MI), published in reputable venues over the past five years. We adopt the PRISMA methodology, categorizing and analyzing the existing body of research in FL-MI. Our analysis identifies common trends, challenges, and emerging strategies for implementing FL in medical imaging, including handling data heterogeneity, privacy concerns, and model performance in non-IID settings. The paper also highlights the most widely used datasets and a comparison of adopted machine learning models. Moreover, we examine FL frameworks in FL-MI applications, such as tumor detection, organ segmentation, and disease classification. We identify several research gaps, including the need for more robust privacy protection. Our findings provide a comprehensive overview of the current state of FL-MI and offer valuable directions for future research and development in this rapidly evolving field.

Medical imaging is a vital diagnostic tool that provides detailed insights into human anatomy but faces challenges affecting its accuracy and efficiency. Advanced generative AI models offer promising solutions. Unlike previous reviews with a narrow focus, a comprehensive evaluation across techniques and modalities is necessary. This systematic review integrates the three state-of-the-art leading approaches, GANs, Diffusion Models, and Transformers, examining their applicability, methodologies, and clinical implications in improving medical image quality. Using the PRISMA framework, 63 studies from 989 were selected via Google Scholar and PubMed, focusing on GANs, Transformers, and Diffusion Models. Articles from ACM, IEEE Xplore, and Springer were analyzed. Generative AI techniques show promise in improving image resolution, reducing noise, and enhancing fidelity. GANs generate high-quality images, Transformers utilize global context, and Diffusion Models are effective in denoising and reconstruction. Challenges include high computational costs, limited dataset diversity, and issues with generalizability, with a focus on quantitative metrics over clinical applicability. This review highlights the transformative impact of GANs, Transformers, and Diffusion Models in advancing medical imaging. Future research must address computational and generalization challenges, emphasize open science, and validate these techniques in diverse clinical settings to unlock their full potential. These efforts could enhance diagnostic accuracy, lower costs, and improve patient outcome.

This bibliometric analysis aims to explore scientific data on Artificial Intelligence (AI) and Head and Neck Cancer (HNC). AI-related HNC articles from the Web of Science Core Collection were searched. VosViewer and Biblioshiny/Bibiometrix for R Studio were used for data synthesis. This analysis covered key characteristics such as sources, authors, affiliations, countries, citations and top cited articles, keyword analysis, and trending topics. A total of 1,019 papers from 1995 to 2024 were included. Among them, 71.6% were original research articles, 7.6% were reviews, and 20.8% took other forms. The fifty most cited documents highlighted radiology as the most explored specialty, with an emphasis on deep learning models for segmentation. The publications have been increasing, with an annual growth rate of 94.4% after 2016. Among the 20 most productive countries, 14 are high-income economies. The keywords of strong citation revealed 2 main clusters: radiomics and radiotherapy. The most frequently keywords include machine learning, deep learning, artificial intelligence, and head and neck cancer, with recent emphasis on diagnosis, survival prediction, and histopathology. There has been an increase in the use of AI in HNC research since 2016 and indicated a notable disparity in publication quantity between high-income and low/middle-income countries. Future research should prioritize clinical validation and standardization to facilitate the integration of AI in HNC management, particularly in underrepresented regions.

The choice of imaging modalities is essential in sarcoma management, as different techniques provide complementary information depending on tumor subtype and anatomical location. This narrative review examines the role of imaging in sarcoma characterization and treatment planning, particularly in the context of radiation therapy (RT). Magnetic resonance imaging (MRI) provides superior soft tissue contrast, enabling detailed assessment of tumor extent and peritumoral involvement. Computed tomography (CT) is particularly valuable for detecting osseous involvement, periosteal reactions, and calcifications, complementing MRI in sarcomas involving bone or calcified lesions. The combination of MRI and CT enhances tumor delineation, particularly for complex sites such as retroperitoneal and uterine sarcomas, where spatial relationships with adjacent organs are critical. In vascularized sarcomas, such as alveolar soft-part sarcomas, the integration of MRI with CT or MR angiography facilitates accurate mapping of tumor margins. Positron emission tomography with [18 F]-fluorodeoxyglucose ([18 F]-FDG PET) provides functional insights, identifying metabolically active regions within tumors to guide dose escalation. Although its role in routine staging is limited, [18 F]-FDG PET and emerging PET tracers offer promise for refining RT planning. Advances in artificial intelligence further enhance imaging precision, enabling more accurate contouring and treatment optimization. This review highlights how the integration of imaging modalities, tailored to specific sarcoma subtypes, supports precise RT delivery while minimizing damage to surrounding tissues. These strategies underline the importance of multidisciplinary approaches in improving sarcoma management and outcomes through multi-image-based RT planning.