Diagnosis of Medical Imaging

1. Introduction

Medical imaging is the cornerstone of modern medicine, offering an unequaled window into the anatomy and physiology of the human body. Computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and advanced microscopy are some of the techniques that have completely revolutionized clinical care by allowing disease to be detected at earlier stages, aiding accurate diagnosis, and guiding personal treatment. The discipline is rapidly evolving due to advances in data acquisition hardware and the incorporation of state-of-the-art computational techniques such as artificial intelligence (AI) and machine learning. This change will lead diagnostics away from subjective, qualitative assessment to quantitative, predictive, and personalized methods [1].

This Special Issue aims to assemble a series of recent advances that exhibit the revolution in the area. The collected articles illustrate that due to increasingly advanced tools for image reconstruction and analysis, optimal segmentation and new functional biomarkers and immersive technologies, materialization is morphing contemporary practice.

2. Recent Trends in Medical Imaging and Diagnosis

The field of medical imaging is at the cusp of a paradigm shift due to a confluence of artificial intelligence, personalized medicine, and immersive technologies.

2.1. Machine Learning and Deep Learning in Medical Imaging

The importance of AI in the diagnostic workflow of radiology has increasingly gained attention for image segmentation, classification, and prediction. Although Convolutional Neural Networks (CNNs) are still a common choice, Vision Transformers (ViTs) and the hybrid CNN-ViT model have quickly proved to be state-of-the-art for tumor and organ segmentation [2]. In addition, Generative Adversarial Networks (GANs) have also proved to be successful in improving images and increasing the sizes of datasets [3]. A recent review of deep learning approaches for multi-organ segmentation corroborates this evidence, demonstrating that the performance of automated pipelines has now largely surpassed traditional approaches [4]. The current role of AI is not only leveraged on raw images upon realistic scenario, but is also focused on seamlessly joining image examination to support holistic medical decision support, thus standing for the new AI-augmented radiology, which is faster, more reliable, and better prepared for clinical decision support [1].

2.2. Toward Adaptive and Personalized Imaging

Medical imaging is increasingly being customized to individual patients. Reinforcement learning has been used to enable in vivo optimization of MRI protocols in real time, adapting MRI scans for specific diagnostic needs [5]. Additionally, in the context of cancer treatment, adaptive radiotherapy uses repeated imaging to optimize the treatment plan over time, which has a positive effect on the treatment outcome [6]. “Digital twins” are now being adopted as an innovative framework that allows patient-specific computer models integrating imaging, genetics, and physiological sensors to model disease progression and treatment response [7]. Such approaches offer the potential for precision imaging that matches an individual patient’s specific anatomy, disease parameters, and biologic response to treatment.

2.3. Clinical Use of Immersive and Mixed Reality

Extended Reality (XR), including virtual reality (VR), augmented reality (AR), and mixed reality (MR), is being more widely used in medicine. For surgical planning and teaching, MR offers the interactive display of complex anatomy and real-time navigation during interventions [8,9], while it can result in reduced learning curves and lower complication rates [10]. Furthermore, beyond the hospital, fully immersive technology is beneficial for both medical education and patient communication resulting in safer training simulations and more understandable presentations of complex diseases [11]. As such platforms mature, they will increasingly become an essential part of the chain between diagnosis, intervention, and training.

3. Contributions of This Special Issue

The papers published in this Special Issue provide a wide range of insights into medical imaging research, highlighting the use of innovative techniques to address long-standing challenges. A significant portion of the effort is devoted to improving CT imaging for more accurate and efficient diagnosis. For example, Kim et al. present a comprehensive review of reconstruction parameters for Cone Beam CT (CBCT), showing the importance of the correct choice of filters and projections angles for improved image quality and reduced radiation dose [12]. This issue also includes work on functional and microscopic analysis, with Hartmann et al. presenting a new Fast Green-enhanced confocal microscopy technique that exceeds the current limits of microscopic imaging to better visualize collagen changes for early cancer detection [13]. Lastly, a paper in this collection highlights the close relationship between imaging and therapeutic management, with Marinozzi et al. proposing a new method to employ Mixed Reality (MR) in intracardiac surgery. They are pioneering a new approach to surgical planning and guidance by translating CT-derived 3D models to interactive holograms, with an intuitive interface enabling surgeons to visualize and interact with complex anatomy with clarity never before imagined [14]. Other contributions in this Special Issue include research on applying deep learning to automate bone segmentation for orthopedic imaging, an evidence-based investigation of factors affecting coronary calcium scores in cardiac CT, a study on using cardiac MRI to assess myocardial strain for early Duchenne muscular dystrophy diagnosis, and a pictorial review of MRI features following non-surgical treatments for hepatocellular carcinoma.

4. Conclusions and Outlook

The works presented in this SI together with the fast progresses of the whole area reveal a future of medical imaging that is adaptive, intelligent, and immersive. Developments in CT reconstruction and cardiac scanning suggest that there will be a growing role for adaptive strategies. Automatic segmentation workflows serve as a basis for the development of a clinical digital twin. In a similar vein, the functional and microscopic biomarkers described pave the way for new predictive and preventative medicine. Immersive technologies will complement such efforts by further developing surgical workflows and medical education. AI will be a primary enabler of such advancements, converting huge quantities of multimodal data into clinically actionable knowledge.

Collectively, these advancements showcase that diagnostic imaging is transforming into an increasingly forward-thinking industry that thrives on personalized patient-centered care as much as on technology. We would like to thank all authors who made excellent contributions, as well as the reviewers for their professional work and helpful advice. We are optimistic that this Special Issue will be able to motivate further creative work, cross-discipline collaboration, and novel findings in the valuable area of medical imaging diagnosis.