La enseñanza de la radiología en los programas docentes integrados / Teaching Radiology in integrated educational programs

La radiología es una disciplina médica, un área de conocimiento transversal integrada en cualquier situación clínica. El aprendizaje óptimo del conocimiento, habilidades y aptitudes en radiología en el Grado en Medicina requiere la integración de cualquier modalidad de imagen en las distintas áreas del conocimiento: desde las asignaturas básicas hasta cualquier asignatura clínica del grado. El presente artículo describe la integración de la docencia en radiología del plan de estudios en todo el grado de medicina de la Universidad de Girona (UdG), describiendo las distintas actividades docentes de radiología que se imparten en las distintas asignaturas; desde primero a sexto curso. Se detallan las actividades específicas de la asignatura de «radiología», incluyendo talleres, seminarios, prácticas, juego de ordenador interactivo, y describiendo las características de la actividad metodológica docente principal de la UdG, el aprendizaje basado en problemas.(AU)

Radiology is a medical discipline, an area of transversal knowledge integrated into any clinical situation. The optimal training of learning knowledge, skills and aptitudes in Radiology in the Degree in Medicine requires the integration of any imaging modality in the different areas of knowledge; from the basic subjects to any clinical subject of the Degree. This article describes the integration of Radiology teaching into the curriculum throughout the Medicine Degree at the University of Girona (UdG), describing the different radiology teaching activities that are taught. The specific activities of the subject «Radiology» are detailed; through workshops, seminars, practices, interactive computer game; and describing the characteristics of the main teaching methodological activity of the UdG, Problem-Based Learning.(AU)

Surface engineered nanodiamonds: mechanistic intervention in biomedical applications for diagnosis and treatment of cancer.

In terms of biomedical tools, nanodiamonds (ND) are a more recent innovation. Their size typically ranges between 4 to 100 nm. ND are produced via a variety of methods and are known for their physical toughness, durability, and chemical stability. Studies have revealed that surface modifications and functionalization have a significant influence on the optical and electrical properties of the nanomaterial. Consequently, surface functional groups of NDs have applications in a variety of domains, including drug administration, gene delivery, immunotherapy for cancer treatment, and bio-imaging to diagnose cancer. Additionally, their biocompatibility is a critical requisite for theirin vivoandin vitrointerventions. This review delves into these aspects and focuses on the recent advances in surface modification strategies of NDs for various biomedical applications surrounding cancer diagnosis and treatment. Furthermore, the prognosis of its clinical translation has also been discussed.

Recent Progress in Peptide-Based Molecular Probes for Disease Bioimaging.

Recent strides in molecular pathology have unveiled distinctive alterations at the molecular level throughout the onset and progression of diseases. Enhancing the in vivo visualization of these biomarkers is crucial for advancing disease classification, staging, and treatment strategies. Peptide-based molecular probes (PMPs) have emerged as versatile tools due to their exceptional ability to discern these molecular changes with unparalleled specificity and precision. In this Perspective, we first summarize the methodologies for crafting innovative functional peptides, emphasizing recent advancements in both peptide library technologies and computer-assisted peptide design approaches. Furthermore, we offer an overview of the latest advances in PMPs within the realm of biological imaging, showcasing their varied applications in diagnostic and therapeutic modalities. We also briefly address current challenges and potential future directions in this dynamic field.

Machine learning approach for proton range verification using real-time prompt gamma imaging with Compton cameras: addressing the total deposited energy information gap.

Objective.Compton camera imaging shows promise as a range verification technique in proton therapy. This work aims to assess the performance of a machine learning model in Compton camera imaging for proton beam range verification improvement.Approach.The presented approach was used to recognize Compton events and estimate more accurately the prompt gamma (PG) energy in the Compton camera to reconstruct the PGs emission profile during proton therapy. This work reports the results obtained from the Geant4 simulation for a proton beam impinging on a polymethyl methacrylate (PMMA) target. To validate the versatility of such an approach, the produced PG emissions interact with a scintillating fiber-based Compton camera.Main results.A trained multilayer perceptron (MLP) neural network shows that it was possible to achieve a notable three-fold increase in the signal-to-total ratio. Furthermore, after event selection by the trained MLP, the loss of full-energy PGs was compensated by means of fitting an MLP energy regression model to the available data from true Compton (signal) events, predicting more precisely the total deposited energy for Compton events with incomplete energy deposition.Significance.A considerable improvement in the Compton camera's performance was demonstrated in determining the distal falloff and identifying a few millimeters of target displacements. This approach has shown great potential for enhancing online proton range monitoring with Compton cameras in future clinical applications.

Artificial intelligence in liver imaging: methods and applications.

Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.

Integrated lithium niobate microwave photonic processing engine.

Integrated microwave photonics (MWP) is an intriguing technology for the generation, transmission and manipulation of microwave signals in chip-scale optical systems1,2. In particular, ultrafast processing of analogue signals in the optical domain with high fidelity and low latency could enable a variety of applications such as MWP filters3-5, microwave signal processing6-9 and image recognition10,11. An ideal integrated MWP processing platform should have both an efficient and high-speed electro-optic modulation block to faithfully perform microwave-optic conversion at low power and also a low-loss functional photonic network to implement various signal-processing tasks. Moreover, large-scale, low-cost manufacturability is required to monolithically integrate the two building blocks on the same chip. Here we demonstrate such an integrated MWP processing engine based on a 4 inch wafer-scale thin-film lithium niobate platform. It can perform multipurpose tasks with processing bandwidths of up to 67 GHz at complementary metal-oxide-semiconductor (CMOS)-compatible voltages. We achieve ultrafast analogue computation, namely temporal integration and differentiation, at sampling rates of up to 256 giga samples per second, and deploy these functions to showcase three proof-of-concept applications: solving ordinary differential equations, generating ultra-wideband signals and detecting edges in images. We further leverage the image edge detector to realize a photonic-assisted image segmentation model that can effectively outline the boundaries of melanoma lesion in medical diagnostic images. Our ultrafast lithium niobate MWP engine could provide compact, low-latency and cost-effective solutions for future wireless communications, high-resolution radar and photonic artificial intelligence.

Advancing Diagnostics and Patient Care: The Role of Biomarkers in Radiology.

The integration of biomarkers into medical practice has revolutionized the field of radiology, allowing for enhanced diagnostic accuracy, personalized treatment strategies, and improved patient care outcomes. This review offers radiologists a comprehensive understanding of the diverse applications of biomarkers in medicine. By elucidating the fundamental concepts, challenges, and recent advancements in biomarker utilization, it will serve as a bridge between the disciplines of radiology and epidemiology. Through an exploration of various biomarker types, such as imaging biomarkers, molecular biomarkers, and genetic markers, I outline their roles in disease detection, prognosis prediction, and therapeutic monitoring. I also discuss the significance of robust study designs, blinding, power and sample size calculations, performance metrics, and statistical methodologies in biomarker research. By fostering collaboration between radiologists, statisticians, and epidemiologists, I hope to accelerate the translation of biomarker discoveries into clinical practice, ultimately leading to improved patient care.

Radiomics in Musculoskeletal Tumors.

Sarcomas are heterogeneous rare tumors predominantly affecting the musculoskeletal (MSK) system. Due to significant variations in their natural history and variable response to conventional treatments, the discovery of novel diagnostic and prognostic biomarkers to guide therapeutic decision-making is an active and ongoing field of research. As new cellular, molecular, and metabolic biomarkers continue to be discovered, quantitative radiologic imaging is becoming increasingly important in sarcoma management. Radiomics offers the potential for discovering novel imaging diagnostic and predictive biomarkers using standard-of-care medical imaging. In this review, we detail the core concepts of radiomics and the application of radiomics to date in MSK sarcoma research. Also described are specific challenges related to radiomic studies, as well as viewpoints on clinical adoption and future perspectives in the field.

In situ theranostic platform combining highly localized magnetic fluid hyperthermia, magnetic particle imaging, and thermometry in 3D.

Theranostic platforms, combining diagnostic and therapeutic approaches within one system, have garnered interest in augmenting invasive surgical, chemical, and ionizing interventions. Magnetic particle imaging (MPI) offers a quite recent alternative to established radiation-based diagnostic modalities with its versatile tracer material (superparamagnetic iron oxide nanoparticles, SPION). It also offers a bimodal theranostic framework that can combine tomographic imaging with therapeutic techniques using the very same SPION. Methods: We show the interleaved combination of MPI-based imaging, therapy (highly localized magnetic fluid hyperthermia (MFH)) and therapy safety control (MPI-based thermometry) within one theranostic platform in all three spatial dimensions using a commercial MPI system and a custom-made heating insert. The heating characteristics as well as theranostic applications of the platform were demonstrated by various phantom experiments using commercial SPION. Results: We have shown the feasibility of an MPI-MFH-based theranostic platform by demonstrating high spatial control of the therapeutic target, adequate MPI-based thermometry, and successful in situ interleaved MPI-MFH application. Conclusions: MPI-MFH-based theranostic platforms serve as valuable tools that enable the synergistic integration of diagnostic and therapeutic approaches. The transition into in vivo studies will be essential to further validate their potential, and it holds promising prospects for future advancements.

Cardiac Sarcoidosis.

Cardiac involvement is a major cause of morbidity and mortality in patients with sarcoidosis. It is important to distinguish between clinical manifest diseases from clinically silent diseases. Advanced cardiac imaging studies are crucial in the diagnostic pathway. In suspected isolated cardiac sarcoidosis, it's key to rule out alternative diagnoses. Therapeutic options can be divided into immunosuppressive agents, guideline-directed medical therapy, antiarrhythmic medications, device/ablation therapy, and heart transplantation.

Practical Applications of Artificial Intelligence in Spine Imaging: A Review.

Artificial intelligence (AI), a transformative technology with unprecedented potential in medical imaging, can be applied to various spinal pathologies. AI-based approaches may improve imaging efficiency, diagnostic accuracy, and interpretation, which is essential for positive patient outcomes. This review explores AI algorithms, techniques, and applications in spine imaging, highlighting diagnostic impact and challenges with future directions for integrating AI into spine imaging workflow.

Motion-resistant three-wavelength spatial frequency domain imaging system with ambient light suppression using an 8-tap CMOS image sensor.

Significance: We present a motion-resistant three-wavelength spatial frequency domain imaging (SFDI) system with ambient light suppression using an 8-tap complementary metal-oxide semiconductor (CMOS) image sensor (CIS) developed at Shizuoka University. The system addresses limitations in conventional SFDI systems, enabling reliable measurements in challenging imaging scenarios that are closer to real-world conditions. Aim: Our study demonstrates a three-wavelength SFDI system based on an 8-tap CIS. We demonstrate and evaluate the system's capability of mitigating motion artifacts and ambient light bias through tissue phantom reflectance experiments and in vivo volar forearm experiments. Approach: We incorporated the Hilbert transform to reduce the required number of projected patterns per wavelength from three to two per spatial frequency. The 8-tap image sensor has eight charge storage diodes per pixel; therefore, simultaneous image acquisition of eight images based on multi-exposure is possible. Taking advantage of this feature, the sensor simultaneously acquires images for planar illumination, sinusoidal pattern projection at three wavelengths, and ambient light. The ambient light bias is eliminated by subtracting the ambient light image from the others. Motion artifacts are suppressed by reducing the exposure and projection time for each pattern while maintaining sufficient signal levels by repeating the exposure. The system is compared to a conventional SFDI system in tissue phantom experiments and then in vivo measurements of human volar forearms. Results: The 8-tap image sensor-based SFDI system achieved an acquisition rate of 9.4 frame sets per second, with three repeated exposures during each accumulation period. The diffuse reflectance maps of three different tissue phantoms using the conventional SFDI system and the 8-tap image sensor-based SFDI system showed good agreement except for high scattering phantoms. For the in vivo volar forearm measurements, our system successfully measured total hemoglobin concentration, tissue oxygen saturation, and reduced scattering coefficient maps of the subject during motion (16.5 cm/s) and under ambient light (28.9 lx), exhibiting fewer motion artifacts compared with the conventional SFDI. Conclusions: We demonstrated the potential for motion-resistant three-wavelength SFDI system with ambient light suppression using an 8-tap CIS.

Analysis of polarization features of human breast cancer tissue by Mueller matrix visualization.

Significance: Breast cancer ranks second in the world in terms of the number of women diagnosed. Effective methods for its early-stage detection are critical for facilitating timely intervention and lowering the mortality rate. Aim: Polarimetry provides much useful information on the structural properties of breast cancer tissue samples and is a valuable diagnostic tool. The present study classifies human breast tissue samples as healthy or cancerous utilizing a surface-illuminated backscatter polarization imaging technique. Approach: The viability of the proposed approach is demonstrated using 95 breast tissue samples, including 35 healthy samples, 20 benign cancer samples, 20 grade-2 malignant samples, and 20 grade-3 malignant samples. Results: The observation results reveal that element m23 in the Mueller matrix of the healthy samples has a deeper color and greater intensity than that in the breast cancer samples. Conversely, element m32 shows a lighter color and reduced intensity. Finally, element m44 has a darker color in the healthy samples than in the cancer samples. The analysis of variance test results and frequency distribution histograms confirm that elements m23, m32, and m44 provide an effective means of detecting and classifying human breast tissue samples. Conclusions: Overall, the results indicate that surface-illuminated backscatter polarization imaging has significant potential as an assistive tool for breast cancer diagnosis and classification.

Exploiting the Photo-Physical Properties of Metal Halide Perovskite Nanocrystals for Bioimaging.

Perovskite nanomaterials have recently been exploited for bioimaging applications due to their unique photo-physical properties, including high absorbance, good photostability, narrow emissions, and nonlinear optical properties. These attributes outperform conventional fluorescent materials such as organic dyes and metal chalcogenide quantum dots and endow them with the potential to reshape a wide array of bioimaging modalities. Yet, their full potential necessitates a deep grasp of their structure-attribute relationship and strategies for enhancing water stability through surface engineering for meeting the stringent and unique requirements of each individual imaging modality. This review delves into this evolving frontier, highlighting how their distinctive photo-physical properties can be leveraged and optimized for various bioimaging modalities, including visible light imaging, near-infrared imaging, and super-resolution imaging.

Magneto-acoustic protein nanostructures for non-invasive imaging of tissue mechanics in vivo.

Measuring cellular and tissue mechanics inside intact living organisms is essential for interrogating the roles of force in physiological and disease processes. Current agents for studying the mechanobiology of intact, living organisms are limited by poor light penetration and material stability. Magnetomotive ultrasound is an emerging modality for real-time in vivo imaging of tissue mechanics. Nonetheless, it has poor sensitivity and spatiotemporal resolution. Here we describe magneto-gas vesicles (MGVs), protein nanostructures based on gas vesicles and magnetic nanoparticles that produce differential ultrasound signals in response to varying mechanical properties of surrounding tissues. These hybrid nanomaterials significantly improve signal strength and detection sensitivity. Furthermore, MGVs enable non-invasive, long-term and quantitative measurements of mechanical properties within three-dimensional tissues and in vivo fibrosis models. Using MGVs as novel contrast agents, we demonstrate their potential for non-invasive imaging of tissue elasticity, offering insights into mechanobiology and its application to disease diagnosis and treatment.

Autoimmune disorders of the gastrointestinal tract: Review of radiological appearances.

Autoimmune gastrointestinal (GI) disorders comprise a heterogeneous group of diseases with non-specific clinical manifestations. These are divided into primary and secondary. A high index of clinical suspicion complemented with endoscopic and radiological imaging may allow early diagnosis. Due to the relatively low incidence of autoimmune disorder, the imaging literature is sparse. In this review, we outline the pathogenesis, classification, and imaging appearances of autoimmune GI disorders.

Shape Anisotropy-Governed High-Performance Nanomagnetosol for In Vivo Magnetic Particle Imaging of Lungs.

Caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has shown extensive lung manifestations in vulnerable individuals, putting lung imaging and monitoring at the forefront of early detection and treatment. Magnetic particle imaging (MPI) is an imaging modality, which can bring excellent contrast, sensitivity, and signal-to-noise ratios to lung imaging for the development of new theranostic approaches for respiratory diseases. Advances in MPI tracers would offer additional improvements and increase the potential for clinical translation of MPI. Here, a high-performance nanotracer based on shape anisotropy of magnetic nanoparticles is developed and its use in MPI imaging of the lung is demonstrated. Shape anisotropy proves to be a critical parameter for increasing signal intensity and resolution and exceeding those properties of conventional spherical nanoparticles. The 0D nanoparticles exhibit a 2-fold increase, while the 1D nanorods have a > 5-fold increase in signal intensity when compared to VivoTrax. Newly designed 1D nanorods displayed high signal intensities and excellent resolution in lung images. A spatiotemporal lung imaging study in mice revealed that this tracer offers new opportunities for monitoring disease and guiding intervention.

Medical image identification methods: A review.

The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging provided a good scheme for medical image analysis over traditional methods that rely on the handcrafted features, it remains challenging due to the diversity of imaging modalities and clinical pathologies. Many medical image identification methods provide a good scheme for medical image analysis. The concepts pertinent of methods, such as the machine learning, deep learning, convolutional neural networks, transfer learning, and other image processing technologies for medical image are analyzed and summarized in this paper. We reviewed these recent studies to provide a comprehensive overview of applying these methods in various medical image analysis tasks, such as object detection, image classification, image registration, segmentation, and other tasks. Especially, we emphasized the latest progress and contributions of different methods in medical image analysis, which are summarized base on different application scenarios, including classification, segmentation, detection, and image registration. In addition, the applications of different methods are summarized in different application area, such as pulmonary, brain, digital pathology, brain, skin, lung, renal, breast, neuromyelitis, vertebrae, and musculoskeletal, etc. Critical discussion of open challenges and directions for future research are finally summarized. Especially, excellent algorithms in computer vision, natural language processing, and unmanned driving will be applied to medical image recognition in the future.