Portable Dynamic Chest Radiography: Literature Review and Potential Bedside Applications

Dynamic digital radiography (DDR) is a high-resolution radiographic imaging technique using pulsed X-ray emission to acquire a multiframe cine-loop of the target anatomical area. The first DDR technology was orthostatic chest acquisitions, but new portable equipment that can be positioned at the patient's bedside was recently released, significantly expanding its potential applications, particularly in chest examination. It provides anatomical and functional information on the motion of different anatomical structures, such as the lungs, pleura, rib cage, and trachea. Native images can be further analyzed with dedicated post-processing software to extract quantitative parameters, including diaphragm motility, automatically projected lung area and area changing rate, a colorimetric map of the signal value change related to respiration and motility, and lung perfusion. The dynamic diagnostic information along with the significant advantages of this technique in terms of portability, versatility, and cost-effectiveness represents a potential game changer for radiological diagnosis and monitoring at the patient's bedside. DDR has several applications in daily clinical practice, and in this narrative review, we will focus on chest imaging, which is the main application explored to date in the literature. However, studies are still needed to understand deeply the clinical impact of this method.

An optimized 1.5 Tesla MRI protocol of the brachial plexus

PURPOSE

Creating an effective MRI protocol for examining the brachial plexus poses significant challenges, and despite the abundance of protocols in the literature, there is a lack of reference standards for basic sequences and essential parameters needed for replication. The aim of this study is to establish a reproducible 1.5 T brachial plexus imaging protocol, including patient positioning, coil selection, imaging planes, and essential sequence parameters.

METHODS

We systematically investigated MRI sequences, testing each parameter through in vivo experiments, examining their effects on signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), visual quality scores, and acquisition time. Sequences were refined based on optimal quality and timing scores. The final protocol was tested on scanners from two other vendors for reliability.

RESULTS

The final protocol included a combination of 2D turbo-spin-echo and 3D SPACE T1, SPACE STIR, and VIBE DIXON sequences. Recommendations for imaging planes, phase encoding, field of view, TR, TE, resolution, number of slices, slice thickness, fat and blood suppression, and acceleration strategies are provided. The protocol was successfully translated to other vendor's scanners with comparable quality.

CONCLUSION

We present an optimized protocol detailing the essential parameters for reproducibility. Our comprehensive list of experiments describes the impact of each parameter on image quality and scan time, addressing common artifacts and potential solutions. This protocol can benefit both young radiologists new to the field and experienced professionals seeking to refine their existing protocols.

Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy

Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer. AI may have different potential applications in lung cancer characterization and outcome prediction. Manual segmentation is a time-consuming task, with high inter-observer variability, that can be replaced by AI-based approaches, including deep learning models such as U-Net, BCDU-Net, and others, to quantify lung nodules and cancers objectively and to extract radiomics features for the characterization of the tissue. AI models have also demonstrated their ability to predict treatment responses, such as immunotherapy and targeted therapy, by integrating radiomic features with clinical data. Additionally, AI-based prognostic models have been developed to identify patients at higher risk and personalize treatment strategies. In conclusion, this review article provides a comprehensive overview of the current state of AI applications in lung cancer management, spanning from segmentation and virtual biopsy to outcome prediction. The evolving role of AI in improving the precision and effectiveness of lung cancer diagnosis and treatment underscores its potential to significantly impact clinical practice and patient outcomes.

The Role of Artificial Intelligence and Texture Analysis in Interventional Radiological Treatments of Liver Masses: A Narrative Review

Liver lesions, including both benign and malignant tumors, pose significant challenges in interventional radiological treatment planning and prognostication. The emerging field of artificial intelligence (AI) and its integration with texture analysis techniques have shown promising potential in predicting treatment outcomes, enhancing precision, and aiding clinical decision-making. This comprehensive review aims to summarize the current state-of-the-art research on the application of AI and texture analysis in determining treatment response, recurrence rates, and overall survival outcomes for patients undergoing interventional radiological treatment for liver lesions. Furthermore, the review addresses the challenges associated with the implementation of AI and texture analysis in clinical practice, including data acquisition, standardization of imaging protocols, and model validation. Future directions and potential advancements in this field are discussed. Integration of multi-modal imaging data, incorporation of genomics and clinical data, and the development of predictive models with enhanced interpretability are proposed as potential avenues for further research. In conclusion, the application of AI and texture analysis in predicting outcomes of interventional radiological treatment for liver lesions shows great promise in augmenting clinical decision-making and improving patient care. By leveraging these technologies, clinicians can potentially enhance treatment planning, optimize intervention strategies, and ultimately improve patient outcomes in the management of liver lesions.

Exploring the Potential of Artificial Intelligence in Breast Ultrasound

Breast ultrasound has emerged as a valuable imaging modality in the detection and characterization of breast lesions, particularly in women with dense breast tissue or contraindications for mammography. Within this framework, artificial intelligence (AI) has garnered significant attention for its potential to improve diagnostic accuracy in breast ultrasound and revolutionize the workflow. This review article aims to comprehensively explore the current state of research and development in harnessing AI's capabilities for breast ultrasound. We delve into various AI techniques, including machine learning, deep learning, as well as their applications in automating lesion detection, segmentation, and classification tasks. Furthermore, the review addresses the challenges and hurdles faced in implementing AI systems in breast ultrasound diagnostics, such as data privacy, interpretability, and regulatory approval. Ethical considerations pertaining to the integration of AI into clinical practice are also discussed, emphasizing the importance of maintaining a patient-centered approach. The integration of AI into breast ultrasound holds great promise for improving diagnostic accuracy, enhancing efficiency, and ultimately advancing patient's care. By examining the current state of research and identifying future opportunities, this review aims to contribute to the understanding and utilization of AI in breast ultrasound and encourage further interdisciplinary collaboration to maximize its potential in clinical practice.

Correlation Analysis on Anatomical Variants of Accessory Foramina in the Sphenoid Bone for Oncological Surgery

The sphenoid bone presents several anatomical variations, including accessory foramina, such as the foramen meningo-orbitale, the foramen of Vesalius, the canaliculus innominatus and the palatovaginal canal, which may be involved in tumor invasion or surgery of surrounding structures. Therefore, clinicians and surgeons have to consider these variants when planning surgical interventions of the cranial base. The prevalence of each variant is reported in the published literature, but very little information is available on the possible correlation among different variants. Here, 300 CT scans of patients (equally divided among males and females) were retrospectively assessed to investigate the presence of the foramen meningo-orbitale, the foramen of Vesalius, the canaliculus innominatus and the palatovaginal canal. Possible differences in the prevalence of each accessory foramen according to sex were assessed, as well as possible correlations among different variants through the Chi-square test (p < 0.01). Overall, the prevalence of the foramen meningo-orbitale, the foramen of Vesalius, the canaliculus innominatus and the palatovaginal canal was 30.7%, 67.7%, 14.0% and 35.3%, respectively, without any difference according to sex (p > 0.01). A significant positive correlation was found between the foramen of Vesalius and canaliculus innominatus, both in males and in females (p < 0.01). In detail, subjects with canaliculus innominatus in 85.7-100.0% of cases also showed the foramen of Vesalius, independently from sex and side. The present study provided novel data about the prevalence of four accessory foramina of the sphenoid bone in an Italian population, and a correlation between the foramen of Vesalius and the canaliculus innominatus was found for the first time. As these accessory foramina host neurovascular structures, the results of this study are thus useful for appropriate planning surgical procedures that are tailored to the anatomical configuration of the patient and for improving techniques to avoid accidental injuries in cranial base surgery. Knowledge of the topography, frequencies and the presence/absence of these additional foramina are pivotal for a successful procedure. Clinicians and surgeons may benefit from these novel data for appropriate recognition of the variants, decision-making, pre-operative and treatment planning, improvement of the procedures, screening of patients and prevention of misdiagnosis.

Deep Learning-Based Versus Iterative Image Reconstruction for Unenhanced Brain CT: A Quantitative Comparison of Image Quality

This exploratory retrospective study aims to quantitatively compare the image quality of unenhanced brain computed tomography (CT) reconstructed with an iterative (AIDR-3D) and a deep learning-based (AiCE) reconstruction algorithm. After a preliminary phantom study, AIDR-3D and AiCE reconstructions (0.5 mm thickness) of 100 consecutive brain CTs acquired in the emergency setting on the same 320-detector row CT scanner were retrospectively analyzed, calculating image noise reduction attributable to the AiCE algorithm, artifact indexes in the posterior cranial fossa, and contrast-to-noise ratios (CNRs) at the cortical and thalamic levels. In the phantom study, the spatial resolution of the two datasets proved to be comparable; conversely, AIDR-3D reconstructions showed a broader noise pattern. In the human study, median image noise was lower with AiCE compared to AIDR-3D (4.7 vs. 5.3, p < 0.001, median 19.6% noise reduction), whereas AIDR-3D yielded a lower artifact index than AiCE (7.5 vs. 8.4, p < 0.001). AiCE also showed higher median CNRs at the cortical (2.5 vs. 1.8, p < 0.001) and thalamic levels (2.8 vs. 1.7, p < 0.001). These results highlight how image quality improvements granted by deep learning-based (AiCE) and iterative (AIDR-3D) image reconstruction algorithms vary according to different brain areas.

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

Non-contrast MR dacryocystography for the evaluation of epiphora and recurrent dacryocystitis: A preliminary study

INTRODUCTION

Obstruction of the lacrimal drainage represents a common ophthalmologic issue. The blockage may interest any level of the lacrimal drainage pathway, and it is important to find the site of obstruction to plan the most appropriate treatment. In this study, findings from magnetic resonance (MR) dacryocystography were compared with findings from endoscopic and surgical procedures to evaluate the accuracy of MR dacryocystography in localizing the site of nasolacrimal duct obstruction.

METHODS

We enrolled twenty-one patients with clinical suspicion of nasolacrimal duct obstruction who underwent dacryoendoscopy and surgery. MR dacryocystography was performed with a heavily T2-weighted fast spin echo sequence in the coronal planes. Before the MRI was performed, a sterile 0.9% NaCl solution was administered into both conjunctival sacs. For each examination, two independent readers (with 8 and 10 years of experience in head and neck imaging) evaluated both heavily 3D space T2-weighted and STIR sequences.

RESULTS

Stenosis/obstruction of nasolacrimal duct or lacrimal sac was diagnosed in all 21 patients who underwent MRI dacryocystography. In particular, the site of the obstruction was classified as lacrimal sac in 12 (57%) patients, nasolacrimal duct in 6 (29%) patients, and canaliculi in 3 (14%) patients by both readers. By comparison with the evidence resulting from the endoscopy, there were differences between MRI dacryocystography and dacryoendoscopy in the evaluation of the obstruction's site in three patients, with an overall accuracy of 85.7%.

CONCLUSION

MR dacryocystography allows a non-invasive evaluation of the lacrimal drainage pathway, valid for the planning of the most appropriate treatment.

Ultrasound elastography in chronic kidney disease: a systematic review and meta-analysis

Ultrasound elastography (USE) is a noninvasive technique for assessing tissue elasticity, and its application in nephrology has aroused growing interest in recent years. The purpose of this article is to systematically review the clinical application of USE in patients with chronic kidney disease (CKD), including native and transplanted kidneys, and quantitatively investigate differences in elasticity values between healthy individuals and CKD patients. Furthermore, we provide a qualitative analysis of the studies included, discussing the potential interplay between renal stiffness, estimated glomerular filtration rate, and fibrosis. In January 2022, a systematic search was carried out on the MEDLINE (PubMed) database, concerning studies on the application of USE in patients with CKD, including patients with transplanted kidneys. The results of the included studies were extracted by two independent researchers and presented mainly through a formal narrative summary. A meta-analysis of nine study parts from six studies was performed. A total of 647 studies were screened for eligibility and, after applying the exclusion and inclusion criteria, 69 studies were included, for a total of 6728 patients. The studies proved very heterogeneous in terms of design and results. The shear wave velocity difference of - 0.82 m/s (95% CI: - 1.72-0.07) between CKD patients and controls was not significant. This result agrees with the qualitative evaluation of included studies that found controversial results for the relationship between renal stiffness and glomerular filtration rate. On the contrary, a clear relationship seems to emerge between USE values and the degree of fibrosis. At present, due to the heterogeneity of results and technical challenges, large-scale application in the monitoring of CKD patients remains controversial.

Computed Tomography Urography: State of the Art and Beyond

Computed Tomography Urography (CTU) is a multiphase CT examination optimized for imaging kidneys, ureters, and bladder, complemented by post-contrast excretory phase imaging. Different protocols are available for contrast administration and image acquisition and timing, with different strengths and limits, mainly related to kidney enhancement, ureters distension and opacification, and radiation exposure. The availability of new reconstruction algorithms, such as iterative and deep-learning-based reconstruction has dramatically improved the image quality and reducing radiation exposure at the same time. Dual-Energy Computed Tomography also has an important role in this type of examination, with the possibility of renal stone characterization, the availability of synthetic unenhanced phases to reduce radiation dose, and the availability of iodine maps for a better interpretation of renal masses. We also describe the new artificial intelligence applications for CTU, focusing on radiomics to predict tumor grading and patients' outcome for a personalized therapeutic approach. In this narrative review, we provide a comprehensive overview of CTU from the traditional to the newest acquisition techniques and reconstruction algorithms, and the possibility of advanced imaging interpretation to provide an up-to-date guide for radiologists who want to better comprehend this technique.

Diagnosis and Treatment of Post-Prostatectomy Lymphedema: What's New?

Lymphedema is a chronic progressive disorder that significantly compromises patients' quality of life. In Western countries, it often results from cancer treatment, as in the case of post-radical prostatectomy lymphedema, where it can affect up to 20% of patients, with a significant disease burden. Traditionally, diagnosis, assessment of severity, and management of disease have relied on clinical assessment. In this landscape, physical and conservative treatments, including bandages and lymphatic drainage have shown limited results. Recent advances in imaging technology are revolutionizing the approach to this disorder: magnetic resonance imaging has shown satisfactory results in differential diagnosis, quantitative classification of severity, and most appropriate treatment planning. Further innovations in microsurgical techniques, based on the use of indocyanine green to map lymphatic vessels during surgery, have improved the efficacy of secondary LE treatment and led to the development of new surgical approaches. Physiologic surgical interventions, including lymphovenous anastomosis (LVA) and vascularized lymph node transplant (VLNT), are going to face widespread diffusion. A combined approach to microsurgical treatment provides the best results: LVA is effective in promoting lymphatic drainage, bridging VLNT delayed lymphangiogenic and immunological effects in the lymphatic impairment site. Simultaneous VLNT and LVA are safe and effective for patients with both early and advanced stages of post-prostatectomy LE. A new perspective is now represented by the combination of microsurgical treatments with the positioning of nano fibrillar collagen scaffolds (BioBridgeTM) to favor restoring the lymphatic function, allowing for improved and sustained volume reduction. In this narrative review, we proposed an overview of new strategies for diagnosing and treating post-prostatectomy lymphedema to get the most appropriate and successful patient treatment with an overview of the main artificial intelligence applications in the prevention, diagnosis, and management of lymphedema.

Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?

Due to its widespread availability, low cost, feasibility at the patient's bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion.

Artificial Intelligence in Emergency Radiology: Where Are We Going?

Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients' lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS-PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients' clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease's severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.

Artificial Intellgence in the Era of Precision Oncological Imaging

Rapid-paced development and adaptability of artificial intelligence algorithms have secured their almost ubiquitous presence in the field of oncological imaging. Artificial intelligence models have been created for a variety of tasks, including risk stratification, automated detection, and segmentation of lesions, characterization, grading and staging, prediction of prognosis, and treatment response. Soon, artificial intelligence could become an essential part of every step of oncological workup and patient management. Integration of neural networks and deep learning into radiological artificial intelligence algorithms allow for extrapolating imaging features otherwise inaccessible to human operators and pave the way to truly personalized management of oncological patients.Although a significant proportion of currently available artificial intelligence solutions belong to basic and translational cancer imaging research, their progressive transfer to clinical routine is imminent, contributing to the development of a personalized approach in oncology. We thereby review the main applications of artificial intelligence in oncological imaging, describe the example of their successful integration into research and clinical practice, and highlight the challenges and future perspectives that will shape the field of oncological radiology.

Computed tomography in traumatic orbital emergencies: a pictorial essay-imaging findings, tips, and report flowchart

Computed tomography (CT) is considered the gold standard technique for the assessment of trauma patients with suspected involvement of the eye and orbit. These traumas can result in dramatic consequences to visual function, ocular motility, and aesthetics. CT is a quick and widely available imaging modality, which provides a detailed evaluation of the orbital bony and soft tissue structures, an accurate assessment of the globes, and is used to guide the patients’ treatment planning. For a timely and accurate diagnosis, radiologists should be aware of fracture patterns and possible associated complications, ocular detachments and hemorrhages, and different appearances of intraorbital foreign bodies. This educational review aims to describe all post-traumatic orbital abnormalities that can be identified on CT, providing a list of tips and a diagnostic flowchart to help radiologists deal with this complex condition.

Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review

The advent of artificial intelligence (AI) represents a real game changer in today's landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.

A rare case of congenital atresia of the left main coronary artery

Congenital atresia of the left main coronary artery is a rare coronary anomaly, in which the left coronary ostium is absent and the blood supply to the entire heart is provided by the right coronary artery. We show the images of the coronary CT angiography performed by a 56-year-old man, with evidence of this vascular abnormality. Coronary CT Angiography has gained a major role in coronary anomalies assessment, thank to high spatial and temporal resolution.