Role of advanced MRI sequences for thyroid lesions assessment. A narrative review.

Despite not being the first imaging modality for thyroid gland assessment, Magnetic Resonance Imaging (MRI), thanks to its optimal tissue contrast and spatial resolution, has provided some advancements in detecting and characterizing thyroid abnormalities. Recent research has been focused on improving MRI sequences and employing advanced techniques for a more comprehensive understanding of thyroid pathology. Although not yet standard practice, advanced MRI sequences have shown high accuracy in preliminary studies, correlating well with histopathological results. They particularly show promise in determining malignancy risk in thyroid lesions, which may reduce the need for invasive procedures like biopsies. In this line, functional MRI sequences like Diffusion Weighted Imaging (DWI), Dynamic Contrast-Enhanced MRI (DCE-MRI), and Arterial Spin Labeling (ASL) have demonstrated their potential usefulness in evaluating both diffuse thyroid conditions and focal lesions. Multicompartmental DWI models, such as Intravoxel Incoherent Motion (IVIM) and Diffusion Kurtosis Imaging (DKI), and novel methods like Amide Proton Transfer (APT) imaging or artificial intelligence (AI)-based analyses are being explored for their potential valuable insights into thyroid diseases. This manuscript reviews the critical physical principles and technical requirements for optimal functional MRI sequences of the thyroid and assesses the clinical utility of each technique. It also considers future prospects in the context of advanced MR thyroid imaging and analyzes the current role of advanced MRI sequences in routine practice.

Increased subtalar rotational motion in patients with symptomatic ankle instability under load and stress conditions.

PURPOSE: Differentiating subtalar and ankle instability in the clinical setting is challenging. This study aims to analyze the rotational laxity of the subtalar joint bilaterally in patients with asymptomatic and symptomatic ankle instability under simulated load and stress-induced position of the subtalar joint. METHODS: A case-control study was conducted using an adjustable load device (ALD). Patients with chronic ankle instability and healthy volunteers were included. Each subject underwent a CT scan under mechanical stress and simulated weight-bearing conditions, maintaining maximum eversion and inversion hindfoot positions. The images were obtained in a single model, allowing calculations of the motion vector as well as the helical axis. The helical axis was defined by a rotation angle and a translation distance. RESULTS: A total of 72 feet were included in the study. Thirty-one patients with unilateral symptoms and five healthy controls were selected, defining two groups: symptomatic (n = 31) and asymptomatic (n = 41). An absolute difference of 4.6º (95%CI 2-11.1) rotation angle was found on the helical axis of the symptomatic vs. asymptomatic group (p = 0.001). No significant differences were detected in the translation distance (n.s.) between the groups. Additionally, a significant positive correlation was found between the rotation angle and translation distance through the helical axis in the asymptomatic group (r = 0.397, p = 0.027). CONCLUSION: Patients with chronic ankle instability suspected of having subtalar joint instability showed a wider subtalar range of laxity in terms of rotation about the helical axis. Furthermore, differences in kinematics between symptomatic and asymptomatic hindfeet was demonstrated when both feet were compared. LEVEL OF EVIDENCE: III.

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver.

Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians' workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.

Hindfoot motion through helical axis image-based on dynamic CT scan using an original simulated weightbearing device.

BACKGROUND: Determining the treatment of subtalar joint (STJ) instability requires a better understanding of the biomechanical principles underlying the condition and, a proper diagnosis. This study aimed to analyze "in vivo" the range of motion of the subtalar joint (STJ) measured on two (2D) and three dimensions (3D) image-based on CT Scan using an original device that maintains a simulated weightbearing. The secondary goal was to correlate the 2D and 3D measurement. METHODS: An observational study was conducted, using an original Dynamic Simulated Weightbearing Device. Asymptomatic ankles were included. Each subject underwent a CT scan under mechanical stress and simulated weightbearing conditions, maintaining maximum eversion and inversion hindfoot positions. The images were obtained, combining both inversion and eversion positions in a single model, which allows for to calculation of the motion vector as well as the helical axis. The helical axis (rotation angle and translation distance), subtalar tilt, anterior drawer, and, subtalar and calcaneocuboid uncoverage were the determinations. RESULTS: Forty asymptomatic ankles were included. The average range of motion of the STJ amounts to 31.5° ± 9.1° of rotation and 1.56 ± 0.8 mm of translation distance. The anterior drawer and subtalar uncoverage variables were statistically significantly related to each other (r = 0.57; P = 0.00001). However, these 2-D measured variables were not related to kinematic measures of rotation through the helical axis (3D) (p = 0.14; p = 0.19) CONCLUSIONS: The average range of motion of the STJ amounts to 31.5° ± 9.1° of rotation and 1.56 ± 0.8 mm of translation distance. We found no significant correlation between 2D and 3D measurements. In our opinion, the rotation angle and translation distance should be considered the most accurate measurements and should be calculated on every STJ instability for comparison with the asymptomatic population LEVEL OF EVIDENCE: Observational study.

The role of Artificial intelligence in the assessment of the spine and spinal cord.

Artificial intelligence (AI) application development is underway in all areas of radiology where many promising tools are focused on the spine and spinal cord. In the past decade, multiple spine AI algorithms have been created based on radiographs, computed tomography, and magnetic resonance imaging. These algorithms have wide-ranging purposes including automatic labeling of vertebral levels, automated description of disc degenerative changes, detection and classification of spine trauma, identification of osseous lesions, and the assessment of cord pathology. The overarching goals for these algorithms include improved patient throughput, reducing radiologist workload burden, and improving diagnostic accuracy. There are several pre-requisite tasks required in order to achieve these goals, such as automatic image segmentation, facilitating image acquisition and postprocessing. In this narrative review, we discuss some of the important imaging AI solutions that have been developed for the assessment of the spine and spinal cord. We focus on their practical applications and briefly discuss some key requirements for the successful integration of these tools into practice. The potential impact of AI in the imaging assessment of the spine and cord is vast and promises to provide broad reaching improvements for clinicians, radiologists, and patients alike.

Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology.

Currently, the use of artificial intelligence (AI) in radiology, particularly machine learning (ML), has become a reality in clinical practice. Since the end of the last century, several ML algorithms have been introduced for a wide range of common imaging tasks, not only for diagnostic purposes but also for image acquisition and postprocessing. AI is now recognized to be a driving initiative in every aspect of radiology. There is growing evidence of the advantages of AI in radiology creating seamless imaging workflows for radiologists or even replacing radiologists. Most of the current AI methods have some internal and external disadvantages that are impeding their ultimate implementation in the clinical arena. As such, AI can be considered a portion of a business trying to be introduced in the health care market. For this reason, this review analyzes the current status of AI, and specifically ML, applied to radiology from the scope of strengths, weaknesses, opportunities, and threats (SWOT) analysis.

Hybrid computed tomography and magnetic resonance imaging 3D printed models for neurosurgery planning.

In the last decade, the clinical applications of three-dimensional (3D) printed models, in the neurosurgery field among others, have expanded widely based on several technical improvements in 3D printers, an increased variety of materials, but especially in postprocessing software. More commonly, physical models are obtained from a unique imaging technique with potential utilization in presurgical planning, generation/creation of patient-specific surgical material and personalized prosthesis or implants. Using specific software solutions, it is possible to obtain a more accurate segmentation of different anatomical and pathological structures and a more precise registration between different medical image sources allowing generating hybrid computed tomography (CT) and magnetic resonance imaging (MRI) 3D printed models. The need of neurosurgeons for a better understanding of the complex anatomy of central nervous system (CNS) and spine is pushing the use of these hybrid models, which are able to combine morphological information from CT and MRI, and also able to add physiological data from advanced MRI sequences, such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion weighted imaging (PWI) and functional MRI (fMRI). The inclusion of physiopathological data from advanced MRI sequences enables neurosurgeons to identify those areas with increased biological aggressiveness within a certain lesion prior to surgery or biopsy procedures. Preliminary data support the use of this more accurate presurgical perspective, to select the better surgical approach, reduce the global length of surgery and minimize the rate of intraoperative complications, morbidities or patient recovery times after surgery. The use of 3D printed models in neurosurgery has also demonstrated to be a valid tool for surgeons training and to improve communication between specialists and patients. Further studies are needed to test the feasibility of this novel approach in common clinical practice and determine the degree of improvement the neurosurgeons receive and the potential impact on patient outcome.

Identification of fracture zones and its application in automatic bone fracture reduction.

BACKGROUND AND OBJECTIVE: The preoperative planning of bone fractures using information from CT scans increases the probability of obtaining satisfactory results, since specialists are provided with additional information before surgery. The reduction of complex bone fractures requires solving a 3D puzzle in order to place each fragment into its correct position. Computer-assisted solutions may aid in this process by identifying the number of fragments and their location, by calculating the fracture zones or even by computing the correct position of each fragment. The main goal of this paper is the development of an automatic method to calculate contact zones between fragments and thus to ease the computation of bone fracture reduction. METHODS: In this paper, an automatic method to calculate the contact zone between two bone fragments is presented. In a previous step, bone fragments are segmented and labelled from CT images and a point cloud is generated for each bone fragment. The calculated contact zones enable the automatic reduction of complex fractures. To that end, an automatic method to match bone fragments in complex fractures is also presented. RESULTS: The proposed method has been successfully applied in the calculation of the contact zone of 4 different bones from the ankle area. The calculated fracture zones enabled the reduction of all the tested cases using the presented matching algorithm. The performed tests show that the reduction of these fractures using the proposed methods leaded to a small overlapping between fragments. CONCLUSIONS: The presented method makes the application of puzzle-solving strategies easier, since it does not obtain the entire fracture zone but the contact area between each pair of fragments. Therefore, it is not necessary to find correspondences between fracture zones and fragments may be aligned two by two. The developed algorithms have been successfully applied in different fracture cases in the ankle area. The small overlapping error obtained in the performed tests demonstrates the absence of visual overlapping in the figures.

Computer assisted preoperative planning of bone fracture reduction: Simulation techniques and new trends.

The development of support systems for surgery significantly increases the likelihood of obtaining satisfactory results. In the case of fracture reduction interventions these systems enable surgery planning, training, monitoring and assessment. They allow improvement of fracture stabilization, a minimizing of health risks and a reduction of surgery time. Planning a bone fracture reduction by means of a computer assisted simulation involves several semiautomatic or automatic steps. The simulation deals with the correct position of osseous fragments and fixation devices for a fracture reduction. Currently, to the best of our knowledge there is no computer assisted methods to plan an entire fracture reduction process. This paper presents an overall scheme of the computer based process for planning a bone fracture reduction, as described above, and details its main steps, the most common proposed techniques and their main shortcomings. In addition, challenges and new trends of this research field are depicted and analyzed.