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The following White Paper will discuss the appropriateness of gadolinium administration in MRI for musculoskeletal indications. Musculoskeletal radiologists should consider the potential risks involved and practice the judicious use of intravenous contrast, restricting administration to cases where there is demonstrable added value. Specific nuances of when contrast is or is not recommended are discussed in detail and listed in table format. Briefly, contrast is recommended for bone and soft tissue lesions. For infection, contrast is reserved for chronic or complex cases. In rheumatology, contrast is recommended for early detection but not for advanced arthritis. Contrast is not recommended for sports injuries, routine MRI neurography, implants/hardware, or spine imaging, but is helpful in complex and post-operative cases. [ABSTRACT FROM AUTHOR]

Children living with achondroplasia are at an increased risk of developing neurological complications, which may be associated with acute and life-altering events. To remediate this risk, the timely acquisition of effective neuroimaging that can help to guide clinical management is essential. We propose imaging protocols and follow-up strategies for evaluating the neuroanatomy of these children and to effectively identify potential neurological complications, including compression at the cervicomedullary junction secondary to foramen magnum stenosis, spinal deformity and spinal canal stenosis. When compiling these recommendations, emphasis has been placed on reducing scan times and avoiding unnecessary radiation exposure. Standardized imaging protocols are important to ensure that clinically useful neuroimaging is performed in children living with achondroplasia and to ensure reproducibility in future clinical trials. The members of the European Society of Pediatric Radiology (ESPR) Neuroradiology Taskforce and European Society of Neuroradiology pediatric subcommittee, together with clinicians and surgeons with specific expertise in achondroplasia, wrote this opinion paper. The research committee of the ESPR also endorsed the final draft. The rationale for these recommendations is based on currently available literature, supplemented by best practice opinion from radiologists and clinicians with subject-specific expertise. [ABSTRACT FROM AUTHOR]

Ultrasound practice is a longstanding tradition for radiology departments, being part of the family of imaging techniques. Ultrasound is widely practiced by non-radiologists but becoming less popular within radiology. The position of ultrasound in radiology is reviewed, and a possible long-term solution to manage radiologist expectations is proposed. An international group of experts in the practice of ultrasound was invited to describe the current organisation of ultrasound within the radiology departments in their own countries and comment on the interaction with non-radiologists and training arrangements. Issues related to regulation, non-medical practitioners, and training principles are detailed. A consensus view was sought from the experts regarding the position of ultrasound within radiology, with the vision of the best scenario for the continuing dominance of radiologists practising ultrasound. Comments were collated from nine different countries. Variable levels of training, practice, and interaction with non-radiologist were reported, with some countries relying on non-physician input to manage the service. All experts recognised there was a diminished desire to practice ultrasound by radiologists. Models varied from practising solely ultrasound and no other imaging techniques to radiology departments being central to the practice of ultrasound by radiologists and non-radiologist, housed within radiology. The consensus view was that the model favoured in select hospitals in Germany would be the most likely setup for ultrasound radiologist to develop and maintain practice. The vision for 20 years hence is for a central ultrasound section within radiology, headed by a trained expert radiologist, with non-radiologist using the facilities. Critical relevance statement The future of ultrasound within the radiology department should encompass all ultrasound users, with radiologists expert in ultrasound, managing the ultrasound section within the radiology department. The current radiology trainees must learn of the importance of ultrasound as a component of the 'holistic' imaging of the patient. Key points: 1. Ultrasound imaging within radiology departments precedes the introduction of CT and MR imaging and was first used over 50 years ago. 2. Non-radiology practitioners deploy ultrasound examinations to either 'problem solve' or perform a comprehensive ultrasound examination; radiologists provide comprehensive examinations or use ultrasound to direct interventional procedures. 3. Radiology does not 'own' ultrasound, but radiologists are best placed to offer a comprehensive patient-focused imaging assessment. 4. A vision of the future of ultrasound within the radiology department is encompassing all ultrasound users under radiologists who are experts in ultrasound, positioned within the radiology department. 5. The current radiology trainee must be aware of the importance of ultrasound as a component of the 'holistic' imaging of the patient. [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance (NMR) is the spectroscopic technique of choice for determining molecular conformations in solution at atomic resolution. As solution NMR spectra are rich in structural and dynamic information, the way in which the data should be acquired and handled to deliver accurate ensembles is not trivial. This Review provides a guide to the NMR experiment selection and parametrization process, the generation of viable theoretical conformer pools and the deconvolution of time-averaged NMR data into a conformer ensemble that accurately represents a flexible molecule in solution. In addition to reviewing the key elements of solution ensemble determination of flexible mid-sized molecules, the feasibility and pitfalls of data deconvolution are discussed with a comparison of the performance of representative algorithms., (© 2023. Springer Nature Limited.)

The use of standardized imaging protocols is paramount in order to facilitate comparable, reproducible images and, consequently, to optimize patient care. Standardized MR protocols are lacking when studying head and neck pathologies in the pediatric population. We propose an international, multicenter consensus paper focused on providing the best combination of acquisition time/technical requirements and image quality. Distinct protocols for different regions of the head and neck and, in some cases, for specific pathologies or clinical indications are recommended. This white paper is endorsed by several international scientific societies and it is the result of discussion, in consensus, among experts in pediatric head and neck imaging. [ABSTRACT FROM AUTHOR]

Copyright of Die Urologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists' workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. Key Points: • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists' workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDA) is the third leading cause of cancer-related death in the United States and is projected to be the second by 2030. Systemic combination chemotherapy is considered an essential first-line treatment for the majority of patients with PDA, in both the neoadjuvant and palliative settings. In addition, a number of novel therapies are being tested in clinical trials for patients with advanced PDA. In all cases, accurate and timely assessment of treatment response is critical to guide therapy, reduce drug toxicities and cost from a failing therapy, and aid adaptive clinical trials. Conventional morphological imaging has significant limitations, especially in the context of determining primary tumor response and resectability following neoadjuvant therapies. In this article, we provide an overview of current therapy options for PDA, highlight several morphological imaging findings that may be helpful to reduce over-staging following neoadjuvant therapy, and discuss a number of emerging imaging, and non-imaging, tools that have shown promise in providing a more precise quantification of disease burden and treatment response in PDA. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging linear-accelerator (MRI-linac) equipment has recently been introduced to multiple centres in Australia and New Zealand. MRI equipment creates hazards for staff, patients and others in the MR environment; these hazards must be well understood, and risks managed by a system of environmental controls, written procedures and a trained workforce. While MRI-linac hazards are similar to the diagnostic paradigm, the equipment, workforce and environment are sufficiently different that additional safety guidance is warranted. In 2019 the Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) formed the Magnetic Resonance Imaging Linear-Accelerator Working Group (MRILWG) to support the safe clinical introduction and optimal use of MR-guided radiation therapy treatment units. This Position Paper is intended to provide safety guidance and education for Medical Physicists and others planning for and working with MRI-linac technology. This document summarises MRI-linac hazards and describes particular effects which arise from the combination of strong magnetic fields with an external radiation treatment beam. This document also provides guidance on safety governance and training, and recommends a system of hazard management tailored to the MRI-linac environment, ancillary equipment, and workforce. [ABSTRACT FROM AUTHOR]

The Liver Imaging and Reporting Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging with the overarching goal of improving communication, clinical care, education, and research relating to patients at risk for or diagnosed with hepatocellular carcinoma (HCC). In 2018, the American Association for the Study of Liver Diseases (AASLD) integrated LI-RADS into its clinical practice guidance for the imaging-based diagnosis of HCC. The harmonization between the AASLD and LI-RADS diagnostic imaging criteria required minor modifications to the recently released LI-RADS v2017 guidelines, necessitating a LI-RADS v2018 update. This article provides an overview of the key changes included in LI-RADS v2018 as well as a look at the LI-RADS v2018 diagnostic algorithm and criteria, technical recommendations, and management suggestions. Substantive changes in LI-RADS v2018 are the removal of the requirement for visibility on antecedent surveillance ultrasound for LI-RADS 5 (LR-5) categorization of 10-19 mm observations with nonrim arterial phase hyper-enhancement and nonperipheral “washout”, and adoption of the Organ Procurement and Transplantation Network definition of threshold growth (≥ 50% size increase of a mass in ≤ 6 months). Nomenclatural changes in LI-RADS v2018 are the removal of -us and -g as LR-5 qualifiers. [ABSTRACT FROM AUTHOR]

This expert opinion paper on cardiac imaging after acute ischemic stroke or transient ischemic attack (TIA) includes a statement of the "Heart and Brain" consortium of the German Cardiac Society and the German Stroke Society. The Stroke Unit-Commission of the German Stroke Society and the German Atrial Fibrillation NETwork (AFNET) endorsed this paper. Cardiac imaging is a key component of etiological work-up after stroke. Enhanced echocardiographic tools, constantly improving cardiac computer tomography (CT) as well as cardiac magnetic resonance imaging (MRI) offer comprehensive non- or less-invasive cardiac evaluation at the expense of increased costs and/or radiation exposure. Certain imaging findings usually lead to a change in medical secondary stroke prevention or may influence medical treatment. However, there is no proof from a randomized controlled trial (RCT) that the choice of the imaging method influences the prognosis of stroke patients. Summarizing present knowledge, the German Heart and Brain consortium proposes an interdisciplinary, staged standard diagnostic scheme for the detection of risk factors of cardio-embolic stroke. This expert opinion paper aims to give practical advice to physicians who are involved in stroke care. In line with the nature of an expert opinion paper, labeling of classes of recommendations is not provided, since many statements are based on expert opinion, reported case series, and clinical experience. [ABSTRACT FROM AUTHOR]

In order for enterprise imaging to be successful across a multitude of specialties, systems, and sites, standards are essential to categorize and classify imaging data. The HIMSS-SIIM Enterprise Imaging Community believes that the Digital Imaging Communications in Medicine (DICOM) Anatomic Region Sequence, or its equivalent in other data standards, is a vital data element for this role, when populated with standard coded values. We believe that labeling images with standard Anatomic Region Sequence codes will enhance the user's ability to consume data, facilitate interoperability, and allow greater control of privacy. Image consumption—when a user views a patient's images, he or she often wants to see relevant comparison images of the same lesion or anatomic region for the same patient automatically presented. Relevant comparison images may have been acquired from a variety of modalities and specialties. The Anatomic Region Sequence data element provides a basis to allow for efficient comparison in both instances. Interoperability—as patients move between health care systems, it is important to minimize friction for data transfer. Health care providers and facilities need to be able to consume and review the increasingly large and complex volume of data efficiently. The use of Anatomic Region Sequence, or its equivalent, populated with standard values enables seamless interoperability of imaging data regardless of whether images are used within a site or across different sites and systems. Privacy—as more visible light photographs are integrated into electronic systems, it becomes apparent that some images may need to be sequestered. Although additional work is needed to protect sensitive images, standard coded values in Anatomic Region Sequence support the identification of potentially sensitive images, enable facilities to create access control policies, and can be used as an interim surrogate for more sophisticated rule-based or attribute-based access control mechanisms. To satisfy such use cases, the HIMSS-SIIM Enterprise Imaging Community encourages the use of a pre-existing body part ontology. Through this white paper, we will identify potential challenges in employing this standard and provide potential solutions for these challenges. [ABSTRACT FROM AUTHOR]

The article presents abstracts on medical topics which include the correlation between intravoxel incoherent motion and dynamic contrast-enhanced MRI in the liver parenchyma, the diagnostic performance of MR elastography, and relative changes in segmental hepatic volume and total splenic volume.

In the human brain, essential iron molecules for proper neurological functioning exist in transferrin (t f) and ferritin (F e 3 +) forms. However, its unusual increment manifests iron overload, which reacts with hydrogen peroxide. This reaction will generate hydroxyl radicals, and iron's higher oxidation states. Furthermore, this reaction causes tissue damage or cognitive decline in the brain and also leads to neurodegenerative diseases. The susceptibility difference due to iron overload within the volume of interest (VOI) responsible for field perturbation of MRI and can benefit in estimating the neural disorder. The quantitative susceptibility mapping (QSM) technique can estimate susceptibility alteration and assist in quantifying the local tissue susceptibility differences. It has attracted many researchers and clinicians to diagnose and detect neural disorders such as Parkinson's, Alzheimer's, multiple sclerosis, and aging. The paper presents a systematic review illustrating QSM fundamentals and its processing steps, including phase unwrapping, background field removal, and susceptibility inversion. Using QSM, the present work delivers novel predictive biomarkers for various neural disorders. It can strengthen new researchers' fundamental knowledge and provides insight into its applicability for cognitive decline disclosure. The paper discusses the future scope of QSM processing stages and their applications in identifying new biomarkers for neural disorders. [ABSTRACT FROM AUTHOR]

Structural alterations have been thoroughly investigated in the brain during the early onset of schizophrenia (SCZ) with the development of neuroimaging methods. The objective of the paper is an efficient classification of SCZ in 2 different classes: cognitive normal (CN) and SCZ using magnetic resonance imaging (MRI) images. This paper proposes a lightweight 3D convolutional neural network (CNN) based framework for SCZ diagnosis using MRI images. In the proposed model, lightweight 3D CNN is used to extract both spatial and spectral features simultaneously from 3D volume MRI scans, and classification is done using an ensemble bagging classifier. Ensemble bagging classifier contributes to preventing overfitting, reduces variance, and improves the model's accuracy. The proposed algorithm is tested on datasets taken from three benchmark databases available as open-source: MCICShare, COBRE, and fBRINPhase-II. All MRI images have undergone preprocessing steps to register all the MRI images to the standard template and reduce the artifacts. The model achieves the highest accuracy 92.22%, sensitivity 94.44%, specificity 90%, precision 90.43%, recall 94.44%, F1-score 92.39%, and G-mean 92.19% as compared to the current state-of-the-art techniques. The performance metrics evidenced the use of this model to assist the clinicians in automatic accurate diagnosis of SCZ. [ABSTRACT FROM AUTHOR]

Considerable progress has been made in developing models of cerebellar function in sensorimotor control, as well as in identifying key problems that are the focus of current investigation. In this consensus paper, we discuss the literature on the role of the cerebellar circuitry in motor control, bringing together a range of different viewpoints. The following topics are covered: oculomotor control, classical conditioning (evidence in animals and in humans), cerebellar control of motor speech, control of grip forces, control of voluntary limb movements, timing, sensorimotor synchronization, control of corticomotor excitability, control of movement-related sensory data acquisition, cerebro-cerebellar interaction in visuokinesthetic perception of hand movement, functional neuroimaging studies, and magnetoencephalographic mapping of cortico-cerebellar dynamics. While the field has yet to reach a consensus on the precise role played by the cerebellum in movement control, the literature has witnessed the emergence of broad proposals that address cerebellar function at multiple levels of analysis. This paper highlights the diversity of current opinion, providing a framework for debate and discussion on the role of this quintessential vertebrate structure. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40 years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods. [ABSTRACT FROM AUTHOR]

Studies the abstracts of several research papers from current literature on abdominal imaging in 2004. Oral contrast agents for small bowel magnetic resonance imaging; Effects of positron emission tomography on the management of primary rectal cancer; Drug smuggling by body packing.

The article presents a list of the abstracts of selected papers from the current medical literature. Some of the abstracts are: "Perforations and fistulae in gastrointestinal tuberculosis," "Magnetic resonance imaging of the effects of infliximab on perianal fistulizing Crohn's disease," "Bowel wall thickening in patients with Crohn's disease: CT patterns and correlation with inflammatory activity," "Magnetic resonance imaging of anatomic and dynamic defects of the pelvic floor in defecatory disorders," "Risk of pancreatic adenocarcinoma in chronic pancreatitis," "Percutaneous management of renal pelvic urothelial tumors: longterm followup," etc.

The article presents abstracts on skeletal radiology topics which include magnetic resonance imaging (MRI) of the lumbar spines of junior tennis players, positron emission tomography (PET) scans of cancer metastasis to the femur, and osteo-meniscal impingement.

Copyright of Der Kardiologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Magnetic resonance imaging (MRI) is a pivotal radiological examination in clinical practice, being widely applied for musculoskeletal examinations. In this setting, strict adherence to standardized protocol is crucial to increase diagnostic performance and minimize variability among different diagnostic centres and readers. The aim of this paper is to provide standardized technical recommendations for musculoskeletal MRI scans proposed by the Italian College of Musculoskeletal Radiology. These recommendations are designed to give a uniform application of MRI protocols over the national territory, to increase reproducibility and improve diagnostic performance. [ABSTRACT FROM AUTHOR]

Radiological imaging is a globally prevalent diagnostic method, yet the free text contained in radiology reports is not frequently used for secondary purposes. Natural Language Processing can provide structured data retrieved from these reports. This paper provides a summary of the current state of research on Large Language Model (LLM) based approaches for information extraction (IE) from radiology reports. We conduct a scoping review that follows the PRISMA-ScR guideline. Queries of five databases were conducted on August 1st 2023. Among the 34 studies that met inclusion criteria, only pre-transformer and encoder-based models are described. External validation shows a general performance decrease, although LLMs might improve generalizability of IE approaches. Reports related to CT and MRI examinations, as well as thoracic reports, prevail. Most common challenges reported are missing validation on external data and augmentation of the described methods. Different reporting granularities affect the comparability and transparency of approaches. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a neurodevelopmental condition that significantly impacts the lives of many children due to its hidden symptoms. Early detection of ASD is challenging because of its complex and heterogeneous nature. Magnetic resonance imaging (MRI) has emerged as a crucial tool for early detection, offering non-invasive imaging with detailed soft tissue information. However, existing approaches face limitations such as overfitting, underfitting, class imbalance, control, domain shift, and behavioral issues. To address these challenges, this paper proposes a novel ASD detection and classification model called the Autism Spectrum Disorder-based Attention Graph Neural Network and Crossover Boosted Meerkat Optimization (ASD-AttGCBMO) algorithm. The proposed method utilizes structural Magnetic Resonance Imaging (sMRI) data from the ABIDE 1 dataset. The data undergoes preprocessing to remove artifacts and noise, ensuring high image quality and consistency. Node feature extraction employs voxel-based morphometry (VBM) and surface-based analysis, which extract relevant features such as surface area, cortical thickness, shape descriptors, and brain volumes. The ASD-AttGCBMO model is trained using preprocessed sMRI images, employing the Adam and Stochastic Gradient Descent (SGD) optimizers to prevent overfitting, reduce classification loss, and improve convergence. The model is designed to enhance the learning process and capture complex patterns for accurate feature classification between ASD and control subjects. To optimize the hyperparameters in the attention-based neural network model, the CBMO algorithm is employed. Experimental validation is conducted using essential performance evaluation measures. The proposed method achieves impressive results, with accuracy, precision, recall, specificity, F1-score, Area under Receiver Operating curve (AUC/ROC), and computational time values of 98.8%, 99%, 98.5%, 98.6%, 98.2%, 0.989, and 3.05 s, respectively. Comparative analysis demonstrates that the proposed method outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Objective: Phase-contrast magnetic resonance imaging (PC-MRI) is a non-invasive method used to compute blood flow velocity and volume. This systematic review aims to discuss the current status of renal PC-MRI and provide practical recommendations which could inform future clinical studies and its adoption in clinical practice. Methodology: A comprehensive search of all the PC-MRI studies in human healthy subjects or patients related to the kidneys was performed. Results: A total of 39 studies were included in which PC-MRI was used to measure renal blood flow (RBF) alongside other derivative hemodynamic parameters. PC-MRI generally showed good correlation with gold standard methods of RBF measurement, both in vitro and in vivo, and good reproducibility. Despite PC-MRI not being routinely used in clinical practice, there are several clinical studies showing its potential to support diagnosis and monitoring of renal diseases, in particular renovascular disease, chronic kidney disease and autosomal dominant polycystic kidney disease. Discussion: Renal PC-MRI shows promise as a non-invasive technique to reliably measure RBF, both in healthy volunteers and in patients with renal disease. Future multicentric studies are needed to provide definitive normative ranges and to demonstrate the clinical potential of PC-MRI, likely as part of a multi-parametric renal MRI protocol. [ABSTRACT FROM AUTHOR]

Objective: To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020. Methods: A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed. Results: Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies. Conclusions: Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage. Critical relevance statement: An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage. Key points: • 2021–2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation. [ABSTRACT FROM AUTHOR]

Objectives: We systematically reviewed the current literature evaluating the ability of fully-automated deep learning (DL) and semi-automated traditional machine learning (TML) MRI-based artificial intelligence (AI) methods to differentiate clinically significant prostate cancer (csPCa) from indolent PCa (iPCa) and benign conditions. Methods: We performed a computerised bibliographic search of studies indexed in MEDLINE/PubMed, arXiv, medRxiv, and bioRxiv between 1 January 2016 and 31 July 2021. Two reviewers performed the title/abstract and full-text screening. The remaining papers were screened by four reviewers using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) for DL studies and Radiomics Quality Score (RQS) for TML studies. Papers that fulfilled the pre-defined screening requirements underwent full CLAIM/RQS evaluation alongside the risk of bias assessment using QUADAS-2, both conducted by the same four reviewers. Standard measures of discrimination were extracted for the developed predictive models. Results: 17/28 papers (five DL and twelve TML) passed the quality screening and were subject to a full CLAIM/RQS/QUADAS-2 assessment, which revealed a substantial study heterogeneity that precluded us from performing quantitative analysis as part of this review. The mean RQS of TML papers was 11/36, and a total of five papers had a high risk of bias. AUCs of DL and TML papers with low risk of bias ranged between 0.80–0.89 and 0.75–0.88, respectively. Conclusion: We observed comparable performance of the two classes of AI methods and identified a number of common methodological limitations and biases that future studies will need to address to ensure the generalisability of the developed models. [ABSTRACT FROM AUTHOR]

In the process of human aging, significant age-related changes occur in brain tissue. To assist individuals in assessing the degree of brain aging, screening for disease risks, and further diagnosing age-related diseases, it is crucial to develop an accurate method for predicting brain age. This paper proposes a multi-scale feature fusion method called Tri-UNet based on the U-Net network structure, as well as a brain region information fusion method based on multi-channel input networks. These methods address the issue of insufficient image feature learning in brain neuroimaging data. They can effectively utilize features at different scales of MRI and fully leverage feature information from different regions of the brain. In the end, experiments were conducted on the Cam-CAN dataset, resulting in a minimum Mean Absolute Error (MAE) of 7.46. The results demonstrate that this method provides a new approach to feature learning at different scales in brain age prediction tasks, contributing to the advancement of the field and holding significance for practical applications in the context of elderly education. [ABSTRACT FROM AUTHOR]

Using ferric chloride (FeCl3) to induce experimental superior sagittal sinus (SSS) thrombosis might interfere with magnetic resonance imaging (MRI)-assisted visualization and evaluation of the thrombus, the brain parenchyma, and the quality of the occlusion. The aim of this study was to investigate whether aluminum chloride (AlCl3)-induced thrombosis of the SSS has comparable properties to those of FeCl3 without causing artifacts in MRI. SSS thrombosis was induced in 14 male Wistar rats by exposure of the SSS and subsequent topical application of a filter paper strip soaked in AlCl3 (n = 7) or FeCl3 (n = 7) over a period of 15 min. The animals with AlCl3-induced SSS thrombosis showed a constant and complete occlusion with in histological analysis large thrombi. Blood flow measurements indicated a significant reduction on the first and seventh postoperative day compared to preoperative measurements. MRI enabled visualization and subsequent evaluation of the thrombus and the surrounding parenchyma. In comparison, FeCl3-induced SSS thrombosis could not be evaluated by MRI due to artifacts caused by the paramagnetic properties and increased susceptibility of FeCl3. The occluded sinus and the surrounding area appeared hypointense. The quality of SSS occlusion by AlCl3 was comparable to that of FeCl3. AlCl3 therefore represents a significant alternative substance in experimental SSS thrombosis ideally suited for studies using MRI. [ABSTRACT FROM AUTHOR]

Schizophrenia (SCZ) is a brain disorder where different people experience different symptoms, such as hallucination, delusion, flat-talk, disorganized thinking, etc. In the long term, this can cause severe effects and diminish life expectancy by more than ten years. Therefore, early and accurate diagnosis of SCZ is prevalent, and modalities like structural magnetic resonance imaging, functional MRI (fMRI), diffusion tensor imaging, and electroencephalogram assist in witnessing the brain abnormalities of the patients. Moreover, for accurate diagnosis of SCZ, researchers have used machine learning (ML) algorithms for the past decade to distinguish the brain patterns of healthy and SCZ brains using MRI and fMRI images. This paper seeks to acquaint SCZ researchers with ML and to discuss its recent applications to the field of SCZ study. This paper comprehensively reviews state-of-the-art techniques such as ML classifiers, artificial neural network, deep learning models, methodological fundamentals, and applications with previous studies. The motivation of this paper is to benefit from finding the research gaps that may lead to the development of a new model for accurate SCZ diagnosis. The paper concludes with the research finding, followed by the future scope that directly contributes to new research directions. [ABSTRACT FROM AUTHOR]

Whole-body magnetic resonance imaging (MRI) is increasingly being used for a number of indications. Our aim was to review and describe indications and scan protocols for diagnostic value of whole-body MRI for multifocal disease in children and adolescents, we conducted a systematic search in Medline, Embase and Cochrane for all published papers until November 2018. Relevant subject headings and free text words were used for the following concepts: 1) whole-body, 2) magnetic resonance imaging and 3) child and/or adolescent. Included were papers in English with a relevant study design that reported on the use and/or findings from whole-body MRI examinations in children and adolescents. This review includes 54 of 1,609 papers identified from literature searches. Chronic nonbacterial osteomyelitis, lymphoma and metastasis were the most frequent indications for performing a whole-body MRI. The typical protocol included a coronal STIR (short tau inversion recovery) sequence with or without a coronal T1-weighted sequence. Numerous studies lacked sufficient data for calculating images resolution and only a few studies reported the acquired voxel volume, making it impossible for others to reproduce the protocol/images. Only a minority of the included papers assessed reliability tests and none of the studies documented whether the use of whole-body MRI affected mortality and/or morbidity. Our systematic review confirms significant variability of technique and the lack of proven validity of MRI findings. The information could potentially be used to boost attempts towards standardization of technique, reporting and guidelines development. [ABSTRACT FROM AUTHOR]

Abstracts of papers submitted during the 32nd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) held October 1-3 in Edinburgh, Great Britain are presented including "Optimization of Contrast-Enhanced MRI of Pancreas and Pancreatic Tumours in Mice," "2D and 3D-MR Microscopy Based Anatomy vs Histology on Tendons: A Juvenile Pig Animal Model" and "Multimodal Imaging of Pancreatic Islets Transplanted into the Artificial Scaffolds."

Purpose: The anterior cruciate ligament is probably one of the ligaments with the lowest healing potential. Many authors have reported cases of spontaneous healing but nowadays it is difficult to predict successful healing of an anterior cruciate ligament rupture and, even more, residual functionality and capability to return to sport. The aim of this study was to investigate cases of spontaneous healing in a population that received non-surgical treatment after anterior cruciate ligament rupture and to perform an updated review of contemporary literature. Methods: The authors retrospectively reviewed patients who suffered from an acute complete anterior cruciate ligament rupture and underwent non-surgical treatment. No specific rehabilitation protocol was prescribed. A new magnetic resonance imaging study was conducted 6 months after the injury for all patients. A literature review was conducted regarding spontaneous healing of the anterior cruciate ligament. The papers included in the analysis were reports of any level of evidence, written in English, Italian, or French languages; articles were excluded if they reported non-human studies, histological studies, studies conducted without magnetic resonance imaging or arthroscopic second look, or partial anterior cruciate ligament tear. Results: Case series: Six patients were enrolled in the study. All patients had a proximal anterior cruciate ligament lesion. The minimum follow-up was 13 months (range 6–20 months). At the last follow-up the mean score on the Lysholm scale was 97, the mean IKDC score was 94, and the mean KOOS score was 96. All patients returned to their own sport activities; no one reported significant differences. The magnetic resonance imaging study at 6 months revealed an end-to-end continuous anterior cruciate ligament with homogeneous signal. No one had any new knee injury at last follow-up. Literature review: A search of comprehensive databases retrieved 1057 articles; 8 full-text articles met the eligibility criteria. The studies were heterogeneous regarding the populations analysed, sport activity level, treatment applied, healing definition, and follow-up. The failure rate of non-surgical management ranged among the papers from 0 to 73%. Conclusions: The study findings show that spontaneous anterior cruciate ligament healing is possible and there are chances of clinical recovery for patients not suitable for surgery. However, there is still a lack of evidence about predictors, clinical outcomes, and adequate rehabilitation protocols. [ABSTRACT FROM AUTHOR]

A review is presented of Management Guide for Incidental Findings on Computed Tomography and Magnetic resonance imaging App by RADIOLOGiQ.

Objectives: To analyse the coverage and main bibliometric indicators of Insights into Imaging in Scopus and the Emerging Sources Citation Index (ESCI) from the Web of Science Core Collection database. Methods: The evolution of journal production in the Scopus database was analysed according to document types, collaboration indexes between authors and institutions, and citation indicators (number of citations, SCImago Journal Rank, quartile, h-index, and most cited works). Networks of collaboration among authors, institutions, and countries were also analysed, as well as those of co-word networks. As a complementary source of information, the Emerging Source Citation Index from the Web of Science database was also considered. Results: Four hundred seventy-four papers were included in Scopus and 292 in ESCI. The index of collaboration was 5.18 for authors and 2.74 for institutions. International collaboration occurred in 24.7% of the papers. The number of citations received in Scopus (4295) exceeds the number of citations received in ESCI (1177). The average number of citations per paper was 9.06 in Scopus versus 4.03 in ESCI. The h-index was 29 in Scopus and 16 in ESCI. Several collaborative groups were identified at the national and international level. Conclusions: There is a progression of Insights into Imaging in the ranking of journals in the area that, if maintained, will allow it to remain in the first quartile in the Scopus database. The main topics of interest were technologies such as 'Magnetic resonance imaging', 'Computed tomography', 'Radiology', and 'Ultrasonography' and diseases such as 'Breast cancer' and those related to 'Paediatrics'. [ABSTRACT FROM AUTHOR]

A major research subject in recent times is Alzheimer's disease (AD) due to the growth and considerable societal impacts on health. So, the detection of AD is essential for medication care. Early detection of AD is critical for effective treatment, and monitoring the time period between normal aging's unavoidable cognitive loss and dementia's more catastrophic degradation is common practice. The deep learning method for early diagnosis and automated categorization of AD has suddenly gained a lot of attention since rapid advancement in the field of GANs approaches has now been used in the clinical research sector. Many recent studies using brain MRI images and convolutional neural networks (CNNs) to identify Alzheimer's disease have yielded promising results. Instead of adequately engaging with the lack of real data, many research papers have focused on prediction. The main purpose of this paper is to do this by generating synthetic MRI images using a series of DCGANs. This paper demonstrates the effectiveness of this concept by cascading DCGANs that imitate different stages of Alzheimer's disease and utilizing SRGANs to enhance the resolution of MRI scans. The purpose of this research is to come forward and tell if an individual might just get Alzheimer's disease. CNN, DCGANs, and SRGANs are used in this paper to present a deep learning-based approach that improves classification and prediction accuracy to 99.7% and also handles the lack of data and the resolution of data. [ABSTRACT FROM AUTHOR]

Background: Research and clinical studies on multiligamentous knee injuries (MLKI) have recently gained interest with several clinical studies reported recently. This study aims to identify, visualize, and characterize the MLKI research, to analyze the knowledge structure of MLKI during 2008–2023 and to identify the emerging research trends from a bibliometric perspective. Methods: All articles reporting MLKI from 2008 to 2023 were curated from the Scopus database, on 1st January 2024. VOS viewer and Microsoft Excel were used to analyze the publications including the participating countries, authors, organizations, journals and research focus. These data were used to generate visual knowledge maps of the outputs. Results: 406 papers on MLKI were published in 115 journals by 483 authors. There has been a slow publication growth in the past 16 years. The United States had more than 50% share in global publications. Knee Surgery Sports Traumatology Arthroscopy, Orthopaedic Journal of Sports Medicine and American Journal of Sports Medicine published the most papers. The three USA institutions, namely Mayo Clinic, Hospital for Special Surgery-New York and Twin Cities Orthopedics contributed the largest number of publications. B.A. Levy (Mayo Clinic, USA) (n = 66) and M.J. Stuart (Mayo Clinic, USA) (n = 63) have registered the highest collaborative links with other authors. Conclusions: This study is the first comprehensive bibliometric study to analyze MLKI. The findings shed light on the growth trajectory of publications, the extent of international collaborations, the influence of highly cited articles, and the key countries, institutions, journals, and authors contributing to the field. [ABSTRACT FROM AUTHOR]

Brain tumors (BT) pose a significant threat to human life, making early detection and classification are critical for effective treatment. Medical imaging plays an important role in brain tumor detection (BTD) by providing invaluable data for diagnosis, surgical planning, research, and training. Maximum accuracy is essential when detecting brain tumors, as even minor diagnostic errors can have serious consequences. Detecting tumors in brain images is inherently complex due to various noise factors that affect image accuracy. This survey examines the significant contributions of deep learning (DL) in the analysis of Magnetic Resonance Imaging (MRI) medical images for brain tumor detection. MRI is the preferred diagnostic tool for examining the brain's intricate structures. DL has emerged as a powerful tool for medical image analysis and brain tumor detection, offering promising solutions to these challenges. This survey paper focuses on the application of DL techniques in the analysis of MRI medical images for brain tumor detection and classification. It presents an overview of diverse DL-based approaches for brain tumor classification (BTC) and their potential to assist radiologists in enhancing research and analysis. This paper reviews recent advancements in DL-based brain tumor detection, addressing current challenges and future prospects in this essential field. Additionally, this survey explores the use of standard datasets in the feature extraction (FE) and classification stages. This review provides a comprehensive analysis of recent developments that address the existing challenges in brain tumor detection using DL approaches. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer's disease neuroimaging initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub. [ABSTRACT FROM AUTHOR]

Stereotyped behaviors should be inhibited under some circumstances in order to encourage appropriate behavior. Psychiatrists have used the modified rock-paper-scissors (RPS) task to examine the inhibition of stereotyped behavior. When subjects are required to lose in response to a gesture, it is difficult for them to lose, and they have a tendency to win involuntarily. It is thought that the win response is the stereotyped response in the RPS task, and the difficulty in making positive attempts to lose is due to the requirement for inhibition of the stereotyped response. In this study, we investigated the brain regions related to inhibition of the stereotyped response using functional magnetic resonance imaging (fMRI). Subjects were assigned to one of two groups: the “win group” or the “lose group.” The lose group showed higher activation of the left dorsolateral prefrontal cortex (DLFPC) when compared to the win group. We also delivered transcranial magnetic stimulation (TMS) while the subjects performed the modified RPS task to investigate whether the left DLPFC (middle frontal gyrus, Brodmann area, BA 9) was directly involved in the inhibition of the stereotyped response. When TMS was delivered before onset of the visual stimulus, the subjects displayed increased response errors. In particular, the subjects had a tendency to win erroneously in a lose condition even though they were required to lose. These results indicate involvement of the left DLPFC in inhibition of the stereotyped responses, which suggests that this region is associated with inhibition of the preparatory setting for stereotyped responses rather than inhibition of ongoing processing to produce a stereotyped response. [ABSTRACT FROM AUTHOR]

The article Phase‑contrast magnetic resonance imaging to assess renal perfusion: a systematic review and statement paper, written by Giulia Villa, Steffen Ringgaard, Ingo Hermann, Rebecca Noble, Paolo Brambilla, Dinah S. Khatir, Frank G. Zöllner, Susan T. Francis, Nicholas M. Selby, Andrea Remuzzi and Anna Caroli, was originally published electronically on the publisher's internet portal on 17 August 2019 without open access [ABSTRACT FROM AUTHOR]

This article presents information about several papers related to pediatric radiology. The first one is "Magnetic Resonance Imaging in Preterm Infants May Predict Later Cerebral Palsy." The purpose of this paper is to assess diffusion tensor imaging as a predictor of cerebral palsy. Another paper is "Prenatal Detection of Spinal Anomalies in Association With Congenital Diaphragmatic Hernia." The paper is aimed at calling attention to the association of spinal anomalies in the setting of congenital diaphragmatic hernia and the significance of prenatal detection.

The advent of precision medicine, increasing clinical needs, and imaging availability among many other factors in the prostate cancer diagnostic pathway has engendered the utilization of artificial intelligence (AI). AI carries a vast number of potential applications in every step of the prostate cancer diagnostic pathway from classifying/improving prostate multiparametric magnetic resonance image quality, prostate segmentation, anatomically segmenting cancer suspicious foci, detecting and differentiating clinically insignificant cancers from clinically significant cancers on a voxel-level, and classifying entire lesions into Prostate Imaging Reporting and Data System categories/Gleason scores. Multiple studies in all these areas have shown many promising results approximating accuracies of radiologists. Despite this flourishing research, more prospective multicenter studies are needed to uncover the full impact and utility of AI on improving radiologist performance and clinical management of prostate cancer. In this narrative review, we aim to introduce emerging medical imaging AI paper quality metrics such as the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Field-Weighted Citation Impact (FWCI), dive into some of the top AI models for segmentation, detection, and classification. [ABSTRACT FROM AUTHOR]

The article presents abstracts on magnetic resonance imaging (MRI) topics including MRI pulse sequences optimization for detection of multiple sclerosis plaques, evaluation of multidimensional model space by dynamic causal modeling, and echo planar spectroscopic imaging of the brain.

The article presents abstracts of research papers on magnetic resonance (MR) imaging (MRI) technology presented at the 2009 European Society for Magnetic Resonance in Medicine Biology (ESMRMB) Congress held in Antalya, Turkey from October 1 to 3, including atypical Virchow-Robin space in brain MRI, intracranial extramedullary hematopoiesis and concurrent cystic astrocytoma on a patient with thalassemia major and MRI of Dyke-Davidoff-Masson syndrome.

The article presents abstracts on medical topics which include "The many faces of TRAP on fetal MRI," "MRI findings of fetal demise," and "3D T1-weighted fetal MRI."