Tuberculosis (TB) is a bacterial infection with Mycobacterium tuberculosis; it is a public health problem worldwide and one of the leading causes of mortality. Since December 2019, the COVID-19 pandemic has created unprecedented health challenges and disrupted the TB health services, especially in high-burden countries with ever-increasing prevalence. Extrapulmonary and even pulmonary TB are an important cause of nonspecific clinical and radiological manifestations and can masquerade as any benign or malignant medical case, thus causing disastrous conditions and diagnostic dilemmas. Clinical manifestations and routine laboratory tests have limitations in directing physicians to diagnose TB. Medical-imaging examinations play an essential role in detecting tissue abnormalities and early suspecting diagnosis of TB in different organs. Radiologists and physicians should be familiar with and aware of the radiological manifestations of TB to contribute to the early suspicion and diagnosis of TB. The purpose of this article is to illustrate the common radiologic patterns of pulmonary and extrapulmonary TB. This article will be beneficial for radiologists, medical students, chest physicians, and infectious-disease doctors who are interested in the diagnosis of TB.

• Cartilage is better visualised with 3D TrueFISP than 3D SPACE sequences. • 3D TrueFISP is a reliable sequence for detecting low- and high-grade cartilage defects. • 3D TrueFISP at 3T provides excellent contrast between cartilage and joint fluid.

Hydatid cyst is a common name for the larval stage of a tapeworm species of the genus Echinococcus granulosus, which is transmitted from animals to humans via the fecal–oral route. Hydatid cysts predominantly affect the liver (75%), followed by the lung (15%), and they can affect many organs in the human body. Medical imaging modalities are the keystone for the diagnosis of hydatid cysts with high sensitivity and specificity. Ultrasound imaging with high resolution is the first choice for diagnosis, differential diagnosis, staging, establishing a role in interventional management, and follow-up, and it can differentiate Type I hydatid cysts from simple liver cysts. Unenhanced computed tomography (CT) is indicated where or when an ultrasound is unsatisfactory, such as with chest or brain hydatid cysts, when detecting calcification, and in obese patients. Magnetic resonance imaging (MRI) is superior for demonstrating cyst wall defects, biliary communication, neural involvement, and differentiating hydatid cysts from simple cysts using diffusion-weighted imaging (DWI) sequences. According to the phase of growth, hydatid cysts occur in different sizes and shapes, which may mimic benign or malignant neoplasms and may create diagnostic challenges in some cases. Hydatid cysts can mimic simple cysts, choledochal cysts, Caroli’s disease, or mesenchymal hamartomas of the liver. They can mimic lung cystic lesions, mycetoma, blood clots, Rasmussen aneurysms, and even lung carcinomas. Differential diagnosis can be difficult for arachnoid cysts, porencephalic cysts, pyogenic abscesses, and even cystic tumors of the brain, and can create diagnostic dilemmas in the musculoskeletal system.

Objective: The aim of this study was to explore opinions and views towards radiology AI among Saudi Arabian radiologists including both consultants and trainees. Methods: A qualitative approach was adopted, with radiologists working in radiology departments in the Western region of Saudi Arabia invited to participate in this interview-based study. Semi-structured interviews (n = 30) were conducted with consultant radiologists and trainees. A qualitative data analysis framework was used based on Miles and Huberman’s philosophical underpinnings. Results: Several factors, such as lack of training and support, were attributed to the non-use of AI-based applications in clinical practice and the absence of radiologists’ involvement in AI development. Despite the expected benefits and positive impacts of AI on radiology, a reluctance to use AI-based applications might exist due to a lack of knowledge, fear of error and concerns about losing jobs and/or power. Medical students’ radiology education and training appeared to be influenced by the absence of a governing body and training programmes. Conclusion: The results of this study support the establishment of a governing body or national association to work in parallel with universities in monitoring training and integrating AI into the medical education curriculum and residency programmes. Advances in knowledge: An extensive debate about AI-based applications and their potential effects was noted, and considerable exceptions of transformative impact may occur when AI is fully integrated into clinical practice. Therefore, future education and training programmes on how to work with AI-based applications in clinical practice may be recommended.

Objectives To investigate the accuracy of Diffusion Weighted Imaging (DWI) using the Readout Segmentation of Long Variable Echo-trains (RESOLVE) sequence in detecting lumbosacral nerve abnormalities. Methods Following institutional ethics committee approval, patients with sciatica-type lower limb radicular symptoms (n = 110) were recruited and prospectively scanned using 3T MRI. Additional participants (n = 17) who underwent neurophysiological testing (EMG/NCV), were also prospectively studied. In addition to routine lumbar spine MRI, a DWI-RESOLVE sequence of the lumbosacral plexus was performed. Two radiologists, blinded to the side of patient symptoms, independently evaluated the MR images. The size and signal intensity changes of the nerves were evaluated using ordinal 4-point Likert-scales. Signal-to-noise ratio (SNR), apparent diffusion coefficient (ADC) and size were measured for affected and normal nerves. Inter-observer agreement was determined with kappa statistics; κ. Results In patients who did not undergo EMG/NCV testing (n = 110), the DWI-RESOLVE sequence detected lumbosacral nerve abnormalities that correlated with symptoms in 36.3% (40/110). This is a similar percentage to patients who underwent EMG/NCV testing, which was positive and correlated with symptoms in 41.2% (7/17). Inter-observer agreement for evaluation of lumbosacral nerve abnormalities was excellent and ranged from 0.87 to 0.94. SNR and nerve size measurements demonstrated statistically significant differences for the L5 and S1 nerves (p value < 0.05) for patients who did not undergo EMG/NCV testing. Conclusion The DWI-RESOLVE sequence is a promising new method that may permit accurate detection and localization of lumbar nerve abnormalities in patients with sciatica.