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Background: To prospectively evaluate ultrasound backscatter coefficients (BSCs) of the supraspinatus and infraspinatus muscles and compare with Goutallier classification on magnetic resonance imaging (MRI)., Methods: Fifty-six participants had shoulder MRI exams and ultrasound exams of the supraspinatus and infraspinatus muscles. Goutallier MRI grades were determined and BSCs were measured. Group means were compared and the strength of relationships between the measures were determined. Using binarized Goutallier groups (0-2 versus 3-4), areas under the receiver operating characteristic curves (AUROCs) were calculated. The nearest integer cutoff value was determined using Youden's index., Results: BSC values were significantly different among most Goutallier grades for the supraspinatus and infraspinatus muscles (both p < 0.001). Strong correlations were found between the BSC values and Goutallier grades for the supraspinatus (τ b  = 0.72, p < 0.001) and infraspinatus (τ b  = 0.79, p < 0.001) muscles. BSC showed excellent performance for classification of the binarized groups (0-2 versus 3-4) for both supraspinatus (AUROC = 0.98, p < 0.0001) and infraspinatus (AUROC = 0.98, p < 0.0001) muscles. Using a cutoff BSC value of -17 dB, sensitivity, specificity, and accuracy for severe fatty infiltration were 87.0%, 90.0%, and 87.5% for the supraspinatus muscle, and 93.6%, 87.5%, and 92.7% for the infraspinatus muscle., Conclusion: BSC can be applied to the rotator cuff muscles for assessment of fatty infiltration. For both the supraspinatus and infraspinatus muscles, BSC values significantly increased with higher Goutallier grades and showed strong performance in distinguishing low versus high Goutallier grades., Relevance Statement: Fatty infiltration of the rotator cuff muscles can be quantified using BSC values, which are higher with increasing Goutallier grades., Key Points: Ultrasound BSC measurements are reliable for the quantification of muscle fatty infiltration. BCS values increased with higher Goutallier MRI grades. BCS values demonstrated high performance for distinguishing muscle fatty infiltration groups., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that can be used to assess microstructural features of skeletal muscle that are related to tissue function. Although widely used, direct correlations between DTI derived metrics such as fractional anisotropy and spatially matched tissue microstructure assessed with histology have not been performed. This study investigated the relationship between scalar-based DTI measurements and histologically derived muscle microstructural measurements in rat rotator cuff muscles. Despite meticulous co-localization of MRI and histology data, negligible correlations were found between DTI metrics and histological measurements including muscle fiber diameter, cross-sectional area, and surface-to-volume ratio. These findings highlight the challenges in validating DTI with histology due to requirements in anatomical co-localization, necessity of high-quality histology, and consideration of diffusion measurement scales. Our findings underscore the need for further research with optimized imaging parameters to enhance our knowledge regarding the sensitivity of DTI to important features of muscle microstructure., Competing Interests: Competing Interests Statement The authors declare no competing interests.

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., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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.

Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients' outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.