One year in review: ultrasound in arthritis

Musculoskeletal ultrasound (MSUS) has become a relevant part of rheumatology practice and research because it substantially allows us to optimize management of rheumatic and musculoskeletal diseases. This non-invasive imaging modality is a valuable point-of-care tool to accurately evaluate intra-articular and periarticular structures involved in a wide range of rheumatic diseases in adults and children. In addition, MSUS is an invaluable bedside aid for guiding accurate and safe musculoskeletal aspirations, injections and biopsies. This review provides an overview of the literature of the last year on the role of MSUS in arthritis

Accuracy of multiparametric magnetic resonance imaging to detect significant prostate cancer and index lesion location

Background: Multiparametric magnetic resonance imaging (mpMRI) of the prostate appears to improve prostate cancer detection, but studies comparing mpMRI to histopathology at the time of radical prostatectomy (RP) are lacking. This retrospective study determined the accuracy of mpMRI predicting Gleason score and index lesion location at the time of RP, the current gold standard for diagnosis. Methods: Between April 2013 and April 2016, a database of all men aged more than 40 years who underwent RP after positive transrectal ultrasound biopsy by an experienced urological surgeon was collated at a single regional centre. This was cross‐referenced with a database of all men who had mpMRIs performed at a single centre and reported according to Prostate Imaging Reporting and Data System (PI‐RADS version 1) during this period to generate a sample size of 64 men. A Spearman\u27s rho test was utilized to calculate correlation. Results: Median age of patients was 64 years, the median prostate‐specific antigen at RP was 6.22 ng/mL. mpMRI was positive (≥PI‐RADS 3) in 85.9% of patients who underwent RP. More than 92% of participants had Gleason ≥7 disease. A positive relationship between mpMRI prostate PI‐RADS score and RP cancer volume was demonstrated. An anatomical location correlation calculated in octants was found to be 89.1% accurate. Conclusion: mpMRI accurately detects prostate cancer location and severity when compared with gold standard histopathology at the time of RP. It thus has an important role in planning for future prostate biopsy and cancer treatment

UK and Ireland Joint Advisory Group (JAG) consensus statements for training and certification in diagnostic endoscopic ultrasound (EUS)

Background and Aims: International endoscopy societies vary in their approach for credentialing individuals in endoscopic ultrasound (EUS) to enable independent practice; however, there is no consensus in this or its implementation. In 2019, the Joint Advisory Group on GI Endoscopy (JAG) commissioned a working group to examine the evidence relating to this process for EUS. The aim of this was to develop evidence-based recommendations for EUS training and certification in the UK.Methods: Under the oversight of the JAG quality assurance team, a modified Delphi process was conducted which included major stakeholders from the UK and Ireland. A formal literature review was made, initial questions for study were proposed and recommendations for training and certification in EUS were formulated after a rigorous assessment using the Grading of Recommendation Assessment, Development and Evaluation tool and subjected to electronic voting to identify accepted statements. These were peer reviewed by JAG and relevant stakeholder societies before consensus on the final EUS certification pathway was achieved.Results: 39 initial questions were proposed of which 33 were deemed worthy of assessment and finally formed the key recommendations. The statements covered four key domains, such as: definition of competence (13 statements), acquisition of competence (10), assessment of competence (5) and postcertification mentorship (5). Key recommendations include: (1) minimum of 250 hands-on cases before an assessment for competency can be made, (2) attendance at the JAG basic EUS course, (3) completing a minimum of one formative direct observation of procedural skills (DOPS) every 10 cases to allow the learning curve in EUS training to be adequately studied, (4) competent performance in summative DOPS assessments and (5) a period of mentorship over a 12-month period is recommended as minimum to support and mentor new service providers.Conclusions: An evidence-based certification pathway has been commissioned by JAG to support and quality assure EUS training. This will form the basis to improve quality of training and safety standards in EUS in the UK and Ireland.</p

Learning Deep Similarity Metric for 3D MR-TRUS Registration

Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image registration. However, it is very challenging to obtain a robust automatic MR-TRUS registration due to the large appearance difference between the two imaging modalities. The work presented in this paper aims to tackle this problem by addressing two challenges: (i) the definition of a suitable similarity metric and (ii) the determination of a suitable optimization strategy. Methods: This work proposes the use of a deep convolutional neural network to learn a similarity metric for MR-TRUS registration. We also use a composite optimization strategy that explores the solution space in order to search for a suitable initialization for the second-order optimization of the learned metric. Further, a multi-pass approach is used in order to smooth the metric for optimization. Results: The learned similarity metric outperforms the classical mutual information and also the state-of-the-art MIND feature based methods. The results indicate that the overall registration framework has a large capture range. The proposed deep similarity metric based approach obtained a mean TRE of 3.86mm (with an initial TRE of 16mm) for this challenging problem. Conclusion: A similarity metric that is learned using a deep neural network can be used to assess the quality of any given image registration and can be used in conjunction with the aforementioned optimization framework to perform automatic registration that is robust to poor initialization.Comment: To appear on IJCAR

Augmented Reality Ultrasound Guidance in Anesthesiology

Real-time ultrasound has become a mainstay in many image-guided interventions and increasingly popular in several percutaneous procedures in anesthesiology. One of the main constraints of ultrasound-guided needle interventions is identifying and distinguishing the needle tip from needle shaft in the image. Augmented reality (AR) environments have been employed to address challenges surrounding surgical tool visualization, navigation, and positioning in many image-guided interventions. The motivation behind this work was to explore the feasibility and utility of such visualization techniques in anesthesiology to address some of the specific limitations of ultrasound-guided needle interventions. This thesis brings together the goals, guidelines, and best development practices of functional AR ultrasound image guidance (AR-UIG) systems, examines the general structure of such systems suitable for applications in anesthesiology, and provides a series of recommendations for their development. The main components of such systems, including ultrasound calibration and system interface design, as well as applications of AR-UIG systems for quantitative skill assessment, were also examined in this thesis. The effects of ultrasound image reconstruction techniques, as well as phantom material and geometry on ultrasound calibration, were investigated. Ultrasound calibration error was reduced by 10% with synthetic transmit aperture imaging compared with B-mode ultrasound. Phantom properties were shown to have a significant effect on calibration error, which is a variable based on ultrasound beamforming techniques. This finding has the potential to alter how calibration phantoms are designed cognizant of the ultrasound imaging technique. Performance of an AR-UIG guidance system tailored to central line insertions was evaluated in novice and expert user studies. While the system outperformed ultrasound-only guidance with novice users, it did not significantly affect the performance of experienced operators. Although the extensive experience of the users with ultrasound may have affected the results, certain aspects of the AR-UIG system contributed to the lackluster outcomes, which were analyzed via a thorough critique of the design decisions. The application of an AR-UIG system in quantitative skill assessment was investigated, and the first quantitative analysis of needle tip localization error in ultrasound in a simulated central line procedure, performed by experienced operators, is presented. Most participants did not closely follow the needle tip in ultrasound, resulting in 42% unsuccessful needle placements and a 33% complication rate. Compared to successful trials, unsuccessful procedures featured a significantly greater (p=0.04) needle-tip to image-plane distance. Professional experience with ultrasound does not necessarily lead to expert level performance. Along with deliberate practice, quantitative skill assessment may reinforce clinical best practices in ultrasound-guided needle insertions. Based on the development guidelines, an AR-UIG system was developed to address the challenges in ultrasound-guided epidural injections. For improved needle positioning, this system integrated A-mode ultrasound signal obtained from a transducer housed at the tip of the needle. Improved needle navigation was achieved via enhanced visualization of the needle in an AR environment, in which B-mode and A-mode ultrasound data were incorporated. The technical feasibility of the AR-UIG system was evaluated in a preliminary user study. The results suggested that the AR-UIG system has the potential to outperform ultrasound-only guidance