Ultrasound-guided nerve blocks in emergency medicine practice: 2022 updates.

OBJECTIVES: In the Emergency Department (ED), ultrasound-guided nerve blocks (UGNBs) have become a cornerstone of multimodal pain regimens. We investigated current national practices of UGNBs across academic medical center EDs, and how these trends have changed over time. METHODS: We conducted a cross-sectional electronic survey of academic EDs with ultrasound fellowships across the United States. Twenty-item questionnaires exploring UGNB practice patterns, training, and complications were distributed between November 2021-June 2022. Data was manually curated, and descriptive statistics were performed. The survey results were then compared to results from Amini et al. 2016 UGNB survey to identify trends. RESULTS: The response rate was 80.5% (87 of 108 programs). One hundred percent of responding programs perform UGNB at their institutions, with 29% (95% confidence interval (CI), 20%-39%) performing at least 5 blocks monthly. Forearm UGNB are most commonly performed (96% of programs (95% CI, 93%-100%)). Pain control for fractures is the most common indication (84%; 95% CI, 76%-91%). Eighty-five percent (95% CI, 77%-92%) of programs report at least 80% of UGNB performed are effective. Eighty-five percent (95% CI, 66%-85%) of programs have had no reported complications from UGNB performed by emergency providers at their institution. The remaining 15% (95% CI, 8%-23%) report an average of 1 complication annually. CONCLUSIONS: All programs participating in our study report performing UGNB in their ED, which is a 16% increase over the last 5 years. UGNB's are currently performed safely and effectively in the ED, however practice improvements can still be made. Creating multi-disciplinary committees at local and national levels can standardize guidelines and practice policies to optimize patient safety and outcomes.

PET/CT Fluoroscopy during PET/CT-Guided Interventions: Initial Experience.

This study assessed the feasibility and functionality of the use of a high-speed image fusion technology to generate and display positron emission tomography (PET)/computed tomography (CT) fluoroscopic images during PET/CT-guided tumor ablation procedures. Thirteen patients underwent 14 PET/CT-guided ablations for the treatment of 20 tumors. A Food and Drug Administration-cleared multimodal image fusion platform received images pushed from a scanner, followed by near-real-time, nonrigid image registration. The most recent intraprocedural PET dataset was fused to each single-rotation CT fluoroscopy dataset as it arrived, and the fused images were displayed on an in-room monitor. PET/CT fluoroscopic images were generated and displayed in all procedures and enabled more confident targeting in 3 procedures. The mean lag time from CT fluoroscopic image acquisition to the in-room display of the fused PET/CT fluoroscopic image was 21 seconds ± 8. The registration accuracy was visually satisfactory in 13 of 14 procedures. In conclusion, PET/CT fluoroscopy was feasible and may have the potential to facilitate PET/CT-guided procedures.

Positron Emission Tomography and Computed Tomography Contributions to Patient Dose and Personnel Exposure to Radiation during PET/CT-Guided Tumor Ablations.

This study sought to quantify the positron emission tomography (PET) and computed tomography (CT) components of patient radiation doses and personnel exposure to radiations during PET/CT-guided tumor ablations and assess the utility of a rolling lead shield for operator protection. Two operators performed 21 PET/CT-guided ablations behind a customized, 25-mm-thick lead shield with midchest-to-midthigh coverage. The mean patient radiation dose per procedure was 3.90 mSv ± 1.13 (11.3%) from PET and 30.51 mSv ± 19.05 (88.7%) from CT. The mean primary and secondary operator exposure outside neck-level thyroid shields was 0.05 and 0.02 mSv per procedure, respectively. The radiation exposure levels behind the rolling lead shield, inside the primary operator's thyroid shield, and on the other personnel were below the measurable threshold cumulatively over 21 procedures. The mean PET exposure level at continuous close proximity to patients was 0.02 mSv per procedure. The PET radiation doses to the patients and personnel were small. Thus, the rolling lead shield provided limited benefit.

ReMIND: The Brain Resection Multimodal Imaging Database.

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation augments the surgeon's ability to achieve this but loses validity as surgery progresses due to brain shift. Moreover, gliomas are often indistinguishable from surrounding healthy brain tissue. Intraoperative magnetic resonance imaging (iMRI) and ultrasound (iUS) help visualize the tumor and brain shift. iUS is faster and easier to incorporate into surgical workflows but offers a lower contrast between tumorous and healthy tissues than iMRI. With the success of data-hungry Artificial Intelligence algorithms in medical image analysis, the benefits of sharing well-curated data cannot be overstated. To this end, we provide the largest publicly available MRI and iUS database of surgically treated brain tumors, including gliomas (n=92), metastases (n=11), and others (n=11). This collection contains 369 preoperative MRI series, 320 3D iUS series, 301 iMRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. This database is expected to help brain shift and image analysis research and neurosurgical training in interpreting iUS and iMRI.

ReMIND: The Brain Resection Multimodal Imaging Database.

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation augments the surgeon's ability to achieve this but loses validity as surgery progresses due to brain shift. Moreover, gliomas are often indistinguishable from surrounding healthy brain tissue. Intraoperative magnetic resonance imaging (iMRI) and ultrasound (iUS) help visualize the tumor and brain shift. iUS is faster and easier to incorporate into surgical workflows but offers a lower contrast between tumorous and healthy tissues than iMRI. With the success of data-hungry Artificial Intelligence algorithms in medical image analysis, the benefits of sharing well-curated data cannot be overstated. To this end, we provide the largest publicly available MRI and iUS database of surgically treated brain tumors, including gliomas (n = 92), metastases (n = 11), and others (n = 11). This collection contains 369 preoperative MRI series, 320 3D iUS series, 301 iMRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. This database is expected to help brain shift and image analysis research and neurosurgical training in interpreting iUS and iMRI.

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound.

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F 1-score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility.

Novice-performed point-of-care ultrasound for home-based imaging.

Patient-performed point-of-care ultrasound (POCUS) may be feasible for use in home-based healthcare. We investigated whether novice users can obtain lung ultrasound (LUS) images via self-scanning with similar interpretability and quality as experts. Adult participants with no prior medical or POCUS training, who were capable of viewing PowerPoint slides in their home and who could hold a probe to their chest were recruited. After training, volunteers self-performed 8-zone LUS and saved images using a hand-held POCUS device in their own home. Each 8-zone LUS scan was repeated by POCUS experts. Clips were independently viewed and scored by POCUS experts blinded to performing sonographers. Quality and interpretability scores of novice- and expert-obtained LUS images were compared. Thirty volunteers with average age of 42.8 years (Standard Deviation (SD) 15.8), and average body mass index of 23.7 (SD 3.1) were recruited. Quality of novice and expert scans did not differ (median score 2.6, interquartile range (IQR) 2.3-2.9 vs. 2.8, IQR 2.3-3.0, respectively p = 0.09). Individual zone quality also did not differ (P > 0.05). Interpretability of LUS was similar between expert and novice scanners (median 7 zones interpretable, IQR 6-8, for both groups, p = 0.42). Interpretability of novice-obtained scans did not differ from expert scans (median 7 out of 8 zones, IQR 6-8, p = 0.42). Novice-users can self-obtain interpretable, expert-quality LUS clips with minimal training. Patient-performed LUS may be feasible for outpatient home monitoring.

Challenges and Opportunities of Intraoperative 3D Ultrasound With Neuronavigation in Relation to Intraoperative MRI.

INTRODUCTION: Neuronavigation greatly improves the surgeons ability to approach, assess and operate on brain tumors, but tends to lose its accuracy as the surgery progresses and substantial brain shift and deformation occurs. Intraoperative MRI (iMRI) can partially address this problem but is resource intensive and workflow disruptive. Intraoperative ultrasound (iUS) provides real-time information that can be used to update neuronavigation and provide real-time information regarding the resection progress. We describe the intraoperative use of 3D iUS in relation to iMRI, and discuss the challenges and opportunities in its use in neurosurgical practice. METHODS: We performed a retrospective evaluation of patients who underwent image-guided brain tumor resection in which both 3D iUS and iMRI were used. The study was conducted between June 2020 and December 2020 when an extension of a commercially available navigation software was introduced in our practice enabling 3D iUS volumes to be reconstructed from tracked 2D iUS images. For each patient, three or more 3D iUS images were acquired during the procedure, and one iMRI was acquired towards the end. The iUS images included an extradural ultrasound sweep acquired before dural incision (iUS-1), a post-dural opening iUS (iUS-2), and a third iUS acquired immediately before the iMRI acquisition (iUS-3). iUS-1 and preoperative MRI were compared to evaluate the ability of iUS to visualize tumor boundaries and critical anatomic landmarks; iUS-3 and iMRI were compared to evaluate the ability of iUS for predicting residual tumor. RESULTS: Twenty-three patients were included in this study. Fifteen patients had tumors located in eloquent or near eloquent brain regions, the majority of patients had low grade gliomas (11), gross total resection was achieved in 12 patients, postoperative temporary deficits were observed in five patients. In twenty-two iUS was able to define tumor location, tumor margins, and was able to indicate relevant landmarks for orientation and guidance. In sixteen cases, white matter fiber tracts computed from preoperative dMRI were overlaid on the iUS images. In nineteen patients, the EOR (GTR or STR) was predicted by iUS and confirmed by iMRI. The remaining four patients where iUS was not able to evaluate the presence or absence of residual tumor were recurrent cases with a previous surgical cavity that hindered good contact between the US probe and the brainsurface. CONCLUSION: This recent experience at our institution illustrates the practical benefits, challenges, and opportunities of 3D iUS in relation to iMRI.