Ultrasound-guided facet block to low back pain: a case report

Ultrasound-guided axial facet joint interventions for chronic spinal pain: A narrative review

Background

Axial facet joint interventions (e.g., medial branch block and radiofrequency ablation, facet joint intra-articular injection) are commonly performed for managing chronic spinal pain. Although traditionally performed with fluoroscopy or computed tomography (CT) guidance, ultrasound-guided techniques have also been developed for these interventions.

Aims

The aim of this study is to present contemporary ultrasound-guided techniques for facet joint interventions and synthesize data addressing their accuracy, safety, and efficacy.

Methods

The PubMed, MEDLINE, CINAHL, Embase, and Cochrane Central Register of Controlled Trials databases were systematically searched for studies of ultrasound-guided facet joint interventions with human subjects from November 1, 1992, to November 1, 2022. Additional sources were drawn from reference lists and citations of relevant studies.

Results

We found 48 studies assessing ultrasound-guided facet joint interventions. Ultrasound guidance for injection of the cervical facet joints and their innervating nerves had favorable accuracy (78%-100%), with lower procedural time compared to fluoroscopy or CT guidance and comparable pain relief. Accuracy with ultrasound-guided lumbar facet joint intra-articular injection (86%-100%) was more reliable than medial branch block (72%-97%); analgesia was comparable to fluoroscopy and CT guidance. In general, these procedures were more challenging for patients with obesity, and deeper structures were more difficult to accurately target (e.g., lower cervical levels, L5 dorsal ramus).

Conclusions

Ultrasound-guided facet joint interventions continue to evolve. Some technically challenging interventions may be impractical for widespread usage or require further technical refinement. The utility of ultrasound guidance with obesity and abnormal anatomy may be reduced.

Automatic Localization of the Needle Target for Ultrasound-Guided Epidural Injections

Accurate identification of the needle target is crucial for effective epidural anesthesia. Currently, epidural needle placement is administered by a manual technique, relying on the sense of feel, which has a significant failure rate. Moreover, misleading the needle may lead to inadequate anesthesia, post dural puncture headaches, and other potential complications. Ultrasound offers guidance to the physician for identification of the needle target, but accurate interpretation and localization remain challenges. A hybrid machine learning system is proposed to automatically localize the needle target for epidural needle placement in ultrasound images of the spine. In particular, a deep network architecture along with a feature augmentation technique is proposed for automatic identification of the anatomical landmarks of the epidural space in ultrasound images. Experimental results of the target localization on planes of 3-D as well as 2-D images have been compared against an expert sonographer. When compared with the expert annotations, the average lateral and vertical errors on the planes of 3-D test data were 1 and 0.4 mm, respectively. On 2-D test data set, an average lateral error of 1.7 mm and vertical error of 0.8 mm were acquired.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.