Bone biopsies guided by augmented reality: a pilot study

US/CT fusion imaging and virtual navigation to guide lumbar intradiscal oxygen-ozone therapy: a pilot study

PURPOSE

To test the feasibility of US/CT fusion imaging to guide lumbar intradiscal O2/O3 therapy to treat discogenic degenerative low back pain due to lumbar disc herniation (LDH).

METHODS

We retrospectively included consecutive patients affected by low back pain and/or sciatica due to LDH resistant to conservative therapies, who underwent to lumbar intradiscal O2/O3 injection under CT/US fusion imaging guidance (Fusion Group) and standard CT guidance (Control Group). For each procedure, we collected procedure operative time, room utilization time, number of CT passes, complications, and O2/O3 intradiscal diffusion adequacy. Technical success was defined as the ability to complete the procedure as initially planned to reach the disc. Technical efficacy was based on O2/O3 intradiscal diffusion adequacy, as demonstrated by the last CT scan.

RESULTS

Six patients (4 males; mean age: 68 ± 15 years) were included in the Fusion group, six (4 males; mean age: 66 ± 12 years) in Control group. No complications were observed in both groups. In Fusion group we found significantly lower room utilization time (30 ± 6 min vs. 46 ± 10 min, p = 0.008), procedure operative time (14 ± 3 min vs. 24 ± 6 min, p = 0.008), and number of CT passes (2 [2,2] vs. 3 [3,3], p = 0.006) than in Control Group, respectively. Technical success and efficacy were 100% in both Groups.

CONCLUSION

CT/US fusion imaging seems to be a feasible and safe guidance for intradiscal O2/O3 injections, allowing decrease of procedure time and number of CT passes.

Mixed Reality Biopsy Navigation System Utilizing Markerless Needle Tracking and Imaging Data Superimposition

Exact biopsy planning and careful execution of needle injection is crucial to ensure successful procedure completion as initially intended while minimizing the risk of complications. This study introduces a solution aimed at helping the operator navigate to precisely position the needle in a previously planned trajectory utilizing a mixed reality headset. A markerless needle tracking method was developed by integrating deep learning and deterministic computer vision techniques. The system is based on superimposing imaging data onto the patient's body in order to directly perceive the anatomy and determine a path from the selected injection site to the target location. Four types of tests were conducted to assess the system's performance: measuring the accuracy of needle pose estimation, determining the distance between injection sites and designated targets, evaluating the efficiency of material collection, and comparing procedure time and number of punctures required with and without the system. These tests, involving both phantoms and physician participation in the latter two, demonstrated the accuracy and usability of the proposed solution. The results showcased a significant improvement, with a reduction in number of punctures needed to reach the target location. The test was successfully completed on the first attempt in 70% of cases, as opposed to only 20% without the system. Additionally, there was a 53% reduction in procedure time, validating the effectiveness of the system.

Artificial intelligence in interventional radiology: state of the art

Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.Relevance statement Exploring AI's transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.Key points• AI adoption in IR is more complex compared to diagnostic radiology.• Current literature about AI in IR is in its early stages.• AI has the potential to revolutionise every aspect of IR.

Navigation and Robotics in Interventional Oncology: Current Status and Future Roadmap

Interventional oncology (IO) is the field of Interventional Radiology that provides minimally invasive procedures under imaging guidance for the diagnosis and treatment of malignant tumors. Sophisticated devices can be utilized to increase standardization, accuracy, outcomes, and "repeatability" in performing percutaneous Interventional Oncology techniques. These technologies can reduce variability, reduce human error, and outperform human hand-to-eye coordination and spatial relations, thus potentially normalizing an otherwise broad diversity of IO techniques, impacting simulation, training, navigation, outcomes, and performance, as well as verification of desired minimum ablation margin or other measures of successful procedures. Stereotactic navigation and robotic systems may yield specific advantages, such as the potential to reduce procedure duration and ionizing radiation exposure during the procedure and, at the same time, increase accuracy. Enhanced accuracy, in turn, is linked to improved outcomes in many clinical scenarios. The present review focuses on the current role of percutaneous navigation systems and robotics in diagnostic and therapeutic Interventional Oncology procedures. The currently available alternatives are presented, including their potential impact on clinical practice as reflected in the peer-reviewed medical literature. A review of such data may inform wiser investment of time and resources toward the most impactful IR/IO applications of robotics and navigation to both standardize and address unmet clinical needs.