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.

Feasibility and accuracy of real-time 3D-holographic graft length measurements.

Aims: Mixed reality (MR) holograms can display high-definition images while preserving the user's situational awareness. New MR software can measure 3D objects with gestures and voice commands; however, these measurements have not been validated. We aimed to assess the feasibility and accuracy of using 3D holograms for measuring the length of coronary artery bypass grafts. Methods and results: An independent core lab analyzed follow-up computer tomography coronary angiograms performed 30 days after coronary artery bypass grafting in 30 consecutive cases enrolled in the FASTTRACK CABG trial. Two analysts, blinded to clinical information, performed holographic reconstruction and measurements using the CarnaLife Holo software (Medapp, Krakow, Poland). Inter-observer agreement was assessed in the first 20 cases. Another analyst performed the validation measurements using the CardIQ W8 CT system (GE Healthcare, Milwaukee, Wisconsin). Seventy grafts (30 left internal mammary artery grafts, 31 saphenous vein grafts, and 9 right internal mammary artery grafts) were measured. Holographic measurements were feasible in 97.1% of grafts and took 3 minutes 36 s ± 50.74 s per case. There was an excellent inter-observer agreement [interclass correlation coefficient (ICC) 0.99 (0.97-0.99)]. There was no significant difference between the total graft length on hologram and CT [187.5 mm (157.7-211.4) vs. 183.1 mm (156.8-206.1), P = 0.50], respectively. Hologram and CT measurements are highly correlated (r = 0.97, P < 0.001) with an excellent agreement [ICC 0.98 (0.97-0.99)]. Conclusion: Real-time holographic measurements are feasible, quick, and accurate even for tortuous bypass grafts. This new methodology can empower clinicians to visualize and measure 3D images by themselves and may provide insights for procedural strategy.

DRU-Net: Pulmonary Artery Segmentation via Dense Residual U-Network with Hybrid Loss Function.

The structure and topology of the pulmonary arteries is crucial to understand, plan, and conduct medical treatment in the thorax area. Due to the complex anatomy of the pulmonary vessels, it is not easy to distinguish between the arteries and veins. The pulmonary arteries have a complex structure with an irregular shape and adjacent tissues, which makes automatic segmentation a challenging task. A deep neural network is required to segment the topological structure of the pulmonary artery. Therefore, in this study, a Dense Residual U-Net with a hybrid loss function is proposed. The network is trained on augmented Computed Tomography volumes to improve the performance of the network and prevent overfitting. Moreover, the hybrid loss function is implemented to improve the performance of the network. The results show an improvement in the Dice and HD95 scores over state-of-the-art techniques. The average scores achieved for the Dice and HD95 scores are 0.8775 and 4.2624 mm, respectively. The proposed method will support physicians in the challenging task of preoperative planning of thoracic surgery, where the correct assessment of the arteries is crucial.

Deep learning-based framework for automatic cranial defect reconstruction and implant modeling.

BACKGROUND AND OBJECTIVE: This article presents a robust, fast, and fully automatic method for personalized cranial defect reconstruction and implant modeling. METHODS: We propose a two-step deep learning-based method using a modified U-Net architecture to perform the defect reconstruction, and a dedicated iterative procedure to improve the implant geometry, followed by an automatic generation of models ready for 3-D printing. We propose a cross-case augmentation based on imperfect image registration combining cases from different datasets. Additional ablation studies compare different augmentation strategies and other state-of-the-art methods. RESULTS: We evaluate the method on three datasets introduced during the AutoImplant 2021 challenge, organized jointly with the MICCAI conference. We perform the quantitative evaluation using the Dice and boundary Dice coefficients, and the Hausdorff distance. The Dice coefficient, boundary Dice coefficient, and the 95th percentile of Hausdorff distance averaged across all test sets, are 0.91, 0.94, and 1.53 mm respectively. We perform an additional qualitative evaluation by 3-D printing and visualization in mixed reality to confirm the implant's usefulness. CONCLUSION: The article proposes a complete pipeline that enables one to create the cranial implant model ready for 3-D printing. The described method is a greatly extended version of the method that scored 1st place in all AutoImplant 2021 challenge tasks. We freely release the source code, which together with the open datasets, makes the results fully reproducible. The automatic reconstruction of cranial defects may enable manufacturing personalized implants in a significantly shorter time, possibly allowing one to perform the 3-D printing process directly during a given intervention. Moreover, we show the usability of the defect reconstruction in a mixed reality that may further reduce the surgery time.

The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report.

The technology of 3D printing and visualization of anatomical structures is rapidly growing in various fields of medicine. A custom-made implant and mixed reality were used to perform complex revision hip arthroplasty in January 2019. The use of mixed reality allowed for a very good visualization of the structures and resulted in precise implant fixation. According to the authors' knowledge, this is the first described case report of the combined use of these two innovations. The diagnosis preceding the qualification for the procedure was the loosening of the left hip's acetabular component. Mixed reality headset and holograms prepared by engineers were used during the surgery. The operation was successful, and it was followed by early verticalization and patient rehabilitation. The team sees opportunities for technology development in joint arthroplasty, trauma, and orthopedic oncology.

3D mixed-reality visualization of medical imaging data as a supporting tool for innovative, minimally invasive surgery for gastrointestinal tumors and systemic treatment as a new path in personalized treatment of advanced cancer diseases.

BACKGROUND: The purpose of this study was to investigate the potential of a combination of 3D mixed-reality visualization of medical images using CarnaLife Holo (MedApp, Poland) system as a supporting tool for innovative, minimally invasive surgery/irreversible electroporation-IRA, Nano-Knife), microwave ablation (MWA)/for advanced gastrointestinal tumors. Eight liver and pancreatic tumor treatments were performed. In all of the patients undergoing laparoscopy or open surgery volume and margin were estimated by preoperative visualization. In all patients, neoplastic lesions were considered unresectable by standard methods. METHODS: Preoperative CT or MRI were transformed into holograms and displayed thanks to the HoloLens 2. During operation, the surgeon's field of view was augmented with a 3D model of the patient's relevant structures. RESULTS: The intraoperative hologram contributed to better presentation of tumor size and locations, more precise setting of needles used to irreversible electroporation and for determining ablation line in case of liver metastases. Surgeons could easily compare the real patient's anatomy to holographic visualization just before the operations. CONCLUSIONS: The combination of 3D mixed-reality visualization using CarnaLife Holo with IRA, MWA and next systemic treatment (chemotherapy) might be a new way in personalized treatment of advanced cancers.

Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network.

Devices and systems secured by biometric factors became a part of our lives because they are convenient, easy to use, reliable, and secure. They use information about unique features of our bodies in order to authenticate a user. It is possible to enhance the security of these devices by adding supplementary modality while keeping the user experience at the same level. Palm vein systems are based on infrared wavelengths used for capturing images of users' veins. It is both convenient for the user, and it is one of the most secure biometric solutions. The proposed system uses IR and UV wavelengths; the images are then processed by a deep convolutional neural network for extraction of biometric features and authentication of users. We tested the system in a verification scenario that consisted of checking if the images collected from the user contained the same biometric features as those in the database. The True Positive Rate (TPR) achieved by the system when the information from the two modalities were combined was 99.5% by the threshold of acceptance set to the Equal Error Rate (EER).

Automatic aorta and left ventricle segmentation for TAVI procedure planning using convolutional neural networks.

Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure which is performed on patients with aortic valve defects that are posing a high-risk for conducting a surgical treatment. Preoperative surgical planning and valve sizing play a crucial role in reducing surgery complications and adverse effects such as paravalvular leakage or stroke. Planning process incorporates performing measurements, detecting landmarks and visualizing relevant structures in 3D. To automatize this process, a segmentation is required. Due to the lack of methods enabling parallel aorta and left ventricle segmentation we propose a fully automatic neural network approach based on 2D U-Net architecture. Convolutional neural network architecture was trained on 44 studies (22 raw CTA datasets and 22 elastic deformed scans) and tested on another 18 stacks of data. During every epoch of network learning process cross validation was performed on 8 stacks. As a result, we achieve 0.95 mean Dice coefficient score with standard deviation 0.02 determining high precision of predicted aorta and left ventricle label maps.