Ultrasound-based navigation for open liver surgery using active liver tracking

Nephron-sparing Surgery for Pediatric Renal Tumors After Centralization of Pediatric Oncology Care in the Netherlands: Improved Outcomes With 3D Modeling

BACKGROUND AND AIM

In this retrospective single center cohort study, we report the surgical outcomes of nephron-sparing surgery (NSS) for Wilms' tumor (WT) patients since centralization of pediatric oncology care in the Netherlands, and implementation of technological advancements. Therewith we describe the influence of experience and innovations for this type of surgery.

METHODS

We retrospectively assessed all NSS procedures from January 1st 2015 until January 1st 2024 for patients who underwent surgery for a renal tumor at the Princess Máxima Center for Pediatric Oncology. Data were gathered on patient characteristics, diagnostic information, radiological characteristics, surgical technique and use of innovations, postoperative outcome, administered treatment and surgical follow-up.

RESULTS

36 patients (58 % female, 42 % male) were included with a total of 43 NSS procedures. Mean (SD) age at diagnosis was 33.3 (23.1) months. 16 procedures were performed without 3D models, of which 3 (18.8 %) resulted in an unexpected positive margin. 27 procedures were preoperatively planned with a 3D model with one (3.7 %) unexpected anticipated positive margins (p = 0.101). Six (13.9 %) procedures had post-operative complications including five urine leakages, one chyle leakage and two (reversible) acute kidney insufficiency. Four patients received a re-intervention (JJ-stent or drain).

CONCLUSIONS

In this retrospective single center cohort study, we show a good surgical outcome after NSS for children with renal tumors after the implementation of 3D models. This study can act as a baseline cohort to harmonize preoperative assessment, intraoperative technique and implementation of innovative surgical technology for further expansion of NSS for WT patients.

Intraoperative patient-specific volumetric reconstruction and 3D visualization for laparoscopic liver surgery

Despite the benefits of minimally invasive surgery, interventions such as laparoscopic liver surgery present unique challenges, like the significant anatomical differences between preoperative images and intraoperative scenes due to pneumoperitoneum, patient pose, and organ manipulation by surgical instruments. To address these challenges, a method for intraoperative three-dimensional reconstruction of the surgical scene, including vessels and tumors, without altering the surgical workflow, is proposed. The technique combines neural radiance field reconstructions from tracked laparoscopic videos with ultrasound three-dimensional compounding. The accuracy of our reconstructions on a clinical laparoscopic liver ablation dataset, consisting of laparoscope and patient reference posed from optical tracking, laparoscopic and ultrasound videos, as well as preoperative and intraoperative computed tomographies, is evaluated. The authors propose a solution to compensate for liver deformations due to pressure applied during ultrasound acquisitions, improving the overall accuracy of the three-dimensional reconstructions compared to the ground truth intraoperative computed tomography with pneumoperitoneum. A unified neural radiance field from the ultrasound and laparoscope data, which allows real-time view synthesis providing surgeons with comprehensive intraoperative visual information for laparoscopic liver surgery, is trained.

Hybrid optical-vision tracking in laparoscopy: accuracy of navigation and ultrasound reconstruction

INTRODUCTION

The use of laparoscopic and robotic liver surgery is increasing. However, it presents challenges such as limited field of view and organ deformations. Surgeons rely on laparoscopic ultrasound (LUS) for guidance, but mentally correlating ultrasound images with pre-operative volumes can be difficult. In this direction, surgical navigation systems are being developed to assist with intra-operative understanding. One approach is performing intra-operative ultrasound 3D reconstructions. The accuracy of these reconstructions depends on tracking the LUS probe.

MATERIAL AND METHODS

This study evaluates the accuracy of LUS probe tracking and ultrasound 3D reconstruction using a hybrid tracking approach. The LUS probe is tracked from laparoscope images, while an optical tracker tracks the laparoscope. The accuracy of hybrid tracking is compared to full optical tracking using a dual-modality tool. Ultrasound 3D reconstruction accuracy is assessed on an abdominal phantom with CT transformed into the optical tracker's coordinate system.

RESULTS

Hybrid tracking achieves a tracking error < 2 mm within 10 cm between the laparoscope and the LUS probe. The ultrasound reconstruction accuracy is approximately 2 mm.

CONCLUSION

Hybrid tracking shows promising results that can meet the required navigation accuracy for laparoscopic liver surgery.

Automatic hepatic tumor segmentation in intra-operative ultrasound: a supervised deep-learning approach

Purpose

Training and evaluation of the performance of a supervised deep-learning model for the segmentation of hepatic tumors from intraoperative US (iUS) images, with the purpose of improving the accuracy of tumor margin assessment during liver surgeries and the detection of lesions during colorectal surgeries.

Approach

In this retrospective study, a U-Net network was trained with the nnU-Net framework in different configurations for the segmentation of CRLM from iUS. The model was trained on B-mode intraoperative hepatic US images, hand-labeled by an expert clinician. The model was tested on an independent set of similar images. The average age of the study population was 61.9 ± 9.9 years. Ground truth for the test set was provided by a radiologist, and three extra delineation sets were used for the computation of inter-observer variability.

Results

The presented model achieved a DSC of 0.84 (p = 0.0037 ), which is comparable to the expert human raters scores. The model segmented hypoechoic and mixed lesions more accurately (DSC of 0.89 and 0.88, respectively) than hyper- and isoechoic ones (DSC of 0.70 and 0.60, respectively) only missing isoechoic or >20 mm in diameter (8% of the tumors) lesions. The inclusion of extra margins of probable tumor tissue around the lesions in the training ground truth resulted in lower DSCs of 0.75 (p = 0.0022 ).

Conclusion

The model can accurately segment hepatic tumors from iUS images and has the potential to speed up the resection margin definition during surgeries and the detection of lesion in screenings by automating iUS assessment.

Ultrasound guidance in navigated liver surgery: toward deep-learning enhanced compensation of deformation and organ motion

PURPOSE

Accuracy of image-guided liver surgery is challenged by deformation of the liver during the procedure. This study aims at improving navigation accuracy by using intraoperative deep learning segmentation and nonrigid registration of hepatic vasculature from ultrasound (US) images to compensate for changes in liver position and deformation.

METHODS

This was a single-center prospective study of patients with liver metastases from any origin. Electromagnetic tracking was used to follow US and liver movement. A preoperative 3D model of the liver, including liver lesions, and hepatic and portal vasculature, was registered with the intraoperative organ position. Hepatic vasculature was segmented using a reduced 3D U-Net and registered to preoperative imaging after initial alignment followed by nonrigid registration. Accuracy was assessed as Euclidean distance between the tumor center imaged in the intraoperative US and the registered preoperative image.

RESULTS

Median target registration error (TRE) after initial alignment was 11.6 mm in 25 procedures and improved to 6.9 mm after nonrigid registration (p = 0.0076). The number of TREs above 10 mm halved from 16 to 8 after nonrigid registration. In 9 cases, registration was performed twice after failure of the first attempt. The first registration cycle was completed in median 11 min (8:00-18:45 min) and a second in 5 min (2:30-10:20 min).

CONCLUSION

This novel registration workflow using automatic vascular detection and nonrigid registration allows to accurately localize liver lesions. Further automation in the workflow is required in initial alignment and classification accuracy.

Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization

Medical image analysis forms the basis of image-guided surgery (IGS) and many of its fundamental tasks. Driven by the growing number of medical imaging modalities, the research community of medical imaging has developed methods and achieved functionality breakthroughs. However, with the overwhelming pool of information in the literature, it has become increasingly challenging for researchers to extract context-relevant information for specific applications, especially when many widely used methods exist in a variety of versions optimized for their respective application domains. By being further equipped with sophisticated three-dimensional (3D) medical image visualization and digital reality technology, medical experts could enhance their performance capabilities in IGS by multiple folds. The goal of this narrative review is to organize the key components of IGS in the aspects of medical image processing and visualization with a new perspective and insights. The literature search was conducted using mainstream academic search engines with a combination of keywords relevant to the field up until mid-2022. This survey systemically summarizes the basic, mainstream, and state-of-the-art medical image processing methods as well as how visualization technology like augmented/mixed/virtual reality (AR/MR/VR) are enhancing performance in IGS. Further, we hope that this survey will shed some light on the future of IGS in the face of challenges and opportunities for the research directions of medical image processing and visualization.

Feasibility of tracked ultrasound registration for pelvic-abdominal tumor navigation: a patient study

PURPOSE

Surgical navigation techniques can guide surgeons in localizing pelvic-abdominal malignancies. For abdominal navigation, accurate patient registration is crucial and is generally performed using an intra-operative cone-beam CT (CBCT). However, this method causes 15-min surgical preparation workflow interruption and radiation exposure, and more importantly, it cannot be repeated during surgery to compensate for large patient movement. As an alternative, the accuracy and feasibility of tracked ultrasound (US) registration are assessed in this patient study.

METHODS

Patients scheduled for surgical navigation during laparotomy of pelvic-abdominal malignancies were prospectively included. In the operating room, two percutaneous tracked US scans of the pelvic bone were acquired: one in supine and one in Trendelenburg patient position. Postoperatively, the bone surface was semiautomatically segmented from US images and registered to the bone surface on the preoperative CT scan. The US registration accuracy was computed using the CBCT registration as a reference and acquisition times were compared. Additionally, both US measurements were compared to quantify the registration error caused by patient movement into Trendelenburg.

RESULTS

In total, 18 patients were included and analyzed. US registration resulted in a mean surface registration error of 1.2 ± 0.2 mm and a mean target registration error of 3.3 ± 1.4 mm. US acquisitions were 4 × faster than the CBCT scans (two-sample t-test P < 0.05) and could even be performed during standard patient preparation before skin incision. Patient repositioning in Trendelenburg caused a mean target registration error of 7.7 ± 3.3 mm, mainly in cranial direction.

CONCLUSION

US registration based on the pelvic bone is accurate, fast and feasible for surgical navigation. Further optimization of the bone segmentation algorithm will allow for real-time registration in the clinical workflow. In the end, this would allow intra-operative US registration to correct for large patient movement.

TRIAL REGISTRATION

This study is registered in ClinicalTrials.gov (NCT05637359).

Three-dimensional ultrasound image reconstruction based on 3D-ResNet in the musculoskeletal system using a 1D probe:ex vivoandin vivofeasibility studies

Objective. Three-dimensional (3D) ultrasound (US) is needed to provide sonographers with a more intuitive panoramic view of the complex anatomical structure, especially the musculoskeletal system. In actual scanning, sonographers may perform fast scanning using a one-dimensional (1D) array probe .at random angles to gain rapid feedback, which leads to a large US image interval and missing regions in the reconstructed volume.Approach.In this study, a 3D residual network (3D-ResNet) modified by a 3D global residual branch (3D-GRB) and two 3D local residual branches (3D-LRBs) was proposed to retain detail and reconstruct high-quality 3D US volumes with high efficiency using only sparse two-dimensional (2D) US images. The feasibility and performance of the proposed algorithm were evaluated onex vivoandin vivosets.Main results. High-quality 3D US volumes in the fingers, radial and ulnar bones, and metacarpophalangeal joints were obtained by the 3D-ResNet, respectively. Their axial, coronal, and sagittal slices exhibited rich texture and speckle details. Compared with kernel regression, voxel nearest-neighborhood, squared distance weighted methods, and a 3D convolution neural network in the ablation study, the mean peak-signal-to-noise ratio and mean structure similarity of the 3D-ResNet were up to 28.53 ± 1.29 dB and 0.98 ± 0.01, respectively, and the corresponding mean absolute error dropped to 0.023 ± 0.003 with a better resolution gain of 1.22 ± 0.19 and shorter reconstruction time.Significance.These results illustrate that the proposed algorithm can rapidly reconstruct high-quality 3D US volumes in the musculoskeletal system in cases of a large amount of data loss. This suggests that the proposed algorithm has the potential to provide rapid feedback and precise analysis of stereoscopic details in complex and meticulous musculoskeletal system scanning with a less limited scanning speed and pose variations for the 1D array probe.