International Journal of Computer Assisted Radiology and Surgery最新文献

Purpose: Traditional surgical robot system relying on computed tomography (CT) navigation suffers from two drawbacks during abdominal organ puncture surgeries. Firstly, the puncture target is displaced under the influence of respiration, thereby reducing the puncture accuracy. Secondly, the puncture process lacks real-time visualization, which may potentially give rise to medical accidents. This paper presents a semi-automatic surgical robot system based on the fusion guidance of real-time ultrasound images and CT, along with the monitoring of the patient's respiratory state, to address these issues.

Method: This system utilizes a six-axis force sensor in contact with the human body, and a respiratory model can be constructed through data got from force sensor to monitor the patient's respiratory phase and recommend the optimal puncture phase. The issue of non-real-time puncture guidance is addressed through the real-time registration and fusion of ultrasound (US) images with preoperative CT images.

Results: Phantom experiments and animal experiments were carried out based on this design. The test results indicate that in these two experiments, the average fusion error between US and CT of the main tissues in the liver is within 3 mm. For puncture accuracy, in the phantom experiment, the average puncture error was 1.0 mm, with a minimum of 0 mm and a maximum of 2.1 mm. In the animal experiment, the average puncture error was 2.5 mm, ranging from a minimum of 1.6 mm to a maximum of 3.0 mm.

Conclusion: The results of two experiments show both the image fusion accuracy and puncture accuracy of this system are within 3mm, which can meet the requirement of 5 mm puncture accuracy in clinical practice. Approximately 70% of the operation are automatically accomplished by robot system, greatly reducing the reliance on the doctor's experience.

Purpose: The goal of our work is to develop a multi-view validation framework for evaluating LLM-generated knowledge graph (KG) triples. The proposed approach aims to address the lack of established validation procedure in the context of LLM-supported KG construction.

Methods: The proposed framework evaluates the LLM-generated triples across three dimensions: semantic plausibility, ontology-grounded type compatibility, and structural importance. We demonstrate the performance for GPT-4 generated concept-specific (e.g., for medications, diagnosis, procedures) triples in the context of chronic kidney disease (CKD).

Results: The proposed approach consistently achieves high-quality results across evaluated GPT-4 generated triples, strong semantic plausibility (semantic score mean: 0.79), excellent type compatibility (type score mean: 0.84), and high structural importance of entities within the CKD knowledge domain (ResourceRank mean: 0.94).

Conclusion: The validation framework offers a reliable and scalable method for evaluating quality and validity of LLM-generated triples across three views: semantic plausibility, type compatibility, and structural importance. The framework demonstrates robust performance in filtering high-quality triples and lays a strong foundation for fast and reliable medical KG construction and validation.

Purpose: The dynamic stress environment in the hip joint is thought to contribute to pain and osteoarthritis (OA) in people with Legg-Calvé-Perthes disease (LCPD) deformity, but is poorly understood, limiting clinical management options. The objective of this study was to develop and evaluate a patient-specific biomechanical "digital twin" model of LCPD to predict chondrolabral shear stress in dynamic and static loading scenarios.

Methods: We produced a digital twin model of both hips in a patient with unilateral LCPD deformity using anatomical magnetic resonance imaging (MRI) and the ArtiSynth modeling platform. We evaluated the model's sensitivity to changes in material properties and joint angles during a typical gait cycle, and verified its stress and femoral translation predictions against upright open MRI of the hip in high-flexion postures.

Results: The model's prediction of the highest chondrolabral shear stress during a gait cycle was 22-93% greater in the LCPD hip than in the unaffected hip. The model was sensitive to changes in material parameters and joint angles, but could accurately reproduce femoral translation and expected stress distribution in extreme static postures.

Conclusion: This study demonstrates the importance of both dynamic motion and morphology in the stress environment of highly aspherical hip joints. Although some challenges remain, digital twin models are a promising tool to study the long-term outcomes of LCPD, and could be applied in future to aid clinical management.

Purpose: Deep learning has been widely used in research on sparse-view computed tomography (CT) image reconstruction. While sufficient training data can lead to high accuracy, collecting medical images is often challenging due to legal or ethical concerns, making it necessary to develop methods that perform well with limited data. To address this issue, we explored the use of nonmedical images for pre-training. Therefore, in this study, we investigated whether fractal images could improve the quality of sparse-view CT images, even with a reduced number of medical images.

Methods: Fractal images generated by an iterated function system (IFS) were used for nonmedical images, and medical images were obtained from the CHAOS dataset. Sinograms were then generated using 36 projections in sparse-view and the images were reconstructed by filtered back-projection (FBP). FBPConvNet and WNet (first module: learning fractal images, second module: testing medical images, and third module: learning output) were used as networks. The effectiveness of pre-training was then investigated for each network. The quality of the reconstructed images was evaluated using two indices: structural similarity (SSIM) and peak signal-to-noise ratio (PSNR).

Results: The network parameters pre-trained with fractal images showed reduced artifacts compared to the network trained exclusively with medical images, resulting in improved SSIM. WNet outperformed FBPConvNet in terms of PSNR. Pre-training WNet with fractal images produced the best image quality, and the number of medical images required for main-training was reduced from 5000 to 1000 (80% reduction).

Conclusion: Using fractal images for network training can reduce the number of medical images required for artifact reduction in sparse-view CT. Therefore, fractal images can improve accuracy even with a limited amount of training data in deep learning.

Purpose: Telesurgery, increasingly valued for enabling remote procedures post-COVID, can be critically affected by communication delays-typically negligible in conventional robot-assisted surgery due to surgeon-patient co-location. While previous studies have assessed the impact of delays on surgical performance, their effects on the operator's cognitive state remain unclear. Therefore, this study assessed delay-induced changes in telesurgery operability based on intraparietal sulcus (IPS) activity.

Methods: A virtual-reality-based surgical assistance simulator was developed using the Unity game engine to replicate the da Vinci surgical robot and colorectal suturing environment. The simulator randomly introduced seven delay conditions to assess their impact on IPS activity during suturing. Eight right-handed participants, all of whom were non-medical students with no prior surgical experience, performed suturing while their IPS activity was measured using functional near-infrared spectroscopy. The left- and right-sided IPS activities were measured separately, and the task completion time and suturing error rate were also recorded for comparison.

Results: Significance was assessed using the nonparametric Jonckheere-Terpstra test. Left- and right-sided IPS activities decreased significantly for 150-300 and 0-300 ms delays, respectively. The task completion time increased significantly for 0-300 ms delays, while the suturing error rate increased significantly for 0-100 ms delays.

Conclusion: These findings confirm that IPS activity can be used to quantify delay-induced operability changes. For delays beyond 150 ms, significant IPS changes indicated that operators perceived degraded control. However, for delays of or shorter than 150 ms, the operators' precision unconsciously declined, indicating that greater caution is required in surgical tasks.

Purpose: The principal objective of this study was to develop and evaluate a deep learning model for segmenting the common iliac vein (CIV) from intraoperative endoscopic videos during oblique lateral interbody fusion for L5/S1 (OLIF51), a minimally invasive surgical procedure for degenerative lumbosacral spine diseases. The study aimed to address the challenge of intraoperative differentiation of the CIV from surrounding tissues to minimize the risk of vascular damage during the surgery.

Methods: We employed two convolutional neural network (CNN) architectures: U-Net and U-Net++ with a ResNet18 backbone, for semantic segmentation. Gamma correction was applied during image preprocessing to improve luminance contrast between the CIV and adjacent tissues. We used a dataset of 614 endoscopic images from OLIF51 surgeries for model training, validation, and testing.

Results: The U-Net++/ResNet18 model outperformed, achieving a Dice score of 0.70, indicating superior ability in delineating the position and shape of the CIV compared to the U-Net/ResNet18 model, which achieved a Dice score of 0.59. Gamma correction increased the differentiation between the CIV and the artery, improving the Dice score from 0.44 to 0.70.

Conclusion: The findings demonstrate that deep learning models, especially the U-Net++ with ResNet18 enhanced by gamma correction preprocessing, can effectively segment the CIV in intraoperative videos. This approach has the potential to significantly improve intraoperative assistance and reduce the risk of vascular injury during OLIF51 procedures, despite the need for further research and refinement of the model for clinical application.

Purpose: While related studies have explored robotic forceps adaptations for narrow surgical workspaces, most have focused on horizontal needle driving, with limited research on optimizing robotic forceps configurations for vertical needle driving in pediatric choledochojejunostomy. Moreover, the impact of inter-joint distance adjustments on motion volume and obstructed value for vertical needle driving remains unclear, necessitating further investigation. We aimed to evaluate the effect of inter-joint distances in robotic forceps on a needle driving task that simulated vertical needle driving in a choledochojejunostomy for congenital biliary dilatation in children using a virtual reality simulator.

Method: We examined the relationship between variations in inter-joint distances, motion volume, and obstructed value. Four pediatric surgeons performed an experimental task, passing a needle through two rings using the right robotic forceps. Based on these results, the inter-joint distances were adjusted through an optimum design approach, which adopted the weighted $l_p$ norm method. We then compared the robotic forceps before and after optimization to evaluate changes in the motion volume and obstructed value.

Results: We observed a trade-off between motion volume and obstructed value in vertical needle driving. Adjusting inter-joint distances improved motion volume for Participants A, B, and C. However, obstructed value did not improve across all participants. This was attributed to the five-joint robotic forceps used in the study. The impact of inter-joint distances on the obstructed value may be limited when the number of joints remains constant.

Conclusion: We verified the impact of inter-joint distances on vertical needle driving, considering the narrow surgical workspace and the specific requirements of pediatric surgery. Our findings suggest that adjusting inter-joint distances can improve motion volume in vertical needle driving. However, further investigation is needed to assess its effects across different joint configurations.

Purpose: This paper introduces a Finite Element Method (FEM) to model the navigation of a surgical guidewire using a Transcatheter (TC) approach in the venous tree. The core objective is to characterize guidewire/vessel walls interactions, to predict reaction forces of the guidewire at the level of operator's grip zones and to correlate them with the model's kinematics.

Methods: The analysis are performed following a dynamic implicit FEM simulation using Abaqus® (SIMULIA™). The venous geometry, from the femoral vein to the right atrium entry, is reconstructed from segmented preoperative CT-Scan data. A commercial super-stiff guidewire is modeled using beam elements with realistic incremental stiffness. To simulate real-life surgical insertion, a velocity-driven boundary condition is applied onto the distal end of the guidewire. Biomimetic material and interaction properties, along with external environmental influences and loads, enable high-fidelity computation.

Results: Deformations remain minimal for venous walls tree while displacement of the guidewire are large. The maximum predicted reaction forces range from 0.5 to 1.4 N, depending on the geometric and kinematic insertion conditions of the guidewire. This magnitude is consistent with values reported in the literature for Minimally Invasive Surgeries. Results validate the applicability of the dynamic implicit FEM in predicting guidewire trajectory, interaction forces and reaction forces relevant to haptic feedback generation.

Conclusion: This work lays the foundation for an image-based, mimetic FEM adapted for guidewire navigation's simulation. The proposed model offers an enhanced understanding of the mechanical behaviour underlying endovascular navigation.

Purpose: The free fibular flap (FFF) is a standard procedure for the oral rehabilitation of segmental bone defects in the mandible caused by diseases such as malignant processes, osteonecrosis, or trauma. Digital guides and computer-assisted surgery (CAS) can improve precision and reduce the time and cost of surgery. This study evaluates how different designs of slot cutting guides, guiding heights, and cutting instruments affect surgical accuracy during mandibular reconstruction.

Methods: Ninety model operations in a three-part fibular transplant for mandibular reconstruction were conducted according to digital planning with three guide designs (standard, flange, and anatomical slots), three guide heights (1 mm, 2 mm, 3 mm), and two osteotomy instruments (piezoelectric instrument and saw). The cut segments were digitized using computed tomography and digitally evaluated to assess surgical accuracy.

Results: For vestibular and lingual segment length, the anatomical slot and the flange appear to be the most accurate, with the flange slightly under-contoured vestibularly and the standard slot over-contoured lingually and vestibularly (p < 0.001). There were only minor differences between the use of saw and piezoelectric instrument for lingual (p = 0.005) and vestibular (p < 0.001) length and proximal angle (p = 0.014). The U-distance after global reconstruction for flanges resulted in a median deviation of 0.0468 mm (IQR 8.15), but was not significant (p = 0.067).

Conclusion: Anatomical slots and flanges are recommended for osteotomy, with guiding effects relying on both haptic and visual control. Unilateral guided flanges also work accurately at high guidance heights. The results of piezoelectric instrument (PI) and saw showed comparable results in the assessment of individual segments and U-reconstruction in this in vitro study without soft tissue, so that the final decision is left to the expertise of the surgeons.

Purpose: Radiotherapy treats cancers through precise delivery of radiation to target volumes. Radiotherapy treatment plans, prescribing the delivery of therapeutic radiation, are presently created primarily from clinical experience and application of clinical protocols through trial-and-error rather than standardized quantitative methods. We developed an informatics infrastructure and decision support system to assist during treatment plan creation by providing access to applicable retrospective radiotherapy cases.

Methods: Radiotherapy treatment planning is based in part on tumor position and spatial relationships to surrounding structural anatomy. Our system data mines retrospective cases to identify cases and treatment plans with similar tumor position and surrounding anatomy (i.e., multi-organ-tumor constellation geometry) for clinicians to use as references during treatment plan creation. The system is based on a database of 390 DICOM RT dosimetry digital radiotherapy datasets with associated extracted quantitative features. Using data mining techniques, overall similarity between cases is calculated with features extracted from tumor volumes and organs at risk (OAR).

Results: We implemented our radiotherapy treatment planning decision support system with a full-stack infrastructure, including a database of best practice retrospective cases, an algorithmic backend for feature extraction and similarity calculation, and a frontend web application for clinical use. Clinicians can upload current planning cases to the web application whereupon their similarity to knowledge base cases is calculated, and the most similar are presented to clinicians for selection as references during current treatment plan creation.

Conclusions: This radiotherapy treatment planning decision support system, by providing access to geometrically similar retrospective best practice reference cases, presents a novel tool to improve treatment planning. Development of a full system infrastructure increases standardization, facilitates creation of high quality plans, and assists clinicians, particularly during the critical initial beam configuration stage.