International Journal of Computer Assisted Radiology and Surgery最新文献

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.

Purpose: Teamwork is fundamental to medical practice and relies on seamless collaboration among professionals with different tasks. Integrating robotic systems into this environment demands smooth interactions. Human action recognition, which infers a person's state without explicit input, can support this. We focus on handovers between medical staff, using the actions as implicit cues for robotic assistance to replace the giving party in such scenarios.

Methods: Skeletal information processed with differing machine learning algorithms makes it possible to derive actions out of sequential image data. Transferred to the medical context, we aim to infer actions defined for each situation in two datasets, a surgery in the operating room and a care intervention in the patient ward, depicting a handover between staff. We aim to abstract movement patterns across individuals through skeletal representation, leveraging the spatiotemporal information of medical handovers to enable future robotic systems to interact based on implicit cues.

Results: We report an F1 score of $0.736\pm 0.045$ for the OR dataset with ST-GCN and an F1 score of $0.941\pm 0.009$ for the Ward dataset with the SkateFormer human action recognition. The defined actions showed distinction in the confusion matrix with limitations on actions with a rapid transition like approach and reach as well as the handover actions in the OR.

Conclusion: The handover phases in two medical contexts, a minimally invasive surgery and a wound dressing on the patient station, are recognized with the proposed framework. This lays a first step for the integration of robotic assistance in the handover of medical material or instruments.

Purpose: Temporomandibular joint (TMJ) prosthesis implantation is an effective procedure for treating temporomandibular joint disorders. Traditionally, preoperative planning for TMJ surgery has been conducted manually by experienced surgeons, which often results in longer operating time and less reliable prosthesis placement. This study proposes an automated surgical planning algorithm for TMJ prosthesis implantation that calculates the optimal position for prosthesis placement.

Methods: Firstly, the STL model of the patient's craniomaxillofacial structure is populated with a point cloud, and the oriented bounding box is calculated. Next, the point cloud that meets specific constraints is filtered based on the characteristics of the anatomical structures. Subsequently, the contact surfaces of the prosthesis with the zygomatic arch and the mandible are generated according to the distribution of the point cloud. Finally, a system of linear equations is established based on the geometric constraints and solved to determine the precise placement position and orientation of the prosthesis.

Results: A group of 12 patients with 24 clinical cases was utilized for automatic planning to evaluate the efficiency and quality of the system. The results demonstrated that the average time required for the automatic planning algorithm was under 30 s for the entire procedure. Furthermore, the calculation of the bone-prosthesis contact area indicated that the quality of the automatic planning is comparable to that of plans created by professional surgeons.

Conclusions: Compared to the cumbersome manual planning methods currently used for TMJ prosthesis implantation, the approach proposed in this paper facilitates efficient and accurate automated preoperative planning. This method simulates the optimal placement of TMJ prosthesis to ensure long-term stability, demonstrating significant clinical potential for future applications in TMJ prosthesis implantation surgery.

Purpose: System calibration, including hand-robot and robot-world calibration, is an essential step that directly influences the location accuracy of surgical robots. Conventional calibration methods for orthognathic robot systems (ORSs) face significant challenges in handling irregularly shaped end tools, leading to manual intervention and compromised accuracy. Therefore, an automatic method has been proposed to improve the calibration efficiency and accuracy of ORSs.

Methods: The core innovation of the proposed method lies in enabling automation of both pre-intraoperative image registration and robotic hand-eye calibration, via aligning the 3D model of irregularly shaped tools to the preoperative CT space. It can effectively minimize errors caused by manual intervention. firstly, the equations of hand-eye-tool calibration were reconstructed using the preoperative graphic information to define tool endpoints (TEPs). Then, the transformation matrices were solved via a robust optimization method based on least squares. finally, the whole calibration process was completed automatically with robot path planning without human involvement.

Results: A group of simulated robot-assisted orthognathic surgery experiments was performed. The proposed method achieved a calibration error of 1.04 ± 0.54 mm, and the total execution error were reduced to 1.56 ± 0.61 mm.

Conclusion: The experimental results proved that the proposed calibration method could not only automate the calibration process, but also effectively improve the accuracy and stability of the system. It is expected to pave the way for more autonomous and efficient surgical procedures. Also, there are some limitations need to be overcome, including dependency on marker-based tracking and small sample size. Future work will integrate markerless tracking and machine learning for further optimization.

Purpose: Prostate cancer is a prevalent malignant tumor in men, and accurate diagnosis and personalized treatment rely on multimodal imaging, such as MRI and TRUS. However, differences in imaging mechanisms and prostate deformation due to ultrasound probe compression pose significant challenges for high-quality registration between the two modalities.

Methods: In this study, we propose a label-aware weakly supervised diffusion model for MRI-TRUS multimodal image registration. First, we align label centroid positions by maximizing the Dice coefficient to correct initial biases. Second, we combine label supervision with a diffusion model to generate high-quality deformation fields. Finally, we incorporate a feature-guided module to better preserve edge structures and improve registration smoothness.

Results: Experiments conducted on the µ-RegPro dataset demonstrate that our method outperforms current state-of-the-art (SOTA) approaches across multiple evaluation metrics. Specifically, it achieves a Dice coefficient of 0.880 and reduces the target registration error (TRE) to 0.940, significantly surpassing unsupervised methods such as VoxelMorph, FSDiffReg, and supervised methods like LocalNet and AutoFuse. The results show that preliminary label centroid alignment effectively enhances the performance of the diffusion-based deformation registration model, reducing the TRE from 3.084 to 0.940. The ablation study demonstrates that the feature-guided diffusion module effectively suppresses deformation field folding, while the label-aware module enhances label alignment. When combined, the proposed framework achieves a favorable balance, substantially improving registration accuracy (Dice = 0.880, TRE = 0.940) with reduced folding (|J|_≤0 = 0.134). This method exhibits strong robustness and generalizability in handling large deformations in target regions while preserving details in nontarget regions.

Conclusion: The proposed label-aware weakly supervised diffusion model enables accurate and efficient MRI-TRUS multimodal image registration, offering strong potential for clinical applications such as prostate cancer diagnosis, targeted biopsy, and image-guided navigation.

Purpose: Virtual reality (VR) has attracted attention in healthcare for many promising applications including pre-surgical planning. Currently, there exists a critical gap in comprehension of the impact of VR on physicians' thinking. Self-reported data from surveys and metrics based on confidence and task completion may not yield sufficiently detailed understanding of the complex decision making and cognitive load experienced by surgeons during VR-based pre-surgical planning.

Methods: Our research aims to address the gap in understanding the impact of VR on physicians' mental models through a novel methodology of self-directed think-aloud protocols, offering deeper perspectives into physicians' thought processes within the virtual 3D environment. We performed qualitative analysis of recorded verbalizations and actions in VR in addition to quantitative measures from the NASA task load index (NASA-TLX). Analysis was conducted to identify thematic sequences in VR which influenced clinical decision making when reviewing patient anatomy.

Results: We find a significant increase in reported physician confidence in understanding of the patient anatomy from before VR to after (p = 0.012) and identified several common patterns of 3D exploration of the anatomy in VR. Physicians also reported low cognitive stress on the NASA-TLX.

Conclusion: Our findings indicate VR has value beyond simulating surgery, helping physicians to confirm findings from conventional medical imaging, visualize approaches with detail, and help make complex decisions while mentally preparing for surgery. These findings provide evidence that VR and related 3D visualization are helpful for pre-surgical planning of complex cases.