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Purpose: Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice., Methods: Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports., Results: The best-performing model using both quantitative evaluation and our visual ranking framework achieved a mean Dice score of 0.959. Quantitative metrics are positively associated with expert visual rankings. However, the predictive value of generated contours was limited with a sensitivity of 0.750 and a specificity of 0.433 when tested against pathology reports., Conclusion: We present a clinical evaluation of deep learning models trained for intraoperative tumor segmentation in breast-conserving surgery. We demonstrate that automatic contouring is limited in predicting pathology margins despite achieving high performance on quantitative metrics., (© 2024. CARS.)

Purpose: Real-time assessment of surgical margins is critical for favorable outcomes in cancer patients. The iKnife is a mass spectrometry device that has demonstrated potential for margin detection in cancer surgery. Previous studies have shown that using deep learning on iKnife data can facilitate real-time tissue characterization. However, none of the existing literature on the iKnife facilitate the use of publicly available, state-of-the-art pretrained networks or datasets that have been used in computer vision and other domains., Methods: In a new framework we call ImSpect, we convert 1D iKnife data, captured during basal cell carcinoma (BCC) surgery, into 2D images in order to capitalize on state-of-the-art image classification networks. We also use self-supervision to leverage large amounts of unlabeled, intraoperative data to accommodate the data requirements of these networks., Results: Through extensive ablation studies, we show that we can surpass previous benchmarks of margin evaluation in BCC surgery using iKnife data, achieving an area under the receiver operating characteristic curve (AUC) of 81%. We also depict the attention maps of the developed DL models to evaluate the biological relevance of the embedding space CONCLUSIONS: We propose a new method for characterizing tissue at the surgical margins, using mass spectrometry data from cancer surgery., (© 2024. CARS.)

Purpose: Bone-targeted radiofrequency ablation (RFA) is widely used in the treatment of vertebral metastases. While radiation therapy utilizes established treatment planning systems (TPS) based on multimodal imaging to optimize treatment volumes, current RFA of vertebral metastases has been limited to qualitative image-based assessment of tumour location to direct probe selection and access. This study aimed to design, develop and evaluate a computational patient-specific RFA TPS for vertebral metastases., Methods: A TPS was developed on the open-source 3D slicer platform, including procedural setup, dose calculation (based on finite element modelling), and analysis/visualization modules. Usability testing was carried out by 7 clinicians involved in the treatment of vertebral metastases on retrospective clinical imaging data using a simplified dose calculation engine. In vivo evaluation was performed in a preclinical porcine model (n = 6 vertebrae)., Results: Dose analysis was successfully performed, with generation and display of thermal dose volumes, thermal damage, dose volume histograms and isodose contours. Usability testing showed an overall positive response to the TPS as beneficial to safe and effective RFA. The in vivo porcine study showed good agreement between the manually segmented thermally damaged volumes vs. the damage volumes identified from the TPS (Dice Similarity Coefficient = 0.71 ± 0.03, Hausdorff distance = 1.2 ± 0.1 mm)., Conclusion: A TPS specifically dedicated to RFA in the bony spine could help account for tissue heterogeneities in both thermal and electrical properties. A TPS would enable visualization of damage volumes in 2D and 3D, assisting clinicians in decisions about potential safety and effectiveness prior to performing RFA in the metastatic spine., (© 2023. CARS.)

Purpose: Up to date, there has been a lack of software infrastructure to connect 3D Slicer to any augmented reality (AR) device. This work describes a novel connection approach using Microsoft HoloLens 2 and OpenIGTLink, with a demonstration in pedicle screw placement planning., Methods: We developed an AR application in Unity that is wirelessly rendered onto Microsoft HoloLens 2 using Holographic Remoting. Simultaneously, Unity connects to 3D Slicer using the OpenIGTLink communication protocol. Geometrical transform and image messages are transferred between both platforms in real time. Through the AR glasses, a user visualizes a patient's computed tomography overlaid onto virtual 3D models showing anatomical structures. We technically evaluated the system by measuring message transference latency between the platforms. Its functionality was assessed in pedicle screw placement planning. Six volunteers planned pedicle screws' position and orientation with the AR system and on a 2D desktop planner. We compared the placement accuracy of each screw with both methods. Finally, we administered a questionnaire to all participants to assess their experience with the AR system., Results: The latency in message exchange is sufficiently low to enable real-time communication between the platforms. The AR method was non-inferior to the 2D desktop planner, with a mean error of 2.1 ± 1.4 mm. Moreover, 98% of the screw placements performed with the AR system were successful, according to the Gertzbein-Robbins scale. The average questionnaire outcomes were 4.5/5., Conclusions: Real-time communication between Microsoft HoloLens 2 and 3D Slicer is feasible and supports accurate planning for pedicle screw placement., (© 2023. The Author(s).)

Purpose: Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technology for clinical margin detection. Deployment of REIMS depends on construction of reliable deep learning models that can categorize tissue according to its metabolomic signature. Challenges associated with developing these models include the presence of noise during data acquisition and the variance in tissue signatures between patients. In this study, we propose integration of uncertainty estimation in deep models to factor predictive confidence into margin detection in cancer surgery., Methods: iKnife is used to collect 693 spectra of cancer and healthy samples acquired from 91 patients during basal cell carcinoma resection. A Bayesian neural network and two baseline models are trained on these data to perform classification as well as uncertainty estimation. The samples with high estimated uncertainty are then removed, and new models are trained using the clean data. The performance of proposed and baseline models, with different ratios of filtered data, is then compared., Results: The data filtering does not improve the performance of the baseline models as they cannot provide reliable estimations of uncertainty. In comparison, the proposed model demonstrates a statistically significant improvement in average balanced accuracy (75.2%), sensitivity (74.1%) and AUC (82.1%) after removing uncertain training samples. We also demonstrate that if highly uncertain samples are predicted and removed from the test data, sensitivity further improves to 88.2%., Conclusions: This is the first study that applies uncertainty estimation to inform model training and deployment for tissue recognition in cancer surgery. Uncertainty estimation is leveraged in two ways: by factoring a measure of input noise in training the models and by including predictive confidence in reporting the outputs. We empirically show that considering uncertainty for model development can help improve the overall accuracy of a margin detection system using REIMS., (© 2022. CARS.)

Purpose: Ultrasound-based navigation is a promising method in breast-conserving surgery, but tumor contouring often requires a radiologist at the time of surgery. Our goal is to develop a real-time automatic neural network-based tumor contouring process for intraoperative guidance. Segmentation accuracy is evaluated by both pixel-based metrics and expert visual rating., Methods: This retrospective study includes 7318 intraoperative ultrasound images acquired from 33 breast cancer patients, randomly split between 80:20 for training and testing. We implement a u-net architecture to label each pixel on ultrasound images as either tumor or healthy breast tissue. Quantitative metrics are calculated to evaluate the model's accuracy. Contour quality and usability are also assessed by fellowship-trained breast radiologists and surgical oncologists. Additionally, the viability of using our u-net model in an existing surgical navigation system is evaluated by measuring the segmentation frame rate., Results: The mean dice similarity coefficient of our u-net model is 0.78, with an area under the receiver-operating characteristics curve of 0.94, sensitivity of 0.95, and specificity of 0.67. Expert visual ratings are positive, with 93% of responses rating tumor contour quality at or above 7/10, and 75% of responses rating contour quality at or above 8/10. Real-time tumor segmentation achieved a frame rate of 16 frames-per-second, sufficient for clinical use., Conclusion: Neural networks trained with intraoperative ultrasound images provide consistent tumor segmentations that are well received by clinicians. These findings suggest that neural networks are a promising adjunct to alleviate radiologist workload as well as improving efficiency in breast-conserving surgery navigation systems., (© 2022. CARS.)

Purpose: One in five women who undergo breast conserving surgery will need a second revision surgery due to remaining tumor. The iKnife is a mass spectrometry modality that produces real-time margin information based on the metabolite signatures in surgical smoke. Using this modality and real-time tissue classification, surgeons could remove all cancerous tissue during the initial surgery, improving many facets of patient outcomes. An obstacle in developing a iKnife breast cancer recognition model is the destructive, time-consuming and sensitive nature of the data collection that limits the size of the datasets., Methods: We address these challenges by first, building a self-supervised learning model from limited, weakly labeled data. By doing so, the model can learn to contextualize the general features of iKnife data from a more accessible cancer type. Second, the trained model can then be applied to a cancer classification task on breast data. This domain adaptation allows for the transfer of learnt weights from models of one tissue type to another., Results: Our datasets contained 320 skin burns (129 tumor burns, 191 normal burns) from 51 patients and 144 breast tissue burns (41 tumor and 103 normal) from 11 patients. We investigate the effect of different hyper-parameters on the performance of the final classifier. The proposed two-step method performed statistically significantly better than a baseline model (p-value < 0.0001), by achieving an accuracy, sensitivity and specificity of 92%, 88% and 92%, respectively., Conclusion: This is the first application of domain transfer for iKnife REIMS data. We showed that having a limited number of breast data samples for training a classifier can be compensated by self-supervised learning and domain adaption on a set of unlabeled skin data. We plan to confirm this performance by collecting new breast samples and extending it to incorporate other cancer tissues.

Purpose: Basal cell carcinoma (BCC) is the most commonly diagnosed skin cancer and is treated by surgical resection. Incomplete tumor removal requires surgical revision, leading to significant healthcare costs and impaired cosmesis. We investigated the clinical feasibility of a surgical navigation system for BCC surgery, based on molecular tissue characterization using rapid evaporative ionization mass spectrometry (REIMS)., Methods: REIMS enables direct tissue characterization by analysis of cell-specific molecules present within surgical smoke, produced during electrocautery tissue resection. A tissue characterization model was built by acquiring REIMS spectra of BCC, healthy skin and fat from ex vivo skin cancer specimens. This model was used for tissue characterization during navigated skin cancer surgery. Navigation was enabled by optical tracking and real-time visualization of the cautery relative to a contoured resection volume. The surgical smoke was aspirated into a mass spectrometer and directly analyzed with REIMS. Classified BCC was annotated at the real-time position of the cautery. Feasibility of the navigation system, and tissue classification accuracy for ex vivo and intraoperative surgery were evaluated., Results: Fifty-four fresh excision specimens were used to build the ex vivo model of BCC, normal skin and fat, with 92% accuracy. While 3 surgeries were successfully navigated without breach of sterility, the intraoperative performance of the ex vivo model was low (< 50%). Hypotheses are: (1) the model was trained on heterogeneous mass spectra that did not originate from a single tissue type, (2) during surgery mixed tissue types were resected and thus presented to the model, and (3) the mass spectra were not validated by pathology., Conclusion: REIMS-navigated skin cancer surgery has the potential to detect and localize remaining tumor intraoperatively. Future work will be focused on improving our model by using a precise pencil cautery tip for burning localized tissue types, and having pathology-validated mass spectra.

Purpose: To evaluate a novel navigation system for breast brachytherapy, based on ultrasound (US)-guided catheter needle implantations followed by electromagnetic (EM) tracking of catheter paths., Methods: Breast phantoms were produced, containing US-visible tumors. Ultrasound was used to localize the tumor pose and volume within the phantom, followed by planning an optimal catheter pattern through the tumor using navigation software. An electromagnetic (EM)-tracked catheter needle was used to insert the catheters in the desired pattern. The inserted catheters were visualized on a post-implant CT, serving as ground truth. Electromagnetic (EM) tracking and reconstruction of the inserted catheter paths were performed by pulling a flexible EM guidewire through each catheter, performed in two clinical brachytherapy suites. The accuracy of EM catheter tracking was evaluated by calculating the Hausdorff distance between the EM-tracked and CT-based catheter paths. The accuracy and clinical feasibility of EM catheter tracking were also evaluated in three breast cancer patients, performed in a separate experiment room., Results: A total of 71 catheter needles were implanted into 12 phantoms using US guidance and EM navigation, in an average ± SD time of 8.1 ± 2.9 min. The accuracy of EM catheter tracking was dependent on the brachytherapy suite: 2.0 ± 1.2 mm in suite 1 and 0.6 ± 0.2 mm in suite 2. EM catheter tracking was successfully performed in three breast brachytherapy patients. Catheter tracking typically took less than 5 min and had an average accuracy of 1.7 ± 0.3 mm., Conclusion: Our preliminary results show a potential role for US guidance and EM needle navigation for implantation of catheters for breast brachytherapy. EM catheter tracking can accurately assess the implant geometry in breast brachytherapy patients. This methodology has the potential to evaluate catheter positions directly after the implantation and during the several fractions of the treatment.

Purpose: Basal cell carcinoma (BCC) is the most commonly diagnosed cancer and the number of diagnosis is growing worldwide due to increased exposure to solar radiation and the aging population. Reduction of positive margin rates when removing BCC leads to fewer revision surgeries and consequently lower health care costs, improved cosmetic outcomes and better patient care. In this study, we propose the first use of a perioperative mass spectrometry technology (iKnife) along with a deep learning framework for detection of BCC signatures from tissue burns., Methods: Resected surgical specimen were collected and inspected by a pathologist. With their guidance, data were collected by burning regions of the specimen labeled as BCC or normal, with the iKnife. Data included 190 scans of which 127 were normal and 63 were BCC. A data augmentation approach was proposed by modifying the location and intensity of the peaks of the original spectra, through noise addition in the time and frequency domains. A symmetric autoencoder was built by simultaneously optimizing the spectral reconstruction error and the classification accuracy. Using t-SNE, the latent space was visualized., Results: The autoencoder achieved an accuracy (standard deviation) of 96.62 (1.35%) when classifying BCC and normal scans, a statistically significant improvement over the baseline state-of-the-art approach used in the literature. The t-SNE plot of the latent space distinctly showed the separability between BCC and normal data, not visible with the original data. Augmented data resulted in significant improvements to the classification accuracy of the baseline model., Conclusion: We demonstrate the utility of a deep learning framework applied to mass spectrometry data for surgical margin detection. We apply the proposed framework to an application with light surgical overhead and high incidence, the removal of BCC. The learnt models can accurately separate BCC from normal tissue.

Background: Suturing is a fundamental skill in undergraduate medical education. It can be taught by faculty-led, peer tutor-led, and holography-augmented methods; however, the most educationally effective and cost-efficient method for proficiency-based teaching of suturing is yet to be determined., Methods: We conducted a randomized controlled trial comparing faculty-led, peer tutor-led, and holography-augmented proficiency-based suturing training in pre-clerkship medical students. Holography-augmented training provided holographic, voice-controlled instructional material. Technical skill was assessed using hand motion analysis every ten sutures and used to construct learning curves. Proficiency was defined by one standard deviation within average faculty surgeon performance. Intervention arms were compared using one-way ANOVA of the number of sutures placed, full-length sutures used, time to proficiency, and incremental costs incurred. Surveys were used to evaluate participant preferences., Results: Forty-four students were randomized to the faculty-led (n = 16), peer tutor-led (n = 14), and holography-augmented (n = 14) intervention arms. At proficiency, there were no differences between groups in the number of sutures placed, full-length sutures used, and time to achieve proficiency. The incremental costs of the holography-augmented method were greater than faculty-led and peer tutor-led instruction ($247.00 ± $12.05, p < 0.001) due to the high cost of the equipment. Faculty-led teaching was the most preferred method (78.0%), while holography-augmented was the least preferred (0%). 90.6% of students reported high confidence in performing simple interrupted sutures, which did not differ between intervention arms (faculty-led 100.0%, peer tutor-led 90.0%, holography-augmented 83.3%, p = 0.409). 93.8% of students felt the program should be offered in the future., Conclusion: Faculty-led and peer tutor-led instructional methods of proficiency-based suturing teaching were superior to holography-augmented method with respect to costs and participants' preferences despite being educationally equivalent.

Objective: Currently, there is a worldwide shift toward competency-based medical education. This necessitates the use of automated skills assessment methods during self-guided interventions training. Making assessment methods that are transparent and configurable will allow assessment to be interpreted into instructional feedback. The purpose of this work is to develop and validate skills assessment methods in ultrasound-guided interventions that are transparent and configurable., Methods: We implemented a method based upon decision trees and a method based upon fuzzy inference systems for technical skills assessment. Subsequently, we validated these methods for their ability to predict scores of operators on a 25-point global rating scale in ultrasound-guided needle insertions and their ability to provide useful feedback for training., Results: Decision tree and fuzzy rule-based assessment performed comparably to state-of-the-art assessment methods. They produced median errors (on a 25-point scale) of 1.7 and 1.8 for in-plane insertions and 1.5 and 3.0 for out-of-plane insertions, respectively. In addition, these methods provided feedback that was useful for trainee learning. Decision tree assessment produced feedback with median usefulness 7 out of 7; fuzzy rule-based assessment produced feedback with median usefulness 6 out of 7., Conclusion: Transparent and configurable assessment methods are comparable to the state of the art and, in addition, can provide useful feedback. This demonstrates their value in self-guided interventions training curricula.

Objective: The main objective of this study was to use the principles of cognitive load theory to design a curriculum that incorporates a progressive part practice approach to teaching ultrasound-guided (USG) internal jugular catheterization (IJC) to novices. A secondary objective was to compare the technical proficiency of residents trained using this curriculum with the technical proficiency of residents trained with the current local standard of a single simulation session., Methods: The experimental group included 16 residents who attended three 2-hour sessions of progressive part practice in a simulation lab. The control group included 46 residents who attended the current local standard of a single 2-hour simulation session just prior to their intensive care unit rotation. Technical proficiency was assessed using hand motion analysis and time to procedure completion., Results: After three sessions, median scores for right hand motion (RHM) (34.5; [27.0-49.0]), left hand motion (LHM) (35.5; [20.0-45.0]), and total time (TT) (117.0 s; [82.7-140.0]) in the experimental group were significantly better than the control group (p<0.001). Results for eight experimental group residents who were assessed for retention at a later date revealed median scores for RHM (45.0; [32.0-58.0]), LHM (33.5; [20.0-63.0]), and TT (150.0 s; [103.0-399.6]), which were significantly better than those of the control group (p=0.01, p<0.01, and p=0.02, respectively)., Conclusion: These results support multiple sessions of progressive part practice in a simulation lab as an effective competency-based approach to teaching USG IJC in preparation for the clinical setting.

Objective: Deep brain stimulation (DBS) is an increasingly common treatment for neurodegenerative diseases. Neurosurgeons must have thorough procedural, anatomical, and functional knowledge to plan electrode trajectories and thus ensure treatment efficacy and patient safety. Developing this knowledge requires extensive training. We propose a training approach with objective assessment of neurosurgeon proficiency in DBS planning., Methods: To assess proficiency, we propose analyzing both the viability of the planned trajectory and the manner in which the operator arrived at the trajectory. To improve understanding, we suggest a self-guided training course for DBS planning using real-time feedback. To validate the proposed measures of proficiency and training course, two experts and six novices followed the training course, and we monitored their proficiency measures throughout., Results: At baseline, experts planned higher quality trajectories and did so more efficiently. As novices progressed through the training course, their proficiency measures increased significantly, trending toward expert measures., Conclusion: We developed and validated measures which reliably discriminate proficiency levels. These measures are integrated into a training course, which quantitatively improves trainee performance. The proposed training course can be used to improve trainees' proficiency, and the quantitative measures allow trainees' progress to be monitored.

Objective: Manipulation of the colonoscope is a technical challenge for novice clinicians which is best learned in a simulated environment. It involves the coordination of scope tip steering with scope insertion, using a rotated image as reference. The purpose of this work is to develop and validate a system which objectively assesses colonoscopy technical skills proficiency in an arbitrary training environment, allowing novices to assess their technical proficiency prior to real patient encounters., Methods: We implemented a motion tracking setup to objectively analyze and assess the way operators perform colonoscopies, including an analysis of wrist and elbow joint motions. Subsequently, we conducted a validation study to verify whether our motion analysis could discriminate novice colonoscopists from experts. Participants navigated a wooden bench-top model using a standard colonoscope while their motions were tracked., Results: The developed motion tracking setup allowed colonoscopists of varying levels of proficiency to have their colonoscope manipulation assessed, and was able to be operated by a trained non-technical operator. Novice operators had significantly greater median times (101.5 vs. 31.5 s) and number of hand movements (62.0 vs. 21.5) than experts. Experts, however, spent a significantly greater proportion of time in extreme ranges of wrist and elbow joint motion than novices., Conclusion: We have developed and implemented a hand and joint motion analysis system that is able to discriminate novices from experts based on objective measures of motion. These metrics could, thus, serve as proxies for technical proficiency during training.

Purpose: Electromagnetic (EM) catheter tracking has recently been introduced in order to enable prompt and uncomplicated reconstruction of catheter paths in various clinical interventions. However, EM tracking is prone to measurement errors which can compromise the outcome of the procedure. Minimizing catheter tracking errors is therefore paramount to improve the path reconstruction accuracy., Methods: An extended Kalman filter (EKF) was employed to combine the nonlinear kinematic model of an EM sensor inside the catheter, with both its position and orientation measurements. The formulation of the kinematic model was based on the nonholonomic motion constraints of the EM sensor inside the catheter. Experimental verification was carried out in a clinical HDR suite. Ten catheters were inserted with mean curvatures varying from 0 to [Formula: see text] in a phantom. A miniaturized Ascension (Burlington, Vermont, USA) trakSTAR EM sensor (model 55) was threaded within each catheter at various speeds ranging from 7.4 to [Formula: see text]. The nonholonomic EKF was applied on the tracking data in order to statistically improve the EM tracking accuracy. A sample reconstruction error was defined at each point as the Euclidean distance between the estimated EM measurement and its corresponding ground truth. A path reconstruction accuracy was defined as the root mean square of the sample reconstruction errors, while the path reconstruction precision was defined as the standard deviation of these sample reconstruction errors. The impacts of sensor velocity and path curvature on the nonholonomic EKF method were determined. Finally, the nonholonomic EKF catheter path reconstructions were compared with the reconstructions provided by the manufacturer's filters under default settings, namely the AC wide notch and the DC adaptive filter., Results: With a path reconstruction accuracy of 1.9 mm, the nonholonomic EKF surpassed the performance of the manufacturer's filters (2.4 mm) by 21% and the raw EM measurements (3.5 mm) by 46%. Similarly, with a path reconstruction precision of 0.8 mm, the nonholonomic EKF surpassed the performance of the manufacturer's filters (1.0 mm) by 20% and the raw EM measurements (1.7 mm) by 53%. Path reconstruction accuracies did not follow an apparent trend when varying the path curvature and sensor velocity; instead, reconstruction accuracies were predominantly impacted by the position of the EM field transmitter ([Formula: see text])., Conclusion: The advanced nonholonomic EKF is effective in reducing EM measurement errors when reconstructing catheter paths, is robust to path curvature and sensor speed, and runs in real time. Our approach is promising for a plurality of clinical procedures requiring catheter reconstructions, such as cardiovascular interventions, pulmonary applications (Bender et al. in medical image computing and computer-assisted intervention-MICCAI 99. Springer, Berlin, pp 981-989, 1999), and brachytherapy.

Hypoplastic left heart syndrome (HLHS) is a single-ventricle congenital heart disease that is fatal if left unpalliated. In HLHS patients, the tricuspid valve is the only functioning atrioventricular valve, and its competence is therefore critical. This work demonstrates the first automated strategy for segmentation, modeling, and morphometry of the tricuspid valve in transthoracic 3D echocardiographic (3DE) images of pediatric patients with HLHS. After initial landmark placement, the automated segmentation step uses multi-atlas label fusion and the modeling approach uses deformable modeling with medial axis representation to produce patient-specific models of the tricuspid valve that can be comprehensively and quantitatively assessed. In a group of 16 pediatric patients, valve segmentation and modeling attains an accuracy (mean boundary displacement) of 0.8 ± 0.2 mm relative to manual tracing and shows consistency in annular and leaflet measurements. In the future, such image-based tools have the potential to improve understanding and evaluation of tricuspid valve morphology in HLHS and guide strategies for patient care.

Objective: To develop a simulation-based curriculum for residents to learn ultrasound-guided (USG) central venous catheter (CVC) insertion, and to study the volume and type of practice that leads to technical proficiency., Methods: Ten post-graduate year two residents from the Departments of Emergency Medicine and Anesthesiology completed four training sessions of two hours each, at two week intervals, where they engaged in a structured program of deliberate practice of the fundamental skills of USG CVC insertion on a simulator. Progress during training was monitored using regular hand motion analysis (HMA) and performance benchmarks were determined by HMA of local experts. Blinded assessment of video recordings was done at the end of training to assess technical competence using a global rating scale., Results: None of the residents met any of the expert benchmarks at baseline. Over the course of training, the HMA metrics of the residents revealed steady and significant improvement in technical proficiency. By the end of the fourth session six of 10 residents had faster procedure times than the mean expert benchmark, and nine of 10 residents had more efficient left and right hand motions than the mean expert benchmarks. Nine residents achieved mean GRS scores rating them competent to perform independently., Conclusion: We successfully developed a simulation-based curriculum for residents learning the skills of USG CVC insertion. Our results suggest that engaging residents in three to four distributed sessions of deliberate practice of the fundamental skills of USG CVC insertion leads to steady and marked improvement in technical proficiency with individuals approaching or exceeding expert level benchmarks.

Purpose: Volar percutaneous scaphoid fracture fixation is conventionally performed under fluoroscopy-based guidance, where surgeons need to mentally determine a trajectory for the insertion of the screw and its depth based on a series of 2D projection images. In addition to challenges associated with mapping 2D information to a 3D space, the process involves exposure to ionizing radiation. Three-dimensional ultrasound has been suggested as an alternative imaging tool for this procedure; however, it has not yet been integrated into clinical routine since ultrasound only provides a limited view of the scaphoid and its surrounding anatomy., Methods: We propose a registration of a statistical wrist shape + scale + pose model to a preoperative CT and intraoperative ultrasound to derive a patient-specific 3D model for guiding scaphoid fracture fixation. The registered model is then used to determine clinically important intervention parameters, including the screw length and the trajectory of screw insertion in the scaphoid bone., Results: Feasibility experiments are performed using 13 cadaver wrists. In 10 out of 13 cases, the trajectory of screw suggested by the registered model meets all clinically important intervention parameters. Overall, an average 94 % of maximum allowable screw length is obtained based on the measurements from gold standard CT. Also, we obtained an average 92 % successful volar accessibility, which indicates that the trajectory is not obstructed by the surrounding trapezium bone., Conclusions: These promising results indicate that determining clinically important screw insertion parameters for scaphoid fracture fixation is feasible using 3D ultrasound imaging. This suggests the potential of this technology in replacing fluoroscopic guidance for this procedure in future applications.

Purpose: Prostate needle biopsy is a commonly performed procedure since it is the most definitive form of cancer diagnosis. Magnetic resonance imaging (MRI) allows target-specific biopsies to be performed. However, needle placements are often inaccurate due to intra-operative prostate motion and the lack of motion compensation techniques. This paper detects and determines the extent of tissue displacement during an MRI-guided biopsy so that the needle insertion plan can be adjusted accordingly., Methods: A multi-slice-to-volume registration algorithm was developed to align the pre-operative planning image volume with three intra-operative orthogonal image slices of the prostate acquired immediately before needle insertion. The algorithm consists of an initial rigid transformation followed by a deformable step., Results: A total of 14 image sets from 10 patients were studied. Based on prostate contour alignment, the registrations were accurate to within 2 mm., Conclusion: This algorithm can be used to increase the needle targeting accuracy by alerting the clinician if the biopsy target has moved significantly prior to needle insertion. The proposed method demonstrated feasibility of intra-operative target localization and motion compensation for MRI-guided prostate biopsy.

Purpose: Electromagnetic (EM) tracking of instruments within a clinical setting is notorious for fluctuating measurement performance. Position location measurement uncertainty of an EM system was characterized in various environments, including control, clinical, cone beam computed tomography (CBCT), and CT scanner environments. Static and dynamic effects of CBCT and CT scanning on EM tracking were evaluated., Methods: Two guidance devices were designed to solely translate or rotate the sensor in a non-interfering fit to decouple pose-dependent tracking uncertainties. These devices were mounted on a base to allow consistent and repeatable tests when changing environments. Using this method, position and orientation measurement accuracies, precision, and 95 % confidence intervals were assessed., Results: The tracking performance varied significantly as a function of the environment-especially within the CBCT and CT scanners-and sensor pose. In fact, at a fixed sensor position in the clinical environment, the measurement error varied from 0.2 to 2.2 mm depending on sensor orientations. Improved accuracies were observed along the vertical axis of the field generator. Calibration of the measurements improved tracking performance in the CT environment by 50-85 %., Conclusion: EM tracking can provide effective assistance to surgeons or interventional radiologists during procedures performed in a clinical or CBCT environment. Applications in the CT scanner demand precalibration to provide acceptable performance.

Purpose: The performance of a fusion-based needle deflection estimation method was experimentally evaluated using prostate brachytherapy phantoms. The accuracy of the needle deflection estimation was determined. The robustness of the approach with variations in needle insertion speed and soft tissue biomechanical properties was investigated., Methods: A needle deflection estimation method was developed to determine the amount of needle bending during insertion into deformable tissue by combining a kinematic deflection model with measurements taken from two electromagnetic trackers placed at the tip and the base of the needle. Experimental verification of this method for use in prostate brachytherapy needle insertion procedures was performed. A total of 21 beveled tip, 18 ga, 200 mm needles were manually inserted at various speeds through a template and toward different targets distributed within 3 soft tissue mimicking polyvinyl chloride prostate phantoms of varying stiffness. The tracked positions of both the needle tip and base were recorded, and Kalman filters were applied to fuse the sensory information. The estimation results were validated using ground truth obtained from fluoroscopy images., Results: The manual insertion speed ranged from 8 to 34 mm/s, needle deflection ranged from 5 to 8 mm at an insertion depth of 76 mm, and the elastic modulus of the soft tissue ranged from 50 to 150 kPa. The accuracy and robustness of the estimation method were verified within these ranges. When compared to purely model-based estimation, we observed a reduction in needle tip position estimation error by [Formula: see text] % (mean [Formula: see text] SD) and the cumulative deflection error by [Formula: see text] %., Conclusions: Fusion of electromagnetic sensors demonstrated significant improvement in estimating needle deflection compared to model-based methods. The method has potential clinical applicability in the guidance of needle placement medical interventions, particularly prostate brachytherapy.

Purpose: Dynamic dosimetry is becoming the standard to evaluate the quality of radioactive implants during brachytherapy. For this, it is essential to obtain a 3D visualization of the implanted seeds and their relative position to the prostate. A method was developed to obtain a robust and precise segmentation of seeds in C-arm images, and this approach was tested using clinical datasets., Method: A region-based implicit active contour approach was used to delineate implanted seeds. Then, a template-based matching was employed to segment iodine implants whereas a K-means algorithm is implemented to resolve palladium seed clusters. To validate the method, 55 C-arm images from 10 patients were used for the segmentation of iodine sources, whereas 225 C-arm images from 16 patients were used for the palladium case., Results: Compared to manual ground truth segmentation of 6,002 iodine seeds and 15,354 palladium seeds, 98.7 % of iodine sources were automatically detected and declustered showing a false-positive rate of only 1.7 %. A total of 98.7 % of palladium sources were automatically detected and declustered with a false-positive rate of only 2.0 %., Conclusion: An automated segmentation method was developed that is able to perform the identification and annotation processes of seeds on par with a human expert. This method was shown to be robust and suitable for integration in the dynamic dosimetry workflow of prostate brachytherapy interventions.

Purpose: X-ray fluoroscopy guidance is frequently used in medical interventions. Image-guided interventional procedures that employ localization for registration require accurate information about the C-arm's rotation angle that provides the data externally in real time. Optical, electromagnetic, and image-based pose tracking systems have limited convenience and accuracy. An alternative method to recover C-arm orientation was developed using an accelerometer as tilt sensor., Methods: The fluoroscopic C-arm's orientation was estimated using a tri-axial acceleration sensor mounted on the X-ray detector as a tilt sensor. When the C-arm is stationary, the measured acceleration direction corresponds to the gravitational force direction. The accelerometer was calibrated with respect to the C-arm's rotation along its two axes, using a high-accuracy optical tracker as a reference. The scaling and offset error of the sensor was compensated using polynomial fitting. The system was evaluated on a GE OEC 9800 C-arm. Results obtained by accelerometer, built-in sensor, and image-based tracking were compared, using optical tracking as ground truth data., Results: The accelerometer-based orientation measurement error for primary angle rotation was -0.1 ± 0.0° and for secondary angle rotation it was 0.1 ± 0.0°. The built-in sensor orientation measurement error for primary angle rotation was -0.1 ± 0.2°, and for secondary angle rotation it was 0.1 ± 0.2°. The image-based orientation measurement error for primary angle rotation was -0.1 ± 1.3°, and for secondary angle rotation it was -1.3 ± 0.3°., Conclusion: The accelerometer provided better results than the built-in sensor and image-based tracking. The accelerometer sensor is small, inexpensive, covers the full rotation range of the C-arm, does not require line of sight, and can be easily installed to any mobile X-ray machine. Therefore, accelerometer tilt sensing is a very promising applicant for orientation angle tracking of C-arm fluoroscopes.

Purpose:    Freehand tracked ultrasound imaging is an inexpensive non-invasive technique used in several guided interventions. This technique requires spatial calibration between the tracker and the ultrasound image plane. Several calibration devices (a.k.a. phantoms) use N-wires that are convenient for automatic procedures since the segmentation of fiducials in the images and the localization of the middle wires in space are straightforward and can be performed in real time. The procedures reported in literature consider only the spatial position of the middle wire. We investigate if better results can be achieved if the information of all the wires is equally taken into account. We also evaluated the precision and accuracy of the implemented methods to allow comparison with other methods., Methods:    We consider a cost function based on the in-plane errors between the intersection of all the wires with the image plane and their respective segmented points in the image. This cost function is minimized iteratively starting from a seed computed with a closed-form solution based on the middle wires., Results:    Mean calibration precision achieved with the N-wire phantom was about 0.5 mm using a shallow probe, and mean accuracy was around 1.4 mm with all implemented methods. Precision was about 2.0 mm using a deep probe., Conclusions:    Precision and accuracy achieved with the N-wire phantom and a shallow probe are at least comparable to that obtained with other methods traditionally considered more precise. Calibration using N-wires can be done more consistently if the parameters are optimized with the proposed cost function.

Purpose:    Ultrasound is prevalent in image-guided therapy as a safe, inexpensive, and widely available imaging modality. However, extensive training in interpreting ultrasound images is essential for successful procedures. An open-source ultrasound image simulator was developed to facilitate the training of ultrasound-guided spinal intervention procedures, thereby eliminating the need for an ultrasound machine from the phantom-based training environment., Methods:    Anatomical structures and surgical tools are converted to surface meshes for data compression. Anatomical data are converted from segmented volumetric images, while the geometry of surgical tools is available as a surface mesh. The pose of the objects are either constants or coming from a pose-tracking device. Intersection points between the surface models and the ultrasound scan lines are determined with a binary space partitioning tree. The scan lines are divided into segments and filled with gray values determined by an intensity calculation accounting for material properties, reflection, and attenuation parameters defined in a configuration file. The scan lines are finally converted to a regular brightness-mode ultrasound image., Results:    The simulator was tested in a tracked ultrasound imaging system, with a mock transducer tracked with an Ascension trakSTAR electromagnetic tracker, on a spine phantom. A mesh model of the spine was created from CT data. The simulated ultrasound images were generated at a speed of 50 frames per second, and a resolution of [Formula: see text] pixels, with 256 scan lines per frame, on a PC with a 3.4 GHz processor. A human subject trial was conducted to compare the learning performance of novice trainees, with real and simulated ultrasound, in the localization of facet joints of a spine phantom. With 22 participants split into two equal groups, and each participant localizing 6 facet joints, there was no statistical difference in the performance of the two groups, indicating that simulated ultrasound could indeed replace the real ultrasound in phantom-based ultrasonography training for spinal interventions., Conclusions:    The ultrasound simulator was implemented and integrated into the open-source Public Library for Ultrasound (PLUS) toolkit.

Purpose: To assess retrospectively the clinical accuracy of an magnetic resonance imaging-guided robotic prostate biopsy system that has been used in the US National Cancer Institute for over 6 years., Methods: Series of 2D transverse volumetric MR image slices of the prostate both pre (high-resolution T2-weighted)- and post (low-resolution)- needle insertions were used to evaluate biopsy accuracy. A three-stage registration algorithm consisting of an initial two-step rigid registration followed by a B-spline deformable alignment was developed to capture prostate motion during biopsy. The target displacement (distance between planned and actual biopsy target), needle placement error (distance from planned biopsy target to needle trajectory), and biopsy error (distance from actual biopsy target to needle trajectory) were calculated as accuracy assessment., Results: A total of 90 biopsies from 24 patients were studied. The registrations were validated by checking prostate contour alignment using image overlay, and the results were accurate to within 2 mm. The mean target displacement, needle placement error, and clinical biopsy error were 5.2, 2.5, and 4.3 mm, respectively., Conclusion: The biopsy error reported suggests that quantitative imaging techniques for prostate registration and motion compensation may improve prostate biopsy targeting accuracy.

Purpose: Facet syndrome is a condition that may cause 15-45 % of chronic lower back pain. It is commonly diagnosed and treated using facet joint injections. This needle technique demands high accuracy, and ultrasound (US) is a potentially useful modality to guide the needle. US-guided injections, however, require physicians to interpret 2-D sonographic images while simultaneously manipulating an US probe and needle. Therefore, US-guidance for facet joint injections needs advanced training methodologies that will equip physicians with the requisite skills., Methods: We used Perk Tutor-an augmented reality training system for US-guided needle insertions-in a configuration for percutaneous procedures of the lumbar spine. In a pilot study of 26 pre-medical undergraduate students, we evaluated the efficacy of Perk Tutor training compared to traditional training., Results: The Perk Tutor Trained group, which had access to Perk Tutor during training, had a mean success rate of 61.5 %, while the Control group, which received traditional training, had a mean success rate of 38.5 % ([Formula: see text]). No significant differences in procedure times or needle path lengths were observed between the two groups., Conclusions: The results of this pilot study suggest that Perk Tutor provides an improved training environment for US-guided facet joint injections on a synthetic model.

Purpose: A clinical augmented reality guidance system was developed for MRI-guided musculoskeletal interventions Magnetic Resonance Image Overlay System (MR-IOS). The purpose of this study was to assess MRI compatibility, system accuracy, technical efficacy, and operator performance of the MR-IOS., Methods and Materials: The impact of the MR-IOS on the MR environment was assessed by measuring image quality with signal-to-noise ratio (SNR) and signal intensity uniformity with the system in various on/off states. The system accuracy was assessed with an in-room preclinical experiment by performing 62 needle insertions on a spine phantom by an expert operator measuring entry, depth, angle, and target errors. Technical efficacy and operator performance were tested in laboratory by running an experiment with 40 novice operators (20 using freehand technique versus 20 MR-IOS-guided) with each operator inserting 10 needles into a geometric phantom. Technical efficacy was measured by comparing the success rates of needle insertions between the two operator groups. Operator performance was assessed by comparing total procedure times, total needle path distance, presumed tissue damage, and speed of individual insertions between the two operator groups., Results: The MR-IOS maximally altered SNR by 2% with no perceptible change in image quality or uniformity. Accuracy assessment showed mean entry error of 1.6 ± 0.6 mm, depth error of 0.7 ± 0.5 mm, angle error of 1.5 ± 1.1°, and target error of 1.9 ± 0.8 mm. Technical efficacy showed a statistically significant difference (p = 0.031) between success rates (freehand 35.0% vs. MR-IOS 80.95%). Operator performance showed: mean total procedure time of 40.3 ± 4.4 (s) for freehand and 37.0 ± 3.7 (s) for MR-IOS (p = 0.584), needle path distances of 152.6 ± 15.0 mm for freehand and 116.9 ± 8.7 mm for MR-IOS (p = 0.074), presumed tissue damage of 7,417.2 ± 955.6 mm(2) for freehand and 6062.2 ± 678.5 mm(2) for MR-IOS (p = 0.347), and speed of insertion 5.9 ± 0.4 mm/s for freehand and 4.3 ± 0.3 mm/s for MR-IOS (p = 0.003)., Conclusion: The MR-IOS is compatible within a clinical MR imaging environment, accurate for needle placement, technically efficacious, and improves operator performance over the unassisted insertion technique. The MR-IOS was found to be suitable for further testing in a clinical setting.

Purpose: In prostate brachytherapy, intraoperative dosimetry would allow for evaluation of the implant quality while the patient is still in treatment position. Such a mechanism, however, requires 3-D visualization of the deposited seeds relative to the prostate. It follows that accurate and robust seed segmentation is of critical importance in achieving intraoperative dosimetry., Methods: Implanted iodine brachytherapy seeds are segmented via a region-based implicit active contour model. Overlapping seed groups are then resolved using a template-based declustering technique., Results: Ground truth seed coordinates were obtained through manual segmentation. A total of 57 clinical C-arm images from 10 patients were used to validate the proposed algorithm. This resulted in two failed images and a 96.0% automatic detection rate with a corresponding 2.2% false-positive rate in the remaining 55 images. The mean centroid error between the manual and automatic segmentations was 1.2 pixels., Conclusions: Robust and accurate iodine seed segmentation can be achieved through the proposed segmentation workflow.

Purpose: To evaluate the targeting accuracy of a small profile MRI-compatible pneumatic robot for needle placement that can angulate a needle insertion path into a large accessible target volume., Methods: We extended our MRI-compatible pneumatic robot for needle placement to utilize its four degrees-of-freedom (4-DOF) mechanism with two parallel triangular structures and support transperineal prostate biopsies in a closed-bore magnetic resonance imaging (MRI) scanner. The robot is designed to guide a needle toward a lesion so that a radiologist can manually insert it in the bore. The robot is integrated with navigation software that allows an operator to plan angulated needle insertion by selecting a target and an entry point. The targeting error was evaluated while the angle between the needle insertion path and the static magnetic field was between -5.7° and 5.7° horizontally and between -5.7° and 4.3° vertically in the MRI scanner after sterilizing and draping the device., Results: The robot positioned the needle for angulated insertion as specified on the navigation software with overall targeting error of 0.8 ± 0.5mm along the horizontal axis and 0.8 ± 0.8mm along the vertical axis. The two-dimensional root-mean-square targeting error on the axial slices as containing the targets was 1.4mm., Conclusions: Our preclinical evaluation demonstrated that the MRI-compatible pneumatic robot for needle placement with the capability to angulate the needle insertion path provides targeting accuracy feasible for clinical MRI-guided prostate interventions. The clinical feasibility has to be established in a clinical study.

Purpose: Magnetic Resonance Imaging (MRI) combined with robotic assistance has the potential to improve on clinical outcomes of biopsy and local treatment of prostate cancer., Methods: We report the workspace optimization and phantom evaluation of a five Degree of Freedom (DOF) parallel pneumatically actuated modular robot for MRI-guided prostate biopsy. To shorten procedure time and consequently increase patient comfort and system accuracy, a prototype of a MRI-compatible master-slave needle driver module using piezo motors was also added to the base robot., Results: Variable size workspace was achieved using appropriate link length, compared with the previous design. The 5-DOF targeting accuracy demonstrated an average error of 2.5 mm (STD = 1.37 mm) in a realistic phantom inside a 3T magnet with a bevel-tip 18G needle. The average position tracking error of the master-slave needle driver was always below 0.1 mm., Conclusion: Phantom experiments showed sufficient accuracy for manual prostate biopsy. Also, the implementation of teleoperated needle insertion was feasible and accurate. These two together suggest the feasibility of accurate fully actuated needle placement into prostate while keeping the clinician supervision over the task.

Purpose: Brachytherapy is an important mode of breast cancer treatment; however, improvements in both treatment planning and delivery are needed. In order to meet these specific needs, integration of pre-operative imaging, supplemented by computerized surgical planning and mathematical optimization were used to develop and test an intra-operative immobilization and catheter guidance system., Method: A custom template specific to each patient with optimally placed guide holes for catheter insertion was designed and fabricated. Creation of the template is based on a virtual reality reconstruction of the patient's anatomy from computed tomography imaging. The template fits on the patient's breast, immobilizing the soft tissue, and provides pre-planned catheter insertion holes for guidance to the tumor site. Agar-based phantom and target models were used for quantitative validation of the template by ascertaining the precision and accuracy of the templates., Results: Tests were performed on agar-based tissue models using computed tomography imaging for template planning and validation. Planned catheter tracks were compared to post-insertion image data and distance measurements from target location were used to create an error measure. Initial results yielded an average error of 4.5mm. Once the workflow and template design were improved, an average error of 2.6mm was observed, bringing the error close to a clinically acceptable range., Conclusion: Use of a patient-specific template for breast brachytherapy is feasible and may improve the procedure accuracy and outcome.

Purpose: Ultrasound section-thickness is the out-of-plane beamwidth causing major roles in creating image artifacts normally appearing around the anechoic areas. These artifacts can introduce errors in localizing the needle tips during any ultrasound-guided procedure. To study how section-thickness and imaging parameters can affect observing and localizing needle tips, we have conducted a typical calibration setup experiment., Method: Multiple needles were inserted orthogonal to the axial image plane, at various distances from the transducer. The experiment was conducted on a brachytherapy stepper for a curvilinear transrectal-ultrasound probe., Result: Experiments demonstrated that the imaging parameters have direct impacts on observing needle tips at different axial locations. They suggest specific settings to minimize the imaging artifacts., Conclusion: The ultrasound section-thickness and side lobes could result in misjudgment of needle insertion depth in an ultrasound-guided procedure. A beam profile could assist in considering the likelihood of position errors, when the effects of side lobes are minimized.

We aim to compute radioactive stranded-implant displacement during and after prostate brachytherapy. We present the methods used to identify corresponding seeds in planned, intra-operative and postimplant patient data that enable us to compute seed displacements. A minimum cost network flow algorithm is used, on 8 patients, for needle track detection to group seeds into needles that can be matched between datasets. An iterative best line detection algorithm is used both to help with needle detection and to register the different datasets. Our results show that there was an average seed misplacement of 5.08 +/- 2.35 mm during the procedure, which then moved another 3.10 +/- 1.91 mm by the time the quality assurance CT was taken. Several directional trends in different regions of the prostate were noted and commented on.

Ultrasound-Fluoroscopy fusion is a key step toward intraoperative dosimetry for prostate brachytherapy. We propose a method for intensity-based registration of fluoroscopy to ultrasound that obviates the need for seed segmentation required for seed-based registration. We employ image thresholding and morphological and Gaussian filtering to enhance the image intensity distribution of ultrasound volume. Finally, we find the registration parameters by maximizing a point-to-volume similarity metric. We conducted an experiment on a ground truth phantom and achieved registration error of 0.7 +/- 0.2 mm. Our clinical results on 5 patient data sets show excellent visual agreement between the registered seeds and the ultrasound volume with a seed-to-seed registration error of 1.8 +/- 0.9mm. With low registration error, high computational speed and no need for manual seed segmentation, our method is promising for clinical application.

Intraoperative dosimetry during prostate brachytherapy is a long standing clinical problem. We propose a novel framework to address this problem by reliable detection of a subset of seeds from 3D transrectal ultrasound and registration to fluoroscopy. Seed detection in ultrasound is achieved through template matching in the RF ultrasound domain followed by thresholding and spatial filtering based on the fixed distance between stranded seeds. This subset of seeds is registered to the complete reconstruction of the implant in C-arm fluoroscopy. We report results, validated with a leave-one-needle-out approach, both in a phantom (average post-registration seed distance of 2.5 mm) and in three clinical patient datasets (average error: 3.9 mm over 113 seeds).

Purpose: Fluoroscopic C-arms are being incorporated in computer-assisted interventions in increasing number. For these applications to work, the relative poses of imaging must be known. To find the pose, tracking methods such as optical cameras, electromagnetic trackers, and radiographic fiducials have been used-all hampered by significant shortcomings., Methods: We propose to recover the rotational pose of the C-arm using the angle-sensing ability of accelerometers, by exploiting the capability of the accelerometer to measure tilt angles. By affixing the accelerometer to a C-arm, the accelerometer tracks the C-arm pose during rotations of the C-arm. To demonstrate this concept, a C-arm analogue was constructed with a webcam device affixed to the C-arm model to mimic X-ray imaging. Then, measuring the offset between the accelerometer angle readings to the webcam pose angle, an angle correction equation (ACE) was created to properly tracking the C-arm rotational pose., Experiments and Results: Several tests were performed on the webcam C-arm model using the ACEs to tracking the primary and secondary angle rotations of the model. We evaluated the capability of linear and polynomial ACEs to tracking the webcam C-arm pose angle for different rotational scenarios. The test results showed that the accelerometer could track the pose of the webcam C-arm model with an accuracy of less than 1.0 degree., Conclusion: The accelerometer was successful in sensing the C-arm's rotation with clinically adequate accuracy in the C-arm webcam model.

During prostate brachytherapy, C-arm flouroscopy images are used for a qualitative assessment of the procedure. Three dimensional reconstruction of the implanted seeds can be used for intraoperative dosimetry and quantitative assessment. Accurate C-arm pose estimation is necessary for 3D localization of the seeds. We propose to measure the C-arm rotation angles and computationally compensate the inevitable C-arm translational motion to estimate the pose. We compensate the oscillation, sagging and wheel motion of the C-arm using a three-level optimization algorithm, without which the reconstruction can fail. We validated our approach on simulated and 10 data sets from 5 patients and gained on average 99.1% success rate, 0.33 mm projection error and computation time of less than one minute per patient, which are clinically excellent results.