An improved camera model for oblique-viewing laparoscopes: high reprojection accuracy independent of telescope rotation.

Objective. Oblique-viewing laparoscopes are popular in laparoscopic surgeries where the target anatomy is located in narrow areas. Their viewing direction can be shifted by telescope rotation without changing the laparoscope pose. This rotation also changes laparoscope camera parameters that are estimated by camera calibration to be able to reproject an anatomical model onto the laparoscopic view, creating augmented reality (AR). The aim of this study was to develop a camera model that accounts for these changes, achieving high reprojection accuracy for any telescope rotation.Approach. Camera parameters were acquired by calibrations encompassing a wide telescope rotation range. For those parameters showing periodic changes upon rotation, interpolation models were created and used to establish an updatable camera model. With this model, corner points of a tracked checkerboard were reprojected onto the checkerboard laparoscopic images, at random rotation angles. Root-mean-square reprojection errors (RMSEs) were calculated between the reprojected and imaged corner points.Main results. Reprojection RMSEs were low and approximately independent on telescope rotation angle, over a wide rotation range of 320°. The mean reprojection RMSE was 2.8±0.7 pixels for a conventional laparoscope and 3.6±0.7 pixels for a chip-on-the-tip (COTT) laparoscope, corresponding to 0.3±0.1 mm and 0.4±0.1 mm in world coordinates respectively. Worst-case reprojection errors were about 9 pixels (0.8 mm) for both laparoscopes.Significance. The camera model developed in this study improves on existing models for oblique-viewing laparoscopes because it provides high reprojection accuracy independent of the telescope rotation angle and is applicable for conventional and chip-on-a-tip oblique-viewing laparoscopes. The work presented here is an important step towards creating accurate AR in image-guided interventions where oblique-viewing laparoscopes are used while simultaneously providing the surgeon the flexibility to rotate the telescope to any desired rotation angle.Acronyms. CC: camera coordinates; CCToolbox: camera calibration toolbox; COTT: chip-on-the-tip; CS: camera sensor; DD: decentering distortion; FL: focal length; OTS: optical tracking system; PP: principal point; RD: radial distortion; SI: supplementary information;tHE:hand-eye translation component.

A simple single-cycle interactive strategy to improve deep learning-based segmentation of organs-at-risk in head-and-neck cancer.

Background and purpose: Interactive segmentation seeks to incorporate human knowledge into segmentation models and thereby reducing the total amount of editing of auto-segmentations. By performing only interactions which provide new information, segmentation performance may increase cost-effectively. The aim of this study was to develop, evaluate and test feasibility of a deep learning-based single-cycle interactive segmentation model with the input being computer tomography (CT) and a small amount of information rich contours. Methods and Materials: A single-cycle interactive segmentation model, which took CT and the most cranial and caudal contour slices for each of 16 organs-at-risk for head-and-neck cancer as input, was developed. A CT-only model served as control. The models were evaluated with Dice similarity coefficient, Hausdorff Distance 95th percentile and average symmetric surface distance. A subset of 8 organs-at-risk were selected for a feasibility test. In this, a designated radiation oncologist used both single-cycle interactive segmentation and atlas-based auto-contouring for three cases. Contouring time and added path length were recorded. Results: The medians of Dice coefficients increased with single-cycle interactive segmentation in the range of 0.004 (Brain)-0.90 (EyeBack_merged) when compared to CT-only. In the feasibility test, contouring time and added path length were reduced for all three cases as compared to editing atlas-based auto-segmentations. Conclusion: Single-cycle interactive segmentation improved segmentation metrics when compared to the CT-only model and was clinically feasible from a technical and usability point of view. The study suggests that it may be cost-effective to add a small amount of contouring input to deep learning-based segmentation models.

Towards interactive deep-learning for tumour segmentation in head and neck cancer radiotherapy.

Background and purpose: With deep-learning, gross tumour volume (GTV) auto-segmentation has substantially been improved, but still substantial manual corrections are needed. With interactive deep-learning (iDL), manual corrections can be used to update a deep-learning tool while delineating, minimising the input to achieve acceptable segmentations. We present an iDL tool for GTV segmentation that took annotated slices as input and simulated its performance on a head and neck cancer (HNC) dataset. Materials and methods: Multimodal image data of 204 HNC patients with clinical tumour and lymph node GTV delineations were used. A baseline convolutional neural network (CNN) was trained (n = 107 training, n = 22 validation) and tested (n = 24). Subsequently, user input was simulated on initial test set by replacing one or more of predicted slices with ground truth delineation, followed by re-training the CNN. The objective was to optimise re-training parameters and simulate slice selection scenarios while limiting annotations to maximally-five slices. The remaining 51 patients were used as an independent test set, where Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff distance (HD95%) were assessed at baseline and after every update. Results: Median segmentation accuracy at baseline was DSC = 0.65, MSD = 4.3 mm, HD95% = 17.5 mm. Updating CNN using three slices equally sampled from the craniocaudal axis of the GTV in the first round, followed by two rounds of annotating one extra slice, gave the best results. The accuracy improved to DSC = 0.82, MSD = 1.6 mm, HD95% = 4.8 mm. Every CNN update took 30 s. Conclusions: The presented iDL tool achieved substantial segmentation improvement with only five annotated slices.

Assessment of Correlation between Dual-Energy Ct (De-Ct)-Derived Iodine Concentration and Local Flourodeoxyglucose (Fdg) Uptake in Patients with Primary Non-Small-Cell Lung Cancer.

(1) The current literature contains several studies investigating the correlation between dual-energy-derived iodine concentration (IC) and positron emission tomography (PET)-derived Flourodeoxyglucose (18F-FDG) uptake in patients with non-small-cell lung cancer (NSCLC). In previously published studies, either the entire tumor volume or a region of interest containing the maximum IC or 18F-FDG was assessed. However, the results have been inconsistent. The objective of this study was to correlate IC with FDG both within the entire volume and regional sub-volumes of primary tumors in patients with NSCLC. (2) In this retrospective study, a total of 22 patients with NSCLC who underwent both dual-energy CT (DE-CT) and 18F-FDG PET/CT were included. A region of interest (ROI) encircling the entire primary tumor was delineated, and a rigid registration of the DE-CT, iodine maps and FDG images was performed for the ROI. The correlation between tumor measurements and area-specific measurements of ICpeak and the peak standardized uptake value (SUVpeak) was found. Finally, a correlation between tumor volume and the distance between SUVpeak and ICpeak centroids was found. (3) For the entire tumor, moderate-to-strong correlations were found between SUVmax and ICmax (R = 0.62, p = 0.002), and metabolic tumor volume vs. total iodine content (R = 0.91, p < 0.001), respectively. For local tumor sub-volumes, a negative correlation was found between ICpeak and SUVpeak (R = −0.58, p = 0.0046). Furthermore, a strong correlation was found between the tumor volume and the distance in millimeters between SUVpeak and ICpeak centroids (R = 0.81, p < 0.0001). (4) In patients with NSCLC, high FDG uptakes and high DE-CT-derived iodine concentrations correlated on a whole-tumor level, but the peak areas were positioned at different locations within the tumor. 18F-FDG PET/CT and DE-CT provide complementary information and might represent different underlying patho-physiologies.

Real-Time Wireless Tumor Tracking in Navigated Liver Resections: An Ex Vivo Feasibility Study.

BACKGROUND: Surgical navigation systems generally require intraoperative steps, such as intraoperative imaging and registration, to link the system to the patient anatomy. Because this hampers surgical workflow, we developed a plug-and-play wireless navigation system that does not require any intraoperative steps. In this ex vivo study on human hepatectomy specimens, the feasibility was assessed of using this navigation system to accurately resect a planned volume with small margins to the lesion. METHODS: For ten hepatectomy specimens, a planning CT was acquired in which a virtual spherical lesion with 5 mm margin was delineated, inside the healthy parenchyma. Using two implanted trackers, the real-time position of this planned resection volume was visualized on a screen, relative to the used tracked pointer. Experienced liver surgeons were asked to accurately resect the nonpalpable planned volume, fully relying on the navigation screen. Resected and planned volumes were compared using CT. RESULTS: The surgeons resected the planned volume while cutting along its border with a mean accuracy of - 0.1 ± 2.4 mm and resected 98 ± 12% of the planned volume. Nine out of ten resections were radical and one case showed a cut of 0.8 mm into the lesion. The sessions took approximately 10 min each, and no considerable technical issues were encountered. CONCLUSIONS: This ex vivo liver study showed that it is feasible to accurately resect virtual hepatic lesions with small planned margins using our novel navigation system, which is promising for clinical applications where nonpalpable hepatic metastases have to be resected with small resection margins.

Diagnostic accuracy of radiography, digital breast tomosynthesis, micro-CT and ultrasound for margin assessment during breast surgery: A systematic review and meta-analysis.

RATIONALE AND OBJECTIVES: Achieving adequate resection margins in breast conserving surgery is challenging and often demands more than one surgical procedure. We evaluated pooled diagnostic sensitivity, and specificity of radiological methods for intraoperative margin assessment and their impact on repeat surgery rate. MATERIALS AND METHODS: We included studies using radiography, digital breast tomosynthesis (DBT), micro-CT, and ultrasound for intraoperative margin assessment with the histological assessment as the reference method. A systematic search was performed in PubMed, Embase, Cochrane Library, Scopus, and Web of Science. Two investigators screened the studies for eligibility criteria and extracted data of the included studies independently. The quality assessment on diagnostic accuracy studies (QUADAS)-2 tool was used. A bivariate random effect model was used to obtained pooled sensitivity and specificity of the index tests in the meta-analysis. RESULTS: The systematic search resulted in screening of 798 unique records. Twenty-two articles with 29 radiological imaging methods were selected for meta-analysis. Pooled sensitivity and specificity and area under the curve were calculated for each of the 4 subgroups in the meta-analysis respectively: Radiography; 52%, 77%, 60%, DBT; 67%, 76%, 76%, micro-CT; 68%, 69%, 72%, and ultrasound; 72%, 78%, 80%. The repeat surgery rate was poorly reported in the included studies. CONCLUSION: Ultrasound showed the highest and radiography the lowest diagnostic performance for intraoperative margin assessment. However, the heterogeneity between studies was high and the subgroups small. The radiological methods for margin assessment need further improvement to provide reliable guidance in the clinical workflow and to prevent repeat surgeries.

Correlation of in-vivo imaging with histopathology: A review.

Despite tremendous advancements in in vivo imaging modalities, there remains substantial uncertainty with respect to tumor delineation on in these images. Histopathology remains the gold standard for determining the extent of malignancy, with in vivo imaging to histopathologic correlation enabling spatial comparisons. In this review, the steps necessary for successful imaging to histopathologic correlation are described, including in vivo imaging, resection, fixation, specimen sectioning (sectioning technique, securing technique, orientation matching, slice matching), microtome sectioning and staining, correlation (including image registration) and performance evaluation. The techniques used for each of these steps are also discussed. Hundreds of publications from the past 20 years were surveyed, and 62 selected for detailed analysis. For these 62 publications, each stage of the correlative pathology process (and the sub-steps of specimen sectioning) are listed. A statistical analysis was conducted based on 19 studies that reported target registration error as their performance metric. While some methods promise greater accuracy, they may be expensive. Due to the complexity of the processes involved, correlative pathology studies generally include a small number of subjects, which hinders advanced developments in this field.

Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation.

BACKGROUND: Manual delineation of gross tumor volume (GTV) is essential for radiotherapy treatment planning, but it is time-consuming and suffers inter-observer variability (IOV). In clinics, CT, PET, and MRI are used to inform delineation accuracy due to their different complementary characteristics. This study aimed to investigate deep learning to assist GTV delineation in head and neck squamous cell carcinoma (HNSCC) by comparing various modality combinations. MATERIALS AND METHODS: This retrospective study had 153 patients with multiple sites of HNSCC including their planning CT, PET, and MRI (T1-weighted and T2-weighted). Clinical delineations of gross tumor volume (GTV-T) and involved lymph nodes (GTV-N) were collected as the ground truth. The dataset was randomly divided into 92 patients for training, 31 for validation, and 30 for testing. We applied a residual 3 D UNet as the deep learning architecture. We independently trained the UNet with four different modality combinations (CT-PET-MRI, CT-MRI, CT-PET, and PET-MRI). Additionally, analogical to post-processing, an average fusion of three bi-modality combinations (CT-PET, CT-MRI, and PET-MRI) was produced as an ensemble. Segmentation accuracy was evaluated on the test set, using Dice similarity coefficient (Dice), Hausdorff Distance 95 percentile (HD95), and Mean Surface Distance (MSD). RESULTS: All imaging combinations including PET provided similar average scores in range of Dice: 0.72-0.74, HD95: 8.8-9.5 mm, MSD: 2.6-2.8 mm. Only CT-MRI had a lower score with Dice: 0.58, HD95: 12.9 mm, MSD: 3.7 mm. The average of three bi-modality combinations reached Dice: 0.74, HD95: 7.9 mm, MSD: 2.4 mm. CONCLUSION: Multimodal deep learning-based auto segmentation of HNSCC GTV was demonstrated and inclusion of the PET image was shown to be crucial. Training on combined MRI, PET, and CT data provided limited improvements over CT-PET and PET-MRI. However, when combining three bimodal trained networks into an ensemble, promising improvements were shown.

Surgical navigation for challenging recurrent or pretreated intra-abdominal and pelvic soft tissue sarcomas.

BACKGROUND: This study assessed whether electromagnetic navigation can be of added value during resection of recurrent or post-therapy intra-abdominal/pelvic soft tissue sarcomas (STS) in challenging locations. MATERIALS AND METHODS: Patients were included in a prospective navigation study. A pre-operatively 3D roadmap was made and tracked using electromagnetic reference markers. During the operation, an electromagnetic pointer was used for the localization of the tumor/critical anatomical structures. The primary endpoint was feasibility, secondary outcomes were safety and usability. RESULTS: Nine patients with a total of 12 tumors were included, 7 patients with locally recurrent sarcoma. Three patients received neoadjuvant radiotherapy and three other patients received neoadjuvant systemic treatment. The median tumor size was 4.6 cm (2.4-10.4). The majority of distances from tumor to critical anatomical structures was <0.5 cm. The tumors were localized using the navigation system without technical or safety issues. Despite the challenging nature of these resections, 89% were R0 resections, with a median blood loss of 100 ml (20-1050) and one incident of vascular damage. Based on the survey, surgeons stated navigation resulted in shorter surgery time and made the resections easier. CONCLUSION: Electromagnetic navigation facilitates resections of challenging lower intra-abdominal/pelvic STS and might be of added value.

CBCT-based navigation system for open liver surgery: Accurate guidance toward mobile and deformable targets with a semi-rigid organ approximation and electromagnetic tracking of the liver.

PURPOSE: The surgical navigation system that provides guidance throughout the surgery can facilitate safer and more radical liver resections, but such a system should also be able to handle organ motion. This work investigates the accuracy of intraoperative surgical guidance during open liver resection, with a semi-rigid organ approximation and electromagnetic tracking of the target area. METHODS: The suggested navigation technique incorporates a preoperative 3D liver model based on diagnostic 4D MRI scan, intraoperative contrast-enhanced CBCT imaging and electromagnetic (EM) tracking of the liver surface, as well as surgical instruments, by means of six degrees-of-freedom micro-EM sensors. RESULTS: The system was evaluated during surgeries with 35 patients and resulted in an accurate and intuitive real-time visualization of liver anatomy and tumor's location, confirmed by intraoperative checks on visible anatomical landmarks. Based on accuracy measurements verified by intraoperative CBCT, the system's average accuracy was 4.0 ± 3.0 mm, while the total surgical delay due to navigation stayed below 20 min. CONCLUSIONS: The electromagnetic navigation system for open liver surgery developed in this work allows for accurate localization of liver lesions and critical anatomical structures surrounding the resection area, even when the liver was manipulated. However, further clinically integrating the method requires shortening the guidance-related surgical delay, which can be achieved by shifting to faster intraoperative imaging like ultrasound. Our approach is adaptable to navigation on other mobile and deformable organs, and therefore may benefit various clinical applications.

Association of Image-Guided Navigation With Complete Resection Rate in Patients With Locally Advanced Primary and Recurrent Rectal Cancer: A Nonrandomized Controlled Trial.

Importance: The percentage of tumor-positive surgical resection margin rates in patients treated for locally advanced primary or recurrent rectal cancer is high. Image-guided navigation may improve complete resection rates. Objective: To ascertain whether image-guided navigation during rectal cancer resection improves complete resection rates compared with surgical procedures without navigation. Design, Setting, and Participants: This prospective single-center nonrandomized controlled trial was conducted at the Netherlands Cancer Institute-Antoni van Leeuwenhoek in Amsterdam, the Netherlands. The prospective or navigation cohort included adult patients with locally advanced primary or recurrent rectal cancer who underwent resection with image-guided navigation between February 1, 2016, and September 30, 2019, at the tertiary referral hospital. Clinical results of this cohort were compared with results of the historical cohort, which was composed of adult patients who received rectal cancer resection without image-guided navigation between January 1, 2009, and December 31, 2015. Intervention: Rectal cancer resection with image-guided navigation. Main Outcomes and Measures: The primary end point was the complete resection rate, measured by the amount of tumor-negative resection margin rates. Secondary outcomes were safety and usability of the system. Safety was evaluated by the number of navigation system-associated surgical adverse events. Usability was assessed from responses to a questionnaire completed by the participating surgeons after each procedure. Results: In total, 33 patients with locally advanced or recurrent rectal cancer were included (23 men [69.7%]; median [interquartile range] age at start of treatment, 61 [55.0-69.0] years). With image-guided navigation, a radical resection (R0) was achieved in 13 of 14 patients (92.9%; 95% CI, 66.1%-99.8%) after primary resection of locally advanced tumors and in 15 of 19 patients (78.9%; 95% CI, 54.4%-94.0%) after resection of recurrent rectal cancer. No navigation system-associated complications occurred before or during surgical procedures. In the historical cohort, 142 patients who underwent resection without image-guided navigation were included (95 men [66.9%]; median [interquartile range] age at start of treatment, 64 [55.0-70.0] years). In these patients, an R0 resection was accomplished in 85 of 101 patients (84.2%) with locally advanced rectal cancer and in 20 of 41 patients (48.8%) with recurrent rectal cancer. A significant difference was found between the navigation and historical cohorts after recurrent rectal cancer resection (21.1% vs 51.2%; P = .047). For locally advanced primary tumor resection, the difference was not significant (7.1% vs 15.8%; P = .69). Surgeons stated in completed questionnaires that the navigation system improved decisiveness and helped with tumor localization. Conclusions and Relevance: Findings of this study suggest that image-guided navigation used during rectal cancer resection is safe and intuitive and may improve tumor-free resection margin rates in recurrent rectal cancer. Trial Registration: Netherlands Trial Register Identifier: NTR7184.

Accurate surgical navigation with real-time tumor tracking in cancer surgery.

In the past decades, image-guided surgery has evolved rapidly. In procedures with a relatively fixed target area, like neurosurgery and orthopedics, this has led to improved patient outcomes. In cancer surgery, intraoperative guidance could be of great benefit to secure radical resection margins since residual disease is associated with local recurrence and poor survival. However, most tumor lesions are mobile with a constantly changing position. Here, we present an innovative technique for real-time tumor tracking in cancer surgery. In this study, we evaluated the feasibility of real-time tumor tracking during rectal cancer surgery. The application of real-time tumor tracking using an intraoperative navigation system is feasible and safe with a high median target registration accuracy of 3 mm. This technique allows oncological surgeons to obtain real-time accurate information on tumor location, as well as critical anatomical information. This study demonstrates that real-time tumor tracking is feasible and could potentially decrease positive resection margins and improve patient outcome.

A workflow for automated segmentation of the liver surface, hepatic vasculature and biliary tree anatomy from multiphase MR images.

Accurate assessment of 3D models of patient-specific anatomy of the liver, including underlying hepatic and biliary tree, is critical for preparation and safe execution of complex liver resections, especially due to high variability of biliary and hepatic artery anatomies. Dynamic MRI with hepatospecific contrast agents is currently the only type of diagnostic imaging that provides all anatomical information required for generation of such a model, yet there is no information in the literature on how the complete 3D model can be generated automatically. In this work, a new automated segmentation workflow for extraction of patient-specific 3D model of the liver, hepatovascular and biliary anatomy from a single multiphase MRI acquisition is developed and quantitatively evaluated. The workflow incorporates course 4D k-means clustering estimation and geodesic active contour refinement of the liver boundary, based on organ's characteristic uptake of gadolinium contrast agents overtime. Subsequently, hepatic vasculature and biliary ducts segmentations are performed using multiscale vesselness filters. The algorithm was evaluated using 15 test datasets of patients with liver malignancies of various histopathological types. It showed good correlation with expert manual segmentation, resulting in an average of 1.76 ± 2.44 mm Hausdorff distance for the liver boundary, and 0.58 ± 0.72 and 1.16 ± 1.98 mm between centrelines of biliary ducts and liver veins, respectively. A workflow for automatic segmentation of the liver, hepatic vasculature and biliary anatomy from a single diagnostic MRI acquisition was developed. This enables automated extraction of 3D models of patient-specific liver anatomy, and may facilitating better perception of organ's anatomy during preparation and execution of liver surgeries. Additionally, it may help to reduce the incidence of intraoperative biliary duct damage due to an unanticipated variation in the anatomy.

Clinical Implementation of In-House Developed MR-Based Patient-Specific 3D Models of Liver Anatomy.

Knowledge of patient-specific liver anatomy is key to patient safety during major hepatobiliary surgery. Three-dimensional (3D) models of patient-specific liver anatomy based on diagnostic MRI images can provide essential vascular and biliary anatomical insight during surgery. However, a method for generating these is not yet publicly available. This paper describes how these 3D models of the liver can be generated using open source software, and then subsequently integrated into a sterile surgical environment. The most common image quality aspects that degrade the quality of the 3D models as well possible ways of eliminating these are also discussed. Per patient, a single diagnostic multiphase MRI scan with hepatospecific contrast agent was used for automated segmentation of liver contour, arterial, portal, and venous anatomy, and the biliary tree. Subsequently, lesions were delineated manually. The resulting interactive 3D model could be accessed during surgery on a sterile covered tablet. Up to now, such models have been used in 335 surgical procedures. Their use simplified the surgical treatment of patients with a high number of liver metastases and contributed to the localization of vanished lesions in cases of a radiological complete response to neoadjuvant treatment. They facilitated perioperative verification of the relationship of tumors and the surrounding vascular and biliary anatomy, and eased decision-making before and during surgery.

Predicting and implications of target volume changes of brain metastases during fractionated stereotactic radiosurgery.

OBJECTIVE: To study the impact of target volume changes in brain metastases during fractionated stereotactic radiosurgery (fSRS) and identify patients that benefit from MRI guidance. MATERIAL AND METHODS: For 15 patients (18 lesions) receiving fSRS only (fSRSonly) and 19 patients (20 lesions) receiving fSRS postoperatively (fSRSpostop), a treatment planning MRI (MR0) and repeated MRI during treatment (MR1) were acquired. The impact of target volume changes on the target coverage was analyzed by evaluating the planned dose distribution (based on MR0) on the planning target volume (PTV) during treatment as defined on MR1. The predictive value of target volume changes before treatment (using the diagnostic MRI (MRD)) was studied to identify patients that experienced the largest changes during treatment. RESULTS: Target volume changes during fSRS did result in large declines of the PTV dose coverage up to -34.8% (median = 3.2%) for fSRSonly patients. For fSRSpostop the variation and declines were smaller (median PTV dose coverage change = -0.5% (-4.5% to 1.9%)). Target volumes changes did also impact the minimum dose in the PTV (fSRSonly; -2.7 Gy (-16.5 to 2.3 Gy), fSRSpostop; -0.4 Gy (-4.2 to 2.5 Gy)). Changes in target volume before treatment (i.e. seen between the MRD and MR0) predicted which patients experienced the largest dose coverage declines during treatment. CONCLUSION: Target volume changes in brain metastases during fSRS can result in worsening of the target dose coverage. Patients benefiting the most from a repeated MRI during treatment could be identified before treatment.

Accuracy assessment of target tracking using two 5-degrees-of-freedom wireless transponders.

PURPOSE: Surgical navigation systems are generally only applied for targets in rigid areas. For non-rigid areas, real-time tumor tracking can be included to compensate for anatomical changes. The only clinically cleared system using a wireless electromagnetic tracking technique is the Calypso® System (Varian Medical Systems Inc., USA), designed for radiotherapy. It is limited to tracking maximally three wireless 5-degrees-of-freedom (DOF) transponders, all used for tumor tracking. For surgical navigation, a surgical tool has to be tracked as well. In this study, we evaluated whether accurate 6DOF tumor tracking is possible using only two 5DOF transponders, leaving one transponder to track a tool. METHODS: Two methods were defined to derive 6DOF information out of two 5DOF transponders. The first method uses the vector information of both transponders (TTV), and the second method combines the vector information of one transponder with the distance vector between the transponders (OTV). The accuracy of tracking a rotating object was assessed for each method mimicking clinically relevant and worst-case configurations. Accuracy was compared to using all three transponders to derive 6DOF (Default method). An optical tracking system was used as a reference for accuracy. RESULTS: The TTV method performed best and was as accurate as the Default method for almost all transponder configurations (median errors < 0.5°, 95% confidence interval < 3°). Only when the angle between the transponders was less than 2°, the TTV method was inaccurate and the OTV method may be preferred. The accuracy of both methods was independent of the angle of rotation, and only the OTV method was sensitive to the plane of rotation. CONCLUSION: These results indicate that accurate 6DOF tumor tracking is possible using only two 5DOF transponders. This encourages further development of a wireless EM surgical navigation approach using a readily available clinical system.

Gross Tumor Delineation in Esophageal Cancer on MRI Compared With 18F-FDG-PET/CT.

PURPOSE: Current delineation of the gross tumor volume (GTV) in esophageal cancer relies on computed tomography (CT) and combination with 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET). There is increasing interest in integrating magnetic resonance imaging (MRI) in radiation treatment, which can potentially obviate CT- or FDG-PET/CT-based delineation. The aim of this study is to evaluate the feasibility of target delineation on T2-weighted (T2W) MRI and T2W including diffusion-weighted MRI (T2W + DW-MRI) compared with current-practice FDG-PET/CT. METHODS: Ten observers delineated primary esophageal tumor GTVs of 6 patients on FDG-PET/CT, T2W-MRI, and T2W + DW-MRI. GTVs, generalized conformity indices, in-slice delineation variation (root mean square), and standard deviations in the position of the most cranial and caudal delineated slice were calculated. RESULTS: Delineations on MRI showed smaller GTVs compared with FDG-PET/CT-based delineations. The main variation was seen at the cranial and caudal border. No differences were observed in conformity indices (FDG-PET/CT, 0.68; T2W-MRI, 0.66; T2W + DW-MRI, 0.68) and in-slice variation (root mean square, 0.13 cm on FDG-PET/CT; 0.10 cm on T2W-MRI; 0.14 cm on T2W + DW-MRI). In the 2 tumors involving the gastroesophageal junction, addition of DW-MRI to T2W-MRI significantly decreased caudal border variation. CONCLUSIONS: MRI-based target delineation of the esophageal tumor is feasible with interobserver variability comparable to that with FDG-PET/CT, despite limited experience with delineation on MRI. Most variation was seen at cranial-caudal borders, and addition of DW-MRI to T2W-MRI may reduce caudal delineation variation of gastroesophageal junction tumors.