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Magnetic resonance image guided radiation therapy (MRIgRT) is a relatively new technology that has already shown outcomes benefits but that has not yet reached its clinical potential. The improved soft-tissue contrast provided with MR, coupled with the immediacy of image acquisition with respect to the treatment, enables expansion of on-table adaptive protocols, currently at a cost of increased treatment complexity, use of human resources, and longer treatment slot times, which translate to decreased throughput. Many approaches are being investigated to meet these challenges, including the development of artificial intelligence (AI) algorithms to accelerate and automate much of the workflow and improved technology that parallelizes workflow tasks, as well as improvements in image acquisition speed and quality. This article summarizes limitations of current available integrated MRIgRT systems and gives an outlook about scientific developments to further expand the use of MRIgRT., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

The development of cone-beam CT guided radiotherapy has transformed radiation oncology in the 20 years since it was first released commercially. The technological pace of change has spurred a massive transformation in our daily clinical practice, forced us to evolve our approach to multi- and inter-disciplinary collaboration, and enabled new treatment paradigms. Further progress in integrating quantitative CT in these robotic platforms promises to do even more by "burying the complexity" of radiotherapy and leveraging the expanding digital fabric that uses machine learning approaches to bring semi-automated expertise to bear on the issues of expertise and quality. This is the only way we will be able to respond to the massive global shortfall in high-quality radiotherapy services across the globe., (© 2023 American Association of Physicists in Medicine.)

Radiation therapy (RT) is an essential component of effective cancer care and is used across nearly all cancer types. The delivery of RT is becoming more precise through rapid advances in both computing and imaging. The direct integration of magnetic resonance imaging (MRI) with linear accelerators represents an exciting development with the potential to dramatically impact cancer research and treatment. These impacts extend beyond improved imaging and dose deposition. Real-time MRI-guided RT is actively transforming the work flows and capabilities of virtually every aspect of RT. It has the opportunity to change entirely the delivery methods and response assessments of numerous malignancies. This review intends to approach the topic of MRI-based RT guidance from a vendor neutral and international perspective. It also aims to provide an introduction to this topic targeted towards oncologists without a speciality focus in RT. Speciality implications, areas for physician education and research opportunities are identified as they are associated with MRI-guided RT. The uniquely disruptive implications of MRI-guided RT are discussed and placed in context. We further aim to describe and outline important future changes to the speciality of radiation oncology that will occur with MRI-guided RT. The impacts on RT caused by MRI guidance include target identification, RT planning, quality assurance, treatment delivery, training, clinical workflow, tumour response assessment and treatment scheduling. In addition, entirely novel research areas that may be enabled by MRI guidance are identified for future investigation., (Published by Elsevier Ltd.)

Radiation oncology is 1 of the most structured disciplines in medicine. It is of a highly technical nature with reliance on robotic systems to deliver intervention, engagement of diverse expertise, and early adoption of digital approaches to optimize and execute the application of this highly effective cancer treatment. As a localized intervention, the dependence on sensitive, specific, and accurate imaging to define the extent of disease, its heterogeneity, and adjacency to normal tissues directly affects the therapeutic ratio. Image-based in vivo temporal monitoring of the response to treatment enables adaptation and further affects the therapeutic ratio. Thus, more precise intervention will enable fractionation schedules that better interoperate with advances such as immunotherapy. In the data set-rich era that promises precision and personalized medicine, the radiation oncology field will integrate these new data into highly protocoled pathways of care that begin with multimodality prediction and enable patient-specific adaptation of therapy based on quantitative measures of the individual's dose-volume temporal trajectory and midtherapy predictions of response. In addition to advancements in computed tomography imaging, emerging technologies, such as ultra-high-field magnetic resonance and molecular imaging will bring new information to the design of treatments. Next-generation image guided radiation therapy systems will inject high specificity and sensitivity data and stimulate adaptive replanning. In addition, a myriad of pre- and peritherapeutic markers derived from advances in molecular pathology (eg, tumor genomics), automated and comprehensive imaging analytics (eg, radiomics, tumor microenvironment), and many other emerging biomarkers (eg, circulating tumor cell assays) will need to be integrated to maximize the benefit of radiation therapy for an individual patient. We present a perspective on the promise and challenges of fully exploiting imaging data in the pursuit of personalized radiation therapy, drawing from the presentations and broader discussions at the 2016 American Society of Therapeutic Radiation Oncology-National Cancer Institute workshop on Precision Medicine in Radiation Oncology (Bethesda, MD)., (Copyright © 2018. Published by Elsevier Inc.)

Purpose: We present and evaluate a fully automated 2D-3D intensity-based registration framework using a single limited field-of-view (FOV) 2D kV radiograph and a 3D kV CBCT for 3D estimation of patient setup errors during brain radiotherapy., Methods: We evaluated two similarity measures, the Pearson correlation coefficient on image intensity values (ICC) and maximum likelihood measure with Gaussian noise (MLG), derived from the statistics of transmission images. Pose determination experiments were conducted on 2D kV radiographs in the anterior-posterior (AP) and left lateral (LL) views and 3D kV CBCTs of an anthropomorphic head phantom. In order to minimize radiation exposure and exclude nonrigid structures from the registration, limited FOV 2D kV radiographs were employed. A spatial frequency band useful for the 2D-3D registration was identified from the bone-to-no-bone spectral ratio (BNBSR) of digitally reconstructed radiographs (DRRs) computed from the 3D kV planning CT of the phantom. The images being registered were filtered accordingly prior to computation of the similarity measures. We evaluated the registration accuracy achievable with a single 2D kV radiograph and with the registration results from the AP and LL views combined. We also compared the performance of the 2D-3D registration solutions proposed to that of a commercial 3D-3D registration algorithm, which used the entire skull for the registration. The ground truth was determined from markers affixed to the phantom and visible in the CBCT images., Results: The accuracy of the 2D-3D registration solutions, as quantified by the root mean squared value of the target registration error (TRE) calculated over a radius of 3 cm for all poses tested, was ICC AP : 0.56 mm, MLG AP : 0.74 mm, ICC LL : 0.57 mm, MLG LL : 0.54 mm, ICC (AP and LL combined): 0.19 mm, and MLG (AP and LL combined): 0.21 mm. The accuracy of the 3D-3D registration algorithm was 0.27 mm. There was no significant difference in mean TRE for the 2D-3D registration algorithms using a single 2D kV radiograph with similarity measure and image view point. There was no significant difference in mean TRE between ICC LL , MLG LL , ICC (AP and LL combined), MLG (AP and LL combined), and the 3D-3D registration algorithm despite the smaller FOV used for the 2D-3D registration. While submillimeter registration accuracy was obtained with both ICC and MLG using a single 2D kV radiograph, combining the results from the two projection views resulted in a significantly smaller (P≤0.05) mean TRE., Conclusions: Our results indicate that it is possible to achieve submillimeter registration accuracy with both ICC and MLG using either single or dual limited FOV 2D kV radiographs of the head in the AP and LL views. The registration accuracy suggests that the 2D-3D registration solutions presented are suitable for the estimation of patient setup errors not only during conventional brain radiation therapy, but also during stereotactic procedures and proton radiation therapy where tighter setup margins are required., (© 2018 American Association of Physicists in Medicine.)

Purpose: To correlate radiation dose to the risk of severe radiologically-evident radiation-induced lung injury (RRLI) using voxel-by-voxel analysis of the follow-up computed tomography (CT) of patients treated for lung cancer with hypofractionated helical Tomotherapy., Methods and Materials: The follow-up CT scans from 32 lung cancer patients treated with various regimens (5, 8, and 25 fractions) were registered to pre-treatment CT using deformable image registration (DIR). The change in density was calculated for each voxel within the combined lungs minus the planning target volume (PTV). Parameters of a Probit formula were derived by fitting the occurrences of changes of density in voxels greater than 0.361gcm -3 to the radiation dose. The model's predictive capability was assessed using the area under receiver operating characteristic curve (AUC), the Kolmogorov-Smirnov test for goodness-of-fit, and the permutation test (P test )., Results: The best-fit parameters for prediction of RRLI 6months post RT were D 50 of 73.0 (95% CI 59.2.4-85.3.7)Gy, and m of 0.41 (0.39-0.46) for hypofractionated (5 and 8 fractions) and D 50 of 96.8 (76.9-123.9)Gy, and m of 0.36 (0.34-0.39) for 25 fractions RT. According to the goodness-of-fit test the null hypothesis of modeled and observed occurrence of RRLI coming from the same distribution could not be rejected. The AUC was 0.581 (0.575-0.583) for fractionated and 0.579 (0.577-0.581) for hypofractionated patients. The predictive models had AUC>upper 95% band of the P test ., Conclusions: The correlation of voxel-by-voxel density increase with dose can be used as a support tool for differential diagnosis of tumor from benign changes in the follow-up of lung IMRT patients., (Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Background and Purpose: Dose-escalated radiotherapy (DE) improves outcomes in localized prostate cancer (PCa). The impact of DE in the context of image-guided radiotherapy (IGRT) remains unknown. Herein, we determined outcomes of three sequential cohorts treated with progressive DE-IGRT., Materials and Methods: We analyzed data from 1998 to 2012. Patients treated with radical radiotherapy were included, with three sequential institutional schedules: (A) 75.6Gy, (B) 79.8Gy, (C) 78Gy, with 1.8, 1.9 and 2Gy/fraction, respectively. IGRT consisted of fiducial markers and daily EPID (A, B) or CBCT (C)., Results: 961 patients were included, with median follow-up of 6.1y. 30.5%, 32.6% and 36.9% were treated in A, B and C, respectively. Risk category distribution was 179 (18.6%) low-, 653 (67.9%) intermediate- and 129 (13.5%) high-risk. PSA, T-category, androgen deprivation use and risk distribution were similar among groups. BCR (biochemical recurrence) was different (p<0.001) between A, B and C with 5-year rates of 23%, 17% and 9%, respectively (HR 2.68 [95% CI 1.87-3.85] and 1.92 [95% CI 1.33-2.78] for A and B compared to C, respectively). Findings were most significant in the intermediate-risk category. Metastasis, cause-specific-death and toxicities were not different between cohorts., Conclusion: Our findings suggest continuous BCR improvement with progressive DE-IGRT. Prospective validation considering further DE with IGRT seems warranted., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Purpose: The present study used cone beam computed tomography (CBCT) to measure the inter- and intrafraction uncertainties for intracranial stereotactic radiosurgery (SRS) using the Leksell Gamma Knife (GK)., Methods and Materials: Using a novel CBCT system adapted to the GK radiosurgery treatment unit, CBCT images were acquired immediately before and after treatment for each treatment session within the context of a research ethics board-approved prospective clinical trial. Patients were immobilized in the Leksell coordinate frame (LCF) for both volumetric CBCT imaging and GK-SRS delivery. The relative displacement of the patient's skull to the stereotactic reference (interfraction motion) was measured for each CBCT scan. Differences between the pre- and post-treatment CBCT scans were used to determine the intrafraction motion., Results: We analyzed 20 pre- and 17 post-treatment CBCT scans in 20 LCF patients treated with SRS. The mean translational pretreatment setup error ± standard deviation in the left-right, anteroposterior, and craniocaudal directions was -0.19 ± 0.32, 0.06 ± 0.27, and -0.23 ± 0.2 mm, with a maximum of -0.74, -0.53, and -0.68 mm, respectively. After an average time between the pre- and post-treatment CBCT scans of 82 minutes (range 27-170), the mean intrafraction error ± standard deviation for the LCF was -0.03 ± 0.05, -0.03 ± 0.18, and -0.03 ± 0.12 mm in the left-right, anteroposterior, and craniocaudual direction, respectively., Conclusions: Using CBCT on a prototype image guided GK Perfexion unit, we were able to measure the inter- and intrafraction positional changes for GK-SRS using the invasive frame. In the era of image guided radiation therapy, the use of CBCT image guidance for both frame- and non-frame-based immobilization systems could serve as a useful quality assurance tool. Our preliminary measurements can guide the application of achievable thresholds for inter- and intrafraction discrepancy when moving to a frameless approach., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

This work measured time taken to integrate image-guidance with CBCTs in routine clinical practice for patients treated between 2007 and 2010 across eight linear accelerators. 117,301 CBCTs from 4592 patients across thirteen disease sites were included. The mean image assessment decision time was 79s. Decision time was correlated with setup displacement magnitude., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Purpose: The widespread use of intensity modulated radiation therapy (IMRT) for cervical cancer has been limited by internal target and normal tissue motion. Such motion increases the risk of underdosing the target, especially as planning margins are reduced in an effort to reduce toxicity. This study explored 2 adaptive strategies to mitigate this risk and proposes a new, automated method that minimizes replanning workload., Methods and Materials: Thirty patients with cervical cancer participated in a prospective clinical study and underwent pretreatment and weekly magnetic resonance (MR) scans over a 5-week course of daily external beam radiation therapy. Target volumes and organs at risk (OARs) were contoured on each of the scans. Deformable image registration was used to model the accumulated dose (the real dose delivered to the target and OARs) for 2 adaptive replanning scenarios that assumed a very small PTV margin of only 3 mm to account for setup and internal interfractional motion: (1) a preprogrammed, anatomy-driven midtreatment replan (A-IMRT); and (2) a dosimetry-triggered replan driven by target dose accumulation over time (D-IMRT)., Results: Across all 30 patients, clinically relevant target dose thresholds failed for 8 patients (27%) if 3-mm margins were used without replanning. A-IMRT failed in only 3 patients and also yielded an additional small reduction in OAR doses at the cost of 30 replans. D-IMRT assured adequate target coverage in all patients, with only 23 replans in 16 patients., Conclusions: A novel, dosimetry-triggered adaptive IMRT strategy for patients with cervical cancer can minimize the risk of target underdosing in the setting of very small margins and substantial interfractional motion while minimizing programmatic workload and cost., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Magnetic resonance (MR) imaging is routinely employed in the design of radiotherapy (RT) treatment plans for many disease sites. It is evident that tighter integration of MR imaging into the RT process would increase confidence in dose placement and facilitate the integration of new MR imaging information (including anatomical and functional imaging) into the therapy process. To this end, a dedicated MR-guided RT (MRgRT) facility has been created that integrates a state-of-the-art linear accelerator delivery system, high-dose rate brachytherapy afterloader, and superconducting MR scanner to allow MR-based online treatment guidance, adaptive replanning, and response monitoring while maintaining the clinical functionality of the existing delivery systems. This system is housed within a dedicated MRgRT suite and operates in a coordinated fashion to assure safe and efficient MRgRT treatments., (Copyright © 2014. Published by Elsevier Inc.)

Purpose: To compare the dosimetric and geometric properties of a commercial x-ray based image-guided small animal irradiation system, installed at three institutions and to establish a complete and broadly accessible commissioning procedure., Methods: The system consists of a 225 kVp x-ray tube with fixed field size collimators ranging from 1 to 44 mm equivalent diameter. The x-ray tube is mounted opposite a flat-panel imaging detector, on a C-arm gantry with 360° coplanar rotation. Each institution performed a full commissioning of their system, including half-value layer, absolute dosimetry, relative dosimetry (profiles, percent depth dose, and relative output factors), and characterization of the system geometry and mechanical flex of the x-ray tube and detector. Dosimetric measurements were made using Farmer-type ionization chambers, small volume air and liquid ionization chambers, and radiochromic film. The results between the three institutions were compared., Results: At 225 kVp, with 0.3 mm Cu added filtration, the first half value layer ranged from 0.9 to 1.0 mm Cu. The dose-rate in-air for a 40 × 40 mm(2) field size, at a source-to-axis distance of 30 cm, ranged from 3.5 to 3.9 Gy/min between the three institutions. For field sizes between 2.5 mm diameter and 40 × 40 mm(2), the differences between percent depth dose curves up to depths of 3.5 cm were between 1% and 4% on average, with the maximum difference being 7%. The profiles agreed very well for fields >5 mm diameter. The relative output factors differed by up to 6% for fields larger than 10 mm diameter, but differed by up to 49% for fields ≤5 mm diameter. The mechanical characteristics of the system (source-to-axis and source-to-detector distances) were consistent between all three institutions. There were substantial differences in the flex of each system., Conclusions: With the exception of the half-value layer, and mechanical properties, there were significant differences between the dosimetric and geometric properties of the three systems. This underscores the need for careful commissioning of each individual system for use in radiobiological experiments.

Purpose: Recent advances in preclinical radiotherapy systems have provided the foundation for scaling many of the elements of clinical radiation therapy practice to the dimensions and energy demanded in small animal studies. Such systems support the technical capabilities to accurately deliver highly complex dose distributions, but methods to optimize and deliver such distributions remain in their infancy. This study developed an optimization method based on empirically measured two-dimensional dose kernel measurements to deliver arbitrary planar dose distributions on a recently developed small animal radiotherapy platform., Methods: A two-dimensional dose kernel was measured with repeated radiochromic film measurements for the circular 1 mm diameter fixed collimator of the small animal radiotherapy system at 1 cm depth in a solid water phantom. This kernel was utilized in a sequential quadratic programming optimization framework to determine optimal beam positions and weights to deliver an arbitrary desired dose distribution. The positions and weights were then translated to a set of stage motions to automatically deliver the optimized dose distribution. End-to-end efficacy of the framework was quantified through five repeated deliveries of two dosimetric challenges: (1) a 5 mm radius bullseye distribution, and (2) a "sock" distribution contained within a 9 × 13 mm bounding box incorporating rectangular, semicircular, and exponentially decaying geometric constructs and a rectangular linear dose gradient region. These two challenges were designed to gauge targeting, geometric, and dosimetric fidelity., Results: Optimization of the bullseye and sock distributions required 2.1 and 5.9 min and utilized 50 and 77 individual beams for delivery, respectively. Automated delivery of the resulting optimized distributions, validated using radiochromic film measurements, revealed an average targeting accuracy of 0.32 mm, and a dosimetric delivery error along four line profiles taken through the sock distribution of 3.9%. Mean absolute delivery error across the 0-1 Gy linear dose gradient over 7.5 mm was 0.01 Gy., Conclusions: The work presented here demonstrates the potential for complex dose distributions to be planned and automatically delivered with millimeter scale heterogeneity at submillimeter accuracy. This capability establishes the technical foundation for preclinical validation of biologically guided radiotherapy investigations and development of unique radiobiological experiments.

Purpose: MR image geometric integrity is one of the building blocks of MRI-guided radiotherapy. In particular, tissue magnetic susceptibility-induced effects are patient-dependent and their behavior is difficult to assess and predict. In this study, the authors investigated in detail the characteristics of susceptibility (χ) distortions in the context of MRIgRT, including the case of two common MR-linac system configurations., Methods: The magnetic field distortions were numerically simulated for several imaging parameters and anatomical sites, i.e., brain, lung, pelvis (with air pockets), and prostate. The simulation process consisted of (a) segmentation of patient CT data into susceptibility relevant anatomical volumes (i.e., soft-tissue, bone and air∕lung), (b) conversion of CT data into susceptibility masks by assigning bulk χ values to the structures defined at (a), (c) numerical computations of the local magnetic fields by using a finite difference algorithm, and (d) generation of the geometric distortion maps from the magnetic field distributions. For each patient anatomy, the distortions were quantified at the interfaces of anatomical structures with significantly different χ values. The analysis was performed for two specific orientations of the external main magnetic field (B(0)) characteristic to the MR-linac systems, specifically along the z-axis for a bore MR scanner and in the (x,y)-plane for a biplanner magnet. The magnetic field local perturbations were reported in ppm. The metrics used to quantify the geometric distortions were the maximum, mean, and range of distortions. The numerical simulation algorithm was validated using phantom data measurements., Results: Susceptibility-induced distortions were determined for both quadratic and patient specific geometries. The numerical simulations showed a good agreement with the experimental data. The measurements were acquired at 1.5 and 3 T and with an encoding gradient varying between 3 and 20 mT∕m by using an annular phantom mimicking the water-air and water-oil χ interfaces. For quadratic geometries, the magnitude of field distortion increased rapidly with the size of the inhomogeneity up to about 10 mm and then tended to plateau. This trend became more evident for materials with a larger Δχ relative to water. The simulations showed only a slight increase in the maximum distortion values when the B(0) orientation was varied with regard to the shape of the χ inhomogeneity. In the case of patient anatomy, the largest distortion values arose at the air-soft-tissue interface. Considering the two MR-linac system configurations and comparing the field distortion values corresponding to all organ structures, the distortions tended to be larger for the biplanar magnet. The authors provide a reference table with ppm values which can be used to easily evaluate the geometric distortions for patient data as a function of B(0) and the strength of the encoding gradient., Conclusions: The susceptibility distortions were quantified as a function of multiple parameters such as the χ inhomogeneity size and shape, the magnitude of B(0) and the readout gradient, and the orientation of B(0) with respect to the sample geometry. The analysis was performed for several anatomical sites and corresponding to two B(0) orientations as featured by MR-linac systems.

Radiotherapy is a highly effective, targeted therapy for the management of cancer. Technological innovations have enabled the direct integration of imaging technology into the radiation treatment device to increase the precision and accuracy of radiation delivery. As well as addressing a clinical need to better control the placement of the dose within the body, image-guided radiotherapy has enabled innovators in the field to accelerate their exploration of a number of different paradigms of radiation delivery, including toxicity reduction, dose escalation, hypofractionation, voxelization, and adaptation. Although these approaches are already innovative trends in radiation oncology, it is anticipated that they will work synergistically with other innovations in cancer management (including biomarker strategies, novel systemic and local therapies) as part of the broader goal of personalized cancer medicine. This Review discusses the rationale for adopting image-guidance approaches in radiotherapy, and the technology for achieving precision and accuracy in the context of different paradigms within the evolving radiation oncology practice. It also examines exciting advances in radiotherapy technology that suggest a convergence of radiotherapy practice in which patient-specific radiotherapy treatment courses are one of the most personalized forms of intervention in cancer medicine.

We have developed a method to register and display 3D parametric data, in particular radiation dose, on two-dimensional endoscopic images. This registration of radiation dose to endoscopic or optical imaging may be valuable in assessment of normal tissue response to radiation, and visualization of radiated tissues in patients receiving post-radiation surgery. Electromagnetic sensors embedded in a flexible endoscope were used to track the position and orientation of the endoscope allowing registration of 2D endoscopic images to CT volumetric images and radiation doses planned with respect to these images. A surface was rendered from the CT image based on the air/tissue threshold, creating a virtual endoscopic view analogous to the real endoscopic view. Radiation dose at the surface or at known depth below the surface was assigned to each segment of the virtual surface. Dose could be displayed as either a colorwash on this surface or surface isodose lines. By assigning transparency levels to each surface segment based on dose or isoline location, the virtual dose display was overlaid onto the real endoscope image. Spatial accuracy of the dose display was tested using a cylindrical phantom with a treatment plan created for the phantom that matched dose levels with grid lines on the phantom surface. The accuracy of the dose display in these phantoms was 0.8-0.99 mm. To demonstrate clinical feasibility of this approach, the dose display was also tested on clinical data of a patient with laryngeal cancer treated with radiation therapy, with estimated display accuracy of ∼2-3 mm. The utility of the dose display for registration of radiation dose information to the surgical field was further demonstrated in a mock sarcoma case using a leg phantom. With direct overlay of radiation dose on endoscopic imaging, tissue toxicities and tumor response in endoluminal organs can be directly correlated with the actual tissue dose, offering a more nuanced assessment of normal tissue toxicities following radiation therapy and accurate registration of radiation dose to the surgical field.

External beam radiation therapy (EBRT) for the treatment of cancer enables accurate placement of radiation dose on the cancerous region. However, the deformation of soft tissue during the course of treatment, such as in cervical cancer, presents significant challenges for the delineation of the target volume and other structures of interest. Furthermore, the presence and regression of pathologies such as tumors may violate registration constraints and cause registration errors. In this paper, automatic segmentation, nonrigid registration and tumor detection in cervical magnetic resonance (MR) data are addressed simultaneously using a unified Bayesian framework. The proposed novel method can generate a tumor probability map while progressively identifying the boundary of an organ of interest based on the achieved nonrigid transformation. The method is able to handle the challenges of significant tumor regression and its effect on surrounding tissues. The new method was compared to various currently existing algorithms on a set of 36 MR data from six patients, each patient has six T2-weighted MR cervical images. The results show that the proposed approach achieves an accuracy comparable to manual segmentation and it significantly outperforms the existing registration algorithms. In addition, the tumor detection result generated by the proposed method has a high agreement with manual delineation by a qualified clinician.

Purpose: Target delineation within volumetric imaging is a critical step in the planning process of intensity modulated radiation therapy. In endoluminal cancers, endoscopy often reveals superficial areas of visible disease beyond what is seen on volumetric imaging. Quantitatively relating these findings to the volumetric imaging is prone to human error during the recall and contouring of the target. We have developed a method to improve target delineation in the radiation therapy planning process by quantitatively registering endoscopic findings contours traced on endoscopic images to volumetric imaging., Methods: Using electromagnetic sensors embedded in an endoscope, 2D endoscopic images were registered to computed tomography (CT) volumetric images by tracking the position and orientation of the endoscope relative to a CT image set. Regions-of-interest (ROI) in the 2D endoscopic view were delineated. A mesh created within the boundary of the ROI was projected onto the 3D image data, registering the ROI with the volumetric image. This 3D ROI was exported to clinical radiation treatment planning software. The precision and accuracy of the procedure was tested on two solid phantoms with superficial markings visible on both endoscopy and CT images. The first phantom was T-shaped tube with X-marks etched on the interior. The second phantom was an anatomically correct skull phantom with a phantom superficial lesion placed on the pharyngeal surface. Markings were contoured on the endoscope images and compared with contours delineated in the treatment planning system based on the CT images. Clinical feasibility was tested on three patients with early stage glottic cancer. Image-based rendering using manually identified landmarks was used to improve the registration., Results: Using the T-shaped phantom with X-markings, the 2D to 3D registration accuracy was 1.5-3.5 mm, depending on the endoscope position relative to the markings. Intraobserver standard variation was 0.5 mm. Rotational accuracy was within 2°. Using the skull phantom, registration accuracy was assessed by calculating the average surface minimum distance between the endoscopy and treatment planning contours. The average surface distance was 0.92 mm with 93% of all points in the 2D-endoscopy ROI within 1.5 mm of any point within the ROI contoured in the treatment planning software. This accuracy is limited by the CT imaging resolution and the electromagnetic (EM) sensor accuracy. The clinical testing demonstrated that endoscopic contouring is feasible. With registration based on em tracking only, accuracy was 5.6-8.4 mm. Image-based registration reduced this error to less than 3.5 mm and enabled endoscopic contouring in all cases., Conclusions: Registration of contours generated on 2D endoscopic images to 3D planning space is feasible, with accuracy smaller than typical set-up margins. Used in addition to standard 3D contouring methods in radiation planning, the technology may improve gross tumour volume (GTV) delineation for superficial tumors in luminal sites that are only visible in endoscopy.