M Deeley

Impact of inhomogeneity corrections on dose coverage in the treatment of lung cancer using stereotactic body radiation therapy.

The purpose of this study is to assess the real target dose coverage when radiation treatments were delivered to lung cancer patients based on treatment planning according to the RTOG-0236 Protocol. We compare calculated dosimetric results between the more accurate anisotropic analytical algorithm (AAA) and the pencil beam algorithm for stereotactic body radiation therapy treatment planning in lung cancer. Ten patients with non-small cell lung cancer were given 60 Gy in three fractions using 6 and 10 MV beams with 8-10 fields. The patients were chosen in accordance with the lung RTOG-0236 protocol. The dose calculations were performed using the pencil beam algorithm with no heterogeneity corrections (PB-NC) and then recalculated with the pencil beam with modified Batho heterogeneity corrections (PB-MB) and the AAA using an identical beam setup and monitor units. The differences in calculated dose to 95% or 99% of the PTV, between using the PB-NC and the AAA, were within 10% of prescribed dose (60 Gy). However, the minimum dose to 95% and 99% of PTV calculated using the PB-MB were consistently overestimated by up to 40% and 36% of the prescribed dose, respectively, compared to that calculated by the AAA. Using the AAA as reference, the calculated maximum doses were underestimated by up to 27% using the PB-NC and overestimated by 19% using the PB-MB. The calculations of dose to lung from PB-NC generally agree with that of AAA except in the small high-dose region where PB-NC underestimates. The calculated dose distributions near the interface using the AAA agree with those from Monte Carlo calculations as well as measured values. This study indicates that the real minimum PTV dose coverage cannot be guaranteed when the PB-NC is used to calculate the monitor unit settings in dose prescriptions.

Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study

The purpose of this work was to characterize expert variation in segmentation of intracranial structures pertinent to radiation therapy, and to assess a registration-driven atlas-based segmentation algorithm in that context. Eight experts were recruited to segment the brainstem, optic chiasm, optic nerves, and eyes, of 20 patients who underwent therapy for large space-occupying tumors. Performance variability was assessed through three geometric measures: volume, Dice similarity coefficient, and Euclidean distance. In addition, two simulated ground truth segmentations were calculated via the simultaneous truth and performance level estimation algorithm and a novel application of probability maps. The experts and automatic system were found to generate structures of similar volume, though the experts exhibited higher variation with respect to tubular structures. No difference was found between the mean Dice similarity coefficient (DSC) of the automatic and expert delineations as a group at a 5% significance level over all cases and organs. The larger structures of the brainstem and eyes exhibited mean DSC of approximately 0.8-0.9, whereas the tubular chiasm and nerves were lower, approximately 0.4-0.5. Similarly low DSCs have been reported previously without the context of several experts and patient volumes. This study, however, provides evidence that experts are similarly challenged. The average maximum distances (maximum inside, maximum outside) from a simulated ground truth ranged from (-4.3, +5.4) mm for the automatic system to (-3.9, +7.5) mm for the experts considered as a group. Over all the structures in a rank of true positive rates at a 2 mm threshold from the simulated ground truth, the automatic system ranked second of the nine raters. This work underscores the need for large scale studies utilizing statistically robust numbers of patients and experts in evaluating quality of automatic algorithms.

Evaluation of multi atlas-based approaches for the segmentation of the thyroid gland in IMRT head-and-neck CT images

Segmenting the thyroid gland in head and neck CT images for IMRT treatment planning is of great importance. In this work, we evaluate and compare multi-atlas methods to segment this structure. The various methods we evaluate range from using a single average atlas representative of the population to selecting one atlas based on three similarity measures. We also compare ways to combine segmentation results obtained with several atlases, i.e., vote rule, and STAPLE, which is a commonly used method to combine multiple segmentations. We show that the best results are obtained when several atlases are combined. We also show that with our data sets, STAPLE does not lead to the best results.

TU‐FF‐A1‐05: The Uncertainty of Dose Measured with An Ionization Chamber in Low‐Density Media

Purpose: An ionization chamber is often used for measuring dose distributions to validate a photon beam dose calculation algorithm for radiation treatment planning. The presence of an ionization chamber can cause dose perturbations at the point of interest especially in low‐density media. This study investigates the magnitude of this type of perturbation as a function of photon beam energy and field size in a low‐density lung medium. Method and Materials: The Monte Carlo codes BEAMnrc/DOSXYZnrc are used to simulate 6–18 MV photon beams and to calculate dose distributions in a heterogeneous phantom. We benchmarked Monte Carlodose calculations against measurements in a lung phantom using a MOSFET detector. The dose to a point of interest in a lung medium is calculated with and without an ionization chamber in order to study the perturbation due to the chamber. The Monte Carlo simulation is also used to validate the Varian Eclipse Anisotropic Analytical Algorithm (AAA). Results: The results show dose increases of up to 6% and 12% due to the presence of an RK ionization chamber at the point of measurement inside lung medium for a small 3×3 cm2 field for 6 and 18 MV incident phantom beams, respectively. However this dose perturbation becomes negligible when beam field size increases to 10×10 cm2. The results of Monte Carlo calculation show that AAA is accurate in predicting dose distributions in lung and at lung‐tissue interface for 6 MV beam. This result contradicts the conclusion by Van Esch et al (Med. Phys. vol.33, pp.4130–48, 2006). Our finding of chamber perturbations explains the discrepancies between their measurements and calculations using AAA. Conclusion:Ionization chambers are not suitable for measuring dose in low‐density medium due to perturbation.

SU‐FF‐T‐320: Measurement of Surface and Exit Dose in Megavoltage X‐Ray Beams Using Micro‐MOSFET Detectors

Purpose: Knowledge of entrance and exit dose, specifically in breast cancers, is of significant clinical importance. New micro‐MOSFET (Thomson & Nielsen Electronics Ltd., Ottawa, Canada) detectors offer an efficient means to accomplish this task. In this study we investigate the use of MOSFETs to measure surface and exit dose in external photon beams. Method and Materials: Ratios of measurements at the surface and a depth of dmax in a solid water phantom were correlated with Monte Carlo (BEAM) generated percentage depth dose curves to determine the water‐equivalent thickness of the micro‐MOSFET detectors. This was done for 6 and 18 MV x‐rays in a 10×10 cm2 field, both normally and obliquely incident. Exit dose was measured similarly and equivalent thickness determined. Results: Correlation of the predicted depth dose and measured ratios indicates a water‐equivalent thickness of 0.8–1.0 mm for the micro‐MOSFET at the surface. All results indicate that the equivalent thickness is independent of angle of incidence and energy. The same detectors show an equivalent thickness that is approximately 0.4 mm and energy independent when measuring exit dose. We anticipate final results to include additional measurements at 10 MV and a field size of 40×40 cm2. Conclusions: This work indicates micro‐MOSFET detectors are a reliable (reproducible within 3%) detector of surface dose and exit dose as they exhibit a water‐equivalent thickness that is independent of energy and angle of incidence. We believe they offer a unique opportunity in their application to in vivo surface dose measurement.

Segmentation of lymph node regions in head-and-neck CT images using a combination of registration and active shape model

Segmenting the lymph node regions in head and neck CT images has been a challenging topic in the area of medical image segmentation. The method proposed herein implements an atlas-based technique constrained by an active shape model (ASM) to segment the level II, III and IV lymph nodes as one structure. A leave-one-out evaluation study performed on 15 data sets shows that the results obtained with this technique are better than those obtained with a pure atlas-based segmentation method, in particular in regions of poor contrast.

MO-D-204B-06: Segmentation Evaluation in the Context of Inter-Physician Variance

Purpose: To evaluate a registration‐based system for intracranial segmentation in the context of multiple physicians and cases of large space‐occupying brain lesions. Method and Materials: Segmentation analyses suffer from the absence of a known ground truth and a single adequate comparison metric. However, these limitations can be overcome by applying multiple metrics and statistical methods to large data sets. We enlisted 8 physicians to delineate the brainstem, chiasm, optic nerves, and eyes in 20 challenging patient volumes. Our system utilizing the adaptive bases algorithm automatically segmented these structures. The expert delineations formed probability maps (p‐maps) that were used to calculate a simulated ground truth. We evaluated inter‐physician and automatic‐physician variation using the Dice similarity coefficient. Each was then evaluated against the ground truth using Dice and Euclidean distance maps. To understand the observed variance at the edges of delineations, we sampled the p‐maps and fitted a linear regression model. Results: Across all structures and patients in pair‐wise Dice comparisons, the automatically derived structures compared as well or better to the physician delineations than did the physicians with one another. The inter‐physician spatial overlap for brainstem and eyes was over 80%, while for the optic chiasm and nerves it was only 40–50 percent. Median Euclidean distances to a simulated ground truth were less than 2 mm for both the automatic and physician delineations, and maximum deviations were generally less than 5 mm. Of the total p‐map variance only 20% was explained by main effects of physician, case and structure, and their interactions, suggesting the delineations are inherently noisy. Conclusion: The fully automatic system produced segmentations geometrically equivalent to those delineated by a group of physician experts. This work underscores the need for multiple physicians and metrics to avoid bias in evaluating segmentation results.

SU‐GG‐I‐99: Registration and Active Shape Model‐Based Segmentation of Lymph Node Regions in Head and Neck CT Images

Purpose: To develop a robust automatic method to segment the level II , III and IV lymph node regions in CTimages for head and neck IMRTtreatment planning. We developed a technique whereby registration‐based methods initialize an active shape model (ASM). Method and Materials: An average atlas was first created from 15 H/N patient (training) volumes with minimally enlarged nodes. To build the ASM model the average atlas was registered with each of the training volumes and correspondence of mesh vertices of lymph node surfaces was determined. Once built, the atlas and ASM can be applied to any target patient image. A patient image is first registered to the atlas through global affine, then global non‐rigid, and finally local non‐rigid transformations. The two non‐rigid registrations produce displacements for vertices on the structure of interest and map them from the atlas onto the affinely aligned image. The ASM segmentation is initialized by constraining the displacements and then refined by iteratively adjusting the vertices through a local gray‐level model search followed by model fitting until convergence. Results: The algorithm was evaluated through a leave‐one‐out experiment. The model‐based and registration‐based segmentations were compared with the manual delineations via a Euclidean distance measure applied to their 3D surfaces. The mean distance errors were reduced from 2.60 mm for the registration‐based method to 2.36 mm for ASM‐based method, and the maximum distance errors were decreased for 13 cases out of 15 cases, with a reduction up to of 47.9%. Conclusion: We have found that purely registration‐based methods suffer gross under/over segmentation in areas of low CT contrast. The results show that an active shape model approach initialized by the registration result can reduce these deviations.

SU‐GG‐J‐111: Initial Dosimetric Validation of An Atlas‐Based Method for Automatic Intracranial Segmentation

Purpose: We have developed a novel method for automatic segmentation of critical structures in the brain. The purpose of this study is to test the feasibility of this method as an alternative to manually‐derived physician contours. We test feasibility by evaluating the dosimetric consequences of auto‐segmentation versus physician‐drawn contours. Method and Materials: Brainstem, eyes,optic nerves and chiasm were segmented through non‐rigid registration of CT and MR‐based atlases to two patients. Patient A presents a challenging case in which a base of skull chondrosarcoma distorts normal brainstem anatomy. Patient B suffers from parotid disease and presents normal critical structureanatomy. Intensity‐modulated radiosurgery treatment plans were derived from physician contours and applied to the automatic contours. Results: For patient B (tumor far from critical structures) calculated doses for manual and automatic contours were within 2% of tumor dose for a given volume. Dose to the eyes,optic nerves, and chiasm of patient A were similar in agreement to those of patient B. The maximum dose to the brainstem of patient A, however, was 13% higher for the automatic contour. These dose differences were clinically negligible for all structures except the brainstem of patient A, in which case the difference was significant but acceptable. Conclusion: Clinical incorporation of our automated method is shown to be feasible dosimetrically. For the tumor lying far from the critical structures, the dose differences between automatically and manually‐derived contours were insignificant. The differences increased for the case in which a critical structure lay directly adjacent to a large tumor. These cases illustrate the system is accurate for critical structures far from the lesion but sensitive to local disturbance and inherently steeper dose gradients when the critical structures lie near the lesion.

SU-GG-J-124: Management of Image-Guidance Dose in Radiotherapy Treatment Planning

Purpose: Recent studies demonstrate that imaging procedures in radiotherapy for patient setup may add significantly to the doses to the normal tissues of the radiotherapy patient, especially using MV and kV cone‐beam CT(CBCT). These imaging procedures add the dose to an already high level of therapeuticradiation to organs. Accounting for image‐guidancedose is becoming increasingly important for radiationoncologists with regards to the total dose limit for normal tissues. This study is to accurately model and quantitate the additional dose from image‐guidance procedures, especially kV CBCT, as part of radiotherapydose in the patient treatment planning. Method and Materials: Unlike megavoltage photon beam in which the dose calculations mainly depend on the electron density of the media, the dose calculations of kilovoltage x‐ray depend on atomic number of the medium due to the photo‐electric effect. Therefore, kV x‐ray dose calculation is not available in current radiotherapytreatment planning systems. The Monte Carlo code BEAMnrc was used to generate kV CBCT beams and DOSXYZnrc was used to calculate dose to the patient using volumetric CBCTimages. The dose‐volume‐histograms (DVHs) for organs were evaluated. Results: For a representative head‐and‐neck cancer patient treated with 33 fractions of IMRT, if the kV‐CBCT procedure was used in each treatment fraction, the accumulative imagingdose can total 230 cGy to 95% of the eye volume, a minimum of 132 cGy to 90% of the soft tissues within the imaged volume, and 528 cGy to 90% of the cervical vertebral volume. Conclusion: The DVH analyses show significant doses to radiosensitive organs from repeated CBCT procedures. Hence, the management of imagingdose during radiotherapy is important to reduce the risk of complications. Total therapeuticdose limits in the treatment planning process may need to include the additional x‐ray dose to radiosensitive organs from image‐guidance procedures.