Purpose: Hyperthermia treatment planning for deep locoregional hyperthermia treatment may assist in phase and amplitude steering to optimize the temperature distribution. This study aims to... Show morePurpose: Hyperthermia treatment planning for deep locoregional hyperthermia treatment may assist in phase and amplitude steering to optimize the temperature distribution. This study aims to incorporate a physically correct description of bladder properties in treatment planning, notably the presence of convection and absence of perfusion within the bladder lumen, and to assess accuracy and clinical implications for non muscle invasive bladder cancer patients treated with locoregional hyperthermia. Methods: We implemented a convective thermophysical fluid model based on the Boussinesq approximation to the Navier-Stokes equations using the (finite element) OpenFOAM toolkit. A clinician delineated the bladder on CT scans obtained from 14 bladder cancer patients. We performed (1) conventional treatment planning with a perfused muscle-like solid bladder, (2) with bladder content properties without and (3) with flow dynamics. Finally, we compared temperature distributions predicted by the three models with temperature measurements obtained during treatment. Results: Much higher and more uniform bladder temperatures are predicted with physically accurate fluid modeling compared to previously employed muscle-like models. The differences reflect the homogenizing effect of convection, and the absence of perfusion. Median steady state temperatures simulated with the novel convective model (3) deviated on average -0.6 degrees C (-12%) from values measured during treatment, compared to -3.7 degrees C (-71%) and +1.5 degrees C (+29%) deviation for the muscle-like (1) and static (2) models, respectively. The Grashof number was 3.2 +/- 1.5 x 10(5) (mean +/- SD). Conclusions: Incorporating fluid modeling in hyperthermia treatment planning yields significantly improved predictions of the temperature distribution in the bladder lumen during hyperthermia treatment. Show less
Schoot, A.J.A.J. van de; Schooneveldt, G.; Wognum, S.; Hoogeman, M.S.; Chai, X.; Stalpers, L.J.A.; ... ; Bel, A. 2014
Purpose: The aim of this study is to develop and validate a generic method for automatic bladder segmentation on cone beam computed tomography (CBCT), independent of gender and treatment position ... Show morePurpose: The aim of this study is to develop and validate a generic method for automatic bladder segmentation on cone beam computed tomography (CBCT), independent of gender and treatment position (prone or supine), using only pretreatment imaging data.Methods: Data of 20 patients, treated for tumors in the pelvic region with the entire bladder visible on CT and CBCT, were divided into four equally sized groups based on gender and treatment position. The full and empty bladder contour, that can be acquired with pretreatment CT imaging, were used to generate a patient-specific bladder shape model. This model was used to guide the segmentation process on CBCT. To obtain the bladder segmentation, the reference bladder contour was deformed iteratively by maximizing the cross-correlation between directional grey value gradients over the reference and CBCT bladder edge. To overcome incorrect segmentations caused by CBCT image artifacts, automatic adaptations were implemented. Moreover, locally incorrect segmentations could be adapted manually. After each adapted segmentation, the bladder shape model was expanded and new shape patterns were calculated for following segmentations. All available CBCTs were used to validate the segmentation algorithm. The bladder segmentations were validated by comparison with the manual delineations and the segmentation performance was quantified using the Dice similarity coefficient (DSC), surface distance error (SDE) and SD of contour-to-contour distances. Also, bladder volumes obtained by manual delineations and segmentations were compared using a Bland-Altman error analysis.Results: The mean DSC, mean SDE, and mean SD of contour-to-contour distances between segmentations and manual delineations were 0.87, 0.27 cm and 0.22 cm (female, prone), 0.85, 0.28 cm and 0.22 cm (female, supine), 0.89, 0.21 cm and 0.17 cm (male, supine) and 0.88, 0.23 cm and 0.17 cm (male, prone), respectively. Manual local adaptations improved the segmentation results significantly (p < 0.01) based on DSC (6.72%) and SD of contour-to-contour distances (0.08 cm) and decreased the 95% confidence intervals of the bladder volume differences. Moreover, expanding the shape model improved the segmentation results significantly (p < 0.01) based on DSC and SD of contour-to-contour distances.Conclusions: This patient-specific shape model based automatic bladder segmentation method on CBCT is accurate and generic. Our segmentation method only needs two pretreatment imaging data sets as prior knowledge, is independent of patient gender and patient treatment position and has the possibility to manually adapt the segmentation locally. (C) 2014 American Association of Physicists in Medicine. Show less
