Estimation of heart-position variability in 3D-surface-image-guided deep-inspiration breath-hold radiation therapy for left-sided breast cancer

To investigate the heart position variability in deep-inspiration breath-hold (DIBH) radiation therapy (RT) for breast cancer when 3D surface imaging would be used for monitoring the BH depth during treatment delivery. For this purpose, surface setup data were compared with heart setup data. Twenty patients treated with DIBH-RT after breast-conserving surgery were included. Retrospectively, heart registrations were performed for cone-beam computed tomography (CBCT) to planning CT. Further, breast-surface registrations were performed for a surface, captured concurrently with CBCT, to planning CT. The resulting setup errors were compared with linear regression analysis. Furthermore, geometric uncertainties of the heart (systematic [Σ] and random [σ]) were estimated relative to the surface registration. Based on these uncertainties planning organ at risk volume (PRV) margins for the heart were calculated: 1.3Σ-0.5σ. Moderate correlation between surface and heart setup errors was found: R(2)=0.64, 0.37, 0.53 in left-right (LR), cranio-caudal (CC), and in anterior-posterior (AP) direction, respectively. When surface imaging would be used for monitoring, the geometric uncertainties of the heart (cm) are [Σ=0.14, σ=0.14]; [Σ=0.66, σ=0.38]; [Σ=0.27, σ=0.19] in LR; CC; AP. This results in PRV margins of 0.11; 0.67; 0.25cm in LR; CC; AP. When DIBH-RT after breast-conserving surgery is guided by the breast-surface position then PRV margins should be used to take into account the heart-position variability relative to the breast-surfac

3D surface imaging for monitoring intrafraction motion in frameless stereotactic body radiotherapy of lung cancer

To investigate the accuracy of surface imaging for monitoring intrafraction motion purposes in frameless stereotactic body radiotherapy (SBRT) of lung cancer by comparison with cone-beam computed tomography (CBCT). Thirty-six patients (18 males, 18 females) were included. During each fraction, three CBCT scans were acquired; CBCT1: before treatment, CBCT2: after correction for tumor misalignment, and CBCT3: after treatment. Intrafraction motion was derived by registering CBCT2 and CBCT3 to the mid-ventilation planning CT scan. Surfaces were captured concurrently with CBCT acquisitions. Retrospectively, for each set of surfaces, an average surface was created: Surface1, Surface2, and Surface3. Subsequently, Surface3 was registered to Surface2 to assess intrafraction motion. For the differences between CBCT- and surface-imaging-derived 3D intrafraction motions, group mean, systematic error, random error and limits of agreement (LOA) were calculated. Group mean, systematic and random errors were smaller for females than for males: 0.4 vs. 1.3, 1.3 vs. 3.1, and 1.7 vs. 3.3mm respectively. For female patients deviations between CBCT-tumor- and 3D-surface-imaging-derived intrafraction motions were between -3.3 and 4.3mm (95% LOA). For male patients these were substantially larger: -5.9-9.5mm. Surface imaging is a promising technology for monitoring intrafraction motion purposes in SBRT for female patient

Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patient-specific bladder model.

In multiple plan adaptive radiotherapy (ART) strategies of bladder cancer, a library of plans corresponding to different bladder volumes is created based on images acquired in early treatment sessions. Subsequently, the plan for the smallest PTV safely covering the bladder on cone-beam CT (CBCT) is selected as the plan of the day. The aim of this study is to develop an automatic bladder segmentation approach suitable for CBCT scans and test its ability to select the appropriate plan from the library of plans for such an ART procedure. Twenty-three bladder cancer patients with a planning CT and on average 11.6 CBCT scans were included in our study. For each patient, all CBCT scans were matched to the planning CT on bony anatomy. Bladder contours were manually delineated for each planning CT (for model building) and CBCT (for model building and validation). The automatic segmentation method consisted of two steps. A patient-specific bladder deformation model was built from the training data set of each patient (the planning CT and the first five CBCT scans). Then, the model was applied to automatically segment bladders in the validation data of the same patient (the remaining CBCT scans). Principal component analysis (PCA) was applied to the training data to model patient-specific bladder deformation patterns. The number of PCA modes for each patient was chosen such that the bladder shapes in the training set could be represented by such number of PCA modes with less than 0.1 cm mean residual error. The automatic segmentation started from the bladder shape of a reference CBCT, which was adjusted by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to fit the bladder in the validation image. A cost function was defined by the absolute difference between the directional gradient field of reference CBCT sampled on the corresponding bladder contour and the directional gradient field of validation CBCT sampled on the segmentation contour candidate. The cost function measured the goodness of fit of the segmentation on the validation image and was minimized using a simplex optimizer. For each validation CBCT image, the segmentations were done five times using a different reference CBCT. The one with the lowest cost function was selected as the final bladder segmentation. Volume- and distance-based metrics and the accuracy of plan selection were evaluated to quantify the performance. Two to four PCA modes were needed to represent the bladder shape variation with less than 0.1 cm average residual error for the training data of each patient. The automatically segmented bladders had a 78.5% mean conformity index with the manual delineations. The mean SD of the local residual error over all patients was 0.24 cm. The agreement of plan selection between automatic and manual bladder segmentations was 77.5%. PCA is an efficient method to describe patient-specific bladder deformation. The statistical-shape-based segmentation approach is robust to handle the relatively poor CBCT image quality and allows for fast and reliable automatic segmentation of the bladder on CBCT for selecting the appropriate plan from a library of plan

Accuracy evaluation of a 3-dimensional surface imaging system for guidance in deep-inspiration breath-hold radiation therapy

To investigate the applicability of 3-dimensional (3D) surface imaging for image guidance in deep-inspiration breath-hold radiation therapy (DIBH-RT) for patients with left-sided breast cancer. For this purpose, setup data based on captured 3D surfaces was compared with setup data based on cone beam computed tomography (CBCT). Twenty patients treated with DIBH-RT after breast-conserving surgery (BCS) were included. Before the start of treatment, each patient underwent a breath-hold CT scan for planning purposes. During treatment, dose delivery was preceded by setup verification using CBCT of the left breast. 3D surfaces were captured by a surface imaging system concurrently with the CBCT scan. Retrospectively, surface registrations were performed for CBCT to CT and for a captured 3D surface to CT. The resulting setup errors were compared with linear regression analysis. For the differences between setup errors, group mean, systematic error, random error, and 95% limits of agreement were calculated. Furthermore, receiver operating characteristic (ROC) analysis was performed. Good correlation between setup errors was found: R(2)=0.70, 0.90, 0.82 in left-right, craniocaudal, and anterior-posterior directions, respectively. Systematic errors were ≤0.17 cm in all directions. Random errors were ≤0.15 cm. The limits of agreement were -0.34-0.48, -0.42-0.39, and -0.52-0.23 cm in left-right, craniocaudal, and anterior-posterior directions, respectively. ROC analysis showed that a threshold between 0.4 and 0.8 cm corresponds to promising true positive rates (0.78-0.95) and false positive rates (0.12-0.28). The results support the application of 3D surface imaging for image guidance in DIBH-RT after BC

Feasibility of geometrical verification of patient set-up using body contours and computed tomography data.

BACKGROUND AND PURPOSE: Body contours can potentially be used for patient set-up verification in external-beam radiotherapy and might enable more accurate set-up of patients prior to irradiation. The aim of this study is to test the feasibility of patient set-up verification using a body contour scanner. MATERIAL AND METHODS: Body contour scans of 33 lung cancer and 21 head-and-neck cancer patients were acquired on a simulator. We assume that this dataset is representative for the patient set-up on an accelerator. Shortly before acquisition of the body contour scan, a pair of orthogonal simulator images was taken as a reference. Both the body contour scan and the simulator images were matched in 3D to the planning computed tomography scan. Movement of skin with respect to bone was quantified based on an analysis of variance method. RESULTS: Set-up errors determined with body-contours agreed reasonably well with those determined with simulator images. For the lung cancer patients, the average set-up errors (mm)+/-1 standard deviation (SD) for the left-right, cranio-caudal and anterior-posterior directions were 1.2+/-2.9, -0.8+/-5.0 and -2.3+/-3.1 using body contours, compared to -0.8+/-3.2, -1.0+/-4.1 and -1.2+/-2.4 using simulator images. For the head-and-neck cancer patients, the set-up errors were 0.5+/-1.8, 0.5+/-2.7 and -2.2+/-1.8 using body contours compared to -0.4+/-1.2, 0.1+/-2.1, -0.1+/-1.8 using simulator images. The SD of the set-up errors obtained from analysis of the body contours were not significantly different from those obtained from analysis of the simulator images. Movement of the skin with respect to bone (1 SD) was estimated at 2.3 mm for lung cancer patients and 1.7 mm for head-and-neck cancer patients. CONCLUSION: Measurement of patient set-up using a body-contouring device is possible. The accuracy, however, is limited by the movement of the skin with respect to the bone. In situations where the error in the patient set-up is relatively large, it is possible to reduce these errors using a computer-aided set-up technique based on contour informatio

Semiautomatic bladder segmentation on CBCT using a population-based model for multiple-plan ART of bladder cancer.

The aim of this study is to develop a novel semiautomatic bladder segmentation approach for selecting the appropriate plan from the library of plans for a multiple-plan adaptive radiotherapy (ART) procedure. A population-based statistical bladder model was first built from a training data set (95 bladder contours from 8 patients). This model was then used as constraint to segment the bladder in an independent validation data set (233 CBCT scans from the remaining 22 patients). All 3D bladder contours were converted into parametric surface representations using spherical harmonic expansion. Principal component analysis (PCA) was applied in the spherical harmonic-based shape parameter space to calculate the major variation of bladder shapes. The number of dominating PCA modes was chosen such that 95% of the total shape variation of the training data set was described. The automatic segmentation started from the bladder contour of the planning CT of each patient, which was modified by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to best fit the bladder boundary in the localization CBCT image. A cost function was defined to measure the goodness of fit of the segmentation on the localization CBCT image. The segmentation was obtained by minimizing this cost function using a simplex optimizer. After automatic segmentation, a fast manual correction method was provided to correct those bladders (parts) that were poorly segmented. Volume- and distance-based metrics and the accuracy of plan selection from multiple plans were evaluated to quantify the performance of the automatic and semiautomatic segmentation methods. For the training data set, only seven PCA modes were needed to represent 95% of the bladder shape variation. The mean CI overlap and residual error (SD) of automatic bladder segmentation over all of the validation data were 70.5% and 0.39 cm, respectively. The agreement of plan selection between automatic bladder segmentation and manual delineation was 56.7%. The automatic segmentation and visual assessment took on average 7.8 and 9.7 s, respectively. In 53.4% of the cases, manual correction was performed after automatic segmentation. The manual correction improved the mean CI overlap, mean residual error and plan selection agreement to 77.7%, 0.30 cm and 80.7%, respectively. Manual correction required on average 8.4 markers and took on average 35.5 s. The statistical shape-based segmentation approach allows automatic segmentation of the bladder on CBCT with moderate accuracy. Limited user intervention can quickly and reliably improve the bladder contours. This segmentation method is suitable to select the appropriate plan for multiple-plan ART of bladder cance

Kilo-voltage cone-beam computed tomography setup measurements for lung cancer patients;:First clinical results and comparison with electronic portal-imaging device.

PURPOSE: Kilovoltage cone-beam computed tomography (CBCT) has been developed to provide accurate soft-tissue and bony setup information. We evaluated clinical CBCT setup data and compared CBCT measurements with electronic portal imaging device (EPID) images for lung cancer patients. METHODS AND MATERIALS: The setup error for CBCT scans at the treatment unit relative to the planning CT was measured for 62 patients (524 scans). For 19 of these patients (172 scans) portal images were also made. The mean, systematic setup error (Sigma), and random setup error (sigma) were calculated for the CBCT and the EPID. The differences between CBCT and EPID and the rotational setup error derived from the CBCT were also evaluated. An offline shrinking action level correction protocol, based on the CBCT measurements, was used to reduce systematic setup errors and the impact of this protocol was evaluated. RESULTS: The CBCT setup errors were significantly larger than the EPID setup errors for the cranial-caudal and anterior-posterior directions (p < 0.05). The mean overall setup errors after correction measured with the CBCT were 0.2 mm (Sigma = 1.6 mm, sigma = 2.9 mm) in the left-right, -0.8 mm (Sigma = 1.7 mm, sigma = 4.0 mm) in cranial-caudal and 0.0 mm (Sigma = 1.5 mm, sigma = 2.0 mm) in the anterior-posterior direction. Using our correction protocol only 2 patients had mean setup errors larger than 5 mm, without this correction protocol 51% of the patients would have had a setup error larger than 5 mm. CONCLUSION: Use of CBCT scans provided more accurate information concerning the setup of lung cancer patients than did portal imagin