Yoshiyuki Umezu

Investigation of interfractional shape variations based on statistical point distribution model for prostate cancer radiation therapy

Purpose The setup errors and organ motion errors pertaining to clinical target volume (CTV) have been considered as two major causes of uncertainties in the determination of the CTV‐to‐planning target volume (PTV) margins for prostate cancer radiation treatment planning. We based our study on the assumption that interfractional target shape variations are not negligible as another source of uncertainty for the determination of precise CTV‐to‐PTV margins. Thus, we investigated the interfractional shape variations of CTVs based on a point distribution model (PDM) for prostate cancer radiation therapy. Materials and methods To quantitate the shape variations of CTVs, the PDM was applied for the contours of 4 types of CTV regions (low‐risk, intermediate‐ risk, high‐risk CTVs, and prostate plus entire seminal vesicles), which were delineated by considering prostate cancer risk groups on planning computed tomography (CT) and cone beam CT (CBCT) images of 73 fractions of 10 patients. The standard deviations (SDs) of the interfractional random errors for shape variations were obtained from covariance matrices based on the PDMs, which were generated from vertices of triangulated CTV surfaces. The correspondences between CTV surface vertices were determined based on a thin‐plate spline robust point matching algorithm. The systematic error for shape variations was defined as the average deviation between surfaces of an average CTV and planning CTVs, and the random error as the average deviation of CTV surface vertices for fractions from an average CTV surface. Results The means of the SDs of the systematic errors for the four types of CTVs ranged from 1.0 to 2.0 mm along the anterior direction, 1.2 to 2.6 mm along the posterior direction, 1.0 to 2.5 mm along the superior direction, 0.9 to 1.9 mm along the inferior direction, 0.9 to 2.6 mm along the right direction, and 1.0 to 3.0 mm along the left direction. Concerning the random errors, the means of the SDs ranged from 0.9 to 1.2 mm along the anterior direction, 1.0 to 1.4 mm along the posterior direction, 0.9 to 1.3 mm along the superior direction, 0.8 to 1.0 mm along the inferior direction, 0.8 to 0.9 mm along the right direction, and 0.8 to 1.0 mm along the left direction. Conclusions Since the shape variations were not negligible for intermediate and high‐risk CTVs, they should be taken into account for the determination of the CTV‐to‐PTV margins in radiation treatment planning of prostate cancer.

Exploration of temporal stability and prognostic power of radiomic features based on electronic portal imaging device images

PURPOSE We aimed to explore the temporal stability of radiomic features in the presence of tumor motion and the prognostic powers of temporally stable features. METHODS We selected single fraction dynamic electronic portal imaging device (EPID) (n = 275 frames) and static digitally reconstructed radiographs (DRRs) of 11 lung cancer patients, who received stereotactic body radiation therapy (SBRT) under free breathing. Forty-seven statistical radiomic features, which consisted of 14 histogram-based features and 33 texture features derived from the graylevel co-occurrence and graylevel run-length matrices, were computed. The temporal stability was assessed by using a multiplication of the intra-class correlation coefficients (ICCs) between features derived from the EPID and DRR images at three quantization levels. The prognostic powers of the features were investigated using a different database of lung cancer patients (n = 221) based on a Kaplan-Meier survival analysis. RESULTS Fifteen radiomic features were found to be temporally stable for various quantization levels. Among these features, seven features have shown potentials for prognostic prediction in lung cancer patients. CONCLUSIONS This study suggests a novel approach to select temporally stable radiomic features, which could hold prognostic powers in lung cancer patients.

Computational analysis of interfractional anisotropic shape variations of the rectum in prostate cancer radiation therapy

PURPOSE To analyze the uncertainties of the rectum due to anisotropic shape variations by using a statistical point distribution model (PDM). MATERIALS AND METHODS The PDM was applied to the rectum contours that were delineated on planning computed tomography (CT) and cone-beam CT (CBCT) at 80 fractions of 11 patients. The standard deviations (SDs) of systematic and random errors of the shape variations of the whole rectum and the region in which the rectum overlapped with the PTV (ROP regions) were derived from the PDMs at all fractions of each patient. The systematic error was derived by using the PDMs of planning and average rectum surface determined from rectum surfaces at all fractions, while the random error was derived by using a PDM-based covariance matrix at all fractions of each patient. RESULTS Regarding whole rectum, the population SDs were larger than 1.0 mm along all directions for random error, and along the anterior, superior, and inferior directions for systematic error. The deviation is largest along the superior and inferior directions for systematic and random errors, respectively. For ROP regions, the population SDs of systematic error were larger than 1.0 mm along the superior and inferior directions. The population SDs of random error for the ROP regions were larger than 1.0 mm except along the right and posterior directions. CONCLUSIONS The anisotropic shape variations of the rectum, especially in the ROP regions, should be considered when determining a planning risk volume (PRV) margins for the rectum associated with the acute toxicities.

Bayesian delineation framework of clinical target volumes for prostate cancer radiotherapy using an anatomical-features-based machine learning technique

Our aim was to develop a Bayesian delineation framework of clinical target volumes (CTVs) for prostate cancer radiotherapy using an anatomical-features-based machine learning (AF-ML) technique. Probabilistic atlases (PAs) of the pelvic bone and the CTV were generated from 43 training cases. Translation vectors, which could move the CTV PAs to CTV locations, were estimated using the AF-ML after a bone-based registration between the PAs and planning computed tomography (CT) images. An input vector derived from 11 AF points was fed to three AF-ML techniques (artificial neural network: ANN, random forest: RF, support vector machine: SVM). The AF points were selected from edge points and centroids of anatomical structures around prostate. Reference translation vectors between centroids of CTV PAs and CTVs were given to the AF-ML as teaching data. The CTV regions were extracted by thresholding posterior probabilities produced by using the Bayesian inference with the translated CTV PA and likelihoods of planning CT values. The framework was evaluated based on a leave-one-out test with CTV contours determined by radiation oncologists. Average location errors of CTV PAs along the anterior-posterior and superior-inferior directions without AF-ML were 5.7±4.6 mm and 5.5±4.3 mm, respectively, whereas the errors along the two directions with ANN, which showed the best performance, were 2.4±1.7 mm and 2.2±2.2 mm, respectively. The average Dice’s similarity coefficient between reference and estimated CTVs for 44 test cases were 0.81±0.062 with ANN. The framework using AF-ML could accurately estimate CTVs of prostate cancer radiotherapy.

Characteristics of Smoothing Filters to Achieve the Guideline Recommended Positron Emission Tomography Image without Harmonization

Objective(s): The aim of this study is to examine the effect of different smoothing filters on the image quality and SUVmax to achieve the guideline recommended positron emission tomography (PET) image without harmonization. Methods: We used a Biograph mCT PET scanner. A National Electrical Manufacturers Association (NEMA) the International Electrotechnical Commission (IEC) body phantom was filled with 18F solution with a background activity of 2.65 kBq/mL and a sphere-to-background ratio of 4. PET images obtained with the Biograph mCT PET scanner were reconstructed using the ordered subsets-expectation maximization (OSEM) algorithm with time-of-flight (TOF) models (iteration, 2; subset, 21); smoothing filters including the Gaussian, Butterworth, Hamming, Hann, Parzen, and Shepp-Logan filters with various full width at half maximum (FWHM) values (1-15 mm) were applied. The image quality was physically assessed according to the percent contrast (QH,10), background variability (N10), standardized uptake value (SUV), and recovery coefficient (RC). The results were compared with the guideline recommended range proposed by the Japanese Society of Nuclear Medicine and the Japanese Society of Nuclear Medicine Technology. The PET digital phantom was developed from the digital reference object (DRO) of the NEMA IEC body phantom smoothed using a Gaussian filter with a 10-mm FWHM and defined as the reference image. The difference in the SUV between the PET image and the reference image was evaluated according to the root mean squared error (RMSE). Results: The FWHMs of the Gaussian, Butterworth, Hamming, Hann, Parzen, and Shepp-Logan filters that satisfied the image quality of the FDG-PET/CT standardization guideline criteria were 8-12 mm, 9-11 mm, 9-13 mm, 10-13 mm, 9-11 mm, and 12-15 mm, respectively. The FWHMs of the Gaussian, Butterworth, Hamming, Hann, Parzen, and Shepp-Logan filters that provided the smallest RMSE between the PET images and the 3D digital phantom were 7 mm, 8 mm, 8 mm, 8 mm, 7 mm, and 11 mm, respectively. Conclusion: The suitable FWHM for image quality or SUVmax depends on the type of smoothing filter that is applied.

Reconstruction of four-dimensional computed tomography images during treatment time using electronic portal imaging device images based on a dynamic 2D/3D registration

The goal of our study was to develop a computational framework for reconstruction of four-dimensional computed tomography (4D-CT) images during treatment time using electronic portal imaging device (EPID) images based on a dynamic 2D/3D registration. The 4D-CT images during treatment time (“treatment” 4D-CT images) were reconstructed by performing an affine transformation-based dynamic 2D/3D registration between dynamic clinical portal dose images (PDIs) derived from the EPID images with planning CT images through planning PDIs for all frames. Elements of the affine transformation matrices (transformation parameters) were optimized using a Levenberg-Marquardt (LM) algorithm so that the planning PDIs could be similar to the dynamic clinical PDIs for all frames. Initial transformation parameters in each frame should be determined for finding optimum transformation parameters in the LM algorithm. In this study, the optimum transformation parameters in a frame employed as the initial transformation parameters for optimizing the transformation parameter in the consecutive frame. Gamma pass rates (3 mm/3%) were calculated for evaluating a similarity of the dose distributions between the dynamic clinical PDIs and “treatment” PDIs, which were calculated from “treatment” 4D-CT images, for all frames. The framework was applied to eight lung cancer patients who were treated with stereotactic body radiation therapy (SBRT). A mean of the average gamma pass rates between the dynamic clinical PDIs and the “treatment” PDIs for all frames was 98.3±1.2% for eight cases. In conclusion, the proposed framework makes it possible to dynamically monitor patients’ movement during treatment time.

Reduction Method of Operator and Medical Staff Radiation Exposure in Balloon Pulmonary Angioplasty for Chronic Thromboembolic Pulmonary Hypertension

The purpose of this study is to investigate a reduction method of radiation exposure for operator and medical staff in balloon pulmonary angioplasty (BPA) for chronic thromboembolic pulmonary hypertension (CTEPH). We devised a new radiation protection, which is U-shaped acrylic supporting table with 0.35 mmPb unleaded radiation protection sheet. A human phantom was put on the bed of cardiac angiography system [C-arm angulation: posteroanterior (PA), L-arm angulation: left anterior oblique (LAO) 60°]. The ambient equivalent dose rate was measured under fluoroscopy with and without three radiation protections: U-shaped acrylic supporting table with 0.35 mmPb unleaded radiation protection sheet, radiation protection for the lower body, and radiation protection for the upper body. With the three radiation protections, the ambient equivalent dose rate was decreased more than 99% at the height of 100 cm above the floor at the operator position (PA: from 186.2 μSv/h to 0.5 μSv/h, LAO 60°: from 350.4 μSv/h to 1.6 μSv/h). Ambient equivalent dose rate at the other points are also decreased effectively. The devised dose reduction method can reduce operator and medical staff radiation exposure effectively and be set up without interference for BPA procedure.

Edge Artifacts in Point Spread Function-based PET Reconstruction in Relation to Object Size and Reconstruction Parameters

Objective(s): We evaluated edge artifacts in relation to phantom diameter and reconstruction parameters in point spread function (PSF)-based positron emission tomography (PET) image reconstruction. Methods: PET data were acquired from an original cone-shaped phantom filled with 18F solution (21.9 kBq/mL) for 10 min using a Biograph mCT scanner. The images were reconstructed using the baseline ordered subsets expectation maximization (OSEM) algorithm and the OSEM with PSF correction model. The reconstruction parameters included a pixel size of 1.0, 2.0, or 3.0 mm, 1-12 iterations, 24 subsets, and a full width at half maximum (FWHM) of the post-filter Gaussian filter of 1.0, 2.0, or 3.0 mm. We compared both the maximum recovery coefficient (RCmax) and the mean recovery coefficient (RCmean) in the phantom at different diameters. Results: The OSEM images had no edge artifacts, but the OSEM with PSF images had a dense edge delineating the hot phantom at diameters 10 mm or more and a dense spot at the center at diameters of 8 mm or less. The dense edge was clearly observed on images with a small pixel size, a Gaussian filter with a small FWHM, and a high number of iterations. At a phantom diameter of 6-7 mm, the RCmax for the OSEM and OSEM with PSF images was 60% and 140%, respectively (pixel size: 1.0 mm; FWHM of the Gaussian filter: 2.0 mm; iterations: 2). The RCmean of the OSEM with PSF images did not exceed 100%. Conclusion: PSF-based image reconstruction resulted in edge artifacts, the degree of which depends on the pixel size, number of iterations, FWHM of the Gaussian filter, and object size.

Evaluation of the distribution of activation inside a compact medical cyclotron

The distribution of activation inside a compact medical cyclotron was evaluated by measuring 1cm dose equivalent rates and γ-ray spectra. Analysis of the distribution of activation showed high activation at the deflector and the magnetic channel. Radionuclides 60Co, 57Co, 65Zn, and 54Mn were detected. Different radionuclides were generated from different components of the cyclotron, and low-activity radionuclides could be detected under low-background-radiation conditions.

SU-D-BRA-04: Computerized Framework for Marker-Less Localization of Anatomical Feature Points in Range Images Based On Differential Geometry Features for Image-Guided Radiation Therapy

PURPOSE To propose a computerized framework for localization of anatomical feature points on the patient surface in infrared-ray based range images by using differential geometry (curvature) features. METHODS The general concept was to reconstruct the patient surface by using a mathematical modeling technique for the computation of differential geometry features that characterize the local shapes of the patient surfaces. A region of interest (ROI) was firstly extracted based on a template matching technique applied on amplitude (grayscale) images. The extracted ROI was preprocessed for reducing temporal and spatial noises by using Kalman and bilateral filters, respectively. Next, a smooth patient surface was reconstructed by using a non-uniform rational basis spline (NURBS) model. Finally, differential geometry features, i.e. the shape index and curvedness features were computed for localizing the anatomical feature points. The proposed framework was trained for optimizing shape index and curvedness thresholds and tested on range images of an anthropomorphic head phantom. The range images were acquired by an infrared ray-based time-of-flight (TOF) camera. The localization accuracy was evaluated by measuring the mean of minimum Euclidean distances (MMED) between reference (ground truth) points and the feature points localized by the proposed framework. The evaluation was performed for points localized on convex regions (e.g. apex of nose) and concave regions (e.g. nasofacial sulcus). RESULTS The proposed framework has localized anatomical feature points on convex and concave anatomical landmarks with MMEDs of 1.91±0.50 mm and 3.70±0.92 mm, respectively. A statistically significant difference was obtained between the feature points on the convex and concave regions (P<0.001). CONCLUSION Our study has shown the feasibility of differential geometry features for localization of anatomical feature points on the patient surface in range images. The proposed framework might be useful for tasks involving feature-based image registration in range-image guided radiation therapy.