A grayscale compression method to segment bone structures for 2D-3D registration of setup images in non-coplanar radiotherapy.

Purpose. To evaluate the performance of an automated 2D-3D bone registration algorithm incorporating a grayscale compression method for quantifying patient position errors in non-coplanar radiotherapy.Methods. An automated 2D-3D registration incorporating a grayscale compression method to segment bone structures was proposed. Portal images containing only bone structures (Portalbone) and digitally reconstructed radiographs containing only bone structures (DRRbone) were used for registration. First, the portal image was filtered by a high-pass finite impulse response (FIR) filter. Then the grayscale range of the filtered portal image was compressed. Thresholds were determined based on the difference in gray values of bone structures in the filtered and compressed portal image to obtainPortalbone.Another threshold was applied to generateDRRbonewhen the CT image uses the ray-casting algorithm to generate DRR images. The compression performance was assessed by registering theDRRbonewith thePortalboneobtained by compressing the portal image into various grayscale ranges. The proposed registration method was quantitatively and visually validated using (1) a CT image of an anthropomorphic head phantom and its portal images obtained in different poses and (2) CT images and pre-treatment portal images of 20 patients treated with non-coplanar radiotherapy.Results. Mean absolute registration errors for the best compression grayscale range test were 0.642 mm, 0.574 mm, and 0.643 mm, with calculation times of 50.6 min, 42.2 min, and 49.6 min for grayscale ranges of 0-127, 0-63 and 0-31, respectively. For the accuracy validation (1), the mean absolute registration errors for couch angles 0°, 45°, 90°, 270°, and 315° were 0.694 mm, 0.839 mm, 0.726 mm, 0.833 mm, and 0.873 mm, respectively. Among the six transformation parameters, the translation error in the vertical direction contributed the most to the registration errors. Visual inspection of the patient registration results revealed success in every instance.Conclusions. The implemented grayscale compression method successfully enhances and segments bone structures in portal images, allowing for accurate determination of patient setup errors in non-coplanar radiotherapy.

Combining autoencoder with clustering analysis for anomaly detection in radiotherapy plans.

Background: To develop an unsupervised anomaly detection method to identify suspicious error-prone treatment plans in radiotherapy. Methods: A total of 577 treatment plans of breast cancer patients were used in this study. They were labeled as either normal or abnormal plans by experienced clinicians. Multiple features of each plan were extracted and selected by the learning algorithms. The training set consisted of feature samples from 400 normal plans and the testing set consisted of feature samples from 158 normal plans and 19 abnormal plans. Using the k-means clustering algorithm in the training stage, 4 normal plan clusters were formed. The distance between the samples in the testing set and the cluster centers were then determined. To evaluate the effect of dimensionality reduction (DR) on detection accuracy, principal component analysis (PCA) and autoencoder (AE) methods were compared. Results: The sensitivity of the anomaly detection model based on PCA and AE methods were 84.2% (16/19) and 94.7% (18/19), respectively. The specificity of the anomaly detection model based on PCA and AE methods were 64.6% (102/158) and 69.0% (109/158), respectively. The areas under the receiver operating characteristic (ROC) curve (AUCs) based on PCA and AE methods were 0.81 and 0.90, respectively. Conclusions: The unsupervised learning method was effective for detecting anomalies from the feature samples. Accuracy could be improved with the introduction of AE-based DR technique. The combination of AE and k-means clustering methods provides an automated way to identify abnormal plans among clinical treatment plans in radiotherapy.

A subregion-based prediction model for local-regional recurrence risk in head and neck squamous cell carcinoma.

BACKGROUND AND PURPOSE: Given that the intratumoral heterogeneity of head and neck squamous cell carcinoma may be related to the local control rate of radiotherapy, the aim of this study was to construct a subregion-based model that can predict the risk of local-regional recurrence, and to quantitatively assess the relative contribution of subregions. MATERIALS AND METHODS: The CT images, PET images, dose images and GTVs of 228 patients with head and neck squamous cell carcinoma from four different institutions of the The Cancer Imaging Archive(TCIA) were included in the study. Using a supervoxel segmentation algorithm called maskSLIC to generate individual-level subregions. After extracting 1781 radiomics and 1767 dosiomics features from subregions, an attention-based multiple instance risk prediction model (MIR) was established. The GTV model was developed based on the whole tumour area and was used to compare the prediction performance with the MIR model. Furthermore, the MIR-Clinical model was constructed by integrating the MIR model with clinical factors. Subregional analysis was carried out through the Wilcoxon test to find the differential radiomic features between the highest and lowest weighted subregions. RESULTS: Compared with the GTV model, the C-index of MIR model was significantly increased from 0.624 to 0.721(Wilcoxon test, p value < 0.0001). When MIR model was combined with clinical factors, the C-index was further increased to 0.766. Subregional analysis showed that for LR patients, the top three differential radiomic features between the highest and lowest weighted subregions were GLRLM_ShortRunHighGrayLevelEmphasis, GRLM_HghGrayLevelRunEmphasis and GLRLM_LongRunHighGrayLevelEmphasis. CONCLUSION: This study developed a subregion-based model that can predict the risk of local-regional recurrence and quantitatively assess relevant subregions, which may provide technical support for the precision radiotherapy in head and neck squamous cell carcinoma.

A two-layer cylinder phantom developed for film-based isocenter verification of radiotherapy machine.

PURPOSE: A two-layer cylinder (TLC) phantom was developed for simplifying film-based isocenter verification of linear accelerators in radiotherapy. METHODS AND MATERIALS: The phantom mainly consists of two parts: (1) two nested solid cylinders between which a radiochromic film can be inserted and irradiated; (2) a tungsten ball supported by a thin rod and located at the phantom center for alignment with the mechanical isocenter. In practice, the phantom was first positioned by the room laser to align the tungsten ball to the mechanical isocenter of the linear accelerator. Then, a radiochromic film was precisely inserted into the gap between the two cylinders of the phantom and irradiated by beams with preset gantry and couch angles. Later the irradiated film was scanned and processed by an in-house developed analysis software. Finally, the offset of the radiation isocenter from the mechanical isocenter was determined by the built-in three-dimensional (3D) reconstruction algorithms. The accuracy of this method was evaluated by positioning the phantom with a known couch shift, then checking the residual error after couch shift correction. The reliability of this method was evaluated by comparing the calculated offset with the corresponding result determined by the traditional star-shot method. RESULTS: For the accuracy test, the residual errors were -0.14 ± 0.03 mm, 0.05 ± 0.06 mm, and 0.05 ± 0.06 mm in the lateral, longitudinal, and vertical axes, respectively. For the reliability test, the differences between the calculated offset and the result determined by the star-shot method were -0.10 mm, 0.12 mm, and 0.12 mm in the lateral, longitudinal, and vertical axes, respectively. CONCLUSION: The proposed method is able to reconstruct beams in 3D with one film, which is more time-saving and accurate. Additionally, with this design, the phantom positioning, film loading, beam delivery, and data analyzing are simpler. This phantom and analysis software provides an efficient and effective way to perform film-based isocenter verification of linear accelerators in radiotherapy.