Classification of chest X-ray images by incorporation of medical domain knowledge into operation branch networks.

BACKGROUND: This study was conducted to alleviate a common difficulty in chest X-ray image diagnosis: The attention region in a convolutional neural network (CNN) does not often match the doctor's point of focus. The method presented herein, which guides the area of attention in CNN to a medically plausible region, can thereby improve diagnostic capabilities. METHODS: The model is based on an attention branch network, which has excellent interpretability of the classification model. This model has an additional new operation branch that guides the attention region to the lung field and heart in chest X-ray images. We also used three chest X-ray image datasets (Teikyo, Tokushima, and ChestX-ray14) to evaluate the CNN attention area of interest in these fields. Additionally, after devising a quantitative method of evaluating improvement of a CNN's region of interest, we applied it to evaluation of the proposed model. RESULTS: Operation branch networks maintain or improve the area under the curve to a greater degree than conventional CNNs do. Furthermore, the network better emphasizes reasonable anatomical parts in chest X-ray images. CONCLUSIONS: The proposed network better emphasizes the reasonable anatomical parts in chest X-ray images. This method can enhance capabilities for image interpretation based on judgment.

Calculating and estimating second cancer risk from breast radiotherapy using Monte Carlo code with internal body scatter for each out-of-field organ.

Out-of-field organs are not commonly designated as dose calculation targets during radiation therapy treatment planning, but they might entail risks of second cancer. Risk components include specific internal body scatter, which is a dominant source of out-of-field doses, and head leakage, which can be reduced by external shielding. Our simulation study quantifies out-of-field organ doses and estimates second cancer risks attributable to internal body scatter in whole-breast radiotherapy (WBRT) with or without additional regional nodal radiotherapy (RNRT), respectively, for right and left breast cancer using Monte Carlo code PHITS. Simulations were conducted using a complete whole-body female model. Second cancer risk was estimated using the calculated doses with a concept of excess absolute risk. Simulation results revealed marked differences between WBRT alone and WBRT plus RNRT in out-of-field organ doses. The ratios of mean doses between them were as large as 3.5-8.0 for the head and neck region and about 1.5-6.6 for the lower abdominal region. Potentially, most out-of-field organs had excess absolute risks of less than 1 per 10,000 persons-year. Our study surveyed the respective contributions of internal body scatter to out-of-field organ doses and second cancer risks in breast radiotherapy on this intact female model.

First experience of 192Ir source stuck event during high-dose-rate brachytherapy in Japan.

PURPOSE: To share the experience of an iridium-192 (192Ir) source stuck event during high-dose-rate (HDR) brachytherapy for cervical cancer. MATERIAL AND METHODS: In 2014, we experienced the first source stuck event in Japan when treating cervical cancer with HDR brachytherapy. The cause of the event was a loose screw in the treatment device that interfered with the gear reeling the source. This event had minimal clinical effects on the patient and staff; however, after the event, we created a normal treatment process and an emergency process. In the emergency processes, each staff member is given an appropriate role. The dose rate distribution calculated by the new Monte Carlo simulation system was used as a reference to create the process. RESULTS: According to the calculated dose rate distribution, the dose rates inside the maze, near the treatment room door, and near the console room were ≅ 10-2 [cGy · h-1], 10-3 [cGy · h-1], and << 10-3 [cGy · h-1], respectively. Based on these findings, in the emergency process, the recorder was evacuated to the console room, and the rescuer waited inside the maze until the radiation source was recovered. This emergency response manual is currently a critical workflow once a year with vendors. CONCLUSIONS: We reported our experience of the source stuck event. Details of the event and proposed emergency process will be helpful in managing a patient safety program for other HDR brachytherapy users.

Hepatic tumor classification using texture and topology analysis of non-contrast-enhanced three-dimensional T1-weighted MR images with a radiomics approach.

The purpose of this study is to evaluate the accuracy for classification of hepatic tumors by characterization of T1-weighted magnetic resonance (MR) images using two radiomics approaches with machine learning models: texture analysis and topological data analysis using persistent homology. This study assessed non-contrast-enhanced fat-suppressed three-dimensional (3D) T1-weighted images of 150 hepatic tumors. The lesions included 50 hepatocellular carcinomas (HCCs), 50 metastatic tumors (MTs), and 50 hepatic hemangiomas (HHs) found respectively in 37, 23, and 33 patients. For classification, texture features were calculated, and also persistence images of three types (degree 0, degree 1 and degree 2) were obtained for each lesion from the 3D MR imaging data. We used three classification models. In the classification of HCC and MT (resp. HCC and HH, HH and MT), we obtained accuracy of 92% (resp. 90%, 73%) by texture analysis, and the highest accuracy of 85% (resp. 84%, 74%) when degree 1 (resp. degree 1, degree 2) persistence images were used. Our methods using texture analysis or topological data analysis allow for classification of the three hepatic tumors with considerable accuracy, and thus might be useful when applied for computer-aided diagnosis with MR images.

Image quality improvement in cone-beam CT using the super-resolution technique.

This study was conducted to improve cone-beam computed tomography (CBCT) image quality using the super-resolution technique, a method of inferring a high-resolution image from a low-resolution image. This technique is used with two matrices, so-called dictionaries, constructed respectively from high-resolution and low-resolution image bases. For this study, a CBCT image, as a low-resolution image, is represented as a linear combination of atoms, the image bases in the low-resolution dictionary. The corresponding super-resolution image was inferred by multiplying the coefficients and the high-resolution dictionary atoms extracted from planning CT images. To evaluate the proposed method, we computed the root mean square error (RMSE) and structural similarity (SSIM). The resulting RMSE and SSIM between the super-resolution images and the planning CT images were, respectively, as much as 0.81 and 1.29 times better than those obtained without using the super-resolution technique. We used super-resolution technique to improve the CBCT image quality.

Fast skin dose estimation system for interventional radiology.

To minimise the radiation dermatitis related to interventional radiology (IR), rapid and accurate dose estimation has been sought for all procedures. We propose a technique for estimating the patient skin dose rapidly and accurately using Monte Carlo (MC) simulation with a graphical processing unit (GPU, GTX 1080; Nvidia Corp.). The skin dose distribution is simulated based on an individual patient's computed tomography (CT) dataset for fluoroscopic conditions after the CT dataset has been segmented into air, water and bone based on pixel values. The skin is assumed to be one layer at the outer surface of the body. Fluoroscopic conditions are obtained from a log file of a fluoroscopic examination. Estimating the absorbed skin dose distribution requires calibration of the dose simulated by our system. For this purpose, a linear function was used to approximate the relation between the simulated dose and the measured dose using radiophotoluminescence (RPL) glass dosimeters in a water-equivalent phantom. Differences of maximum skin dose between our system and the Particle and Heavy Ion Transport code System (PHITS) were as high as 6.1%. The relative statistical error (2 σ) for the simulated dose obtained using our system was ≤3.5%. Using a GPU, the simulation on the chest CT dataset aiming at the heart was within 3.49 s on average: the GPU is 122 times faster than a CPU (Core i7-7700K; Intel Corp.). Our system (using the GPU, the log file, and the CT dataset) estimated the skin dose more rapidly and more accurately than conventional methods.