Head CT dose reduction with organ-based tube current modulation.

BACKGROUND: A helical head CT examination uses a pitch factor (PF) of <1.0, resulting in a part of the slice being directly irradiated twice. This raises the possibility of double irradiation, which may increase the amount of radiation to the lens. Organ-based tube current modulation (OBTCM) is an effective method for reducing lens exposure because it reduces the dose to the anterior aspect of the patient. However, it is challenging to visualize the complex dose distribution when factoring in double irradiation. PURPOSE: To visualize twice-irradiated areas in helical head CT in three dimensions and to clarify the exposure reduction effect of OBTCM. MATERIAL AND METHODS: A leuco crystal violet (LCV) dosimeter was placed into an empty polyethylene terephthalate bottle 16.5 cm in diameter. Helical scans were performed without and with OBTCM using the following parameters: tube voltage 120 kV, tube current 600 mA, pitch factor 0.637, rotation time 0.5 s, 80 (detector rows) × 0.5 mm (detector collimation), and ten scans. Exposed areas were visualized using an optical computed tomography (OCT) system designed by our group. The dose reduction rate of OBTCM was defined as the ratio of the average values of the histogram with the dose value on the x-axis and the frequency on the y-axis without and with OBTCM at 90° to the anterior midline. RESULTS: The LCV dosimeter visualized the spiral-shaped twice-irradiated areas. Double irradiation resulted in a dose of 2.19/1.90 and 1.38/1.19 Gy (15.0% and 15.9% increase) without and with OBTCM, respectively. The dose reduction using OBTCM was 29.6% at 90° anterolateral. CONCLUSION: The LCV dosimeter visualized the complex three-dimensional irradiated areas and enabled dose measurement in twice-irradiated areas. Increased exposure from double irradiation was attenuated by OBTCM.

Mixed Reality Visualization of Radiation Dose for Health Professionals and Patients in Interventional Radiology.

For interventional radiology, dose management has persisted as a crucially important issue to reduce radiation exposure to patients and medical staff. This study designed a real-time dose visualization system for interventional radiology designed with mixed reality technology and Monte Carlo simulation. An earlier report described a Monte-Carlo-based estimation system, which simulates a patient's skin dose and air dose distributions, adopted for our system. We also developed a system of acquiring fluoroscopic conditions to input them into the Monte Carlo system. Then we combined the Monte Carlo system with a wearable device for three-dimensional holographic visualization. The estimated doses were transferred sequentially to the device. The patient's dose distribution was then projected on the patient body. The visualization system also has a mechanism to detect one's position in a room to estimate the user's exposure dose to detect and display the exposure level. Qualitative tests were conducted to evaluate the workload and usability of our mixed reality system. An end-to-end system test was performed using a human phantom. The acquisition system accurately recognized conditions that were necessary for real-time dose estimation. The dose hologram represents the patient dose. The user dose was changed correctly, depending on conditions and positions. The perceived overall workload score (33.50) was lower than the scores reported in the literature for medical tasks (50.60) for computer activities (54.00). Mixed reality dose visualization is expected to improve exposure dose management for patients and health professionals by exhibiting the invisible radiation exposure in real space.

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.

Classification of optical coherence tomography images using a capsule network.

BACKGROUND: Classification of optical coherence tomography (OCT) images can be achieved with high accuracy using classical convolution neural networks (CNN), a commonly used deep learning network for computer-aided diagnosis. Classical CNN has often been criticized for suppressing positional relations in a pooling layer. Therefore, because capsule networks can learn positional information from images, we attempted application of a capsule network to OCT images to overcome that shortcoming. This study is our attempt to improve classification accuracy by replacing CNN with a capsule network. METHODS: From an OCT dataset, we produced a training dataset of 83,484 images and a test dataset of 1000 images. For training, the dataset comprises 37,205 images with choroidal neovascularization (CNV), 11,348 with diabetic macular edema (DME), 8616 with drusen, and 26,315 normal images. The test dataset has 250 images from each category. The proposed model was constructed based on a capsule network for improving classification accuracy. It was trained using the training dataset. Subsequently, the test dataset was used to evaluate the trained model. RESULTS: Classification of OCT images using our method achieved accuracy of 99.6%, which is 3.2 percentage points higher than that of other methods described in the literature. CONCLUSION: The proposed method achieved classification accuracy results equivalent to those reported for other methods for CNV, DME, drusen, and normal images.

Immersive Radiation Experience for Interventional Radiology with Virtual Reality Radiation Dose Visualization Using Fast Monte Carlo Dose Estimation.

For interventional radiology (IR), understanding the precise dose distribution is crucial to reduce the risks of radiation dermatitis to patients and staff. Visualization of dose distribution is expected to support radiation safety efforts immensely. This report presents techniques for perceiving the dose distribution using virtual reality (VR) technology and for estimating the air dose distribution accurately using Monte Carlo simulation for VR dose visualization. We adopted an earlier reported Monte-Carlo-based estimation system for IR and simulated the dose in a geometrical area resembling an IR room with fluoroscopic conditions. Users of our VR system experienced a simulated air dose distribution in the IR room while the irradiation angle, irradiation timing, and lead shielding were controlled. The estimated air dose was evaluated through comparison with measurements taken using a radiophotoluminescence glass dosimeter. Our dose estimation results were consistent with dosimeter readings, showing a 13.5% average mutual difference. The estimated air dose was visualized in VR: users could view a virtual IR room and walk around in it. Using our VR system, users experienced dose distribution changes dynamically with C-arm rotation. Qualitative tests were conducted to evaluate the workload and usability of our VR system. The perceived overall workload score (18.00) was lower than the scores reported in the literature for medical tasks (50.60) and computer activities (54.00). This VR visualization is expected to open new horizons for understanding dose distributions intuitively, thereby aiding the avoidance of radiation injury.

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.

Estimation of free radical formation by beta-ray irradiation in rat liver.

In vivo free radical reactions in rat liver as a result of exposure to low-dose beta-radiation was evaluated with electron paramagnetic resonance (EPR) spectroscopy by monitoring the reduction of the nitroxyl spin probe after intravenous administration. The EPR signal intensity of a nitroxyl probe as a function of time in bile flow was monitored by cannulating the bile duct through the cavity of an X-band EPR spectrometer. The results show that the rate of nitroxyl signal loss was higher in rats whose livers were exposed to beta-rays compared to unexposed rats. However, the rate of signal loss was lower in animals whose organs were exposed to air by opening the abdominal cavity. In vitro experiments also showed that the nitroxyl EPR signal loss was greater in an atmosphere of nitrogen than in air. Results suggest that under low levels of tissue oxygen, exposure to beta-rays results in nitroxyl signal loss, which may be mediated by free radical dependent pathways. When tissue oxygen were higher, hydrogen peroxide mediated oxidation of hydroxylamine may predominate resulting in a signal loss of smaller magnitudes. This study shows possible evidence of reactive oxygen species formation by low-dose beta-ray irradiation in a living animal.