Radiological Physics and Technology最新文献

FLASH radiotherapy (FLASH-RT) has great potential to improve patient outcomes. It delivers radiation doses at an ultra-high dose rate (UHDR: ≥ 40 Gy/s) in a single instant or a few pulses. Much higher irradiation doses can be administered to tumors with FLASH-RT than with conventional dose rate (0.01-0.40 Gy/s) radiotherapy. UHDR irradiation can suppress toxicity in normal tissues while sustaining antitumor efficiency, which is referred to as the FLASH effect. However, the mechanisms underlying the effects of the FLASH remain unclear. To clarify these mechanisms, the development of simulation models that can contribute to treatment planning for FLASH-RT is still underway. Previous studies indicated that transient oxygen depletion or augmented reactions between secondary reactive species produced by irradiation may be involved in this process. To discuss the possible mechanisms of the FLASH effect and its clinical potential, we summarized the physicochemical, chemical, and biological perspectives as well as the development of simulation modeling for FLASH-RT.

In this work, an open beam-limiting device, consisting of a rectangular collimator to be coupled to an intraoral dental X-ray device, was made using recycled lead sheets as a radiation-absorbing element. The collimator was designed for 3D printing, and using Spektr 3.0 software, the number of lead sheets needed to absorb excess radiation was calculated. The rectangular collimator reduced the radiation dose to patients by 65% when using four layers of recycled lead sheets (saturating with a 70% reduction in radiation dose at the limit of eight or more sheets of lead). The rectangular collimator does not negatively impact the quality of the radiological image, is available as an open design for 3D printing, and can be built with materials that are easily accessible to the dentist, facilitating its use in clinical practice and reducing the patient's exposure to ionizing radiation.

Head holder attenuation affects brain perfusion single-photon emission computed tomography (SPECT) image quality. Here, we proposed a head holder-attenuation correction (AC) method using attenuation coefficient maps calculated by Chang's method from CT images. Then, we evaluated the effectiveness of the head holder-AC method by numerical phantom and clinical cerebral perfusion SPECT studies. In the numerical phantom, the posterior counts were 10.7% lower than the anterior counts without head holder-AC method. However, by performing head holder-AC, the posterior count recovered by approximately 6.8%, approaching the true value. In the clinical study, the normalized count ratio was significantly increased by performing the head holder-AC method in the posterior-middle cerebral artery, posterior cerebral artery and cerebellum regions. There were no significant increases in other regions. The head holder-AC method can correct the counts attenuated by the head holder.

This study aimed to evaluate the ability of deep learning reconstruction (DLR) compared to that of hybrid iterative reconstruction (IR) to depict small vessels on computed tomography (CT). DLR and two types of hybrid IRs were used for image reconstruction. The target vessels were the basilar artery (BA), superior cerebellar artery (SCA), anterior inferior cerebellar artery (AICA), and posterior inferior cerebellar artery (PICA). The peak value, ΔCT values defined as the difference between the peak value and background, and full width at half maximum (FWHM), were obtained from the profile curves. In all target vessels, the peak and ΔCT values of DLR were significantly higher than those of the two types of hybrid IR (p < 0.001). Compared to that associated with hybrid IR, the FWHM of DLR was significantly lower in the SCA (p < 0.001), AICA (p < 0.001), and PICA (p < 0.001). In conclusion, DLR has the potential to improve visualization of small vessels.

Computed tomography (CT) scanning protocols should be optimized to minimize the radiation dose necessary for imaging. The addition of computationally generated noise to the CT images facilitates dose reduction. The objective of this study was to develop a noise addition method that reproduces the complexity of the noise texture present in clinical images with directionality that varies over images according to the underlying anatomy, requiring only Digital Imaging and Communications in Medicine (DICOM) images as input data and commonly available phantoms for calibration. The developed method is based on the estimation of projection data by forward projection from images, the addition of Poisson noise, and the reconstruction of new images. The method was validated by applying it to images acquired from cylindrical and thoracic phantoms using source images with exposures up to 49 mAs and target images between 39 and 5 mAs. 2D noise spectra were derived for regions of interest in the generated low-dose images and compared with those from the scanner-acquired low-dose images. The root mean square difference between the standard deviations of noise was 4%, except for very low exposures in peripheral regions of the cylindrical phantom. The noise spectra from the corresponding regions of interest exhibited remarkable agreement, indicating that the complex nature of the noise was reproduced. A practical method for adding noise to CT images was presented, and the magnitudes of noise and spectral content were validated. This method may be used to optimize CT imaging.

To improve image quality for low-count bone scintigraphy using deep learning and evaluate their clinical applicability. Six hundred patients (training, 500; validation, 50; evaluation, 50) were included in this study. Low-count original images (75%, 50%, 25%, 10%, and 5% counts) were generated from reference images (100% counts) using Poisson resampling. Output (DL-filtered) images were obtained after training with U-Net using reference images as teacher data. Gaussian-filtered images were generated for comparison. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) to the reference image were calculated to determine image quality. Artificial neural network (ANN) value, bone scan index (BSI), and number of hotspots (Hs) were computed using BONENAVI analysis to assess diagnostic performance. Accuracy of bone metastasis detection and area under the curve (AUC) were calculated. PSNR and SSIM for DL-filtered images were highest in all count percentages. BONENAVI analysis values for DL-filtered images did not differ significantly, regardless of the presence or absence of bone metastases. BONENAVI analysis values for original and Gaussian-filtered images differed significantly at ≦25% counts in patients without bone metastases. In patients with bone metastases, BSI and Hs for original and Gaussian-filtered images differed significantly at ≦10% counts, whereas ANN values did not. The accuracy of bone metastasis detection was highest for DL-filtered images in all count percentages; the AUC did not differ significantly. The deep learning method improved image quality and bone metastasis detection accuracy for low-count bone scintigraphy, suggesting its clinical applicability.

Magnetic resonance imaging (MRI) is an indispensable diagnostic imaging technique used in the clinical setting. MRI is advantageous over X-ray and computed tomography (CT), because the contrast provided depends on differences in the density of various organ tissues. In addition to MRI systems in hospitals, more than 100 systems are used for research purposes in Japan in various fields, including basic scientific research, molecular and clinical investigations, and life science research, such as drug discovery, veterinary medicine, and food testing. For many years, additional preclinical imaging studies have been conducted in basic research in the fields of radiation technology, medical physics, and radiology. The preclinical MRI research includes studies using small-bore and whole-body MRI systems. In this review, we focus on the animal study using small-bore MRI systems as "preclinical MRI". The preclinical MRI can be used to elucidate the pathophysiology of diseases and for translational research. This review will provide an overview of previous preclinical MRI studies such as brain, heart, and liver disease assessments. Also, we provide an overview of the utility of preclinical MRI studies in radiological physics and technology.

Neutron capture therapy (NCT) with various concentrations of gadolinium (¹⁵⁷Gd) is one of the treatment modalities for glioblastoma (GBM) tumors. Current study aims to evaluate how variations of ¹⁵⁷Gd concentration and cell oxygen levels can affect the relative biological effectiveness (RBE) of gadolinium neutron capture therapy (GdNCT) technique through a hybrid Monte Carlo (MC) simulation approach. At first, Snyder phantom including a spherical tumor was simulated by Geant4 MC code and relevant energy electron spectra to different ¹⁵⁷Gd concentrations including 100, 250, 500, and 1000 ppm were calculated following the neutron irradiation of simulated phantom. Scored energy electron spectra were then imported to Monte Carlo damage simulation (MCDS) code to estimate RBE values (both RBE_SSB and RBE_DSB) at different gadolinium concentrations and oxygen levels from 10 to 100%. The results indicate that variations of ¹⁵⁷Gd can affect the energy spectrum of released secondary electrons including Auger electrons. Variation of gadolinium concentration from 100 to 1000 ppm in tumor region can change RBE_SSB and RBE_DSB values by about 0.1% and 0.5%, respectively. Besides, maximum variations of 4.3% and 2% were calculated for RBE_DSB and RBE_SSB when cell oxygen level changed from 10 to 100%. From the results, variations of considered gadolinium and oxygen concentrations during GdNCT can influence RBE values. Nevertheless, due to the not remarkable changes in the intensity of Auger electrons, a slight difference in RBE values would be expected at various ¹⁵⁷Gd concentrations, although considerable RBE changes were calculated relevant to the oxygen alternations inside tumor tissue.

This study evaluated the validity of internal target volumes (ITVs) defined by three- (3DCT) and four-dimensional computed tomography (4DCT), and subsequently compared them with actual movements during treatment. Five patients with upper lobe lung tumors were treated with stereotactic body radiotherapy (SBRT) at 48 Gy in four fractions. Planning 3DCT images were acquired with peak-exhale and peak-inhale breath-holds, and 4DCT images were acquired in the cine mode under free breathing. Cine images were acquired using an electronic portal imaging device during irradiation. Tumor coverage was evaluated based on the manner in which the peak-to-peak breathing amplitude on the planning CT covered the range of tumor motion (± 3 SD) during irradiation in the left-right, anteroposterior, and cranio-caudal (CC) directions. The mean tumor coverage of the 4DCT-based ITV was better than that of the 3DCT-based ITV in the CC direction. The internal margin should be considered when setting the irradiation field for 4DCT. The proposed 4DCT-based ITV can be used as an efficient approach in free-breathing SBRT for upper-lobe tumors of the lung because its coverage is superior to that of 3DCT.