Radiological Physics and Technology最新文献

In positron emission tomography (PET), measurements of depth-of-interaction (DOI) information and time-of-flight (TOF) information are important. DOI information reduces the parallax error, and TOF information reduces noise by measuring the arrival time difference of the annihilation photons. Historically, these have been studied independently, and there has been less implementation of both DOI and TOF capabilities because previous DOI detectors did not have good TOF resolution. However, recent improvements in PET detector performance have resulted in commercial PET scanners achieving a coincidence resolving time of around 200 ps, which result in an effect even for small objects. This means that TOF information can now be utilized even for a brain PET scanner, which also requires DOI information. Therefore, various methods have been proposed to obtain better DOI and TOF information. In addition, the cost of PET detectors is also an important factor to consider, since several hundred detectors are used per PET scanner. In this paper, we review the latest DOI-TOF detectors including the history of detector development. When put into practical use, these DOI-TOF detectors are expected to contribute to the improvement of imaging performance in brain PET scanners.

Deep learning, particularly convolutional neural networks (CNNs), has advanced positron emission tomography (PET) image reconstruction. However, it requires extensive, high-quality training datasets. Unsupervised learning methods, such as deep image prior (DIP), have shown promise for PET image reconstruction. Although DIP-based PET image reconstruction methods demonstrate superior performance, they involve highly time-consuming calculations. This study proposed a two-step optimization method to accelerate end-to-end DIP-based PET image reconstruction and improve PET image quality. The proposed two-step method comprised a pre-training step using conditional DIP denoising, followed by an end-to-end reconstruction step with fine-tuning. Evaluations using Monte Carlo simulation data demonstrated that the proposed two-step method significantly reduced the computation time and improved the image quality, thereby rendering it a practical and efficient approach for end-to-end DIP-based PET image reconstruction.

In cone-beam computed tomography (CBCT) for image-guided radiation therapy (IGRT) of the head, we evaluated the exposure dose reduction effect to the crystalline lens and position-matching accuracy by narrowing one side (X2) of the X-ray aperture (blade) in the X-direction. We defined the ocular surface dose of the head phantom as the crystalline lens exposure dose and measured using a radiophotoluminescence dosimeter (RPLD, GD-352 M) in the preset field (13.6 cm) and in each of the fields when blade X2 aperture was reduced in 0.5 cm increments from 10.0 to 5.0 cm. Auto-bone matching was performed on CBCT images acquired five times with blade X2 aperture set to 13.6 cm and 5.0 cm at each position when the head phantom was moved from - 5.0 to + 5.0 mm in 1.0 mm increment. The maximum reduction rate in the crystalline lens exposure dose was - 38.7% for the right lens and - 13.2% for the left lens when blade X2 aperture was 5.0 cm. The maximum difference in the amount of position correction between blade X2 aperture of 13.6 cm and 5.0 cm was 1 mm, and the accuracy of auto-bone matching was similar. In CBCT of the head, reduced blade X2 aperture is a useful technique for reducing the crystalline lens exposure dose while ensuring the accuracy of position matching.

In this study, we aimed to conduct a survey on the current clinical practice of, staffing for, commissioning of, and staff training for online adaptive radiotherapy (oART) in the institutions that installed commercial oART systems in Japan, and to share the information with institutions that will implement oART systems in future. A web-based questionnaire, containing 107 questions, was distributed to nine institutions in Japan. Data were collected from November to December 2023. Three institutions each with the MRIdian (ViewRay, Oakwood Village, OH, USA), Unity (Elekta AB, Stockholm, Sweden), and Ethos (Varian Medical Systems, Palo Alto, CA, USA) systems completed the questionnaire. One institution (MRIdian) had not performed oART by the response deadline. Each institution had installed only one oART system. Hypofractionation, and moderate hypofractionation or conventional fractionation were employed in the MRIdian/Unity and Ethos systems, respectively. The elapsed time for the oART process was faster with the Ethos than with the other systems. All institutions added additional staff for oART. Commissioning periods differed among the oART systems owing to provision of beam data from the vendors. Chambers used during commissioning measurements differed among the institutions. Institutional training was provided by all nine institutions. To the best of our knowledge, this was the first survey about oART performed using commercial systems in Japan. We believe that this study will provide useful information to institutions that installed, are installing, or are planning to install oART systems.

We investigated whether magnetic resonance imaging can visualize the distribution in the vitreous cavity via eye drops of ophthalmic solutions, gadolinium-based contrast agent, and ¹⁷O-water, and to clarify the usefulness of ultra-heavily T2-weighted sequences in the research of intraocular distribution. Five different solutions (V-ROHTO, TRAVATANZ, gadobutrol, H₂¹⁷O, and saline) were administered to excised pig eye specimens. The samples were scanned using T1 mapping, T2 mapping, 3D T2-weighted (echo times (TE): 500, 3200, and 4500 ms), a half-Fourier single-shot turbo-spin echo sequence (HASTE; TE: 440 and 3000 ms), and 3D-real inversion-recovery before eye drops administration. Subsequently, we used a plastic dropper to drop a 0.5 mL solution each, and images were obtained up to 26 h later. Temporal changes in the T1 and T2 values of the anterior chamber and vitreous cavity were compared. The other sequences were evaluated by determining temporal signal changes as signal intensity ratio (SIR) compared to "No drop." The T1 and T2 values of samples treated with gadobutrol and H₂¹⁷O decreased over time. The SIR of samples treated with gadobutrol and H₂¹⁷O showed remarkable changes in the 3D T2-weighted images, whereas no remarkable temporal changes were observed in the other solutions. Longer TEs resulted in remarkable changes. We demonstrated that visualization of distribution in the vitreous cavity via eye drops could be achieved with excised pig eyes using gadobutrol and H₂¹⁷O, but not with ophthalmic solutions. Ultra-heavily T2-weighted sequences may be promising for the early and highly sensitive visualization of the intraocular distribution of eye drops.

During the radiographic examination of the chest and bones in hospitals, communicating and maintaining posture is difficult for some patients, and movement before or during X-ray irradiation may necessitate re-exposure owing to body wobbling movements or breathing movements. To prevent the need for re-exposure during radiography and to determine the exposure timing, a body movement detection system that considers breathing movements was developed in this study. The posture of a patient was monitored using an RGB camera. The acquired video data was analyzed to detect body movement using either an inter-frame difference method or an optical flow estimation method. The performance of the system was evaluated by detecting the body and breathing movements during positioning. Consequently, the inter-frame difference method detected 179.8-1222.2 pixels during body movements, and the optical flow estimation method confirmed that the feature points moved by 5.5-26.6 mm (4.2-20.3 pixels). When detecting breathing movements, 82-585 pixels were detected by the inter-frame difference method, and the optical flow estimation method showed that the feature points moved by 5.2 mm (2-4 pixels). Therefore, the proposed method can detect body movements during radiography to prevent re-exposure due to body wobble and breathing movements. For healthcare providers, it will lead to reduce not only concerns about patient exposure but also unnecessary radiographic workload.

We aimed to evaluate the accuracy and repeatability of the T1, T2*, and proton density (PD) values obtained by quantitative parameter mapping (QPM) using the ISMRM/NIST MRI system phantom and compared them with computer simulations. We compared the relaxation times and PD obtained through QPM with the reference values of the ISMRM/NIST MRI system phantom and conventional methods. Furthermore, we evaluated the presence or absence of influences other than noise in T1 and T2* values obtained by QPM by comparing the obtained coefficient of variation (CV) with simulation results. The T1, T2*, and PD values by QPM showed a strong correlation with the measured values and the referenced values. The simulated CVs of QPM calculated for each sphere showed similar trends to those of the actual scans.

This study aimed to compare the image quality and detection performance of pancreatic cystic lesions between computed tomography (CT) images reconstructed by deep learning reconstruction (DLR) and filtered back projection (FBP). This retrospective study included 54 patients (mean age: 67.7 ± 13.1) who underwent contrast-enhanced CT from May 2023 to August 2023. Among eligible patients, 30 and 24 were positive and negative for pancreatic cystic lesions, respectively. DLR and FBP were used to reconstruct portal venous phase images. Objective image quality analyses calculated quantitative image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) using regions of interest on the abdominal aorta, pancreatic lesion, and pancreatic parenchyma. Three blinded radiologists performed subjective image quality assessment and lesion detection tests. Lesion depiction, normal structure illustration, subjective image noise, and overall image quality were utilized as subjective image quality indicators. DLR significantly reduced quantitative image noise compared with FBP (p < 0.001). SNR and CNR were significantly improved in DLR compared with FBP (p < 0.001). Three radiologists rated significantly higher scores for DLR in all subjective image quality indicators (p ≤ 0.029). Performance of DLR and FBP were comparable in lesion detection, with no statistically significant differences in the area under the receiver operating characteristic curve, sensitivity, specificity and accuracy. DLR reduced image noise and improved image quality with a clearer depiction of pancreatic structures. These improvements may have a positive effect on evaluating pancreatic cystic lesions, which can contribute to appropriate management of these lesions.

This study aims to evaluate the feasibility of using a commercially available boron neutron capture therapy (BNCT) dose calculation program (NeuCure^® Dose Engine) in terms of calculation accuracy and computation time. Treatment planning was simulated under the following calculation parameters: 1.5-5.0 mm grid sizes and 1-10% statistical uncertainties. The calculated monitor units (MUs) and computation times were evaluated. The MUs calculated on grid sizes larger than 2 mm were overestimated by 2% compared with the result of 1.5 mm grid. We established the two-step method for the routine administration of BNCT: multiple calculations involved in beam optimization should be done at a 5 mm grid and a 10% statistical uncertainty (the shortest computation time: 10.3 ± 2.1 min) in the first-step, and final dose calculations should be performed at a 2 mm grid and a 10% statistical uncertainty (satisfied clinical accuracy: 6.9 ± 0.3 h) in the second-step.

Urinary toxicities are one of the serious complications of radiotherapy for prostate cancer, and dose-volume histogram of prostatic urethra has been associated with such toxicities in previous reports. Previous research has focused on estimating the prostatic urethra, which is difficult to delineate in CT images; however, these studies, which are limited in number, mainly focused on cases undergoing brachytherapy uses low-dose-rate sources and do not involve external beam radiation therapy (EBRT). In this study, we aimed to develop a deep learning-based method of determining the position of the prostatic urethra in patients eligible for EBRT. We used contour data from 430 patients with localized prostate cancer. In all cases, a urethral catheter was placed when planning CT to identify the prostatic urethra. We used 2D and 3D U-Net segmentation models. The input images included the bladder and prostate, while the output images focused on the prostatic urethra. The 2D model determined the prostate's position based on results from both coronal and sagittal directions. Evaluation metrics included the average distance between centerlines. The average centerline distances for the 2D and 3D models were 2.07 ± 0.87 mm and 2.05 ± 0.92 mm, respectively. Increasing the number of cases while maintaining equivalent accuracy as we did in this study suggests the potential for high generalization performance and the feasibility of using deep learning technology for estimating the position of the prostatic urethra.