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Purpose: Entrance dose (or skin dose) is an important part of patient quality assurance in external beam radiation therapy. However, entrance dose verification in proton beam is not routinely performed. In this study, the OneDose single use MOSFET detector system for in vivo dosimetry measurement in proton therapy is investigated. Methods: Using a solid water phantom, several fundamental dosimetric characteristics of the OneDose system are studied with a proton beam: The reproducibility (consistency) of the dosimeter, the linearity with dose and dose rate, energy dependence, directional dependence, LET dependence, and fading (delay readout with time) is studied. Results: OneDose detectors show dose and dose rate linearity but exhibit pronounced energy dependence at depth and a large variation in dose response with LET. On the other hand, the detector response remain relatively constant (within 3%) at surface over a wide range of energies. There is also a slight angular dependence (about 2%) up to 60° angle of incidence. However, detector orientation such that incidence along the long axis of the detector should be avoided as the proton beam will have to traverse a large amount of the copper backing. Since most in vivo dosimetry involves entrance dose measurement, the OneDose at surface appears to be well suited for such application. OneDose exhibits small intrabatch variation (≤2% at one SD) indicating that it is only necessary to calibration a few detectors from each batch. The interbatch variation is generally within 3%. Conclusions: The small detector size and its relatively flexible design of OneDose allow dose measurement to be performed on a curved surface or in small cavities that is otherwise difficult with the conventional diode detectors. The slight drawback in its angular dependence can be easily handled by angular dependence table. However, since OneDose is a single use detector, the intrabatch consistency must be verified before the remaining detectors from the same batch could be used for in vivo dosimetry. It is advisable that the detectors from the same batch be taken for the same application to reduce the dosimetric uncertainty. For detectors from different batches, interbatch consistency should also be verified to obtain reliable results. OneDose provides an opportunity to measure in vivo dose with proton beam within acceptable clinical criterion of ±(5.0%–6.5%). [ABSTRACT FROM AUTHOR]

Background: Lung cancer is the deadliest and second most common cancer in the United States due to the lack of symptoms for early diagnosis. Pulmonary nodules are small abnormal regions that can be potentially correlated to the occurrence of lung cancer. Early detection of these nodules is critical because it can significantly improve the patient's survival rates. Thoracic thin-sliced computed tomography (CT) scanning has emerged as a widely used method for diagnosing and prognosis lung abnormalities., Purpose: The standard clinical workflow of detecting pulmonary nodules relies on radiologists to analyze CT images to assess the risk factors of cancerous nodules. However, this approach can be error-prone due to the various nodule formation causes, such as pollutants and infections. Deep learning (DL) algorithms have recently demonstrated remarkable success in medical image classification and segmentation. As an ever more important assistant to radiologists in nodule detection, it is imperative ensure the DL algorithm and radiologist to better understand the decisions from each other. This study aims to develop a framework integrating explainable AI methods to achieve accurate pulmonary nodule detection., Methods: A robust and explainable detection (RXD) framework is proposed, focusing on reducing false positives in pulmonary nodule detection. Its implementation is based on an explanation supervision method, which uses nodule contours of radiologists as supervision signals to force the model to learn nodule morphologies, enabling improved learning ability on small dataset, and enable small dataset learning ability. In addition, two imputation methods are applied to the nodule region annotations to reduce the noise within human annotations and allow the model to have robust attributions that meet human expectations. The 480, 265, and 265 CT image sets from the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset are used for training, validation, and testing., Results: Using only 10, 30, 50, and 100 training samples sequentially, our method constantly improves the classification performance and explanation quality of baseline in terms of Area Under the Curve (AUC) and Intersection over Union (IoU). In particular, our framework with a learnable imputation kernel improves IoU from baseline by 24.0% to 80.0%. A pre-defined Gaussian imputation kernel achieves an even greater improvement, from 38.4% to 118.8% from baseline. Compared to the baseline trained on 100 samples, our method shows less drop in AUC when trained on fewer samples. A comprehensive comparison of interpretability shows that our method aligns better with expert opinions., Conclusions: A pulmonary nodule detection framework was demonstrated using public thoracic CT image datasets. The framework integrates the robust explanation supervision (RES) technique to ensure the performance of nodule classification and morphology. The method can reduce the workload of radiologists and enable them to focus on the diagnosis and prognosis of the potential cancerous pulmonary nodules at the early stage to improve the outcomes for lung cancer patients., (© 2024 American Association of Physicists in Medicine.)

Background: Improving imaging speed has always been the focus of research in CT technology, which is related to the radiation dose and imaging quality of moving organs, including heart and blood vessels. However, it is difficult to achieve further improvement by increasing the rotation speed of the gantry due to its structural strength limitation. Differing from the conventional CTs, the static CT employs dozens of ray sources to acquire projection data from different angular ranges, and each source only needs to be rotated in a small range to finish a full 360° scan, thus greatly increasing the scanning speed., Purpose: As sources of static CT need to be evenly distributed over 360°, the sources and detectors have to be arranged on two parallel rings independently. Such a geometry can be considered as a special case of CT systems with a significantly large cone angle, that is, a part of the detector is missing in the vicinity of the mid-plane. Due to restriction of upper and lower bounds of the cone angle of the static CT, there are uneven projection data varying in each portion of the reconstruction volume, the conventional analytical or iterative reconstruction methods may introduce artifacts in the reconstructed outcomes., Methods: Following the weighting approach extended FDK (xFDK) by Grimmer et al., we propose an improved bilateral xFDK algorithm (bixFDK), which focuses on the reconstruction of the expanded volume. With the same philosophy as xFDK in terms of weighting function design, bixFDK takes the longitudinal offset of the detector with respect to the source into consideration, making our method applicable to a wide range of CT geometries, especially for the static CT. Based on the proposed bixFDK, a new iterative scheme bixFDK-IR is also constructed to extend the applications to a wide range of scan protocols such as sparse-view scan., Results: The proposed method has been validated with the simulated phantom data and the actual clinical data of the static CT, and demonstrates that it can ensure good image quality and enlarge the reconstruction volume in z-direction of the static CT., Conclusions: The bixFDK algorithm is an ideal reconstruction approach for static CT geometry, and the iterative scheme of bixFDK-IR is applicable to a wide range of CT geometries and scan protocols, thus providing a wide range of application scenarios., (© 2023 American Association of Physicists in Medicine.)

Background: Delineation of Organs-at-Risks (OARs) is an important step in radiotherapy treatment planning. As manual delineation is time-consuming, labor-intensive and affected by inter- and intra-observer variability, a robust and efficient automatic segmentation algorithm is highly desirable for improving the efficiency and repeatability of OAR delineation., Purpose: Automatic segmentation of OARs in medical images is challenged by low contrast, various shapes and imbalanced sizes of different organs. We aim to overcome these challenges and develop a high-performance method for automatic segmentation of 10 OARs required in radiotherapy planning for brain tumors., Methods: A novel two-stage segmentation framework is proposed, where a coarse and simultaneous localization of all the target organs is obtained in the first stage, and a fine segmentation is achieved for each organ, respectively, in the second stage. To deal with organs with various sizes and shapes, a stratified segmentation strategy is proposed, where a High- and Low-Resolution Residual Network (HLRNet) that consists of a multiresolution branch and a high-resolution branch is introduced to segment medium-sized organs, and a High-Resolution Residual Network (HRRNet) is used to segment small organs. In addition, a label fusion strategy is proposed to better deal with symmetric pairs of organs like the left and right cochleas and lacrimal glands., Results: Our method was validated on the dataset of MICCAI ABCs 2020 challenge for OAR segmentation. It obtained an average Dice of 75.8% for 10 OARs, and significantly outperformed several state-of-the-art models including nnU-Net (71.6%) and FocusNet (72.4%). Our proposed HLRNet and HRRNet improved the segmentation accuracy for medium-sized and small organs, respectively. The label fusion strategy led to higher accuracy for symmetric pairs of organs., Conclusions: Our proposed method is effective for the segmentation of OARs of brain tumors, with a better performance than existing methods, especially on medium-sized and small organs. It has a potential for improving the efficiency of radiotherapy planning with high segmentation accuracy., (© 2023 American Association of Physicists in Medicine.)

Purpose: Laryngoscopy, the most common diagnostic method for vocal cord lesions (VCLs), is based mainly on the visual subjective inspection of otolaryngologists. This study aimed to establish a highly objective computer-aided VCLs diagnosis system based on deep convolutional neural network (DCNN) and transfer learning., Methods: To classify VCLs, our method combined the DCNN backbone with transfer learning on a system specifically finetuned for a laryngoscopy image dataset. Laryngoscopy image database was collected to train the proposed system. The diagnostic performance was compared with other DCNN-based models. Analysis of F1 score and receiver operating characteristic curves were conducted to evaluate the performance of the system., Results: Beyond the existing VCLs diagnosis method, the proposed system achieved an overall accuracy of 80.23%, an F1 score of 0.7836, and an area under the curve (AUC) of 0.9557 for four fine-grained classes of VCLs, namely, normal, polyp, keratinization, and carcinoma. It also demonstrated robust classification capacity for detecting urgent (keratinization, carcinoma) and non-urgent (normal, polyp), with an overall accuracy of 0.939, a sensitivity of 0.887, a specificity of 0.993, and an AUC of 0.9828. The proposed method also outperformed clinicians in the classification of normal, polyps, and carcinoma at an extremely low time cost., Conclusion: The VCLs diagnosis system succeeded in using DCNN to distinguish the most common VCLs and normal cases, holding a practical potential for improving the overall diagnostic efficacy in VCLs examinations. The proposed VCLs diagnosis system could be appropriately integrated into the conventional workflow of VCLs laryngoscopy as a highly objective auxiliary method., (© 2021 American Association of Physicists in Medicine.)

Purpose: In the spirit of overcoming the signal-to-noise limitations of active matrix, flat-panel imagers (AMFPIs) which employ array circuits based on a-Si:H thin-film transistors (TFTs), an empirical investigation of the noise properties of prototype active pixel arrays based on polycrystalline silicon (poly-Si) TFTs is reported. Like a-Si:H, poly-Si supports fabrication of large area, monolithic x-ray imaging arrays and offers good radiation damage resistance, while providing electron and hole mobility orders of magnitude higher. Compared to pixel circuits typically consisting of a single addressing switch in an AMFPI array, the pixel circuit of an active pixel array includes an amplifier that magnifies the imaging signal prior to readout by external acquisition electronics. Also, while readout erases signal stored in the pixels for AMFPI arrays, active pixel arrays allow multiple nondestructive readout, which can be used to reduce noise. The prototype arrays investigated in this paper were developed to explore the effect of variation in amplifier design on noise., Methods: A pair of prototype arrays incorporating single-stage and two-stage poly-Si pixel amplifiers were examined. The arrays incorporate various amplifier designs in which dimensions of some of the three (or four) poly-Si TFTs per pixel circuit for the single-stage array, and some of the five poly-Si TFTs for the two-stage array, were varied. The arrays were operated using a recently developed electronic data acquisition system that allows variation of operational conditions such as voltages and timing of control signals. The arrays were operated in the absence of radiation in various correlated multiple sampling modes, with and without the injection of charge directly into the pixel circuits for measurements of in-pixel gain and pixel noise. Pixel noise, referred back to the input of the pixel amplifier, was compared to predictions generated by a sophisticated circuit simulation model., Results: Across the various pixel circuit designs, the median in-pixel gain for the single-stage and two-stage arrays was measured to be ×9.3 and ×25, respectively. These gain levels were sufficient to reduce the contribution of external noise, defined as the electronic additive noise in the absence of noise contributions from circuitry in the pixel and referred back to the input of the pixel amplifier, to less than 340 e. As a result, median pixel noise results as low as ~695 e and 866 e, acquired using eight samples, were observed from the best-performing single-stage and two-stage designs, respectively. While the magnitude of pixel noise predicted by simulation was lower than the measured results, there was generally good agreement between simulation and measurement for the functional dependence of noise on operating voltages, timing, and sampling mode., Conclusions: The single-stage and two-stage arrays examined in this study demonstrated pixel noise well below that typically demonstrated by AMFPIs. Through proper design, it should be possible to maintain the noise levels observed in this study irrespective of the size and pitch of an active pixel array. Further reduction in pixel noise may be possible through more optimized pixel circuit design, faster readout, or improvements in fabrication., (© 2020 American Association of Physicists in Medicine.)