Prevalence and impact of viral myocarditis in patients with severe fever with thrombocytopenia syndrome.

To explore the association and impact between viral myocarditis and mortality in patients with severe fever with thrombocytopenia syndrome. A dynamic analysis was conducted between fatal group and nonfatal group regarding the daily epidemiology data, clinical symptoms, and electrocardiogram (ECG), echocardiogram, and laboratory findings. Outcomes of patients with and without viral myocarditis were compared. The association between viral myocarditis and mortality was analyzed. Among 183 severe fever with thrombocytopenia syndrome patients, 32 were in the fatal group and 151 in the nonfatal group; there were 26 (81.25%) with viral myocarditis in the fatal group, 66 (43.70%) with viral myocarditis in the nonfatal group (p < 0.001), 79.35% of patients had abnormal ECG results. The abnormal rate of ECG in the fatal group was 100%, and in the nonfatal group was 74.83%. Univariate analysis found that the number of risk factors gradually increased on Day 7 of the disease course and reached the peak on Day 10. Combined with the dynamic analysis of the disease course, alanine aminotransferase, aspartate aminotransferase, creatine kinase, creatine kinase fraction, lactate dehydrogenase, hydroxybutyrate dehydrogenase, neutrophil count, serum creatinine, Na, Ca, carbon dioxide combining power, amylase, lipase, activated partial thromboplastin time and thrombin time had statistically significant impact on prognosis. The incidence of fever with thrombocytopenia syndrome combined with viral myocarditis is high, especially in the fatal group of patients. Viral myocarditis is closely related to prognosis and is an early risk factor. The time point for changes in myocarditis is Day 7 of the course of the disease.

A deep learning approach to remove contrast from contrast-enhanced CT for proton dose calculation.

PURPOSE: Non-Contrast Enhanced CT (NCECT) is normally required for proton dose calculation while Contrast Enhanced CT (CECT) is often scanned for tumor and organ delineation. Possible tissue motion between these two CTs raises dosimetry uncertainties, especially for moving tumors in the thorax and abdomen. Here we report a deep-learning approach to generate NCECT directly from CECT. This method could be useful to avoid the NCECT scan, reduce CT simulation time and imaging dose, and decrease the uncertainties caused by tissue motion between otherwise two different CT scans. METHODS: A deep network was developed to convert CECT to NCECT. The network receives a 3D image from CECT images as input and generates a corresponding contrast-removed NCECT image patch. Abdominal CECT and NCECT image pairs of 20 patients were deformably registered and 8000 image patch pairs extracted from the registered image pairs were utilized to train and test the model. CTs of clinical proton patients and their treatment plans were employed to evaluate the dosimetric impact of using the generated NCECT for proton dose calculation. RESULTS: Our approach achieved a Cosine Similarity score of 0.988 and an MSE value of 0.002. A quantitative comparison of clinical proton dose plans computed on the CECT and the generated NCECT for five proton patients revealed significant dose differences at the distal of beam paths. V100% of PTV and GTV changed by 3.5% and 5.5%, respectively. The mean HU difference for all five patients between the generated and the scanned NCECTs was ∼4.72, whereas the difference between CECT and the scanned NCECT was ∼64.52, indicating a ∼93% reduction in mean HU difference. CONCLUSIONS: A deep learning approach was developed to generate NCECTs from CECTs. This approach could be useful for the proton dose calculation to reduce uncertainties caused by tissue motion between CECT and NCECT.

Image quality characterization of an ultra-high-speed kilovoltage cone-beam computed tomography imaging system on an O-ring linear accelerator.

PURPOSE: The quality of on-board imaging systems, including cone-beam computed tomography (CBCT), plays a vital role in image-guided radiation therapy (IGRT) and adaptive radiotherapy. Recently, there has been an upgrade of the CBCT systems fused in the O-ring linear accelerators called HyperSight, featuring a high imaging performance. As the characterization of a new imaging system is essential, we evaluated the image quality of the HyperSight system by comparing it with Halcyon 3.0 CBCT and providing benchmark data for routine imaging quality assurance. METHODS: The HyperSight features ultra-fast scan time, a larger kilovoltage (kV) detector, a more substantial kV tube, and an advanced reconstruction algorithm. Imaging protocols in the two modes of operation, treatment mode with IGRT and the CBCT for planning (CBCTp) mode were evaluated and compared with Halcyon 3.0 CBCT. Image quality metrics, including spatial resolution, contrast resolution, uniformity, noise, computed tomography (CT) number linearity, and calibration error, were assessed using a Catphan and an electron density phantom and analyzed with TotalQA software. RESULTS: HyperSight demonstrated substantial improvements in contrast-to-noise ratio and noise in both IGRT and CBCTp modes compared to Halcyon 3.0 CBCT. CT number calibration error of HyperSight CBCTp mode (1.06%) closely matches that of a full CT scanner (0.72%), making it suitable for adaptive planning. In addition, the advanced hardware of HyperSight, such as ultra-fast scan time (5.9 s) or 2.5 times larger heat unit capacity, enhanced the clinical efficiency in our experience. CONCLUSIONS: HyperSight represented a significant advancement in CBCT imaging. With its image quality, CT number accuracy, and ultra-fast scans, HyperSight has a potential to transform patient care and treatment outcomes. The enhanced scan speed and image quality of HyperSight are expected to significantly improve the quality and efficiency of treatment, particularly benefiting patients.

EStore: A User-Friendly Encrypted Storage Scheme for Distributed File Systems.

In this paper, we propose a user-friendly encrypted storage scheme named EStore, which is based on the Hadoop distributed file system. Users can make use of cloud-based distributed file systems to collaborate with each other. However, most data are processed and stored in plaintext, which is out of the owner's control after it has been uploaded and shared. Meanwhile, simple encryption guarantees the confidentiality of uploaded data but reduces availability. Furthermore, it is difficult to deal with complex key management as there is the problem whereby a single key encrypts different files, thus increasing the risk of leakage. In order to solve the issues above, we put forward an encrypted storage model and a threat model, designed with corresponding system architecture to cope with these requirements. Further, we designed and implemented six sets of protocols to meet users' requirements for security and use. EStore manages users and their keys through registration and authentication, and we developed a searchable encryption module and encryption/decryption module to support ciphertext retrieval and secure data outsourcing, which will only minimally increase the calculation overhead of the client and storage redundancy. Users are invulnerable compared to the original file system. Finally, we conducted a security analysis of the protocols to demonstrate that EStore is feasible and secure.

microRNA-seq of cartilage reveals an overabundance of miR-140-3p which contains functional isomiRs.

miR-140 is selectively expressed in cartilage. Deletion of the entire Mir140 locus in mice results in growth retardation and early-onset osteoarthritis-like pathology; however, the relative contribution of miR-140-5p or miR-140-3p to the phenotype remains to be determined. An unbiased small RNA sequencing approach identified miR-140-3p as significantly more abundant (>10-fold) than miR-140-5p in human cartilage. Analysis of these data identified multiple miR-140-3p isomiRs differing from the miRBase annotation at both the 5' and 3' end, with >99% having one of two seed sequences (5' bases 2-8). Canonical (miR-140-3p.2) and shifted (miR-140-3p.1) seed isomiRs were overexpressed in chondrocytes and transcriptomics performed to identify targets. miR-140-3p.1 and miR-140-3p.2 significantly down-regulated 694 and 238 genes, respectively, of which only 162 genes were commonly down-regulated. IsomiR targets were validated using 3'UTR luciferase assays. miR-140-3p.1 targets were enriched within up-regulated genes in rib chondrocytes of Mir140-null mice and within down-regulated genes during human chondrogenesis. Finally, through imputing the expression of miR-140 from the expression of the host gene WWP2 in 124 previously published data sets, an inverse correlation with miR-140-3p.1 predicted targets was identified. Together these data suggest the novel seed containing isomiR miR-140-3p.1 is more functional than original consensus miR-140-3p seed containing isomiR.

Visually guided respiratory motion management for Ethos adaptive radiotherapy.

PURPOSE: Ethos adaptive radiotherapy (ART) is emerging with AI-enhanced adaptive planning and high-quality cone-beam computed tomography (CBCT). Although a respiratory motion management solution is critical for reducing motion artifacts on abdominothoracic CBCT and improving tumor motion control during beam delivery, our institutional Ethos system has not incorporated a commercial solution. Here we developed an institutional visually guided respiratory motion management system to coach patients in regular breathing or breath hold during intrafractional CBCT scans and beam delivery with Ethos ART. METHODS: The institutional visual-guidance respiratory motion management system has three components: (1) a respiratory motion detection system, (2) an in-room display system, and (3) a respiratory motion trace management software. Each component has been developed and implemented in the clinical Ethos ART workflow. The applicability of the solution was demonstrated in installation, routine QA, and clinical workflow. RESULTS: An air pressure sensor has been utilized to detect patient respiratory motion in real time. Either a commercial or in-house software handled respiratory motion trace display, collection and visualization for operators, and visual guidance for patients. An extended screen and a projector on an adjustable stand were installed as the in-room visual guidance solution for the closed-bore ring gantry medical linear accelerator utilized by Ethos. Consistent respiratory motion traces and organ positions on intrafractional CBCTs demonstrated the clinical suitability of the proposed solution in Ethos ART. CONCLUSION: The study demonstrated the utilization of an institutional visually guided respiratory motion management system for Ethos ART. The proposed solution can be easily applied for Ethos ART and adapted for use with any closed bore-type system, such as computed tomography and magnetic resonance imaging, through incorporation with appropriate respiratory motion sensors.

Application programming interface guided QA plan generation and analysis automation.

PURPOSE: Linear accelerator quality assurance (QA) in radiation therapy is a time consuming but fundamental part of ensuring the performance characteristics of radiation delivering machines. The goal of this work is to develop an automated and standardized QA plan generation and analysis system in the Oncology Information System (OIS) to streamline the QA process. METHODS: Automating the QA process includes two software components: the AutoQA Builder to generate daily, monthly, quarterly, and miscellaneous periodic linear accelerator QA plans within the Treatment Planning System (TPS) and the AutoQA Analysis to analyze images collected on the Electronic Portal Imaging Device (EPID) allowing for a rapid analysis of the acquired QA images. To verify the results of the automated QA analysis, results were compared to the current standard for QA assessment for the jaw junction, light-radiation coincidence, picket fence, and volumetric modulated arc therapy (VMAT) QA plans across three linacs and over a 6-month period. RESULTS: The AutoQA Builder application has been utilized clinically 322 times to create QA patients, construct phantom images, and deploy common periodic QA tests across multiple institutions, linear accelerators, and physicists. Comparing the AutoQA Analysis results with our current institutional QA standard the mean difference of the ratio of intensity values within the field-matched junction and ball-bearing position detection was 0.012 ± 0.053 (P = 0.159) and is 0.011 ± 0.224 mm (P = 0.355), respectively. Analysis of VMAT QA plans resulted in a maximum percentage difference of 0.3%. CONCLUSION: The automated creation and analysis of quality assurance plans using multiple APIs can be of immediate benefit to linear accelerator quality assurance efficiency and standardization. QA plan creation can be done without following tedious procedures through API assistance, and analysis can be performed inside of the clinical OIS in an automated fashion.

Portal dosimetry scripting application programming interface (PDSAPI) for Winston-Lutz test employing ceramic balls.

PURPOSE: Stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) treatments require a high degree of accuracy. Mechanical, imaging, and radiation isocenter coincidence is especially important. As a common method, the Winston-Lutz (WL) test plays an important role. However, weekly or daily WL test can be very time consuming. We developed novel methods using Portal Dosimetry Scripting Application Programming Interface (PDSAPI) to facilitate the test as well as documentation. METHODS: Winston-Lutz PDSAPI was developed and tested on our routine weekly WL imaging. The results were compared against two commercially available software RIT (Radiological Imaging Technology, Colorado Springs, CO) and DoseLab (Varian Medical Systems, Inc. Palo Alto, CA). Two manual methods that served as ground truth were used to verify PDSAPI results. Twenty WL test image data sets (10 fields per tests, and 200 images in total) were analyzed by these five methods in this report. RESULTS: More than 99.5% of WL PDSAPI 1D shifts agreed with each of four other methods within ±0.33 mm, which is roughly the pixel width of a-Si 1200 portal imager when source to imager distance (SID) is at 100 cm. 1D shifts agreement for ±0.22 mm and 0.11 mm were 96% and 63%, respectively. Same trend was observed for 2D displacement. CONCLUSIONS: Winston-Lutz PDSAPI delivers similar accuracy as two commercial applications for WL test. This new application can save time spent transferring data and has the potential to implement daily WL test with reasonable test time. It also provides the data storage capability, and enables easy access to imaging and shift data.

Automated and robust beam data validation of a preconfigured ring gantry linear accelerator using a 1D tank with synchronized beam delivery and couch motions.

PURPOSE: To develop an efficient and automated methodology for beam data validation for a preconfigured ring gantry linear accelerator using scripting and a one-dimensional (1D) tank with automated couch motions. MATERIALS AND METHODS: Using an application programming interface, a program was developed to allow the user to choose a set of beam data to validate with measurement. Once selected the program generates a set of instructions for radiation delivery with synchronized couch motions for the linear accelerator in the form of an extensible markup language (XML) file to be delivered on the ring gantry linear accelerator. The user then delivers these beams while measuring with the 1D tank and data logging electrometer. The program also automatically calculates this set of beams on the measurement geometry within the treatment planning system (TPS) and extracts the corresponding calculated dosimetric data for comparison to measurement. Once completed the program then returns a comparison of the measurement to the predicted result from the TPS to the user and prints a report. In this work lateral, longitudinal, and diagonal profiles were taken for fields sizes of 6 × 6, 8 × 8, 10 × 10, 20 × 20, and 28 × 28 cm2 at depths of 1.3, 5, 10, 20, and 30 cm. Depth dose profiles were taken for all field sizes. RESULTS: Using this methodology, the TPS was validated to agree with measurement. All compared points yielded a gamma value less than 1 for a 1.5%/1.5 mm criteria (100% passing rate). Off axis profiles had >98.5% of data points producing a gamma value <1 with a 1%/1 mm criteria. All depth profiles produced 100% of data points with a gamma value <1 with a 1%/1 mm criteria. All data points measured were within 1.5% or 2 mm distance to agreement. CONCLUSIONS: This methodology allows for an increase in automation in the beam data validation process. Leveraging the application program interface allows the user to use a single system to create the measurement files, predict the result, and then compare to actual measurement increasing efficiency and reducing the chance for user input errors.

Single-Cell Analysis of the Impact of Host Cell Heterogeneity on Infection with Foot-and-Mouth Disease Virus.

Viral infection and replication are affected by host cell heterogeneity, but the mechanisms underlying the effects remain unclear. Using single-cell analysis, we investigated the effects of host cell heterogeneity, including cell size, inclusion, and cell cycle, on foot-and-mouth disease virus (FMDV) infection (acute and persistent infections) and replication. We detected various viral genome replication levels in FMDV-infected cells. Large cells and cells with a high number of inclusions generated more viral RNA copies and viral protein and a higher proportion of infectious cells than other cells. Additionally, we found that the viral titer was 10- to 100-fold higher in cells in G2/M than those in other cell cycle phases and identified a strong correlation between cell size, inclusion, and cell cycle heterogeneity, which all affected the infection and replication of FMDV. Furthermore, we demonstrated that host cell heterogeneity influenced the adsorption of FMDV due to differences in the levels of FMDV integrin receptors expression. Collectively, these results further our understanding of the evolution of a virus in a single host cell.IMPORTANCE It is important to understand how host cell heterogeneity affects viral infection and replication. Using single-cell analysis, we found that viral genome replication levels exhibited dramatic variability in foot-and-mouth disease virus (FMDV)-infected cells. We also found a strong correlation between heterogeneity in cell size, inclusion number, and cell cycle status and that all of these characteristics affect the infection and replication of FMDV. Moreover, we found that host cell heterogeneity influenced the viral adsorption as differences in the levels of FMDV integrin receptors' expression. This study provided new ideas for the studies of correlation between FMDV infection mechanisms and host cells.

Myxovirus resistance protein A inhibits hepatitis C virus replication through JAK-STAT pathway activation.

The interferon-inducible dynamin-like GTPase myxovirus resistance protein A (MxA) exhibits activity against multiple viruses. However, its role in the life cycle of hepatitis C virus (HCV) is unclear, and the mechanisms underlying the anti-HCV activity of MxA require further investigation. In this study, we demonstrated that exogenous MxA expression in the Huh7 and Huh7.5.1 hepatoma cell lines significantly decreased the levels of HCV RNA and core proteins, whereas MxA knockdown exerted the opposite effect. MxA-mediated inhibition of HCV replication was found to involve the JAK-STAT pathway: STAT1 phosphorylation and the expression of IFN-stimulated genes (ISGs) such as guanylate-binding protein 1 and 2'-5'-oligoadenylate synthetase 1 were augmented by MxA overexpression and reduced by endogenous MxA silencing. Treatment with the JAK inhibitor ruxolitinib abrogated the MxA-mediated suppression of HCV replication and activation of the JAK-STAT pathway. Additionally, transfection with an MxA mutant with disrupted GTP-binding consensus motifs abrogated activation of the JAK-STAT pathway and resistance to HCV replication. This study shows that MxA inhibits HCV replication by activating the JAK-STAT signaling pathway through a mechanism involving its GTPase function.

A comprehensive lung CT landmark pair dataset for evaluating deformable image registration algorithms.

PURPOSE: Deformable image registration (DIR) is a key enabling technology in many diagnostic and therapeutic tasks, but often does not meet the required robustness and accuracy for supporting clinical tasks. This is in large part due to a lack of high-quality benchmark datasets by which new DIR algorithms can be evaluated. Our team was supported by the National Institute of Biomedical Imaging and Bioengineering to develop DIR benchmark dataset libraries for multiple anatomical sites, comprising of large numbers of highly accurate landmark pairs on matching blood vessel bifurcations. Here we introduce our lung CT DIR benchmark dataset library, which was developed to improve upon the number and distribution of landmark pairs in current public lung CT benchmark datasets. ACQUISITION AND VALIDATION METHODS: Thirty CT image pairs were acquired from several publicly available repositories as well as authors' institution with IRB approval. The data processing workflow included multiple steps: (1) The images were denoised. (2) Lungs, airways, and blood vessels were automatically segmented. (3) Bifurcations were directly detected on the skeleton of the segmented vessel tree. (4) Falsely identified bifurcations were filtered out using manually defined rules. (5) A DIR was used to project landmarks detected on the first image onto the second image of the image pair to form landmark pairs. (6) Landmark pairs were manually verified. This workflow resulted in an average of 1262 landmark pairs per image pair. Estimates of the landmark pair target registration error (TRE) using digital phantoms were 0.4 mm ± 0.3 mm. DATA FORMAT AND USAGE NOTES: The data is published in Zenodo at https://doi.org/10.5281/zenodo.8200423. Instructions for use can be found at https://github.com/deshanyang/Lung-DIR-QA. POTENTIAL APPLICATIONS: The dataset library generated in this work is the largest of its kind to date and will provide researchers with a new and improved set of ground truth benchmarks for quantitatively validating DIR algorithms within the lung.

Trends and focal points in pelvic floor reconstruction for pelvic organ prolapse: A bibliometric analysis.

OBJECTIVE: This study aims to investigate the current research trends and focal points in the field of pelvic floor reconstruction for the management of pelvic organ prolapse (POP). METHODS: To achieve this objective, a bibliometric analysis was conducted on relevant literature using the Citespace database. The analysis led to the creation of a knowledge map, offering a comprehensive overview of scientific advancements in this research area. RESULTS: The study included a total of 607 publications, revealing a consistent increase in articles addressing pelvic floor reconstruction for POP treatment. Most articles originated from the United States (317 articles), followed by Chinese scholars (40 articles). However, it is important to note that the overall number of articles remains relatively low. The organization with the highest publication frequency was the Cleveland Clinic in Ohio, where Matthew D. Barber leads the academic group. Barber himself has the highest number of published articles (18 articles), followed by Zhu Lan, a Chinese scholar (10 articles). Key topics with high frequency and mediated centrality include stress urinary incontinence, quality of life, impact, and age. The journal with the largest number of papers from both domestic and international researchers is INT UROGYNECOL J. The study's hotspots mainly focus on the impact of pelvic floor reconstruction on the treatment and quality of life of POP patients. The United States leads in this field, but there is a lack of cooperation between countries, institutions, and authors. Moving forward, cross-institutional, cross-national, and cross-disciplinary exchanges and cooperation should be strengthened to further advance the field of pelvic floor reconstructive surgery for POP research.

A quality assurance framework for routine monitoring of deep learning cardiac substructure computed tomography segmentation models in radiotherapy.

BACKGROUND: For autosegmentation models, the data used to train the model (e.g., public datasets and/or vendor-collected data) and the data on which the model is deployed in the clinic are typically not the same, potentially impacting the performance of these models by a process called domain shift. Tools to routinely monitor and predict segmentation performance are needed for quality assurance. Here, we develop an approach to perform such monitoring and performance prediction for cardiac substructure segmentation. PURPOSE: To develop a quality assurance (QA) framework for routine or continuous monitoring of domain shift and the performance of cardiac substructure autosegmentation algorithms. METHODS: A benchmark dataset consisting of computed tomography (CT) images along with manual cardiac substructure delineations of 241 breast cancer radiotherapy patients were collected, including one "normal" image domain of clean images and five "abnormal" domains containing images with artifact (metal, contrast), pathology, or quality variations due to scanner protocol differences (field of view, noise, reconstruction kernel, and slice thickness). The QA framework consisted of an image domain shift detector which operated on the input CT images and a shape quality detector on the output of an autosegmentation model, and a regression model for predicting autosegmentation model performance. The image domain shift detector was composed of a trained denoising autoencoder (DAE) and two hand-engineered image quality features to detect normal versus abnormal domains in the input CT images. The shape quality detector was a variational autoencoder (VAE) trained to estimate the shape quality of the auto-segmentation results. The output from the image domain shift and shape quality detectors was used to train a regression model to predict the per-patient segmentation accuracy, measured by Dice coefficient similarity (DSC) to physician contours. Different regression techniques were investigated including linear regression, Bagging, Gaussian process regression, random forest, and gradient boost regression. Of the 241 patients, 60 were used to train the autosegmentation models, 120 for training the QA framework, and the remaining 61 for testing the QA framework. A total of 19 autosegmentation models were used to evaluate QA framework performance, including 18 convolutional neural network (CNN)-based and one transformer-based model. RESULTS: When tested on the benchmark dataset, all abnormal domains resulted in a significant DSC decrease relative to the normal domain for CNN models ( p < 0.001 $p < 0.001$ ), but only for some domains for the transformer model. No significant relationship was found between the performance of an autosegmentation model and scanner protocol parameters ( p = 0.42 $p = 0.42$ ) except noise ( p = 0.01 $p = 0.01$ ). CNN-based autosegmentation models demonstrated a decreased DSC ranging from 0.07 to 0.41 with added noise, while the transformer-based model was not significantly affected (ANOVA, p = 0.99 $p=0.99$ ). For the QA framework, linear regression models with bootstrap aggregation resulted in the highest mean absolute error (MAE) of 0.041 ± 0.002 $0.041 \pm 0.002$ , in predicted DSC (relative to true DSC between autosegmentation and physician). MAE was lowest when combining both input (image) detectors and output (shape) detectors compared to output detectors alone. CONCLUSIONS: A QA framework was able to predict cardiac substructure autosegmentation model performance for clinically anticipated "abnormal" domain shifts.

Millimeter wave-based patient setup verification and motion tracking during radiotherapy.

BACKGROUND: Position verification and motion monitoring are critical for safe and precise radiotherapy (RT). Existing approaches to these tasks based on visible light or x-ray are suboptimal either because they cannot penetrate obstructions to the patient's skin or introduce additional radiation exposure. The low-cost mmWave radar is an ideal solution for these tasks as it can monitor patient position and motion continuously throughout the treatment delivery. PURPOSE: To develop and validate frequency-modulated continuous wave (FMCW) mmWave radars for position verification and motion tracking during RT delivery. METHODS: A 77 GHz FMCW mmWave module was used in this study. Chirp Z Transform-based (CZT) algorithm was developed to process the intermediate frequency (IF) signals. Absolute distances to flat Solid Water slabs and human shape phantoms were measured. The accuracy of absolute distance and relative displacement were evaluated. RESULTS: Without obstruction, mmWave based on the CZT algorithm was able to detect absolute distance within 1 mm for a Solid Water slab that simulated the reflectivity of the human body. Through obstructive materials, the mmWave device was able to detect absolute distance within 5 mm in the worst case and within 3.5 mm in most cases. The CZT algorithm significantly improved the accuracy of absolute distance measurement compared with Fast Fourier Transform (FFT) algorithm and was able to achieve submillimeter displacement accuracy with and without obstructions. The surface-to-skin distance (SSD) measurement accuracy was within 8 mm in the anterior of the phantom. CONCLUSIONS: With the CZT signal processing algorithm, the mmWave radar is able to measure the absolute distance to a flat surface within 1 mm. But the absolute distance measurement to a human shape phantom is as large as 8 mm at some angles. Further improvement is necessary to improve the accuracy of SSD measurement to uneven surfaces by the mmWave radar.

High spatiotemporal resolution scintillation imaging of pulsed pencil beam scanning proton beams produced by a gantry-mounted synchrocyclotron.

BACKGROUND: A dosimeter with high spatial and temporal resolution would be of significant interest for pencil beam scanning (PBS) proton beams' characterization, especially when facing small fields and beams with high temporal dynamics. Optical imaging of scintillators has potential in providing sub-millimeter spatial resolution with pulse-by-pulse basis temporal resolution when the imaging system is capable of operating in synchrony with the beam-producing accelerator. PURPOSE: We demonstrate the feasibility of imaging PBS proton beams as they pass through a plastic scintillator detector to simultaneously obtain multiple beam parameters, including proton range, pencil beam's widths at different depths, spot's size, and spot's position on a pulse-by-pulse basis with sub-millimeter resolution. MATERIALS AND METHODS: A PBS synchrocyclotron was used for proton irradiation. A BC-408 plastic scintillator block with 30 × 30 × 5 cm3 size, and another block with 30 × 30 × 0.5 cm3 size, positioned in an optically sealed housing, were used sequentially to measure the proton range, and spot size/location, respectively. A high-speed complementary metal-oxide-semiconductor (CMOS) camera system synchronized with the accelerator's pulses through a gating module was used for imaging. Scintillation images, captured with the camera directly facing the 5-cm-thick scintillator, were corrected for background (BG), and ionization quenching of the scintillator to obtain the proton range. Spots' position and size were obtained from scintillation images of the 0.5-cm-thick scintillator when a 45° mirror was used to reflect the scintillation light toward the camera. RESULTS: Scintillation images with 0.16 mm/pixel resolution corresponding to all proton pulses were captured. Pulse-by-pulse analysis showed that variations of the range, spots' position, and size were within ± 0.2% standard deviation of their average values. The absolute ranges were within ± 1 mm of their expected values. The average spot-positions were mostly within ± 0.8 mm and spots' sigma agreed within 0.2 mm of the expected values. CONCLUSION: Scintillation-imaging PBS beams with high-spatiotemporal resolution is feasible and may help in efficient and cost-effective acceptance testing and commissioning of existing and even emerging technologies such as FLASH, grid, mini-beams, and so forth.

A clinical and time savings evaluation of a deep learning automatic contouring algorithm.

Automatic contouring algorithms may streamline clinical workflows by reducing normal organ-at-risk (OAR) contouring time. Here we report the first comprehensive quantitative and qualitative evaluation, along with time savings assessment for a prototype deep learning segmentation algorithm from Siemens Healthineers. The accuracy of contours generated by the prototype were evaluated quantitatively using the Sorensen-Dice coefficient (Dice), Jaccard index (JC), and Hausdorff distance (Haus). Normal pelvic and head and neck OAR contours were evaluated retrospectively comparing the automatic and manual clinical contours in 100 patient cases. Contouring performance outliers were investigated. To quantify the time savings, a certified medical dosimetrist manually contoured de novo and, separately, edited the generated OARs for 10 head and neck and 10 pelvic patients. The automatic, edited, and manually generated contours were visually evaluated and scored by a practicing radiation oncologist on a scale of 1-4, where a higher score indicated better performance. The quantitative comparison revealed high (> 0.8) Dice and JC performance for relatively large organs such as the lungs, brain, femurs, and kidneys. Smaller elongated structures that had relatively low Dice and JC values tended to have low Hausdorff distances. Poor performing outlier cases revealed common anatomical inconsistencies including overestimation of the bladder and incorrect superior-inferior truncation of the spinal cord and femur contours. In all cases, editing contours was faster than manual contouring with an average time saving of 43.4% or 11.8 minutes per patient. The physician scored 240 structures with > 95% of structures receiving a score of 3 or 4. Of the structures reviewed, only 11 structures needed major revision or to be redone entirely. Our results indicate the evaluated auto-contouring solution has the potential to reduce clinical contouring time. The algorithm's performance is promising, but human review and some editing is required prior to clinical use.

Characterization of the Targeting Accuracy of a Neuronavigation-Guided Transcranial FUS System In Vitro, In Vivo, and In Silico.

Focused ultrasound (FUS)-enabled liquid biopsy (sonobiopsy) is an emerging technique for the noninvasive and spatiotemporally controlled diagnosis of brain cancer by inducing blood-brain barrier (BBB) disruption to release brain tumor-specific biomarkers into the blood circulation. The feasibility, safety, and efficacy of sonobiopsy were demonstrated in both small and large animal models using magnetic resonance-guided FUS devices. However, the high cost and complex operation of magnetic resonance-guided FUS devices limit the future broad application of sonobiopsy in the clinic. In this study, a neuronavigation-guided sonobiopsy device is developed and its targeting accuracy is characterized in vitro, in vivo, and in silico. The sonobiopsy device integrated a commercially available neuronavigation system (BrainSight) with a nimble, lightweight FUS transducer. Its targeting accuracy was characterized in vitro in a water tank using a hydrophone. The performance of the device in BBB disruption was verified in vivo using a pig model, and the targeting accuracy was quantified by measuring the offset between the target and the actual locations of BBB opening. The feasibility of the FUS device in targeting glioblastoma (GBM) tumors was evaluated in silico using numerical simulation by the k-Wave toolbox in glioblastoma patients. It was found that the targeting accuracy of the neuronavigation-guided sonobiopsy device was 1.7 ± 0.8 mm as measured in the water tank. The neuronavigation-guided FUS device successfully induced BBB disruption in pigs with a targeting accuracy of 3.3 ± 1.4 mm. The targeting accuracy of the FUS transducer at the GBM tumor was 5.5 ± 4.9 mm. Age, sex, and incident locations were found to be not correlated with the targeting accuracy in GBM patients. This study demonstrated that the developed neuronavigation-guided FUS device could target the brain with a high spatial targeting accuracy, paving the foundation for its application in the clinic.

Initial clinical experience building a dual CT- and MR-guided adaptive radiotherapy program.

Introduction: Our institution was the first in the world to clinically implement MR-guided adaptive radiotherapy (MRgART) in 2014. In 2021, we installed a CT-guided adaptive radiotherapy (CTgART) unit, becoming one of the first clinics in the world to build a dual-modality ART clinic. Herein we review factors that lead to the development of a high-volume dual-modality ART program and treatment census over an initial, one-year period. Materials and Methods: The clinical adaptive service at our institution is enabled with both MRgART (MRIdian, ViewRay, Inc, Mountain View, CA) and CTgART (ETHOS, Varian Medical Systems, Palo Alto, CA) platforms. We analyzed patient and treatment information including disease sites treated, radiation dose and fractionation, and treatment times for patients on these two platforms. Additionally, we reviewed our institutional workflow for creating, verifying, and implementing a new adaptive workflow on either platform. Results: From October 2021 to September 2022, 256 patients were treated with adaptive intent at our institution, 186 with MRgART and 70 with CTgART. The majority (106/186) of patients treated with MRgART had pancreatic cancer, and the most common sites treated with CTgART were pelvis (23/70) and abdomen (20/70). 93.0% of treatments on the MRgART platform were stereotactic body radiotherapy (SBRT), whereas only 72.9% of treatments on the CTgART platform were SBRT. Abdominal gated cases were allotted a longer time on the CTgART platform compared to the MRgART platform, whereas pelvic cases were allotted a shorter time on the CTgART platform when compared to the MRgART platform. Our adaptive implementation technique has led to six open clinical trials using MRgART and seven using CTgART. Conclusions: We demonstrate the successful development of a dual platform ART program in our clinic. Ongoing efforts are needed to continue the development and integration of ART across platforms and disease sites to maximize access and evidence for this technique worldwide.

Improvement of IMRT QA prediction using imaging-based neural architecture search.

PURPOSE: Machine learning (ML) has been used to predict the gamma passing rate (GPR) of intensity-modulated radiation therapy (IMRT) QA results. In this work, we applied a novel neural architecture search to automatically tune and search for the best deep neural networks instead of using hand-designed deep learning architectures. METHOD AND MATERIALS: One hundred and eighty-two IMRT plans were created and delivered with portal dosimetry. A total of 1497 fields for multiple treatment sites were delivered and measured by portal imagers. Gamma criteria of 2%/2 mm with a 5% threshold were used. Fluence maps calculated for each plan were used as inputs to a convolution neural network (CNN). Auto-Keras was implemented to search for the best CNN architecture for fluence image regression. The network morphism was adopted in the searching process, in which the base models were ResNet and DenseNet. The performance of this CNN approach was compared with tree-based ML models previously developed for this application, using the same dataset. RESULTS: The deep-learning-based approach had 98.3% of predictions within 3% of the measured 2%/2-mm GPRs with a maximum error of 3.1% and a mean absolute error of less than 1%. Our results show that this novel architecture search approach achieves comparable performance to the machine-learning-based approaches with handcrafted features. CONCLUSIONS: We implemented a novel CNN model using imaging-based neural architecture for IMRT QA prediction. The imaging-based deep-learning method does not require a manual extraction of relevant features and is able to automatically select the best network architecture.