An endophytic fungus Schizophyllum commune isolated from Panax ginseng enhances hairy roots growth and ginsenoside biosynthesis.

Using endophytic fungal elicitors to increase the accumulation of valuable secondary metabolites in plant tissue culture is an effective biotechnology strategy. In this study, a collection of 56 strains of endophytic fungi were isolated from different organs of cultivated Panax ginseng, of which seven strains can be symbiotically co-cultured with the hairy roots of P. ginseng. Further experiments observed that strain 3R-2, identified as endophytic fungus Schizophyllum commune, can not only infect hairy roots but also promote the accumulation of specific ginsenosides. This was further verified because S. commune colonization significantly affected the overall metabolic profile of ginseng hairy roots. By comparing the effects of S. commune mycelia and its mycelia extract (EM) on ginsenoside production in P. ginseng hairy roots, the EM was confirmed to be a relatively better stimulus elicitor. Additionally, the introduction of EM elicitor can significantly enhance the expressions of key enzyme genes of pgHMGR, pgSS, pgSE, and pgSD involved in the biosynthetic pathway of ginsenosides, which was deemed the most relevant factor for promoting ginsenosides production during the elicitation period. In conclusion, this study is the first to show that the EM of endophytic fungus S. commune can be considered as an effective endophytic fungal elicitor for increasing the biosynthesis of ginsenosides in hairy root cultures of P. ginseng.

First report of bacterial leaf spot on Hami melon caused by Pseudomonas aeruginosa in China.

Hami melon (Cucumis melon var. saccharinus) is an economically important crop all over the world. It is being extensively planted in greenhouse in the southwest part of Hainan province, China. A new bacterial leaf spot was observed in a 20 hm2 Hami melon plantation in Huangliu town, Ledong county, Hainan province, in January 2022. The incidence of the disease was approximately 5%. Symptoms were irregularly shaped, brown lesions with yellow haloes on mature leaves, and first appeared as small, dark-green, water-soaking spots. Specimens from the lesion margin were disinfected by submersion in 0.1% mercuric chloride for 1 min, then soaked with 75% alcohol for 30 s, and rinsed with sterilized distilled water. The tissues were then ground in 2 ml of sterile water and allowed to stand for 5min. The supernatant was streaked onto nutrient agar (NA) and incubated for 48h at 30°C. Colonies were round, smooth, colorless, nearly transparent, bead-shaped at first, and then became lightly blue. After being cultured for days on NA at 30℃, the bacteria can turn the media brown. Yellow green pigments (pyoverdin) that fluoresce under ultraviolet light could be produced by the isolates in the Luria Broth. The bacteria were gram-negative, rod shaped with a single polar flagellum, 0.4 to 1.1 × 1.4 to 3.4 µm. Its physiological and biochemical characteristics were as follows: positive for the oxidase, aerobic, arginine dihydrolase, gelatin liquefaction, denitrification, lipase, growth at 41℃, utilization of mannitol, and production of pyocyanin tests; negative for the hydrolysis of starch, levan formation, lecithinase, growth at 4℃, growth in media supplemented with 8.5% NaCl, and utilization of maltose, xylose, and ethylene glycol tests. The 16S rRNA (1,437 bp), gyrB (1,181 bp), and rpoB genes (1,510 bp) were amplified with 27F/1492R (Zhang et al. 2016）, UP-1s/UP-2sr（Hannula M，2007）, and rpoB-F/rpoB-R (Ogier, JC. et al., 2019) primer sets respectively. One of the 5 isolates collected was sequenced. A BLASTn search of GenBank revealed that the sequence of 16S rRNA gene (OQ918303) had 99.7% identity and 98% coverage comparing with the best hit Pseudomonas aeruginosa strain DSM 50071(NR_117678.1), and both gyrB (OR261077) and rpoB (OR261078) had 99.9% identity and over 98% coverage comparing with P. aeruginosa E90 (CP044006.1). A pathogenicity test was conducted by spraying a suspension of the bacteria (108 CFU/mL) onto 10 Hami melon seedlings with two true leaves. Controls were inoculated with sterile water. All inoculated plants were maintained at 28℃ with 80 to 85% relative humidity in a greenhouse. Dark-green, water soaking spots appeared on the cotyledon and stems of treated seedlings 3-5 days after inoculation, and dark green lesions with halos were observed on the true leaves at the same time. Symptoms did not occur on the control plants. The bacteria which were re-isolated from the inoculated plants were confirmed as P. aeruginosa based on the 16S rRNA gene sequence. The bacterium was not isolated from control plants. P. aeruginosa has been reported to cause disease on a variety of plants including tomato (Zhang et al., 2021), poplar (Liu, et al., 2019), ginseng (Gao et al., 2014), tinda (Mondal et al., 2012), onion (Abd-Alla et al., 2011), tobacco (Yu et al., 2008) and sweet basil (Walker et al., 2004). As far as we know, this is the first report of P. aeruginosa causing leaf spot on Hami melon in China.. This report will contribute to the recognition and diagnosis of the new disease for the Hami melon growers.

Magnetic resonance imaging-guided stereotactic body radiotherapy for prostate cancer (mirage): a phase iii randomized trial.

BACKGROUND: Stereotactic body radiotherapy (SBRT) is becoming increasingly used in treating localized prostate cancer (PCa), with evidence showing similar toxicity and efficacy profiles when compared with longer courses of definitive radiation. Magnetic resonance imaging (MRI)-guided radiotherapy has multiple potential advantages over standard computed tomography (CT)-guided radiotherapy, including enhanced prostate visualization (abrogating the need for fiducials and MRI fusion), enhanced identification of the urethra, the ability to track the prostate in real-time, and the capacity to perform online adaptive planning. However, it is unknown whether these potential advantages translate into improved outcomes. This phase III randomized superiority trial is designed to prospectively evaluate whether toxicity is lower after MRI-guided versus CT-guided SBRT. METHODS: Three hundred men with localized PCa will be randomized in a 1:1 ratio to SBRT using CT or MRI guidance. Randomization will be stratified by baseline International Prostate Symptom Score (IPSS) (≤15 or > 15) and prostate gland volume (≤50 cc or > 50 cc). Five fractions of 8 Gy will be delivered to the prostate over the course of fourteen days, with or without hormonal therapy and elective nodal radiotherapy (to a dose of 5 Gy per fraction) as per the investigator's discretion. The primary endpoint is the incidence of physician-reported acute grade ≥ 2 genitourinary (GU) toxicity (during the first 90 days after SBRT), as assessed by the CTCAE version 4.03 scale. Secondary clinical endpoints include incidence of acute grade ≥ 2 gastrointestinal (GI) toxicity, 5-year cumulative incidences of physician-reported late grade ≥ 2 GU and GI toxicity, temporal changes in patient-reported quality of life (QOL) outcomes, 5-year biochemical recurrence-free survival and the proportion of fractions of MRI-guided SBRT in which online adaptive radiotherapy is used. DISCUSSION: The MIRAGE trial is the first randomized trial comparing MRI-guided with standard CT-guided SBRT for localized PCa. The primary hypothesis is that MRI-guided SBRT will lead to an improvement in the cumulative incidence of acute grade ≥ 2 GU toxicity when compared to CT-guided SBRT. The pragmatic superiority design focused on an acute toxicity endpoint will allow an early comparison of the two technologies. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT04384770. Date of registration: May 12, 2020. https://clinicaltrials.gov/ct2/show/NCT04384770 PROTOCOL VERSION: Version 2.1, Aug 28, 2020.

Clinical assessment of geometric distortion for a 0.35T MR-guided radiotherapy system.

PURPOSE: To estimate the overall spatial distortion on clinical patient images for a 0.35 T MR-guided radiotherapy system. METHODS: Ten patients with head-and-neck cancer underwent CT and MR simulations with identical immobilization. The MR images underwent the standard systematic distortion correction post-processing. The images were rigidly registered and landmark-based analysis was performed by an anatomical expert. Distortion was quantified using Euclidean distance between each landmark pair and tagged by tissue interface: bone-tissue, soft tissue, or air-tissue. For baseline comparisons, an anthropomorphic phantom was imaged and analyzed. RESULTS: The average spatial discrepancy between CT and MR landmarks was 1.15 ± 1.14 mm for the phantom and 1.46 ± 1.78 mm for patients. The error histogram peaked at 0-1 mm. 66% of the discrepancies were <2 mm and 51% <1 mm. In the patient data, statistically significant differences (p-values < 0.0001) were found between the different tissue interfaces with averages of 0.88 ± 1.24 mm, 2.01 ± 2.20 mm, and 1.41 ± 1.56 mm for the air/tissue, bone/tissue, and soft tissue, respectively. The distortion generally correlated with the in-plane radial distance from the image center along the longitudinal axis of the MR. CONCLUSION: Spatial distortion remains in the MR images after systematic distortion corrections. Although the average errors were relatively small, large distortions observed at bone/tissue interfaces emphasize the need for quantitative methods for assessing and correcting patient-specific spatial distortions.

Multidimensional MR spectroscopic imaging of prostate cancer in vivo.

Prostate cancer (PCa) is the second most common type of cancer among men in the United States. A major limitation in the management of PCa is an inability to distinguish, early on, cancers that will progress and become life threatening. One-dimensional (1D) proton ((1)H) MRS of the prostate provides metabolic information such as levels of choline (Ch), creatine (Cr), citrate (Cit), and spermine (Spm) that can be used to detect and diagnose PCa. Ex vivo high-resolution magic angle spinning (HR-MAS) of PCa specimens has revealed detection of more metabolites such as myo-inositol (mI), glutamate (Glu), and glutamine (Gln). Due to the J-modulation and signal overlap, it is difficult to quantitate Spm and other resonances in the prostate clearly by single- and multivoxel-based 1D MR spectroscopy. This limitation can be minimized by adding at least one more spectral dimension by which resonances can be spread apart, thereby increasing the spectral dispersion. However, recording of multivoxel-based two-dimensional (2D) MRS such as J-resolved spectroscopy (JPRESS) and correlated spectroscopy (L-COSY) combined with 2D or three-dimensional (3D) magnetic resonance spectroscopic imaging (MRSI) using conventional phase-encoding can be prohibitively long to be included in a clinical protocol. To reduce the long acquisition time required for spatial encoding, the echo-planar spectroscopic imaging (EPSI) technique has been combined with correlated spectroscopy to give four-dimensional (4D) echo-planar correlated spectroscopic imaging (EP-COSI) as well as J-resolved spectroscopic imaging (EP-JRESI) and the multi-echo (ME) variants. Further acceleration can be achieved using non-uniform undersampling (NUS) and reconstruction using compressed sensing (CS). Earlier versions of 2D MRS, theory of 2D MRS, spectral apodization filters, newer developments and the potential role of multidimensional MRS in PCa detection and management will be reviewed here.

Pulmonary toxicity in a rabbit model of stereotactic lung radiation therapy: efficacy of a radioprotector.

This study aimed to assess the efficacy of the radioprotector amifostine in limiting radiation toxicity in a rabbit model of lung stereotactic body radiation therapy (SBRT) by correlating contrast-enhanced magnetic resonance angiography (ce-MRA), computed tomography (CT), and helium-3 (He-3) magnetic resonance imaging (MRI) with histopathology. Multiple MRI techniques were tested to obtain complementing physiologic information. Thirteen rabbits received SBRT to the right lower lobe of the lung. Specifically, 4 received 3 × 11 Gray (Gy), 6 received 3 × 11 Gy and 50 mg/kg of amifostine pre-SRBT, and 3 received 3 × 7, 3 × 9, or 3 × 13 Gy. Imaging was performed at baseline and 4, 8, 12, and 16 weeks post-SBRT. Ce-MRA perfusion difference between lungs in the irradiated group at 16 weeks post-treatment was statistically significant (P = .04) whereas the difference in the irradiated + amifostine group was not (P = .30). Histologically observed low red blood cell (RBC) count and CT hypodensity suggests changes were primarily related to perfusion; however, structural changes, such as increased alveolar size, were also present. No changes in He-3 MRI lung ventilation were observed in either group. Although radiation-induced injury detected in rabbits as CT hypodensity contrasted with increased density observed in humans/rodents, the changes in ce-MRA and CT were still significantly reduced after the addition of amifostine to SBRT. Use of CT and selected MRI techniques helped to pinpoint primary physiologic changes.

[Insertion/deletion polymorphism of IL1A 3'-UTR associated with the susceptibility of prostate cancer ].

OBJECTIVE: To identify the association between the insertion/deletion polymorphism of interleukin- 1A gene (IL1A) and the susceptibility of prostate cancer (PCA). METHODS: We performed a case-control study enrolling 131 PCA patients and 229 healthy control subjects in a Chinese Han population. The TTCA insertion/ deletion polymorphism (rs3783553) in 3'-UTR of IL1A gene was genotyped by PCR-RFLP method. RESULTS: The genotype distribution of rs3783553 in both groups met the requirements of Hardy-Weinberg equilibrium. Significantly reduced PCA risk was associated with D/I and I/I genotype compared to D/D genotype (P<0. 001, OR=O. 48, 95%CI: 0.31-0.74), and allele I is associated with the reduced PCA risk (P=0. 001,OR=0. 56,95% CI: 0. 40-0. 79). CONCLUSION: TTCA insertion allele of rs3783553 contributes to the reduction of the susceptibility to prostate cancer.

Anatomy-constrained synthesis for spleen segmentation improvement in unpaired mouse micro-CT scans with 3D CycleGAN.

Objective. Auto-segmentation in mouse micro-CT enhances the efficiency and consistency of preclinical experiments but often struggles with low-native-contrast and morphologically complex organs, such as the spleen, resulting in poor segmentation performance. While CT contrast agents can improve organ conspicuity, their use complicates experimental protocols and reduces feasibility. We developed a 3D Cycle Generative Adversarial Network (CycleGAN) incorporating anatomy-constrained U-Net models to leverage contrast-enhanced CT (CECT) insights to improve unenhanced native CT (NACT) segmentation.Approach.We employed a standard CycleGAN with an anatomical loss function to synthesize virtual CECT images from unpaired NACT scans at two different resolutions. Prior to training, two U-Nets were trained to automatically segment six major organs in NACT and CECT datasets, respectively. These pretrained 3D U-Nets were integrated during the CycleGAN training, segmenting synthetic images, and comparing them against ground truth annotations. The compound loss within the CycleGAN maintained anatomical fidelity. Full image processing was achieved for low-resolution datasets, while high-resolution datasets employed a patch-based method due to GPU memory constraints. Automated segmentation was applied to original NACT and synthetic CECT scans to evaluate CycleGAN performance using the Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD95p).Main results.High-resolution scans showed improved auto-segmentation, with an average DSC increase from 0.728 to 0.773 and a reduced HD95p from 1.19 mm to 0.94 mm. Low-resolution scans benefited more from synthetic contrast, showing a DSC increase from 0.586 to 0.682 and an HD95preduction from 3.46 mm to 1.24 mm.Significance.Implementing CycleGAN to synthesize CECT scans substantially improved the visibility of the mouse spleen, leading to more precise auto-segmentation. This approach shows the potential in preclinical imaging studies where contrast agent use is impractical.

Robust localization of poorly visible tumor in fiducial free stereotactic body radiation therapy.

BACKGROUND AND PURPOSE: Effective respiratory motion management reduces healthy tissue toxicity and ensures sufficient dose delivery to lung cancer cells in pulmonary stereotactic body radiation therapy (SBRT) with high fractional doses. An articulated robotic arm paired with an X-ray imaging system is designed for real-time motion-tracking (RTMT) dose delivery. However, small tumors (<15 mm) or tumors at challenging locations may not be visible in the X-ray images, disqualifying patients with such tumors from RTMT dose delivery unless fiducials are implanted via an invasive procedure. To track these practically invisible lung tumors in SBRT, we hereby develop a deep learning-enabled template-free tracking framework, SAFE Track. METHODS: SAFE Track is a fully supervised framework that trains a generalizable prior for template-free target localization. Two sub-stages are incorporated in SAFE Track, including the initial pretraining on two large-scale medical image datasets (DeepLesion and Node21) followed by fine-tuning on our in-house dataset. A two-stage detector, Faster R-CNN, with a backbone of ResNet50, was selected as our detection network. 94 patients (415 fractions; 40,348 total frames) with low tumor visibility who thus had implanted fiducials were included. The cohort is categorized by the longest dimension of the tumor (<10 mm, 10-15 mm and > 15 mm). The patients were split into training (n = 66) and testing (n = 28) sets. We simulated fiducial-free tumors by removing the fiducials from the X-ray images. We classified the patients into two groups - fiducial implanted inside tumors and implanted outside tumors. To ensure the rigor of our experiment design, we only conducted fiducial removal simulation in training patients and utilized patients with fiducial implanted outside of the tumors for testing. Commercial Xsight Lung Tracking (XLT) and a Deep Match were included for comparison. RESULTS: SAFE Track achieves promising outcomes to as accurate as 1.23±1.32 mm 3D distance in testing patients with tumor size > 15 mm where Deep Match is at 4.75±1.67 mm and XLT is at 12.23±4.58 mm 3D distance. Even for the most challenging tumor size (<10 mm), SAFE Track maintains its robustness at 1.82 plus or minus 1.67 mm 3D distance, where Deep Match is at 5.32 plus or minus 2.32 mm, and XLT is at 24.83±12.95 mm 3D distance. Moreover, SAFE Track can detect some considerably challenging cases where the tumor is almost invisible or overlapped with dense anatomies. CONCLUSION: SAFE Track is a robust, clinically compatible, fiducial-free, and template-free tracking framework that is applicable to patients with small tumors or tumors obscured by overlapped anatomies in SBRT.

Hypoxia-informed RBE-weighted beam orientation optimization for intensity modulated proton therapy.

BACKGROUND: Variable relative biological effectiveness (RBE) models in treatment planning have been proposed to optimize the therapeutic ratio of proton therapy. It has been reported that proton RBE decreases with increasing tumor oxygen level, offering an opportunity to address hypoxia-related radioresistance with RBE-weighted optimization. PURPOSE: Here, we obtain a voxel-level estimation of partial oxygen pressure to weigh RBE values in a single biologically informed beam orientation optimization (BOO) algorithm. METHODS: Three glioblastoma patients with [18 F]-fluoromisonidazole (FMISO)-PET/CT images were selected from the institutional database. Oxygen values were derived from tracer uptake using a nonlinear least squares curve fitting. McNamara RBE, calculated from proton dose, was then weighed using oxygen enhancement ratios (OER) for each voxel and incorporated into the dose fidelity term of the BOO algorithm. The nonlinear optimization problem was solved using a split-Bregman approach, with FISTA as the solver. The proposed hypoxia informed RBE-weighted method (HypRBE) was compared to dose fidelity terms using the constant RBE of 1.1 (cRBE) and the normoxic McNamara RBE model (RegRBE). Tumor homogeneity index (HI), maximum biological dose (Dmax), and D95%, as well as OAR therapeutic index (TI = gEUDCTV /gEUDOAR ) were evaluated along with worst-case statistics after normalization to normal tissue isotoxicity. RESULTS: Compared to [cRBE, RegRBE], HypRBE increased tumor HI, Dmax, and D95% across all plans by on average [31.3%, 31.8%], [48.6%, 27.1%], and [50.4%, 23.8%], respectively. In the worst-case scenario, the parameters increase on average by [12.5%, 14.7%], [7.3%,-8.9%], and [22.3%, 2.1%]. Despite increased OAR Dmean and Dmax by [8.0%, 3.0%] and [13.1%, -0.1%], HypRBE increased average TI by [22.0%, 21.1%]. Worst-case OAR Dmean, Dmax, and TI worsened by [17.9%, 4.3%], [24.5%, -1.2%], and [9.6%, 10.5%], but in the best cases, HypRBE escalates tumor coverage significantly without compromising OAR dose, increasing the therapeutic ratio. CONCLUSIONS: We have developed an optimization algorithm whose dose fidelity term accounts for hypoxia-informed RBE values. We have shown that HypRBE selects bE:\Alok\aaeams better suited to deliver high physical dose to low RBE, hypoxic tumor regions while sparing the radiosensitive normal tissue.

MUsculo-Skeleton-Aware (MUSA) deep learning for anatomically guided head-and-neck CT deformable registration.

Deep-learning-based deformable image registration (DL-DIR) has demonstrated improved accuracy compared to time-consuming non-DL methods across various anatomical sites. However, DL-DIR is still challenging in heterogeneous tissue regions with large deformation. In fact, several state-of-the-art DL-DIR methods fail to capture the large, anatomically plausible deformation when tested on head-and-neck computed tomography (CT) images. These results allude to the possibility that such complex head-and-neck deformation may be beyond the capacity of a single network structure or a homogeneous smoothness regularization. To address the challenge of combined multi-scale musculoskeletal motion and soft tissue deformation in the head-and-neck region, we propose a MUsculo-Skeleton-Aware (MUSA) framework to anatomically guide DL-DIR by leveraging the explicit multiresolution strategy and the inhomogeneous deformation constraints between the bony structures and soft tissue. The proposed method decomposes the complex deformation into a bulk posture change and residual fine deformation. It can accommodate both inter- and intra- subject registration. Our results show that the MUSA framework can consistently improve registration accuracy and, more importantly, the plausibility of deformation for various network architectures. The code will be publicly available at https://github.com/HengjieLiu/DIR-MUSA.

Robust treatment planning for small animal radio-neuromodulation using focused kV x-ray beams.

BACKGROUND: In preclinical radio-neuromodulation research, small animal experiments are pivotal for unraveling radiobiological mechanism, investigating prescription and planning techniques, and assessing treatment effects and toxicities. However, the target size inside a rat brain is typically in the order of sub-millimeters. The small target inside the visual cortex neural region in rat brain with a diameter of around 1 mm was focused in this work to observe the physiological change of this region. Delivering uniform doses to the small target while sparing health tissues is challenging. Focused kV x-ray technique based on modern x-ray polycapillary focusing lens is a promising modality for small animal radio-neuromodulation. PURPOSE: The current manual planning method could lead to sub-optimal plans, and the positioning uncertainties due to mechanical accuracy limitations, animal immobilization, and robotic arm motion are not considered. This work aims to design a robust inverse planning method to optimize the intensities of focused kV x-ray beams located in beam trajectories to irradiate small mm-sized targets in rat brains for radio-neuromodulation. METHODS: Focused kV x-ray beams were generated through polycapillary x-ray focusing lenses on achieving small (≤0.3 mm) focus perpendicular to the beam. The beam trajectories were manually designed in 3D space in scanning-while-rotating mode. Geant4 Monte Carlo (MC) simulation generated a dose calculation matrix for each focused kV x-ray beam located in beam trajectories. In the proposed robust inverse planning method, an objective function combining a voxel-wise stochastic programming approach and L1 norm regularization was established to overcome the positioning uncertainties and obtain a high-quality plan. The fast iterative shrinkage thresholding algorithm (FISTA) was utilized to solve the objective function and obtain the optimal intensities. Four cases were employed to validate the feasibility and effectiveness of the proposed method. The manual and non-robust inverse planning methods were also implemented for comparison. RESULTS: The proposed robust inverse planning method achieved superior dose homogeneity and higher robustness against positioning uncertainties. On average, the clinical target volume (CTV) homogeneity index (HI) of robust inverse plan improved to 13.3 from 22.9 in non-robust inverse plan and 53.8 in manual plan if positioning uncertainties were also present. The average bandwidth at D90 was reduced by 6.5 Gy in the robust inverse plan, compared to 9.6 Gy in non-robust inverse plan and 12.5 Gy in manual plan. The average bandwidth at D80 was reduced by 3.4 Gy in robust inverse plan, compared to 5.5 Gy in non-robust inverse plan and 8.5 Gy in manual plan. Moreover, the dose delivery time of manual plan was reduced by an average reduction of 54.7% with robust inverse plan and 29.0% with non-robust inverse plan. CONCLUSION: Compared to manual and non-robust inverse planning methods, the robust inverse planning method improved the dose homogeneity and delivery efficiency and was resistant to the uncertainties, which are crucial for radio-neuromodulation utilizing focused kV x-rays.

Deep match: A zero-shot framework for improved fiducial-free respiratory motion tracking.

BACKGROUND AND PURPOSE: Motion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body radiation therapy (SBRT). Lung SBRT using an articulated robotic arm allows dynamic tracking during radiation dose delivery. Two stereoscopic X-ray tracking modes are available - fiducial-based and fiducial-free tracking. Although X-ray detection of implanted fiducials is robust, the implantation procedure is invasive and inapplicable to some patients and tumor locations. Fiducial-free tracking relies on tumor contrast, which challenges the existing tracking algorithms for small (e.g., <15 mm) and/or tumors obscured by overlapping anatomies. To markedly improve the performance of fiducial-free tracking, we proposed a deep learning-based template matching algorithm - Deep Match. METHOD: Deep Match consists of four self-definable stages - training-free feature extractor, similarity measurements for location proposal, local refinements, and uncertainty level prediction for constructing a more trustworthy and versatile pipeline. Deep Match was validated on a 10 (38 fractions; 2661 images) patient cohort whose lung tumor was trackable on one X-ray view, while the second view did not offer sufficient conspicuity for tumor tracking using existing methods. The patient cohort was stratified into subgroups based on tumor sizes (<10 mm, 10-15 mm, and >15 mm) and tumor locations (with/without thoracic anatomy overlapping). RESULTS: On X-ray views that conventional methods failed to track the lung tumor, Deep Match achieved robust performance as evidenced by >80 % 3 mm-Hit (detection within 3 mm superior/inferior margin from ground truth) for 70 % of patients and <3 mm superior/inferior distance (SID) ∼1 mm standard deviation for all the patients. CONCLUSION: Deep Match is a zero-shot learning network that explores the intrinsic neural network benefits without training on patient data. With Deep Match, fiducial-free tracking can be extended to more patients with small tumors and with tumors obscured by overlapping anatomy.

Exploring Automated Contouring Across Institutional Boundaries: A Deep Learning Approach with Mouse Micro-CT Datasets.

Image-guided mouse irradiation is essential to understand interventions involving radiation prior to human studies. Our objective is to employ Swin UNEt Transformers (Swin UNETR) to segment native micro-CT and contrast-enhanced micro-CT scans and benchmark the results against 3D no-new-Net (nnU-Net). Swin UNETR reformulates mouse organ segmentation as a sequence-to-sequence prediction task, using a hierarchical Swin Transformer encoder to extract features at 5 resolution levels, and connects to a Fully Convolutional Neural Network (FCNN)-based decoder via skip connections. The models were trained and evaluated on open datasets, with data separation based on individual mice. Further evaluation on an external mouse dataset acquired on a different micro-CT with lower kVp and higher imaging noise was also employed to assess model robustness and generalizability. Results indicate that Swin UNETR consistently outperforms nnU-Net and AIMOS in terms of average dice similarity coefficient (DSC) and Hausdorff distance (HD95p), except in two mice of intestine contouring. This superior performance is especially evident in the external dataset, confirming the model's robustness to variations in imaging conditions, including noise and quality, thereby positioning Swin UNETR as a highly generalizable and efficient tool for automated contouring in pre-clinical workflows.

Minimum reporting standards should be expected for preclinical radiobiology irradiators and dosimetry in the published literature.

The cornerstones of science advancement are rigor in performing scientific research, reproducibility of research findings and unbiased reporting of design and results of the experiments. For radiation research, this requires rigor in describing experimental details as well as the irradiation protocols for accurate, precise and reproducible dosimetry. Most institutions conducting radiation biology research in in vitro or animal models do not have describe experimental irradiation protocols in sufficient details to allow for balanced review of their publication nor for other investigators to replicate published experiments. The need to increase and improve dosimetry standards, traceability to National Institute of Standards and Technology (NIST) standard beamlines, and to provide dosimetry harmonization within the radiation biology community has been noted for over a decade both within the United States and France. To address this requirement subject matter experts have outlined minimum reporting standards that should be included in published literature for preclinical irradiators and dosimetry.

Dosimetric predictors for genitourinary toxicity in MR-guided stereotactic body radiation therapy (SBRT): Substructure with fraction-wise analysis.

BACKGROUND: MR-guided radiation therapy (MRgRT) systems provide superior soft tissue contrast than x-ray based systems and can acquire real-time cine for treatment gating. These features allow treatment planning margins to be reduced, allowing for improved critical structure sparing and reduced treatment toxicity. Despite this improvement, genitourinary (GU) toxicity continues to affect many patients. PURPOSE: (1) To identify dosimetric predictors, potentially in combination with clinical parameters, of GU toxicity following SBRT by leveraging MRgRT to accurately monitor daily dose, beyond predicted dose calculated during planning. (2) Improve awareness of toxicity-sensitive bladder substructures, specifically the trigone and urethra. METHODS: Sixty-nine prostate cancer patients (NCT04384770 clinical trial) were treated on a ViewRay MRIdian MRgRT system, with 40 Gy prescribed to 95% of the PTV in over five fractions. Overall, 17 (24.6%) prostate patients reported acute grade 2 GU toxicity. The CTV, PTV, bladder, bladder wall, trigone, urethra, rectum, and rectal wall were contoured on the planning and daily treatment MRIs. Planning and daily treatment DVHs (0.1 Gy increments), organ doses (min, max, mean), and organ volumes were recorded. Daily dose was estimated by transferring the planning dose distributions to the daily MRI based on the daily setup alignment. Patients were partitioned into a training (55) and testing set (14). Dose features were pre-filtered using a t-test followed by maximum relevance minimum redundancy (MRMR) algorithm. Logistic regression was investigated with regularization to select dosimetric predictors. Specifically, two approaches: time-group least absolute shrinkage and selection (LASSO), and interactive grouped greedy algorithm (IGA) were investigated. Shared features across the planning and five treatment fractions were grouped to encourage consistency and stability. The conventional flat non-temporally grouped LASSO was also evaluated to provide a solid benchmark. After feature selection, a final logistic regression model was trained. Dosimetric regression models were compared to a clinical regression model with only clinical parameters (age, baseline IPSS, prostate gland size, ADT usage, etc.) and a hybrid model, combining the best performing dosimetric features with the clinical parameters, was evaluated. Final model performance was evaluated on the testing set using accuracy, sensitivity, and specificity determined by the optimal threshold of the training set. RESULTS: IGA had the best testing performance with an accuracy/sensitivity/specificity of 0.79/0.67/0.82, selecting 12 groups covering the bladder (V19.8 Gy, V20.5 Gy), bladder wall (19.7 Gy), trigone (15.9, 18.2, 43.3 Gy), urethra (V41.4 Gy, V41.7 Gy), CTV (V41.9 Gy), rectum (V8.5 Gy), and rectal wall (1.2, 44.1 Gy) dose features. Absolute bladder V19.8 Gy and V20.5 Gy were the most important features, followed by relative trigone 15.9  and 18.2 Gy. Inclusion of clinical parameters in the hybrid model with IGA did not significantly change regression performance. CONCLUSION: Overall, IGA feature selection resulted in the best GU toxicity prediction performance. This exploratory study demonstrated the feasibility of identification and analysis of dosimetric toxicity predictors with awareness to sensitive substructures and daily dose to potentially provide consistent and stable dosimetric metrics to guide treatment planning. Further patient accruement is warranted to further assess dosimetric predictor and perform validation.

Intra-fractional geometric and dose/volume metric variations of magnetic resonance imaging-guided stereotactic radiotherapy of prostate bed after radical prostatectomy.

Background and purpose: Magnetic Resonance Imaging (MRI)-guided Stereotactic body radiotherapy (SBRT) treatment to prostate bed after radical prostatectomy has garnered growing interests. The aim of this study is to evaluate intra-fractional anatomic and dose/volume metric variations for patients receiving this treatment. Materials and methods: Nineteen patients who received 30-34 Gy in 5 fractions on a 0.35T MR-Linac were included. Pre- and post-treatment MRIs were acquired for each fraction (total of 75 fractions). The Clinical Target Volume (CTV), bladder, rectum, and rectal wall were contoured on all images. Volumetric changes, Hausdorff distance, Mean Distance to Agreement (MDA), and Dice similarity coefficient (DSC) for each structure were calculated. Median value and Interquartile range (IQR) were recorded. Changes in target coverage and Organ at Risk (OAR) constraints were compared and evaluated using Wilcoxon rank sum tests at a significant level of 0.05. Results: Bladder had the largest volumetric changes, with a median volume increase of 48.9 % (IQR 28.9-76.8 %) and a median MDA of 5.1 mm (IQR 3.4-7.1 mm). Intra-fractional CTV volume remained stable with a median volume change of 1.2 % (0.0-4.8 %). DSC was 0.97 (IQR 0.94-0.99). For the dose/volume metrics, there were no statistically significant changes observed except for an increase in bladder hotspot and a decrease of bladder V32.5 Gy and mean dose. The CTV V95% changed from 99.9 % (IQR 98.8-100 %) to 99.6 % (IQR 93.9-100 %). Conclusion: Despite intra-fractional variations of OARs, CTV coverage remained stable during MRI-guided SBRT treatments for the prostate bed.

Quantification of Dosimetry Improvement With or Without Patient Surface Guidance.

Purpose: Noncoplanar beams and arcs are routinely used to improve dosimetry for intracranial cases, but their application for extracranial cases has been hampered by the risk of collision. This has led to conservative beam selection whose impact on plan dosimetry has not been previously studied. Methods and Materials: A full-body 3-dimensional patient surface was acquired using optical cameras for a single lung patient at the time of computed tomography simulation. Eight stereotactic body radiation therapy (SBRT) plans were created for the patient, with varying degrees of noncoplanarity and deliverability. The plans included volumetric modulated arc therapy and intensity modulated radiation therapy (IMRT) plans ranging from simple, coplanar arcs to multiple noncoplanar arcs and IMRT beams. A total of 70 fields were created across the 8 plans, of which 21 fields were undeliverable with a 5-cm buffer. Organs-at-risk (OARs) metrics including R50, Dmax 2 cm from the PTV, lung V20, and chest wall V30 were evaluated. Five expert SBRT dosimetrists from 5 institutions evaluated field deliverability, with or without the guidance of the clearance map. Results: In the dosimetry evaluation, a clear trend in increasing dosimetric compactness and OAR sparing is observed with increasing plan noncoplanarity. R50, Dmax 2 cm, lung V20, and chest wall V30 decreased 41%, 39%, 43%, and 57%, respectively, from plan 1 (2 coplanar partial arcs) to plan 8 (19 noncoplanar IMRT beams). In the observer tests, the expert dosimetrists' ability to accurately discern beam deliverability because of collision significantly increases with the clearance map. The errors in predicting colliding fields were eliminated using the whole-body surface and clearance map, and the user was able to select fields based on plan quality and patient comfort instead of being overly conservative. Conclusion: The study shows that incorporating a personalized, whole-body clearance map in the treatment planning workflow can facilitate the adoption of noncoplanar beams or arcs that benefit the SBRT plan dosimetry.

Numerical simulation of the temporal and spatial evolution of sandstone pore type reservoir damage types and severity.

During the oilfield development process, various factors can cause different types of reservoir damage, leading to reduced oil well production or even shutdown, and decreased water injection capacity in water wells, resulting in significant economic losses for the oilfield. However, formation damage control measures must be based on the quantitative diagnosis of the types and degrees of reservoir damage. This paper establishes a spatiotemporal evolution numerical model for 12 common types of damage during the oil and gas exploration and development process, based on the material balance theory and Fick's diffusion law in reservoir damage processes. This model achieves numerical simulation of the degree of various reservoir damage types in different spatiotemporal domains. The overall damage degree of a specific well's evolution with time and space is further simulated in simple superposition way. The sequential core flow experiment was carried out in the laboratory, and then compared with the calculation results. The accuracy is above 90%. Finally, using field test data, the simulation results show a 95% or higher degree of agreement with the actual field measurements, proving that the reservoir damage spatiotemporal evolution quantitative simulation technology established in this paper has high accuracy and practicality.

Paired conditional generative adversarial network for highly accelerated liver 4D MRI.

Purpose.4D MRI with high spatiotemporal resolution is desired for image-guided liver radiotherapy. Acquiring densely sampling k-space data is time-consuming. Accelerated acquisition with sparse samples is desirable but often causes degraded image quality or long reconstruction time. We propose the Reconstruct Paired Conditional Generative Adversarial Network (Re-Con-GAN) to shorten the 4D MRI reconstruction time while maintaining the reconstruction quality.Methods.Patients who underwent free-breathing liver 4D MRI were included in the study. Fully- and retrospectively under-sampled data at 3, 6 and 10 times (3×, 6× and 10×) were first reconstructed using the nuFFT algorithm. Re-Con-GAN then trained input and output in pairs. Three types of networks, ResNet9, UNet and reconstruction swin transformer (RST), were explored as generators. PatchGAN was selected as the discriminator. Re-Con-GAN processed the data (3D +t) as temporal slices (2D +t). A total of 48 patients with 12 332 temporal slices were split into training (37 patients with 10 721 slices) and test (11 patients with 1611 slices). Compressed sensing (CS) reconstruction with spatiotemporal sparsity constraint was used as a benchmark. Reconstructed image quality was further evaluated with a liver gross tumor volume (GTV) localization task using Mask-RCNN trained from a separate 3D static liver MRI dataset (70 patients; 103 GTV contours).Results.Re-Con-GAN consistently achieved comparable/better PSNR, SSIM, and RMSE scores compared to CS/UNet models. The inference time of Re-Con-GAN, UNet and CS are 0.15, 0.16, and 120 s. The GTV detection task showed that Re-Con-GAN and CS, compared to UNet, better improved the dice score (3× Re-Con-GAN 80.98%; 3× CS 80.74%; 3× UNet 79.88%) of unprocessed under-sampled images (3× 69.61%).Conclusion.A generative network with adversarial training is proposed with promising and efficient reconstruction results demonstrated on an in-house dataset. The rapid and qualitative reconstruction of 4D liver MR has the potential to facilitate online adaptive MR-guided radiotherapy for liver cancer.