Purpose: Metallic implants have been correlated to local control failure for spinal sarcoma and chordoma patients due to the uncertainty of implant delineation from computed tomography (CT). Such uncertainty can compromise the proton Monte Carlo dose calculation (MCDC) accuracy. A component method is proposed to determine the dimension and volume of the implants from CT images. Methods: The proposed component method leverages the knowledge of surgical implants from medical supply vendors to predefine accurate contours for each implant component, including tulips, screw bodies, lockers, and rods. A retrospective patient study was conducted to demonstrate the feasibility of the method. The reference implant materials and samples were collected from patient medical records and vendors, Medtronic and NuVasive. Additional CT images with extensive features, such as extended Hounsfield units and various reconstruction diameters, were used to quantify the uncertainty of implant contours. Results: For in vivo patient implant estimation, the reference and the component method differences were 0.35, 0.17, and 0.04 cm3 for tulips, screw bodies, and rods, respectively. The discrepancies by a conventional threshold method were 5.46, 0.76, and 0.05 cm3, respectively. The mischaracterization of implant materials and dimensions can underdose the clinical target volume coverage by 20 cm3 for a patient with eight lumbar implants. The tulip dominates the dosimetry uncertainty as it can be made from titanium or cobalt–chromium alloys by different vendors. Conclusions: A component method was developed and demonstrated using phantom and patient studies with implants. The proposed method provides more accurate implant characterization for proton MCDC and can potentially enhance the treatment quality for proton therapy. The current proof-of-concept study is limited to the implant characterization for lumbar spine. Future investigations could be extended to cervical spine and dental implants for head-and-neck patients where tight margins are required to spare organs at risk.

Proton therapy requires accurate dose calculation for treatment planning to ensure the conformal doses are precisely delivered to the targets. The conversion of CT numbers to material properties is a significant source of uncertainty for dose calculation. The aim of this study is to develop a physics-informed deep learning (PIDL) framework to derive accurate mass density and relative stopping power maps from dual-energy computed tomography (DECT) images. The PIDL framework allows deep learning (DL) models to be trained with a physics loss function, which includes a physics model to constrain DL models. Five DL models were implemented including a fully connected neural network (FCNN), dual-FCNN (DFCNN), and three variants of residual networks (ResNet): ResNet-v1 (RN-v1), ResNet-v2 (RN-v2), and dual-ResNet-v2 (DRN-v2). An artificial neural network (ANN) and the five DL models trained with and without physics loss were explored to evaluate the PIDL framework. Two empirical DECT models were implemented to compare with the PIDL method. DL training data were from CIRS electron density phantom 062M (Computerized Imaging Reference Systems, Inc., Norfolk, VA). The performance of DL models was tested by CIRS adult male, adult female, and 5-year-old child anthropomorphic phantoms. For density map inference, the physics-informed RN-v2 was 3.3%, 2.9% and 1.9% more accurate than ANN for the adult male, adult female, and child phantoms. The physics-informed DRN-v2 was 0.7%, 0.6%, and 0.8% more accurate than DRN-v2 without physics training for the three phantoms, respectfully. The results indicated that physics-informed training could reduce uncertainty when ANN/DL models without physics training were insufficient to capture data structures or derived significant errors. DL models could also achieve better image noise control compared to the empirical DECT parametric mapping methods. The proposed PIDL framework can potentially improve proton range uncertainty by offering accurate material properties conversion from DECT.

FLASH radiotherapy (RT) is a novel technique in which the ultrahigh dose rate (UHDR) (≥40 Gy/s) is delivered to the entire treatment volume. Recent outcomes of in vivo studies show that the UHDR RT has the potential to spare normal tissue without sacrificing tumor control. There is a growing interest in the application of FLASH RT, and the ultrahigh dose irradiation delivery has been achieved by a few experimental and modified linear accelerators. The underlying mechanism of FLASH effect is yet to be fully understood, but the oxygen depletion in normal tissue providing extra protection during FLASH irradiation is a hypothesis that attracts most attention currently. Monte Carlo simulation is playing an important role in FLASH, enabling the understanding of its dosimetry calculations and hardware design. More advanced Monte Carlo simulation tools are under development to fulfill the challenge of reproducing the radiolysis and radiobiology processes in FLASH irradiation. FLASH RT may become one of standard treatment modalities for tumor treatment in the future. This paper presents the history and status of FLASH RT studies with a focus on FLASH irradiation delivery modalities, underlying mechanism of FLASH effect, in vivo and vitro experiments, and simulation studies. Existing challenges and prospects of this novel technique are discussed in this manuscript.

Purpose: Proton vertebral body sparing craniospinal irradiation (CSI) treats the thecal sac while avoiding the anterior vertebral bodies in an effort to reduce myelosuppression and growth inhibition. However, robust treatment planning needs to compensate for proton range uncertainty, which contributes unwanted doses within the vertebral bodies. This work aimed to develop an early in vivo radiation damage quantification method using longitudinal magnetic resonance (MR) scans to quantify the dose effect during fractionated CSI. Methods and Materials: Ten pediatric patients were enrolled in a prospective clinical trial of proton vertebral body sparing CSI, in which they received 23.4 to 36 Gy. Monte Carlo robust planning was used, with spinal clinical target volumes defined as the thecal sac and neural foramina. T1/T2-weighted MR scans were acquired before, during, and after treatments to detect a transition from hematopoietic to less metabolically active fatty marrow. MR signal intensity histograms at each time point were analyzed and fitted by multi-Gaussian models to quantify radiation damage. Results: Fatty marrow filtration was observed in MR images as early as the fifth fraction of treatment. Maximum radiation-induced marrow damage occurred 40 to 50 days from the treatment start, followed by marrow regeneration. The mean damage ratios were 0.23, 0.41, 0.59, and 0.54, corresponding to 10, 20, 40, and 60 days from the treatment start. Conclusions: We demonstrated a noninvasive method for identifying early vertebral marrow damage based on radiation-induced fatty marrow replacement. The proposed method can be potentially used to quantify the quality of CSI vertebral sparing and preserve metabolically active hematopoietic bone marrow.

Whilst allowing for easy access to synthetically versatile motifs and for modification of bioactive molecules, the chemoselective benzylic oxidation reactions of functionalized alkyl arenes remain challenging. Reported in this study is a new non-heme Mn catalyst stabilized by a bipiperidine-based tetradentate ligand, which enables methylene oxidation of benzylic compounds by H2O2, showing high activity and excellent chemoselectivity under mild conditions. The protocol tolerates an unprecedentedly wide range of functional groups, including carboxylic acid and derivatives, ketone, cyano, azide, acetate, sulfonate, alkyne, amino acid, and amine units, thus providing a low-cost, more sustainable and robust pathway for the facile synthesis of ketones, increase of complexity of organic molecules, and late-stage modification of drugs.

Purpose: For patients with high-risk bladder cancer (pT3þ or Nþ), local regional failure remains a challenge after chemotherapy and cystectomy. An ongoing prospective phase 2 trial (NCT01954173) is examining the role of postoperative photon radiation therapy for high-risk patients using volumetric modulated arc therapy. Proton beam therapy (PBT) may be beneficial in this setting to reduce hematologic toxicity. We evaluated for dosimetric relationships with pelvic bone marrow (PBM) and changes in hematologic counts before and after pelvic radiation therapy and explored the potential of PBT treatment plans to achieve reductions in PBM dose. Materials and Methods: All enrolled patients were retrospectively analyzed after pelvic radiation per protocol with 50.4 to 55.8 Gy in 28 to 31 fractions. Comparative PBT plans were generated using pencil-beam scanning and a 3-beam multifield optimization technique. Changes in hematologic nadirs were assessed using paired t test. Correlation of mean nadirs and relative PBM dose levels were assessed using the Pearson correlation coefficient (CC). Results: Eighteen patients with a median age of 70 were analyzed. Mean cell count values after radiation therapy decreased compared with preradiation therapy values for white blood cells (WBCs), absolute neutrophil count (ANC), absolute lymphocyte count (all P,.001), and platelets (P ¼.03). Increased mean PBM dose was associated with lower nadirs in WBC (Pearson CC -0.593, P ¼.02), ANC (Pearson CC -0.597, P ¼.02), and hemoglobin (Pearson CC -0.506, P ¼.046), whereas the PBM V30 to V40 correlated with lower WBC (Pearson CC -0.512 to -0.618, P,.05), and V20 to V30 correlated with lower ANC (Pearson CC -0.569 to -0.598, P,.04). Comparative proton therapy plans decreased the mean PBM dose from 26.5 Gy to 16.1 Gy (P,.001) and had significant reductions in the volume of PBM receiving doses from 5 to 40 Gy (P,.001). Conclusion: Increased PBM mean dose and V20 to V40 were associated with lower hematologic nadirs. PBT plans reduced PBM dose and may be a valuable strategy to reduce the risk of hematologic toxicity in these patients.

Ultrasound imaging of the lung has played an important role in managing patients with COVID-19–associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.

Objectives: The purpose of this study is to investigate the dosimetric effect and clinical impact of delivering a focal radiotherapy boost dose to multiparametric MRI (mp-MRI)- defined dominant intraprostatic lesions (DILs) in prostate cancer using proton therapy. Methods: We retrospectively investigated 36 patients with pre-treatment mp-MRI and CT images who were treated using pencil beam scanning (PBS) proton radiation therapy to the whole prostate. DILs were contoured on co-registered mp-MRIs. Simultaneous integrated boost (SIB) plans using intensity-modulated proton therapy (IMPT) were created based on conventional whole-prostate- irradiation for each patient and optimized with additional DIL coverage goals and urethral constraints. DIL dose coverage and organ-at- risk (OAR) sparing were compared between conventional and SIB plans. Tumor control probability (TCP) and normal tissue complication probability (NTCP) were estimated to evaluate the clinical impact of the SIB plans. Results: Optimized SIB plans significantly escalated the dose to DILs while meeting OAR constraints. SIB plans were able to achieve 125, 150 and 175% of prescription dose coverage in 74, 54 and 17% of 36 patients, respectively. This was modeled to result in an increase in DIL TCP by 7.3-13.3% depending on -/- and DIL risk level. Conclusion: The proposed mp-MRI- guided DIL boost using proton radiation therapy is feasible without violating OAR constraints and demonstrates a potential clinical benefit by improving DIL TCP. This retrospective study suggested the use of IMPT-based DIL SIB may represent a strategy to improve tumor control.

Target delineation for radiation therapy treatment planning often benefits from magnetic resonance imaging (MRI) in addition to x-ray computed tomography (CT) due to MRI's superior soft tissue contrast. MRI-based treatment planning could reduce systematic MR-CT co-registration errors, medical cost, radiation exposure, and simplify clinical workflow. However, MRI-only based treatment planning is not widely used to date because treatment-planning systems rely on the electron density information provided by CTs to calculate dose. Additionally, air and bone regions are difficult to separategiven their similar intensities in MR imaging. The purpose of this work is to develop a learning-based method to generate patient-specific synthetic CT (sCT) from a routine anatomical MRI for use in MRI-only radiotherapy treatment planning. An auto-context model with patch-based anatomical features was integrated into a classification random forest to generate and improve semantic information. The semantic information along with anatomical features was then used to train a series of regression random forests based on the auto-context model. After training, the sCT of a new MRI can be generated by feeding anatomical features extracted from the MRI into the well-trained classification and regression random forests. The proposed algorithm was evaluated using 14 patient datasets withT1-weighted MR and corresponding CT images of the brain. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross correlation (NCC) were 57.45 ± 8.45 HU, 28.33 ± 1.68 dB, and 0.97 ± 0.01. We also compared the difference between dose maps calculated on the sCT and those on the original CT, using the same plan parameters. The average DVH differences among all patients are less than 0.2 Gy for PTVs, and less than 0.02 Gy for OARs. The sCT generation by the proposed method allows for dose calculation based MR imaging alone, and may be a useful tool for MRI-based radiation treatment planning.

Purpose: While intensity modulated proton therapy can deliver simultaneous integrated boost (SIB) to the dominant intraprostatic lesion (DIL) with high precision, it is sensitive to anatomic changes. We investigated the dosimetric effects from these changes based on pretreatment cone-beam computed tomographic (CBCT) images and identified the most important factors using a multilayer perceptron neural network (MLPNN). Methods and Materials: DILs were contoured based on coregistered multiparametric magnetic resonance images for 25 previously treated prostate cancer patients. SIB plans were created with (1) prostate clinical target volume − V70 Gy = 98%; (2) DIL − V98 Gy > 95%; and (3) all organs at risk (OARs)"?> within clinical constraints. SIB plans were applied to daily CBCT-based deformed planning computed tomography (CT)"?>. DIL − V98 Gy, bladder/rectum maximum dose (Dmax) and volume changes, femur shifts, and the distance from DIL to organs at riskOARs"?> in both planning computed tomogramsCT"?> and CBCT were calculated. Wilcoxon signed-ranks tests were used to compare the changes. MLPNNs were used to model the change in ΔDIL − V98 Gy > 10% and bladder/rectum Dmax > 80 Gy, and the relative importance factors for the model were provided. The performances of the models were evaluated with receiver operating characteristic curves. Results: Comparing initial plan to the average from evaluation plans, respectively, DIL − V98 Gy was 89.3% ± 19.9% versus 86.2% ± 21.3% (P =.151); bladder Dmax 71.9 ± 0.6 Gy versus 74.5 ± 2.9 Gy (P <.001); and rectum Dmax 70.1 ± 2.4 Gy versus 74.9 ± 9.1Gy (P =.007). Bladder and rectal volumes were 99.6% ± 39.5% and 112.8% ± 27.2%, respectively, of their initial volume. The femur shift was 3.16 ± 2.52 mm. In the modeling of ΔDIL V98 Gy > 10%, DIL to rectum distance changes, DIL to bladder distance changes, and rectum volume changes ratio are the 3 most important factors. The areas under the receiver operating characteristic curves were 0.89, 1.00, and 0.99 for the modeling of ΔDIL − V98 Gy > 10%, and bladder and rectum Dmax > 80 Gy, respectively. Conclusions: Dosimetric changes in DIL SIB with intensity modulated proton therapy can be modeled and classified based on anatomic changes on pretreatment images by an MLPNN.