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1.
Artigo em Inglês | MEDLINE | ID: mdl-33545301

RESUMO

PURPOSE: The differential response of normal and tumor tissues to ultra-high dose rate radiation (FLASH) has raised new hope for treating solid tumors but, to date, the mechanism remains elusive. One leading hypothesis is that FLASH radiochemically depletes oxygen from irradiated tissues faster than it is replenished through diffusion. The purpose of this study is to investigate these effects within hypoxic multicellular tumor spheroids, through simulations and experiments. MATERIALS AND METHODS: Physicobiological equations were derived to model (i) the diffusion and metabolism of oxygen within spheroids; (ii) its depletion through reactions involving radiation-induced radicals; and (iii) the increase in radioresistance of spheroids, modeled according to the classical oxygen enhancement ratio and linear-quadratic response. These predictions were then tested experimentally in A549 spheroids exposed to electron irradiation at conventional (0.075 Gy/s) or FLASH (90 Gy/s) dose rates. Clonogenic survival, cell viability, and spheroid growth were scored post-radiation. Clonogenic survival of two other cell lines was also investigated. RESULTS: The existence of a hypoxic core in unirradiated tumor spheroids is predicted by simulations and visualized by fluorescence microscopy. Upon FLASH irradiation, this hypoxic core transiently expands, engulfing a large number of well-oxygenated cells. In contrast, oxygen is steadily replenished during slower conventional irradiation. Experimentally, clonogenic survival was around 3-fold higher in FLASH-irradiated spheroid compared to conventional irradiation, but no significant difference was observed for well-oxygenated 2D-cultured cells. This differential survival is consistent with the predictions of the computational model. FLASH irradiation of spheroids resulted in a dose-modifying factor of around 1.3 for doses above 10 Gy. CONCLUSION: Tumor spheroids can be used as a model to study FLASH irradiation in vitro . The improved survival of tumor spheroids receiving FLASH radiation confirms that ultra-fast radiochemical oxygen depletion and its slow replenishment are critical components of the FLASH effect.

2.
Med Phys ; 2021 Feb 05.
Artigo em Inglês | MEDLINE | ID: mdl-33544390

RESUMO

PURPOSE: To develop and evaluate a deep unsupervised learning (DUL) framework based on a regional deformable model for automated prostate contour propagation from planning computed tomography (pCT) to cone-beam CT (CBCT). METHODS: We introduce a DUL model to map the prostate contour from pCT to on-treatment CBCT. The DUL framework used a regional deformable model via narrow band mapping to augment the conventional strategy. 251 anonymized CBCT images from prostate cancer patients were retrospectively selected and divided into three sets: 180 were used for training, 12 for validation, and 59 for testing. The testing dataset was divided into two Groups. Group one contained 50 CBCT volumes, with one physician-generated prostate contour on CBCT image. Group two contained 9 CBCT images, each including prostate contours delineated by four independent physicians and a consensus contour generated using the STAPLE method. Results were compared between the proposed DUL and physician-generated contours through the Dice similarity coefficients (DSC), the Hausdorff distances, and the distances of the center-of-mass. RESULTS: The average DSCs between DUL-based prostate contours and reference contours for test data in Group one and Group two-consensus were 0.83 ± 0.04, and 0.85 ± 0.04, respectively. Correspondingly, the mean center-of-mass distances were 3.52 mm ± 1.15 mm, and 2.98 mm ± 1.42 mm, respectively. CONCLUSIONS: This novel DUL technique can automatically propagate the contour of the prostate from pCT to CBCT. The proposed method shows that highly accurate contour propagation for CBCT-guided adaptive radiotherapy is achievable via the deep learning technique.

3.
IEEE Trans Biomed Eng ; PP2021 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-33523802

RESUMO

OBJECTIVE: Radiation therapy treatment planning is a time-consuming, iterative process with potentially high inter-planner variability. Fully automated treatment planning processes could reduce a planner's active treatment planning time and remove inter-planner variability, with the potential to tremendously improve patient turnover and quality of care. In developing fully automated algorithms for treatment planning, we have two main objectives: to produce plans that are 1) Pareto optimal and 2) clinically acceptable. Here, we propose the Pareto optimal projection search (POPS) algorithm, which provides a general framework for directly searching the Pareto front. METHODS: Our POPS algorithm is a novel automated planning method that combines two main search processes: 1) gradient-free search in the decision variable space and 2) projection of decision variables to the Pareto front using the bisection method. We demonstrate the performance of POPS by comparing with clinical treatment plans. As one possible quantitative measure of treatment plan quality, we construct a clinical acceptability scoring function (SF) modified from the previously developed general evaluation metric (GEM). RESULTS: On a dataset of 21 prostate cases collected as part of clinical workflow, our proposed POPS algorithm produces Pareto optimal plans that are clinically acceptable in regards to dose conformity, dose homogeneity, and sparing of organs-at-risk. CONCLUSION: Our proposed POPS algorithm provides a general framework for fully automated treatment planning that achieves clinically acceptable dosimetric quality without requiring active planning from human planners. SIGNIFICANCE: Our fully automated POPS algorithm addresses many key limitations of other automated planning approaches, and we anticipate that it will substantially improve treatment planning workflow.

4.
Phys Med Biol ; 2021 Feb 17.
Artigo em Inglês | MEDLINE | ID: mdl-33596553

RESUMO

Prior-image-based reconstruction (PIBR) methods are powerful in reducing radiation dose and improving image quality for low-dose CT. Besides anatomical changes, the prior and current images can also have different attenuation due to different scanners or the same scanner but with different x-ray beam quality (e.g., kVp setting, beam filtration) during data acquisitions. PIBR is challenged in such scenarios with attenuation mismatched prior. In this work, we investigate a specific PIBR method, called statistical image reconstruction using normal dose image induced nonlocal means regularization (SIR-ndiNLM), to address PIBR with such attenuation mismatched prior and achieve quantitative low-dose CT imaging. We proposed two corrective schemes for the original SIR-ndiNLM method, 1) a global histogram matching approach and 2) a local attenuation correction approach, to account for the attenuation differences between the prior and current images in PIBR. We validated the efficacy of the proposed schemes using images acquired from dual-energy CT scanners to emulate attenuation mismatches. Meanwhile, we utilized different CT slices to emulate anatomical mismatches/changes between the prior and the current low-dose images. We observed that the original SIR-ndiNLM introduces artifacts to the reconstruction when using attenuation mismatched prior. Furthermore, we found that larger attenuation mismatch between the prior and current images results in more severe artifacts in the SIR-ndiNLM reconstruction. Our proposed two corrective schemes enabled SIR-ndiNLM to effectively handle attenuation mismatch and anatomical changes between two images and successfully eliminate the artifacts. We demonstrated that the proposed techniques permit SIR-ndiNLM to leverage the attenuation mismatched prior and achieve quantitative low-dose CT reconstruction from both low-flux and sparse-view data acquisitions. This work permits robust and reliable PIBR for CT data acquired using different beam settings.

5.
Med Phys ; 2021 Feb 17.
Artigo em Inglês | MEDLINE | ID: mdl-33595900

RESUMO

PURPOSE: High-performance computed tomography (CT) plays a vital role in clinical decision making. However, the performance of CT imaging is adversely affected by the non-ideal focal spot size of the X-ray source or degraded by an enlarged focal spot size due to aging. In this work, we aim to develop a deep learning-based strategy to mitigate the problem so that high spatial resolution CT images can be obtained even in the case of a non-ideal X-ray source. METHODS: To reconstruct high-quality CT images from blurred sinograms via joint image and sinogram learning, a cross-domain hybrid model is formulated via deep learning into a modularized data-driven reconstruction (MDR) framework. The proposed MDR framework comprises several blocks, and all the blocks share the same network architecture and network parameters. In essence, each block utilizes two sub-models to generate an estimated blur kernel and a high-quality CT image simultaneously. In this way, our framework generates not only a final high-quality CT image but also a series of intermediate images with gradually improved anatomical details, enhancing the visual perception for clinicians through the dynamic process. We used simulated training datasets to train our model in an end-to-end manner and tested our model on both simulated and realistic experimental datasets. RESULTS: On the simulated testing datasets, our approach increases the information fidelity criterion (IFC) by up to 34.2%, the universal quality index (UQI) by up to 20.3%, the signal-to-noise (SNR) by up to 6.7%, and reduces the root mean square error (RMSE) by up to 10.5% as compared with FBP. Compared with the iterative deconvolution method (NSM), MDR increases IFC by up to 24.7%, UQI by up to 16.7%, SNR by up to 6.0%, and reduces RMSE by up to 9.4%. In the modulation transfer function (MTF) experiment, our method improves the MTF50% by 34.5% and MTF10% by 18.7% as compared with FBP, Similarly remarkably, our method improves MTF50% by 14.3% and MTF10% by 0.9% as compared with NSM. Also, our method shows better imaging results in the edge of bony structures and other tiny structures in the experiments using phantom consisting of ham and a bottle of peanuts. CONCLUSIONS: A modularized data-driven CT reconstruction framework is established to mitigate the blurring effect caused by a non-ideal X-ray source with relatively large focal spot. The proposed method enables us to obtain high-resolution images with less ideal X-ray source.

6.
J Hazard Mater ; 412: 125237, 2021 Jan 26.
Artigo em Inglês | MEDLINE | ID: mdl-33540266

RESUMO

Herein, a Fe3O4 decorated attapulgite adsorbent (FA) is fabricated for the removal of Cd(II) from wastewater, and subsequently a feasible strategy for converting the saturated waste adsorbent to CdS/FA photocatalyst is reported. Owing to the in situ growth of Fe3O4 on the attapulgite (ATP), the FA adsorbents exhibit enlarged surface area and increased adsorption sites. More importantly, the strong interaction between Fe3O4 and ATP leads to changes of coordination environment around the O‒Fe‒O bond with the ATP. Based on the results of density functional theory calculations, the electrons are more readily transferred from Fe to O, and the hanging O atoms with more electronegativity act as the efficient adsorption sites for Cd(II), efficiently improving the adsorption performance of the Fe3O4 phases. Furthermore, the waste FA adsorbent could be conveniently separated from the treated water by magnets and converted to CdS/FA photocatalyst, which exhibits satisfying degradation efficiency for tetracycline with low concentration. This work provides a potential strategy to optimize the ATP-based materials for heavy ions adsorption and reutilize the waste adsorbents.

7.
J Control Release ; 331: 460-471, 2021 Feb 03.
Artigo em Inglês | MEDLINE | ID: mdl-33545218

RESUMO

Cisplatin is one of the most used first-line anticancer drugs for various solid tumor therapies. However, cisplatin-based chemotherapy can induce tumor cells to secrete excessive prostaglandin E2 (PGE2) catalyzed by cyclooxygenase-2 (COX-2), which, in turn, counteracts its chemotherapeutic effect and further accelerates tumor metastasis. Here, we report a carrier-free self-delivered nanoprodrug based on platinum (II) coordination bonding coupled with tolfenamic acid (Tolf) (named Tolfplatin). Tolfplatin can spontaneously assemble into uniformly sized nanoparticles (NPs) with a high drug-loading capacity. Compared with cisplatin, Tolfplatin NPs can facilitate cellular uptake, significantly decrease PGE2 secretion by COX-2 inhibition, which further downregulate tumorous anti-apoptotic and metastasis-associated proteins, thereby efficiently inducing apoptotic cell death and significantly inhibit tumor metastasis in vitro and in vivo. Therefore, as the carrier-free nanoprodrug, Tolfplatin NPs are promising anti-tumoral agents to inhibit tumor proliferation and metastasis by enriching the function and promoting the anti-tumor activity of cisplatin.

8.
Comp Biochem Physiol C Toxicol Pharmacol ; 243: 108995, 2021 Feb 02.
Artigo em Inglês | MEDLINE | ID: mdl-33545344

RESUMO

In teleost fish, radial glial cells (RGCs) are progenitor cells for neurons and the major cell type synthesizing neuroestrogens. We hypothesized that chemical exposure impairs mitochondrial bioenergetics of RGCs, which then may lead to downstream consequences for neuroestrogen production. Here we provide proof of concept that mitochondria of RGCs can be perturbed by fungicides. We isolated RGCs from a mixed sex population of goldfish (Carassius auratus) and measured metabolic capacity of primary cells to a model mitotoxin fluazinam, a broad-spectrum fungicide that inhibits mitochondria electron transport chain (or ETC) Complex I. Using immunocytochemistry and real-time PCR, we demonstrate that the goldfish primary cell cultures are highly enriched for glia after multiple passages. Cytotoxicity assays revealed that glia treated with >25 µM fluazinam for 24 and 48-h showed reduced viability. As such, metabolic assays were conducted with non-cytotoxic concentrations (0.25-12.5 µM). Fluazinam did not affect oxygen consumption rates of RGCs at 24 h, but after 48 h, oligomycin induced ATP-linked respiration was decreased by both 6.25 and 12.5 µM fluazinam. Moreover, concentrations as low as 0.25 µM disrupted the mitochondrial membrane potential of RGCs, reflecting strong uncoupling effects of the fungicide on mitochondria. Here we provide proof of concept that mitochondrial bioenergetics of teleostean RGCs can be responsive to agrochemicals. Additional studies are required to address low-dose exposures in vivo and to determine if metabolic disruption impairs neuroendocrine functions of RGCs. We propose this mechanism constitutes a novel aspect of neuroendocrine disruption, significant because dysregulation of neuron-glia communication is expected to contribute to neuroendocrine disruption.

9.
J Atheroscler Thromb ; 2021 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-33455996

RESUMO

AIMS: Recent studies suggested plaque erosion with noncritical stenosis could be treated distinctly from that with critical stenosis, but their morphological features remained largely unknown. The present study aimed to investigate morphological features of eroded plaques with different lumen stenosis using optical coherence tomography (OCT). METHODS: A total of 348 ST-segment elevated myocardial infarction patients with culprit OCT-defined plaque erosion (OCT-erosion) were analyzed. Based on the severity of lumen area stenosis, all patients with OCT-erosions were divided into the following three groups: Group A (area stenosis <50%, n=50); Group B (50% ≤area stenosis <75%, n=146); Group C (area stenosis ≥ 75%, n=152). RESULTS: Compared with patients in Groups A and B, patients in Group C were older (p=0.008) and had higher prevalence of hypertension (p=0.029). Angiographic analysis showed that 72.0% of the eroded plaques in Group A were located in the left anterior descending artery, followed by 67.8% in Group B, and 53.9% in Group C (p=0.039). OCT analysis showed that Group A had the highest prevalence of fibrous plaques (p<0.001) and nearby bifurcation (p=0.036), but the lowest prevalence of lipid-rich plaques (p<0.001), macrophage accumulation (p<0.001), microvessels (p=0.009), cholesterol crystals (p<0.001), and calcification (p=0.023). Multivariable regression analysis showed fibrous plaque (odds ratio [OR]: 3.014, 95% confidence interval [CI]: 1.932-4.702, p<0.001) and nearby bifurcation (OR: 1.750, 95% CI: 1.109-2.761, p=0.016) were independently associated with OCT-erosion with an area stenosis of <75%. CONCLUSIONS: More than half of OCT-erosions presented with <75% area stenosis, having distinct morphological features from those of OCT-erosions with critical stenosis. Fibrous plaque and nearby bifurcation were independently associated with noncritically stenotic OCT-erosion, suggesting that eroded plaques might need individualized treatment.

10.
Eur J Pharm Sci ; 159: 105719, 2021 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-33465475

RESUMO

The safety problem of injections caused by clarity has lately become a widely shared concern. Due to the synthesis process, polysorbate 80 had a wide molecular weight distribution, which is also related to the clinical anaphylaxis. In this paper, ultrasonic-assisted ultrafiltration (UAU) technology was firstly applied to regulate colloidal structure and remove macromolecules from polysorbate 80 to improve injection clarity. In the separation process, ultrafiltration molecular weight cut off (MWCO), ultrasonic power and polysorbate 80 concentraion were selected as variables to adjust the separation efficiency. The ultrasonic frequency and power were provided by KQ-700DE ultrasonic system, based on the data analysis by response surface methodology (RSM), the optimal UAU parameters were as follows: ultrafiltration MWCO of 50,000, ultrasonic power of 900 W and polysorbate 80 concentration of 15.00 mg/mL. The experimental transmittance of polysorbate 80 was 87.6% and the qualification rate of clarity was 94.5%, which solved the separation contradiction among yield, clarity and safety. As an innovation in colloidal separation fields, UAU had a vast range of prospects for making use in pharmaceutical area.

11.
J Proteome Res ; 20(2): 1371-1381, 2021 Feb 05.
Artigo em Inglês | MEDLINE | ID: mdl-33356298

RESUMO

This study aims to identify biomarkers for evaluating the therapeutic efficacy of mesalazine on ulcerative colitis by metabolomics and lipidomics. A dextran sulfate sodium-induced mouse model was used. The disease status was assessed by a disease activity index, the TNF-α level of colon was measured by an enzyme-linked immunosorbent assay, and the pathological changes of colon tissue was examined by hematoxylin-eosin staining. Serum metabolomics and lipidomics analysis based on ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry were applied to decipher the metabolic profile changes. Multivariate analysis was applied to differentiate the metabolites of controls, models, and mesalazine-treated mice. By the receiver operating characteristic (ROC) analysis, 40 differential metabolites with an area under curve (AUC) >0.80 were screened out between control and model groups. Among them, four potential biomarkers (palmitoyl glucuronide, isobutyrylglycine, PC (20:3 (5Z, 8Z, 11Z)/15:0) and L-arginine) had a signficantly reversed level of peak areas in the mesalazine group, and three of them were closely correlated with mesalazine efficacy by linear regression analysis. Furthermore, metabolic pathway analysis revealed several dysregulated pathways in colitis mice, including glycerophospholipid metabolism, pyrimidine metabolism, linoleic acid metabolism, arginine biosynthesis, etc. This study indicates that serum metabolomics is a useful approach that can noninvasively evaluate the therapeutic effect and provide unique insights into the underlying mechanism of mesalazine.

12.
Bioresour Technol ; 319: 124117, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-32979594

RESUMO

Azo dyes pose hazards to ecosystems and human health and the cosubstrate strategy has become the focus for the bioremediation of azo dyes. Herein, Brilliant Crocein (BC), a model pollutant, was biodegraded by Providencia rettgeri domesticated from activated sludge. Additional ethanol, as a cosubstrate, could accelerate P. rettgeri growth and BC biodegradation, as reflected by the Gompertz models. This phenomenon was attributed to the smaller metabolites and greater number of potential pathways observed under the synergistic effect of ethanol. Genomic analysis of P. rettgeri showed that functional genes related to azo bond cleavage, redox reactions, ring opening and hydrolysis played crucial roles in azo dye biodegradation. Furthermore, the mechanism proposed was that ethanol might stimulate the production of additional reducing power via the expression of related genes, leading to the cleavage of azo bonds and aromatic rings. However, biodegradation without ethanol could only partly cleave the azo bonds.


Assuntos
Etanol , Providencia , Compostos Azo , Biodegradação Ambiental , Corantes , Ecossistema , Genômica , Humanos , Cinética , Providencia/genética
13.
Radiology ; : 202861, 2020 Dec 08.
Artigo em Inglês | MEDLINE | ID: mdl-33289613

RESUMO

Background Hyperpolarized noble gas MRI helps measure lung ventilation, but clinical translation remains limited. Free-breathing proton MRI may help quantify lung function using existing MRI systems without contrast material and may assist in providing information about ventilation not visible to the eye or easily extracted with segmentation methods. Purpose To explore the use of deep convolutional neural networks (DCNNs) to generate synthetic MRI ventilation scans from free-breathing MRI (deep learning [DL] ventilation MRI)-derived specific ventilation maps as a surrogate of noble gas MRI and to validate this approach across a wide range of lung diseases. Materials and Methods In this secondary analysis of prospective trials, 114 paired noble gas MRI and two-dimensional free-breathing MRI scans were obtained in healthy volunteers with no history of chronic or acute respiratory disease and in study participants with a range of different obstructive lung diseases, including asthma, bronchiectasis, chronic obstructive pulmonary disease, and non-small-cell lung cancer between September 2013 and April 2018 (ClinicalTrials.gov identifiers: NCT03169673, NCT02351141, NCT02263794, NCT02282202, NCT02279329, and NCT02002052). A U-Net-based DCNN model was trained to map free-breathing proton MRI to hyperpolarized helium 3 (3He) MRI ventilation and validated using a sixfold validation. During training, the DCNN ventilation maps were compared with noble gas MRI scans using the Pearson correlation coefficient (r) and mean absolute error. DCNN ventilation images were segmented for ventilation and ventilation defects and were compared with noble gas MRI scans using the Dice similarity coefficient (DSC). Relationships were evaluated with the Spearman correlation coefficient (rS). Results One hundred fourteen study participants (mean age, 56 years ± 15 [standard deviation]; 66 women) were evaluated. As compared with 3He MRI, DCNN model ventilation maps had a mean r value of 0.87 ± 0.08. The mean DSC for DL ventilation MRI and 3He MRI ventilation was 0.91 ± 0.07. The ventilation defect percentage for DL ventilation MRI was highly correlated with 3He MRI ventilation defect percentage (rS = 0.83, P < .001, mean bias = -2.0% ± 5). Both DL ventilation MRI (rS = -0.51, P < .001) and 3He MRI (rS = -0.61, P < .001) ventilation defect percentage were correlated with the forced expiratory volume in 1 second. The DCNN model required approximately 2 hours for training and approximately 1 second to generate a ventilation map. Conclusion In participants with diverse pulmonary pathologic findings, deep convolutional neural networks generated ventilation maps from free-breathing proton MRI trained with a hyperpolarized noble-gas MRI ventilation map data set. The maps showed correlation with noble gas MRI ventilation and pulmonary function measurements. © RSNA, 2020 See also the editorial by Vogel-Claussen in this issue.

15.
Phys Med Biol ; 2020 Dec 23.
Artigo em Inglês | MEDLINE | ID: mdl-33361563

RESUMO

This study aims to demonstrate a low-cost camera-based radioluminescence imaging system (CRIS) for high-quality beam visualization that encourages accurate pre-treatment verifications on radiation delivery in external beam radiotherapy. To ameliorate the optical image that suffers from mirror glare and edge blurring caused by photon scattering, a deep learning model is proposed and trained to learn from an on-board electronic portal imaging device (EPID). Beyond the typical purposes of an on-board EPID, the developed system maintains independent measurement with co-planar detection ability by involving a cylindrical receptor. Three task-aware modules are integrated into the network design to enhance its robustness against the artifacts that exist in an EPID running at the cine mode for efficient image acquisition. The training data consists of various designed beam fields that were modulated via the multi-leaf collimator (MLC). Validation experiments are performed for five regular fields ranging from 2 × 2 cm2 to 10 × 10 cm2 and three clinical IMRT cases. The captured CRIS images are compared to the high-quality images collected from an EPID running at the integration-mode, in terms of gamma index and other typical similarity metrics. The mean 2% / 2mm gamma pass rate is 99.14% (range between 98.6% and 100%) and 97.1% (ranging between 96.3% and 97.9%), for the regular fields and IMRT cases, respectively. The CRIS is further applied as a tool for MLC leaf-end position verification. A rectangular field with introduced leaf displacement is designed, and the measurements using CRIS and EPID agree within 0.100 mm ± 0.072 mm with maximum of 0.292 mm. Coupled with its simple system design and low-cost nature, the technique promises to provide viable choice for routine quality assurance in radiation oncology practice.

16.
IEEE Trans Med Imaging ; PP2020 Dec 22.
Artigo em Inglês | MEDLINE | ID: mdl-33351753

RESUMO

Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources in practice, which makes it challenging to build a universal model with a varied set of input data. To tackle this problem, we propose a general approach to complete the random missing domain(s) data in real applications. Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared content encoding and separate style encoding across multiple domains. We further illustrate that the learned representation in multi-domain image completion could be leveraged for high-level tasks, e.g., segmentation, by introducing a unified framework consisting of image completion and segmentation with a shared content encoder. The experiments demonstrate consistent performance improvement on three datasets for brain tumor segmentation, prostate segmentation, and facial expression image completion respectively.

17.
Sci Rep ; 10(1): 20381, 2020 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-33230262

RESUMO

Luneburg lenses and Maxwell fisheye lenses possess distinct properties of focusing, well beyond conventional lenses made of uniform materials. In this paper, a planar broadband bifunctional Luneburg-fisheye lens synthesized by gradient anisotropic metasurface is proposed. The proposed anisotropic metasurface is formed by non-resonant anisotropic cells, so that it can independently realize the equivalent gradient refractive indexes of Luneburg lens and Maxwell fisheye lens along orthogonal directions in a broad band, respectively. To verify the performance of the bifunctional lens, a prototype associated with a feeding log-periodic dipole antenna has been fabricated. Experimental results show that the proposed lens functions well over a wide frequency range with high efficiency and low profile, which coincides well with theoretical predictions and simulated results. It is expected that the proposed design will facilitate the applications of multifunctional metadevices in microwave and optical ranges.

18.
Med Phys ; 2020 Nov 07.
Artigo em Inglês | MEDLINE | ID: mdl-33159812

RESUMO

Recent advances in artificial intelligence (AI) have transformed it from an indispensable tool in research and development into a mainstream technology that is fundamentally altering how we work and live. AI has already been incorporated into some common medical physics tools used to support key tasks such as diagnosis, treatment planning, and quality assurance. A paradigm shift in clinical practice in which AI is used more widely is likely to occur in the near future. Some therefore advocate incorporating AI into the medical physics graduate program curriculum to better adapt workers' skill sets to this new paradigm. However, others have reservations about such curricular adaptation. This is the premise debated in this month's Point/Counterpoint.

19.
Phys Med Biol ; 65(22): 225005, 2020 Nov 17.
Artigo em Inglês | MEDLINE | ID: mdl-33200751

RESUMO

The purpose of this study is to leverage radioluminescence imaging for the development of an automated high-dose-rate (HDR) brachytherapy quality assurance (QA) system that enables simultaneous measurements of dwell position, dwell time, wire velocity, and relative source strength in a single test. The system consists of a radioluminescence phosphor sheet (a mixture of Gd2O2S:Tb and PDMS) positioned atop a HDR needle applicator, a complementary metal-oxide-semiconductor digital camera used to capture the emitted radioluminescence signals from the scintillator sheet, and an in-house graphical user interface for signal processing. The signal processing was used to extract source intensity, location, and elapsed time, yielding the final measurements on dwell position, dwell time, and wire velocity. The source strength relative to the well chamber calibration (in unit of Air-Kerma strength, Sk ) is measured by establishing a calibration curve that correlates Sk with the detector response. Validation experiments are performed using three customized treatment plans. With these plans, the dwell position and dwell time are verified for a range of 110.0 cm-117.5 cm and 2 s-16 s, respectively, and the linear correlation with Sk is demonstrated for the source strength varying between 28 348 U (cGy cm2 h-1) and 41 906 U. The wire velocity, i.e. the speed of the radioactive source averaged over the distance in between dwell positions, is calculated for various distances ranging from 5 mm to 50 mm. Results show that the mean deviations of the measured dwell position and dwell time are 0.1 mm (range from 0 to 0.2 mm) and 32.5 ms (range from 0 to 60.0 ms) with respect to the planned values, respectively, and the system response is highly linear with Sk ( R2 = 0.998). Moreover, the measured wire velocities are comparable to previously reported values. Benefitting from the compact hardware design and image processing algorithms, the system provides a practical, reliable, and comprehensive solution for HDR QA.

20.
Phys Med Biol ; 2020 Nov 12.
Artigo em Inglês | MEDLINE | ID: mdl-33181506

RESUMO

Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiation therapy. However, all the dose calculation algorithms commonly used in current clinical practice have to compromise between calculation accuracy and efficiency, which may result in unsatisfactory dose accuracy or highly intensive computation time in many clinical situations. The purpose of this work is to develop a novel dose calculation algorithm based on the deep learning method for radiation therapy. In this study we performed a feasibility investigation on implementing a fast and accurate dose calculation based on a deep learning technique. A two dimensional (2D) fluence map was first converted into a three dimensional (3D) volume using ray traversal algorithm. A 3D U-Net like deep residual network was then established to learn a mapping between this converted 3D volume, CT and 3D dose distribution. Therefore an indirect relationship was built between a fluence map and its corresponding 3D dose distribution without using significantly complex neural networks. 200 patients, including nasopharyngeal, lung, rectum and breast cancer cases, were collected and applied to train the proposed network. Additional 47 patients were randomly selected to evaluate the accuracy of the proposed method through comparing dose distributions, dose volume histograms (DVH) and clinical indices with the results from a treatment planning system (TPS), which was used as the ground truth in this study. The proposed deep learning based dose calculation algorithm achieved good predictive performance. For 47 tested patients, the average per-voxel bias of the deep learning calculated value and standard deviation (normalized to the prescription), relative to the TPS calculation, is 0.17%±2.28%. The average deep learning calculated values and standard deviations for relevant clinical indices were compared with the TPS calculated results and the t-test p-values demonstrated the consistency between them.

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