Deconvolution of Camera Instrument Response Functions.

Temporal sequences of fluorescence intensities in single-molecule experiments are often obtained from stacks of camera images. The dwell times of different macromolecular structural or functional states, correlated with characteristic fluorescence intensities, are extracted from the images and combined into dwell time distributions that are fitted by kinetic functions to extract corresponding rate constants. The frame rate of the camera limits the time resolution of the experiment and thus the fastest rate processes that can be reliably detected and quantified. However, including the influence of discrete sampling (framing) on the detected time series in the fitted model enables rate processes near to the frame rate to be reliably estimated. This influence, similar to the instrument response function in other types of instruments, such as pulsed emission decay fluorometers, is easily incorporated into the fitted model. The same concept applies to any temporal data that is low-pass filtered or decimated to improve signal to noise ratio.

MEMLET: An Easy-to-Use Tool for Data Fitting and Model Comparison Using Maximum-Likelihood Estimation.

We present MEMLET (MATLAB-enabled maximum-likelihood estimation tool), a simple-to-use and powerful program for utilizing maximum-likelihood estimation (MLE) for parameter estimation from data produced by single-molecule and other biophysical experiments. The program is written in MATLAB and includes a graphical user interface, making it simple to integrate into the existing workflows of many users without requiring programming knowledge. We give a comparison of MLE and other fitting techniques (e.g., histograms and cumulative frequency distributions), showing how MLE often outperforms other fitting methods. The program includes a variety of features. 1) MEMLET fits probability density functions (PDFs) for many common distributions (exponential, multiexponential, Gaussian, etc.), as well as user-specified PDFs without the need for binning. 2) It can take into account experimental limits on the size of the shortest or longest detectable event (i.e., instrument "dead time") when fitting to PDFs. The proper modification of the PDFs occurs automatically in the program and greatly increases the accuracy of fitting the rates and relative amplitudes in multicomponent exponential fits. 3) MEMLET offers model testing (i.e., single-exponential versus double-exponential) using the log-likelihood ratio technique, which shows whether additional fitting parameters are statistically justifiable. 4) Global fitting can be used to fit data sets from multiple experiments to a common model. 5) Confidence intervals can be determined via bootstrapping utilizing parallel computation to increase performance. Easy-to-follow tutorials show how these features can be used. This program packages all of these techniques into a simple-to-use and well-documented interface to increase the accessibility of MLE fitting.

Control of myosin-I force sensing by alternative splicing.

Myosin-Is are molecular motors that link cellular membranes to the actin cytoskeleton, where they play roles in mechano-signal transduction and membrane trafficking. Some myosin-Is are proposed to act as force sensors, dynamically modulating their motile properties in response to changes in tension. In this study, we examined force sensing by the widely expressed myosin-I isoform, myo1b, which is alternatively spliced in its light chain binding domain (LCBD), yielding proteins with lever arms of different lengths. We found the actin-detachment kinetics of the splice isoforms to be extraordinarily tension-sensitive, with the magnitude of tension sensitivity to be related to LCBD splicing. Thus, in addition to regulating step-size, motility rates, and myosin activation, the LCBD is a key regulator of force sensing. We also found that myo1b is substantially more tension-sensitive than other myosins with similar length lever arms, indicating that different myosins have different tension-sensitive transitions.

Tracking multiple conformations occurring on angstrom-and-millisecond scales in single amino-acid-transporter molecules.

Most membrane protein molecules undergo conformational changes as they transition from one functional state to another one. An understanding of the mechanism underlying these changes requires the ability to resolve individual conformational states, whose changes often occur on millisecond and angstrom scales. Tracking such changes and acquiring a sufficiently large amount of data remain challenging. Here, we use the amino-acid transporter AdiC as an example to demonstrate the application of a high-resolution fluorescence-polarization-microscopy method in tracking multistate conformational changes of a membrane protein. We have successfully resolved four conformations of AdiC by monitoring the emission-polarization changes of a fluorophore label and quantified their probabilities in the presence of a series of concentrations of its substrate arginine. The acquired data are sufficient for determining all equilibrium constants that fully establish the energetic relations among the four states. The KD values determined for arginine in four individual conformations are statistically comparable to the previously reported overall KD determined using isothermal titration calorimetry. This demonstrated strong resolving power of the present polarization-microscopy method will enable an acquisition of the quantitative information required for understanding the expected complex conformational mechanism underlying the transporter's function, as well as those of other membrane proteins.

Left Anterior Descending Coronary Artery Radiation Dose Association With All-Cause Mortality in NRG Oncology Trial RTOG 0617.

PURPOSE: A left anterior descending (LAD) coronary artery volume (V) receiving 15 Gy (V15 Gy) ≥10% has been recently observed to be an independent risk factor of major adverse cardiac events and all-cause mortality in patients with locally advanced non-small cell lung cancer treated with radiation therapy. However, this dose constraint has not been validated in independent or prospective data sets. METHODS AND MATERIALS: The NRG Oncology/Radiation Therapy Oncology Group (RTOG) 0617 data set from the National Clinical Trials Network was used. The LAD coronary artery was manually contoured. Multivariable Cox regression was performed, adjusting for known prognostic factors. Kaplan-Meier estimates of overall survival (OS) were calculated. For assessment of baseline cardiovascular risk, only age, sex, and smoking history were available. RESULTS: There were 449 patients with LAD dose-volume data and clinical outcomes available after 10 patients were excluded owing to unreliable LAD dose statistics. The median age was 64 years. The median LAD V15 Gy was 38% (interquartile range, 15%-62%), including 94 patients (21%) with LAD V15 Gy <10% and 355 (79%) with LAD V15 Gy ≥10%. Adjusting for prognostic factors, LAD V15 Gy ≥10% versus <10% was associated with an increased risk of all-cause mortality (hazard ratio [HR], 1.43; 95% confidence interval, 1.02-1.99; P = .037), whereas a mean heart dose ≥10 Gy versus <10 Gy was not (adjusted HR, 1.12; 95% confidence interval, 0.88-1.43; P = .36). The median OS for patients with LAD V15 Gy ≥10% versus <10% was 20.2 versus 25.1 months, respectively, with 2-year OS estimates of 47% versus 67% (P = .004), respectively. CONCLUSIONS: In a reanalysis of RTOG 0617, LAD V15 Gy ≥10% was associated with an increased risk of all-cause mortality. These findings underscore the need for improved cardiac risk stratification and aggressive risk mitigation strategies, including implementation of cardiac substructure dose constraints in national guidelines and clinical trials.

Calcium regulation of myosin-I tension sensing.

Myo1b is a myosin that is exquisitely sensitive to tension. Its actin-attachment lifetime increases > 50-fold when its working stroke is opposed by 1 pN of force. The long attachment lifetime of myo1b under load raises the question: how are actin attachments that last >50 s in the presence of force regulated? Like most myosins, forces are transmitted to the myo1b motor through a light-chain binding domain that is structurally stabilized by calmodulin, a calcium-binding protein. Thus, we examined the effect of calcium on myo1b motility using ensemble and single-molecule techniques. Calcium accelerates key biochemical transitions on the ATPase pathway, decreases the working-stroke displacement, and greatly reduces the ability of myo1b to sense tension. Thus, calcium provides an effective mechanism for inhibiting motility and terminating long-duration attachments.

Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study.

A method for generating fluoroscopic (time-varying) volumetric images using patient-specific motion models derived from four-dimensional cone-beam CT (4D-CBCT) images was developed. 4D-CBCT images acquired immediately prior to treatment have the potential to accurately represent patient anatomy and respiration during treatment. Fluoroscopic 3D image estimation is performed in two steps: (1) deriving motion models and (2) optimization. To derive motion models, every phase in a 4D-CBCT set is registered to a reference phase chosen from the same set using deformable image registration (DIR). Principal components analysis (PCA) is used to reduce the dimensionality of the displacement vector fields (DVFs) resulting from DIR into a few vectors representing organ motion found in the DVFs. The PCA motion models are optimized iteratively by comparing a cone-beam CT (CBCT) projection to a simulated projection computed from both the motion model and a reference 4D-CBCT phase, resulting in a sequence of fluoroscopic 3D images. Patient datasets were used to evaluate the method by estimating the tumor location in the generated images compared to manually defined ground truth positions. Experimental results showed that the average tumor mean absolute error (MAE) along the superior-inferior (SI) direction and the 95th percentile in two patient datasets were 2.29 and 5.79 mm for patient 1, and 1.89 and 4.82 mm for patient 2. This study demonstrated the feasibility of deriving 4D-CBCT-based PCA motion models that have the potential to account for the 3D non-rigid patient motion and localize tumors and other patient anatomical structures on the day of treatment.

Dosimetric Planning Tradeoffs to Reduce Heart Dose Using Machine Learning-Guided Decision Support Software in Patients with Lung Cancer.

PURPOSE: Cardiac toxicity is a well-recognized risk after radiation therapy (RT) in patients with non-small cell lung cancer (NSCLC). However, the extent to which treatment planning optimization can reduce mean heart dose (MHD) without untoward increases in lung dose is unknown. METHODS AND MATERIALS: Retrospective analysis of RT plans from 353 consecutive patients with locally advanced NSCLC treated with intensity modulated RT (IMRT) or 3-dimensional conformal RT. Commercially available machine learning-guided clinical decision support software was used to match RT plans. A leave-one-out predictive model was used to examine lung dosimetric tradeoffs necessary to achieve a MHD reduction. RESULTS: Of all 232 patients, 91 patients (39%) had RT plan matches showing potential MHD reductions of >4 to 8 Gy without violating the upper limit of lung dose constraints (lung volume [V] receiving 20 Gy (V20 Gy) <37%, V5 Gy <70%, and mean lung dose [MLD] <20 Gy). When switching to IMRT, 75 of 103 patients (72.8%) had plan matches demonstrating improved MHD (average 2.0 Gy reduction, P < .0001) without violating lung constraints. Examining specific lung dose tradeoffs, a mean ≥3.7 Gy MHD reduction was achieved with corresponding absolute increases in lung V20 Gy, V5 Gy, and MLD of 3.3%, 5.0%, and 1.0 Gy, respectively. CONCLUSIONS: Nearly 40% of RT plans overall, and 73% when switched to IMRT, were predicted to have reductions in MHD >4 Gy with potentially clinically acceptable tradeoffs in lung dose. These observations demonstrate that decision support software for optimizing heart-lung dosimetric tradeoffs is feasible and may identify patients who might benefit most from more advanced RT technologies.

Ovarian Transposition Before Pelvic Radiation Therapy: Spatial Distribution and Dose Volume Analysis.

PURPOSE: There is a paucity of data analyzing the anatomic locations and dose volume metrics achieved for surgically transposed ovaries in patients desiring fertility or hormonal preservation receiving pelvic radiation therapy (RT), which were examined herein. METHODS AND MATERIALS: This is a retrospective study including women who underwent ovarian transposition before pelvic RT between 2010 to 2020. The craniocaudal (CC) distance of the ovary centroid to the (1) plane of the sacral promontory, (2) iliac crest, and (3) the nearest distance between the ovary edge and RT planning target volume (PTV) were measured (cm). The area under the receiver operating characteristic curve and cut-point analysis estimating ovary location outside the PTV was performed. RESULTS: Thirty-one ovaries were analyzed from 18 patients. Thirteen (72.2%) were treated with intensity modulated RT, and 5 (27.8%) were treated with 3-dimensional conformal radiation therapy. Most ovaries were located above the sacral promontory (64.5%, n = 20), below the iliac crest (96.8%, n = 30), and outside the PTV (64.5%, n = 20). The median distance from the ovaries to the sacral promontory, iliac crest, and PTV was 0.8 cm (interquartile range [IQR], -0.83 to 1.59 cm), -3.22 cm (IQR, -5.12 to -1.84 cm), and 0.9 cm (IQR, -1.0 to 1.9 cm), respectively. The area under the receiver operating characteristic curve and cut-point analysis demonstrated that distance from the iliac crest predicted an ovary to be outside the PTV with an optimal cut-point of -3.0 cm (C-index = 0.82). The median mean and maximum (Dmax) ovary doses were 15.5 Gy (IQR, 9.6-20.2 Gy) and 32.2 Gy (IQR 24.8-46.5 Gy), respectively. CONCLUSIONS: Despite most transposed ovaries being located outside the PTV, nearly all remained below the iliac crest and received RT doses associated with a high risk of ovarian failure. These findings deepen our understanding of the spatial relationship between transposed ovaries and dose to inform surgical and pre-RT planning and suggest that more aggressive ovary-sparing strategies are warranted.

Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning Scans Predicts Mortality.

PURPOSE: Coronary artery calcium (CAC) quantified on computed tomography (CT) scans is a robust predictor of atherosclerotic coronary disease; however, the feasibility and relevance of quantitating CAC from lung cancer radiotherapy planning CT scans is unknown. We used a previously validated deep learning (DL) model to assess whether CAC is a predictor of all-cause mortality and major adverse cardiac events (MACEs). METHODS: Retrospective analysis of non-contrast-enhanced radiotherapy planning CT scans from 428 patients with locally advanced lung cancer is performed. The DL-CAC algorithm was previously trained on 1,636 cardiac-gated CT scans and tested on four clinical trial cohorts. Plaques ≥ 1 cubic millimeter were measured to generate an Agatston-like DL-CAC score and grouped as DL-CAC = 0 (very low risk) and DL-CAC ≥ 1 (elevated risk). Cox and Fine and Gray regressions were adjusted for lung cancer and cardiovascular factors. RESULTS: The median follow-up was 18.1 months. The majority (61.4%) had a DL-CAC ≥ 1. There was an increased risk of all-cause mortality with DL-CAC ≥ 1 versus DL-CAC = 0 (adjusted hazard ratio, 1.51; 95% CI, 1.01 to 2.26; P = .04), with 2-year estimates of 56.2% versus 45.4%, respectively. There was a trend toward increased risk of major adverse cardiac events with DL-CAC ≥ 1 versus DL-CAC = 0 (hazard ratio, 1.80; 95% CI, 0.87 to 3.74; P = .11), with 2-year estimates of 7.3% versus 1.2%, respectively. CONCLUSION: In this proof-of-concept study, CAC was effectively measured from routinely acquired radiotherapy planning CT scans using an automated model. Elevated CAC, as predicted by the DL model, was associated with an increased risk of mortality, suggesting a potential benefit for automated cardiac risk screening before cancer therapy begins.

3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy.

PURPOSE: To evaluate an algorithm for real-time 3D tumor localization from a single x-ray projection image for lung cancer radiotherapy. METHODS: Recently, we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection [Li et al., Med. Phys. 37, 2822-2826 (2010)]. We have demonstrated its feasibility using a digital respiratory phantom with regular breathing patterns. In this work, we present a detailed description and a comprehensive evaluation of the improved algorithm. The algorithm was improved by incorporating respiratory motion prediction. The accuracy and efficiency of using this algorithm for 3D tumor localization were then evaluated on (1) a digital respiratory phantom, (2) a physical respiratory phantom, and (3) five lung cancer patients. These evaluation cases include both regular and irregular breathing patterns that are different from the training dataset. RESULTS: For the digital respiratory phantom with regular and irregular breathing, the average 3D tumor localization error is less than 1 mm which does not seem to be affected by amplitude change, period change, or baseline shift. On an NVIDIA Tesla C1060 graphic processing unit (GPU) card, the average computation time for 3D tumor localization from each projection ranges between 0.19 and 0.26 s, for both regular and irregular breathing, which is about a 10% improvement over previously reported results. For the physical respiratory phantom, an average tumor localization error below 1 mm was achieved with an average computation time of 0.13 and 0.16 s on the same graphic processing unit (GPU) card, for regular and irregular breathing, respectively. For the five lung cancer patients, the average tumor localization error is below 2 mm in both the axial and tangential directions. The average computation time on the same GPU card ranges between 0.26 and 0.34 s. CONCLUSIONS: Through a comprehensive evaluation of our algorithm, we have established its accuracy in 3D tumor localization to be on the order of 1 mm on average and 2 mm at 95 percentile for both digital and physical phantoms, and within 2 mm on average and 4 mm at 95 percentile for lung cancer patients. The results also indicate that the accuracy is not affected by the breathing pattern, be it regular or irregular. High computational efficiency can be achieved on GPU, requiring 0.1-0.3 s for each x-ray projection.

Three-dimensional multipath DenseNet for improving automatic segmentation of glioblastoma on pre-operative multimodal MR images.

PURPOSE: Convolutional neural networks have achieved excellent results in automatic medical image segmentation. In this study, we proposed a novel three-dimensional (3D) multipath DenseNet for generating the accurate glioblastoma (GBM) tumor contour from four multimodal pre-operative MR images. We hypothesized that the multipath architecture could achieve more accurate segmentation than a singlepath architecture. METHODS: Two hundred and fifty-eight GBM patients were included in this study. Each patient had four MR images (T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and FLAIR) and the manually segmented tumor contour. We built a 3D multipath DenseNet that could be trained to achieve an end-to-end mapping from four MR images to the corresponding GBM tumor contour. A 3D singlepath DenseNet was also built for comparison. Both DenseNets were based on the encoder-decoder architecture. All four images were concatenated and fed into a single encoder path in the singlepath DenseNet, while each input image had its own encoder path in the multipath DenseNet. The patient cohort was randomly split into a training set of 180 patients, a validation set of 39 patients, and a testing set of 39 patients. Model performance was evaluated using the Dice similarity coefficient (DSC), average surface distance (ASD), and 95% Hausdorff distance (HD95% ). Wilcoxon signed-rank tests were conducted to assess statistical significances. RESULTS: The singlepath DenseNet achieved the DSC of 0.911 ± 0.060, ASD of 1.3 ± 0.7 mm, and HD95% of 5.2 ± 7.1 mm, while the multipath DenseNet achieved the DSC of 0.922 ± 0.041, ASD of 1.1 ± 0.5 mm, and HD95% of 3.9 ± 3.3 mm. The P-values of all Wilcoxon signed-rank tests were less than 0.05. CONCLUSIONS: Both DenseNets generated GBM tumor contours in good agreement with the manually segmented contours from multimodal MR images. The multipath DenseNet achieved more accurate tumor segmentation than the singlepath DenseNet. Here presented the 3D multipath DenseNet that demonstrated an improved accuracy over comparable algorithms in the clinical task of GBM tumor segmentation.

An Automatic Deep Learning-Based Workflow for Glioblastoma Survival Prediction Using Preoperative Multimodal MR Images: A Feasibility Study.

PURPOSE: Most radiomic studies use the features extracted from the manually drawn tumor contours for classification or survival prediction. However, large interobserver segmentation variations lead to inconsistent features and hence introduce more challenges in constructing robust prediction models. Here, we proposed an automatic workflow for glioblastoma (GBM) survival prediction based on multimodal magnetic resonance (MR) images. METHODS AND MATERIALS: Two hundred eighty-five patients with glioma (210 GBM, 75 low-grade glioma) were included. One hundred sixty-three of the patients with GBM had overall survival data. Every patient had 4 preoperative MR images and manually drawn tumor contours. A 3-dimensional convolutional neural network, VGG-Seg, was trained and validated using 122 patients with glioma for automatic GBM segmentation. The trained VGG-Seg was applied to the remaining 163 patients with GBM to generate their autosegmented tumor contours. The handcrafted and deep learning (DL)-based radiomic features were extracted from the autosegmented contours using explicitly designed algorithms and a pretrained convolutional neural network, respectively. One hundred sixty-three patients with GBM were randomly split into training (n = 122) and testing (n = 41) sets for survival analysis. Cox regression models were trained to construct the handcrafted and DL-based signatures. The prognostic powers of the 2 signatures were evaluated and compared. RESULTS: The VGG-Seg achieved a mean Dice coefficient of 0.86 across 163 patients with GBM for GBM segmentation. The handcrafted signature achieved a C-index of 0.64 (95% confidence interval, 0.55-0.73), whereas the DL-based signature achieved a C-index of 0.67 (95% confidence interval, 0.57-0.77). Unlike the handcrafted signature, the DL-based signature successfully stratified testing patients into 2 prognostically distinct groups. CONCLUSIONS: The VGG-Seg generated accurate GBM contours from 4 MR images. The DL-based signature achieved a numerically higher C-index than the handcrafted signature and significant patient stratification. The proposed automatic workflow demonstrated the potential of improving patient stratification and survival prediction in patients with GBM.

Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy.

PURPOSE: To develop an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. METHODS: Given a set of volumetric images of a patient at N breathing phases as the training data, deformable image registration was performed between a reference phase and the other N-1 phases, resulting in N-1 deformation vector fields (DVFs). These DVFs can be represented efficiently by a few eigenvectors and coefficients obtained from principal component analysis (PCA). By varying the PCA coefficients, new DVFs can be generated, which, when applied on the reference image, lead to new volumetric images. A volumetric image can then be reconstructed from a single projection image by optimizing the PCA coefficients such that its computed projection matches the measured one. The 3D location of the tumor can be derived by applying the inverted DVF on its position in the reference image. The algorithm was implemented on graphics processing units (GPUs) to achieve real-time efficiency. The training data were generated using a realistic and dynamic mathematical phantom with ten breathing phases. The testing data were 360 cone beam projections corresponding to one gantry rotation, simulated using the same phantom with a 50% increase in breathing amplitude. RESULTS: The average relative image intensity error of the reconstructed volumetric images is 6.9% +/- 2.4%. The average 3D tumor localization error is 0.8 +/- 0.5 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for reconstructing a volumetric image from each projection is 0.24 s (range: 0.17 and 0.35 s). CONCLUSIONS: The authors have shown the feasibility of reconstructing volumetric images and localizing tumor positions in 3D in near real-time from a single x-ray image.

A generative adversarial network-based (GAN-based) architecture for automatic fiducial marker detection in prostate MRI-only radiotherapy simulation images.

PURPOSE: Clinical sites utilizing magnetic resonance imaging (MRI)-only simulation for prostate radiotherapy planning typically use fiducial markers for pretreatment patient positioning and alignment. Fiducial markers appear as small signal voids in MRI images and are often difficult to discern. Existing clinical methods for fiducial marker localization require multiple MRI sequences and/or manual interaction and specialized expertise. In this study, we develop a robust method for automatic fiducial marker detection in prostate MRI simulation images and quantify the pretreatment alignment accuracy using automatically detected fiducial markers in MRI. METHODS AND MATERIALS: In this study, a deep learning-based algorithm was used to convert MRI images into labeled fiducial marker volumes. Seventy-seven prostate cancer patients who received marker implantation prior to MRI and CT simulation imaging were selected for this study. Multiple-Echo T1 -VIBE MRI images were acquired, and images were stratified (at the patient level) based on the presence of intraprostatic calcifications. Ground truth (GT) contours were defined by an expert on MRI using CT images. Training was done using the pix2pix generative adversarial network (GAN) image-to-image translation package and model testing was done using fivefold cross validation. For performance comparison, an experienced medical dosimetrist and a medical physicist each manually contoured fiducial markers in MRI images. The percent of correct detections and F1 classification scores are reported for markers detected using the automatic detection algorithm and human observers. The patient positioning errors were quantified by calculating the target registration errors (TREs) from fiducial marker driven rigid registration between MRI and CBCT images. Target registration errors were quantified for fiducial marker contours defined on MRI by the automatic detection algorithm and the two expert human observers. RESULTS: Ninety-six percent of implanted fiducial markers were correctly identified using the automatic detection algorithm. Two expert raters correctly identified 97% and 96% of fiducial markers, respectively. The F1 classification score was 0.68, 0.75, and 0.72 for the automatic detection algorithm and two human raters, respectively. The main source of false discoveries was intraprostatic calcifications. The mean TRE differences between alignments from automatic detection algorithm and human detected markers and GT were <1 mm. CONCLUSIONS: We have developed a deep learning-based approach to automatically detect fiducial markers in MRI-only simulation images in a clinically representative patient cohort. The automatic detection algorithm-predicted markers can allow for patient setup with similar accuracy to independent human observers.

A generalized system of tissue-mimicking materials for computed tomography and magnetic resonance imaging.

The advent of technologies such as magnetic resonance imaging (MRI)-guided radiation therapy has led to the need for phantom materials that are capable of producing tissue-like contrast on both MRI and computed tomography (CT) imaging modalities. The purpose of this work is to develop a system of easily made and formed materials with adjustable T1 and T2 relaxation times, and x-ray attenuation properties, for mimicking soft tissue and bone with both MRI and CT imaging modalities. The effects on T1/T2 relaxation times and CT numbers were quantified for a range of gadolinium contrast (0-25 µmol g-1), agarose (0%-8% w/w), glass microspheres (0%-10% w/w) and CaCO3 (0%-50% w/w) concentrations in a carrageenan-based gel. 105 gel samples were prepared with the additives, carrageenan and water. Samples were imaged in a 3D-printed holding structure to find the attainable range of T1/T2 relaxation time and CT number combinations. T1 and T2 relaxation time maps were generated using voxel-wise inversion-recovery and spin-echo techniques, respectively. A multivariate linear regression model was generated to allow the materials system to be generalized to semi-arbitrary T1/T2 relaxation times and CT numbers. Nine diverse tissue types were mimicked for fit model validation. The achievable T1/T2 relaxation times and CT numbers for the additive concentrations tested in this study spanned from 82 to 2180 ms, 12 to 475 ms, and -117 to +914 Hounsfield units (HU), respectively. The mean absolute error between the fit model predicted and measured T1/T2 relaxation times and CT numbers for the nine tested tissue types was 113 ± 64 ms, 16 ± 26 ms and 11 ± 14 HU, respectively. In conclusion, we have created a system of materials capable of producing tissue-like contrast for 3.0 T MRI and CT imaging modalities.

A novel anthropomorphic multimodality phantom for MRI-based radiotherapy quality assurance testing.

PURPOSE: Increased utilization of magnetic resonance imaging (MRI) in radiotherapy has caused a growing need for phantoms that provide tissue-like contrast in both computed tomography (CT) and MRI images. Such phantoms can be used to compare MRI-based processes with CT-based clinical standards. Here, we develop and demonstrate the clinical utility of a three-dimensional (3D)-printed anthropomorphic pelvis phantom containing materials capable of T1 , T2 , and electron density matching for a clinically relevant set of soft tissues and bone. METHODS: The phantom design was based on a male pelvic anatomy template with thin boundaries separating tissue types. Slots were included to allow insertion of various dosimeters. The phantom structure was created using a 3D printer. The tissue compartments were filled with carrageenan-based materials designed to match the T1 and T2 relaxation times and electron densities of the corresponding tissues. CT and MRI images of the phantom were acquired and used to compare phantom T1 and T2 relaxation times and electron densities to literature-reported values for human tissue. To demonstrate clinical utility, the phantom was used for end-to-end testing of an MRI-only treatment simulation and planning workflow. Based on a T2 -weighted MRI image, synthetic CT (sCT) images were created using a statistical decomposition algorithm (MRIPlanner, Spectronic Research AB, Sweden) and used for dose calculation of volumetric-modulated arc therapy (VMAT) and seven-field intensity-modulated radiation therapy (IMRT) prostate plans. The plans were delivered on a Truebeam STX (Varian Medical Systems, Palo Alto, CA), with film and a 0.3 cc ion chamber used to measure the delivered dose. Doses calculated on the CT and sCTs were compared using common dose volume histogram metrics. RESULTS: T1 and T2 relaxation time and electron density measurements for the muscle, prostate, and bone agreed well with literature-reported in vivo measurements. Film analysis resulted in a 99.7% gamma pass rate (3.0%, 3.0 mm) for both plans. The ion chamber-measured dose discrepancies at the isocenter were 0.36% and 1.67% for the IMRT and VMAT plans, respectively. The differences in PTV D98% and D95% between plans calculated on the CT and 1.5T/3.0 T-derived sCT images were under 3%. CONCLUSION: The developed phantom provides tissue-like contrast on MRI and CT and can be used to validate MRI-based processes through comparison with standard CT-based processes.

Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy.

Electron density maps must be accurately estimated to achieve valid dose calculation in MR-only radiotherapy. The goal of this study is to assess whether two deep learning models, the conditional generative adversarial network (cGAN) and the cycle-consistent generative adversarial network (cycleGAN), can generate accurate abdominal synthetic CT (sCT) images from 0.35T MR images for MR-only liver radiotherapy. A retrospective study was performed using CT images and 0.35T MR images of 12 patients with liver (n = 8) and non-liver abdominal (n = 4) cancer. CT images were deformably registered to the corresponding MR images to generate deformed CT (dCT) images for treatment planning. Both cGAN and cycleGAN were trained using MR and dCT transverse slices. Four-fold cross-validation testing was conducted to generate sCT images for all patients. The HU prediction accuracy was evaluated by voxel-wise similarity metric between each dCT and sCT image for all 12 patients. dCT-based and sCT-based dose distributions were compared using gamma and dose-volume histogram (DVH) metric analysis for 8 liver patients. sCTcycleGAN achieved the average mean absolute error (MAE) of 94.1 HU, while sCTcGAN achieved 89.8 HU. In both models, the average gamma passing rates within all volumes of interest were higher than 95% using a 2%, 2 mm criterion, and 99% using a 3%, 3 mm criterion. The average differences in the mean dose and DVH metrics were within ±0.6% for the planning target volume and within ±0.15% for evaluated organs in both models. Results: demonstrated that abdominal sCT images generated by both cGAN and cycleGAN achieved accurate dose calculation for 8 liver radiotherapy plans. sCTcGAN images had smaller average MAE and achieved better dose calculation accuracy than sCTcyleGAN images. More abdominal patients will be enrolled in the future to further evaluate the two models.

Quantification of fiducial marker visibility for MRI-only prostate radiotherapy simulation.

To objectively compare the suitability of MRI pulse sequences and commercially available fiducial markers (FMs) for MRI-only prostate radiotherapy simulation. Most FMs appear as small signal voids in MRI images making them difficult to differentiate from tissue heterogeneities such as calcifications. In this study we use quantitative metrics to objectively evaluate the visibility of FMs in 27 patients and an anthropomorphic phantom with a variety of standard clinical MRI pulse sequences and commercially available FMs. FM visibility was quantified using the local contrast-to-noise-ratio (lCNR), the difference between the 80th and 20th percentile iso-intensity FM volumes (V fall) and the largest iso-intensity volume that can be distinguished from background: apparent-marker-volume (AMV). A larger lCNR and AMV, and smaller V fall represents a more easily identifiable FM. The number of non-marker objects visualized by each pulse sequence was calculated using FM-derived template-matching. The FM-based target-registration-error (TRE) between each MRI and the planning-CT image was calculated. Fiducial marker visibility was rated by two medical physicists with over three years of experience examining MRI-only prostate simulation images. The rater's classification accuracy was quantified using the F 1 score, which is the harmonic mean of the rater's precision and recall. These quantitative metrics and human observer ratings were used to evaluate FM identifiability in images from nine subtypes of T 1-weighted, T 2-weighted and gradient echo (GRE) pulse sequences in a 27-patient study. A phantom study was conducted to quantify the visibility of 8 commercially available FMs. In the patient study, the largest mean lCNR and AMV and, smallest normalized V fall were produced by the 3.0 T multiple-echo GRE pulse sequence (T 1-VIBE, 2° flip angle, 1.23 ms and 2.45 ms echo-times). This pulse sequence produced no false marker detections and TREs less than 2 mm in the left-right, anterior-posterior and cranial-caudal directions, respectively. Human observers rated the 1.23 ms echo-time GRE images with the best average marker visibility score of 100% and an F 1 score of 1. In the phantom study, the Gold-Anchor GA-200X-20-B (deployed in a folded configuration) produced the largest sequence averaged lCNR and AMV measurements at 16.1 and 16.7 mm3, respectively. Using quantitative visibility and distinguishability metrics and human observer ratings, the patient study demonstrated that multiple-echo GRE images produced the best gold FM visibility and distinguishability. The phantom study demonstrated that markers manufactured from platinum or iron-doped gold quantitatively produced superior visibility compared to their pure gold counterparts.

A theoretical model for respiratory motion artifacts in free-breathing CT scans.

Successful radiotherapy treatment depends heavily upon the accuracy of patient geometry captured during treatment simulation using computed tomography (CT) scans. Radiotherapy patients are often scanned under free breathing, and respiratory motion can cause severe artifacts in CT scans, including shortening, elongation or splitting of the shapes and shifting of the midpoint positions of the tumor and organs. This paper presents a theoretical model that explains the source of motion artifacts and the relationship between motion artifacts and the motion parameters of the scanner, treatment couch and tumor/organ. It is shown that an understanding of the relationship between the translational table velocity and the maximum tumor/organ velocity might enable one to mitigate certain types of motion artifacts. We show that splitting artifacts can be eliminated if the scanning speed is above the maximum tumor/organ velocity. Slow scanning speeds are shown to be useful for obtaining accurate internal target volumes (ITVs), and fast scanning speeds are shown to be useful for obtaining accurate tumor/organ shapes. In both cases, an upper bound on the maximum possible error is calculated as a function of the scanning speed. A set of special scanning speeds which allow for an accurate representation of tumor/organ length along the craniocaudal direction is obtained, and a relationship between the maximum displacement of a tumor/organ image's midpoint position and the magnitude of its length distortion is derived.