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.

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.

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.

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.

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.

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.

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.

Integrating spatiotemporal features of a ligand-regulated, multi-state allosteric protein.

Dynamic protein molecules are defined by their spatiotemporal characteristics and should thus be represented by models incoporating both characteritics. Structural biology enables determination of atomic structures of individual conformational states of a given protein. Obtaining the complementary temporal information of a given time resolution, which can be directly linked to the corresponding atomic structures, requires identifying at each time point the specific conformational state adopted by the protein. Here, we examine individual regulator of conductance to K+ (RCK) domains in the regulatory module of the MthK channel by monitoring in real time the orientation of an α-helix that is conformational-state-specific. The acquired dynamic information that specifies an RCK domain's multi-state conformational changes, combined with already available corresponding atomic structures, enables us to establish an experiment-based spatiotemporal representation of an RCK domain, and to deduce a quantitative mechanistic model of the channel.

Resolution of ångström-scale protein conformational changes by analyzing fluorescence anisotropy.

Conformational changes within typical protein molecules are rapid and small, making their quantitative resolution challenging. These changes generally involve rotational motions and may thus be resolved by determining changes in the orientation of a fluorescent label that assumes a unique orientation in each conformation. Here, by analyzing fluorescence intensities collected using a polarization microscope at a rate of 50 frames per second, we follow the changes of 10-16° in the orientation of a single bifunctional rhodamine molecule attached to a regulator of conductance to K+ (RCK) domain of the MthK channel, and thus, the transitions between its three conformational states, with effective standard deviation (σ) of 2-5°. Based on available crystal structures, the position of the fluorophore's center differs by 3.4-8.1 Å among the states. Thus, the present approach allows the resolution of protein conformational changes involving ångström-scale displacements.

Energetics of ångström-scale conformational changes in an RCK domain of the MthK K+ channel.

Allosteric proteins transition among different conformational states in a ligand-dependent manner. Upon resolution of a protein's individual states, one can determine the probabilities of these states, thereby dissecting the energetic mechanisms underlying their conformational changes. Here we examine individual regulator of conductance to K+ (RCK) domains that form the regulatory module of the Ca2+-activated MthK channel. Each domain adopts multiple conformational states differing on an ångström scale. The probabilities of these different states of the domain, assessed in different Ca2+ concentrations, allowed us to fully determine a six-state model that is minimally required to account for the energetic characteristics of the Ca2+-dependent conformational changes of an RCK domain. From the energetics of this domain, we deduced, in the framework of statistical mechanics, an analytic model that quantitatively predicts the experimentally observed Ca2+ dependence of the channel's open probability.

Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging.

PURPOSE: The improved soft tissue contrast of magnetic resonance imaging (MRI) compared to computed tomography (CT) makes it a useful imaging modality for radiotherapy treatment planning. Even when MR images are acquired for treatment planning, the standard clinical practice currently also requires a CT for dose calculation and x-ray-based patient positioning. This increases workloads, introduces uncertainty due to the required inter-modality image registrations, and involves unnecessary irradiation. While it would be beneficial to use exclusively MR images, a method needs to be employed to estimate a synthetic CT (sCT) for generating electron density maps and patient positioning reference images. We investigated 2D and 3D convolutional neural networks (CNNs) to generate a male pelvic sCT using a T1-weighted MR image and compare their performance. METHODS: A retrospective study was performed using CTs and T1-weighted MR images of 20 prostate cancer patients. CTs were deformably registered to MR images to create CT-MR pairs for training networks. The proposed 2D CNN, which contained 27 convolutional layers, was modified from the state-of-the-art 2D CNN to save computational memory and prepare for building the 3D CNN. The proposed 2D and 3D models were trained from scratch to map intensities of T1-weighted MR images to CT Hounsfield Unit (HU) values. Each sCT was generated in a fivefold cross-validation framework and compared with the corresponding deformed CT (dCT) using voxel-wise mean absolute error (MAE). The sCT geometric accuracy was evaluated by comparing bone regions, defined by thresholding at 150 HU in the dCTs and the sCTs, using dice similarity coefficient (DSC), recall, and precision. To evaluate sCT patient positioning accuracy, bone regions in dCTs and sCTs were rigidly registered to the corresponding cone-beam CTs. The resulting paired Euler transformation vectors were compared by calculating translation vector distances and absolute differences of Euler angles. Statistical tests were performed to evaluate the differences among the proposed models and Han's model. RESULTS: Generating a pelvic sCT required approximately 5.5 s using the proposed models. The average MAEs within the body contour were 40.5 ± 5.4 HU (mean ± SD) and 37.6 ± 5.1 HU for the 2D and 3D CNNs, respectively. The average DSC, recall, and precision for the bone region (thresholding the CT at 150 HU) were 0.81 ± 0.04, 0.85 ± 0.04, and 0.77 ± 0.09 for the 2D CNN, and 0.82 ± 0.04, 0.84 ± 0.04, and 0.80 ± 0.08 for the 3D CNN, respectively. For both models, mean translation vector distances are less than 0.6 mm with mean absolute differences of Euler angles less than 0.5°. CONCLUSIONS: The 2D and 3D CNNs generated accurate pelvic sCTs for the 20 patients using T1-weighted MR images. Statistical tests indicated that the proposed 3D model was able to generate sCTs with smaller MAE and higher bone region precision compared to 2D models. Results of patient alignment tests suggested that sCTs generated by the proposed CNNs can provide accurate patient positioning. The accuracy of the dose calculation using generated sCTs will be tested and compared for the proposed models in the future.

Reconstruction of a high-quality volumetric image and a respiratory motion model from patient CBCT projections.

PURPOSE: To develop and evaluate a method of reconstructing a patient- and treatment day- specific volumetric image and motion model from free-breathing cone-beam projections and respiratory surrogate measurements. This Motion-Compensated Simultaneous Algebraic Reconstruction Technique (MC-SART) generates and uses a motion model derived directly from the cone-beam projections, without requiring prior motion measurements from 4DCT, and can compensate for both inter- and intrabin deformations. The motion model can be used to generate images at arbitrary breathing points, which can be used for estimating volumetric images during treatment delivery. METHODS: The MC-SART was formulated using simultaneous image reconstruction and motion model estimation. For image reconstruction, projections were first binned according to external surrogate measurements. Projections in each bin were used to reconstruct a set of volumetric images using MC-SART. The motion model was estimated based on deformable image registration between the reconstructed bins, and least squares fitting to model parameters. The model was used to compensate for motion in both projection and backprojection operations in the subsequent image reconstruction iterations. These updated images were then used to update the motion model, and the two steps were alternated between. The final output is a volumetric reference image and a motion model that can be used to generate images at any other time point from surrogate measurements. RESULTS: A retrospective patient dataset consisting of eight lung cancer patients was used to evaluate the method. The absolute intensity differences in the lung regions compared to ground truth were 50.8 ± 43.9 HU in peak exhale phases (reference) and 80.8 ± 74.0 in peak inhale phases (generated). The 50th percentile of voxel registration error of all voxels in the lung regions with >5 mm amplitude was 1.3 mm. The MC-SART was also applied to measured patient cone-beam projections acquired with a linac-mounted CBCT system. Results from this patient data demonstrate the feasibility of MC-SART and showed qualitative image quality improvements compared to other state-of-the-art algorithms. CONCLUSION: We have developed a simultaneous image reconstruction and motion model estimation method that uses Cone-beam computed tomography (CBCT) projections and respiratory surrogate measurements to reconstruct a high-quality reference image and motion model of a patient in treatment position. The method provided superior performance in both HU accuracy and positional accuracy compared to other existing methods. The resultant reference image and motion model can be combined with respiratory surrogate measurements to generate volumetric images representing patient anatomy at arbitrary time points.

Safety-oriented design of in-house software for new techniques: A case study using a model-based 4DCT protocol.

PURPOSE: In-house software is commonly employed to implement new imaging and therapy techniques before commercial solutions are available. Risk analysis methods, as detailed in the TG-100 report of the American Association of Physicists in Medicine, provide a framework for quality management of processes but offer little guidance on software design. In this work, we examine a novel model-based four-dimensional computed tomography (4DCT) protocol using the TG-100 approach and describe two additional methods for promoting safety of the associated in-house software. METHODS: To implement a previously published model-based 4DCT protocol, in-house software was necessary for tasks such as synchronizing a respiratory signal to computed tomography images, deformable image registration (DIR), model parameter fitting, and interfacing with a treatment planning system. A process map was generated detailing the workflow. Failure modes and effects analysis (FMEA) was performed to identify critical steps and guide quality interventions. Software system safety was addressed through writing "use cases," narratives that characterize the behavior of the software, for all major operations to elicit safety requirements. Safety requirements were codified using the easy approach to requirements syntax (EARS) to ensure testability and eliminate ambiguity. RESULTS: Sixty-one failure modes were identified and assigned risk priority numbers using FMEA. Resultant quality management interventions include integration of a comprehensive reporting and logging system into the software, mandating daily and monthly equipment quality assurance procedures, and a checklist to be completed at image acquisition. Use cases and resulting safety requirements informed the design of needed in-house software as well as a suite of tests performed during the image generation process. CONCLUSIONS: TG-100 methods were used to construct a process-level quality management program for a 4DCT imaging protocol. Two supplemental tools from the field of requirements engineering facilitated elicitation and codification of safety requirements that informed the design and testing of in-house software necessary to implement the protocol. These general tools can be applied to promote safety when in-house software is needed to bring new techniques to the clinic.

Comparison of lung tumor motion measured using a model-based 4DCT technique and a commercial protocol.

PURPOSE: To compare lung tumor motion measured with a model-based technique to commercial 4-dimensional computed tomography (4DCT) scans and describe a workflow for using model-based 4DCT as a clinical simulation protocol. METHODS AND MATERIALS: Twenty patients were imaged using a model-based technique and commercial 4DCT. Tumor motion was measured on each commercial 4DCT dataset and was calculated on model-based datasets for 3 breathing amplitude percentile intervals: 5th to 85th, 5th to 95th, and 0th to 100th. Internal target volumes (ITVs) were defined on the 4DCT and 5th to 85th interval datasets and compared using Dice similarity. Images were evaluated for noise and rated by 2 radiation oncologists for artifacts. RESULTS: Mean differences in tumor motion magnitude between commercial and model-based images were 0.47 ± 3.0, 1.63 ± 3.17, and 5.16 ± 4.90 mm for the 5th to 85th, 5th to 95th, and 0th to 100th amplitude intervals, respectively. Dice coefficients between ITVs defined on commercial and 5th to 85th model-based images had a mean value of 0.77 ± 0.09. Single standard deviation image noise was 11.6 ± 9.6 HU in the liver and 6.8 ± 4.7 HU in the aorta for the model-based images compared with 57.7 ± 30 and 33.7 ± 15.4 for commercial 4DCT. Mean model error within the ITV regions was 1.71 ± 0.81 mm. Model-based images exhibited reduced presence of artifacts at the tumor compared with commercial images. CONCLUSION: Tumor motion measured with the model-based technique using the 5th to 85th percentile breathing amplitude interval corresponded more closely to commercial 4DCT than the 5th to 95th or 0th to 100th intervals, which showed greater motion on average. The model-based technique tended to display increased tumor motion when breathing amplitude intervals wider than 5th to 85th were used because of the influence of unusually deep inhalations. These results suggest that care must be taken in selecting the appropriate interval during image generation when using model-based 4DCT methods.