MB-DECTNet: a model-based unrolling network for accurate 3D dual-energy CT reconstruction from clinically acquired helical scans.

Objective.Over the past several decades, dual-energy CT (DECT) imaging has seen significant advancements due to its ability to distinguish between materials. DECT statistical iterative reconstruction (SIR) has exhibited potential for noise reduction and enhanced accuracy. However, its slow convergence and substantial computational demands render the elapsed time for 3D DECT SIR often clinically unacceptable. The objective of this study is to accelerate 3D DECT SIR while maintaining subpercentage or near-subpercentage accuracy.Approach.We incorporate DECT SIR into a deep-learning model-based unrolling network for 3D DECT reconstruction (MB-DECTNet), which can be trained end-to-end. This deep learning-based approach is designed to learn shortcuts between initial conditions and the stationary points of iterative algorithms while preserving the unbiased estimation property of model-based algorithms. MB-DECTNet comprises multiple stacked update blocks, each containing a data consistency layer (DC) and a spatial mixer layer, with the DC layer functioning as a one-step update from any traditional iterative algorithm.Main results.The quantitative results indicate that our proposed MB-DECTNet surpasses both the traditional image-domain technique (MB-DECTNet reduces average bias by a factor of 10) and a pure deep learning method (MB-DECTNet reduces average bias by a factor of 8.8), offering the potential for accurate attenuation coefficient estimation, akin to traditional statistical algorithms, but with considerably reduced computational costs. This approach achieves 0.13% bias and 1.92% mean absolute error and reconstructs a full image of a head in less than 12 min. Additionally, we show that the MB-DECTNet output can serve as an initializer for DECT SIR, leading to further improvements in results.Significance.This study presents a model-based deep unrolling network for accurate 3D DECT reconstruction, achieving subpercentage error in estimating virtual monoenergetic images for a full head at 60 and 150 keV in 30 min, representing a 40-fold speedup compared to traditional approaches. These findings have significant implications for accelerating DECT SIR and making it more clinically feasible.

Motion-compensated scheme for sequential scanned statistical iterative dual-energy CT reconstruction.

Objective.Dual-energy computed tomography (DECT) has been widely used to reconstruct numerous types of images due its ability to better discriminate tissue properties. Sequential scanning is a popular dual-energy data acquisition method as it requires no specialized hardware. However, patient motion between two sequential scans may lead to severe motion artifacts in DECT statistical iterative reconstructions (SIR) images. The objective is to reduce the motion artifacts in such reconstructions.Approach.We propose a motion-compensation scheme that incorporates a deformation vector field into any DECT SIR. The deformation vector field is estimated via the multi-modality symmetric deformable registration method. The precalculated registration mapping and its inverse or adjoint are then embedded into each iteration of the iterative DECT algorithm.Main results.Results from a simulated and clinical case show that the proposed framework is capable of reducing motion artifacts in DECT SIRs. Percentage mean square errors in regions of interest in the simulated and clinical cases were reduced from 4.6% to 0.5% and 6.8% to 0.8%, respectively. A perturbation analysis was then performed to determine errors in approximating the continuous deformation by using the deformation field and interpolation. Our findings show that errors in our method are mostly propagated through the target image and amplified by the inverse matrix of the combination of the Fisher information and Hessian of the penalty term.Significance.We have proposed a novel motion-compensation scheme to incorporate a 3D registration method into the joint statistical iterative DECT algorithm in order to reduce motion artifacts caused by inter-scan motion, and successfully demonstrate that interscan motion corrections can be integrated into the DECT SIR process, enabling accurate imaging of radiological quantities on conventional SECT scanners, without significant loss of either computational efficiency or accuracy.

Towards subpercentage uncertainty proton stopping-power mapping via dual-energy CT: Direct experimental validation and uncertainty analysis of a statistical iterative image reconstruction method.

PURPOSE: To assess the potential of a joint dual-energy computerized tomography (CT) reconstruction process (statistical image reconstruction method built on a basis vector model (JSIR-BVM)) implemented on a 16-slice commercial CT scanner to measure high spatial resolution stopping-power ratio (SPR) maps with uncertainties of less than 1%. METHODS: JSIR-BVM was used to reconstruct images of effective electron density and mean excitation energy from dual-energy CT (DECT) sinograms for 10 high-purity samples of known density and atomic composition inserted into head and body phantoms. The measured DECT data consisted of 90 and 140 kVp axial sinograms serially acquired on a Philips Brilliance Big Bore CT scanner without beam-hardening corrections. The corresponding SPRs were subsequently measured directly via ion chamber measurements on a MEVION S250 superconducting synchrocyclotron and evaluated theoretically from the known sample compositions and densities. Deviations of JSIR-BVM SPR values from their theoretically calculated and directly measured ground-truth values were evaluated for our JSIR-BVM method and our implementation of the Hünemohr-Saito (H-S) DECT image-domain decomposition technique for SPR imaging. A thorough uncertainty analysis was then performed for five different scenarios (comparison of JSIR-BVM stopping-power ratio/stopping power (SPR/SP) to International Commission on Radiation Measurements and Units benchmarks; comparison of JSIR-BVM SPR to measured benchmarks; and uncertainties in JSIR-BVM SPR/SP maps for patients of unknown composition) per the Joint Committee for Guides in Metrology and the Guide to Expression of Uncertainty in Measurement, including the impact of uncertainties in measured photon spectra, sample composition and density, photon cross section and I-value models, and random measurement uncertainty. Estimated SPR uncertainty for three main tissue groups in patients of unknown composition and the weighted proportion of each tissue type for three proton treatment sites were then used to derive a composite range uncertainty for our method. RESULTS: Mean JSIR-BVM SPR estimates deviated by less than 1% from their theoretical and directly measured ground-truth values for most inserts and phantom geometries except for high-density Delrin and Teflon samples with SPR error relative to proton measurements of 1.1% and -1.0% (head phantom) and 1.1% and -1.1% (body phantom). The overall root-mean-square (RMS) deviations over all samples were 0.39% and 0.52% (head phantom) and 0.43% and 0.57% (body phantom) relative to theoretical and directly measured ground-truth SPRs, respectively. The corresponding RMS (maximum) errors for the image-domain decomposition method were 2.68% and 2.73% (4.68% and 4.99%) for the head phantom and 0.71% and 0.87% (1.37% and 1.66%) for the body phantom. Compared to H-S SPR maps, JSIR-BVM yielded 30% sharper and twofold sharper images for soft tissues and bone-like surrogates, respectively, while reducing noise by factors of 6 and 3, respectively. The uncertainty (coverage factor k = 1) of the DECT-to-benchmark values comparison ranged from 0.5% to 1.5% and is dominated by scanning-beam photon-spectra uncertainties. An analysis of the SPR uncertainty for patients of unknown composition showed a JSIR-BVM uncertainty of 0.65%, 1.21%, and 0.77% for soft-, lung-, and bony-tissue groups which led to a composite range uncertainty of 0.6-0.9%. CONCLUSIONS: Observed JSIR-BVM SPR estimation errors were all less than 50% of the estimated k = 1 total uncertainty of our benchmarking experiment, demonstrating that JSIR-BVM high spatial resolution, low-noise SPR mapping is feasible and is robust to variations in the geometry of the scanned object. In contrast, the much larger H-S SPR estimation errors are dominated by imaging noise and residual beam-hardening artifacts. While the uncertainties characteristic of our current JSIR-BVM implementation can be as large as 1.5%, achieving < 1% total uncertainty is feasible by improving the accuracy of scanner-specific scatter-profile and photon-spectrum estimates. With its robustness to beam-hardening artifact, image noise, and variations in phantom size and geometry, JSIR-BVM has the potential to achieve high spatial-resolution SPR mapping with subpercentage accuracy and estimated uncertainty in the clinical setting.

Impact of bowtie filter and detector collimation on multislice CT scatter profiles: A simulation study.

PURPOSE: To investigate via Monte Carlo simulations, the impact of scan subject size, antiscatter grid (ASG), collimator size, and bowtie filter on the distribution of scatter radiation in a typical realistically modeled third generation 16 slice diagnostic computed tomography (CT) scanner. METHODS: Full radiation transport was simulated with Geant4 in a realistic CT scanner geometric model, including the imaging phantom, bowtie filter (BTF), collimators and detector assembly, except for the ASGs. An analytical method was employed to quantify the probable transmission through the ASG of each photon intersecting the detector array. Normalized scatter profiles (NSP) and scatter-to-primary-ratio (SPR) profiles were simulated for 90 and 140 kVp beams for different size phantoms and slice thicknesses. The impact of CT scatter on the reconstructed attenuation coefficient factor was also studied as were the modulating effects of phantom- and patient-tissue heterogeneities on scatter profiles. A method to characterize the relative spatial frequency content of sinogram signals was developed to assess the latter. RESULTS: For the 21.4-cm diameter phantom, NSP and SPR increase linearly with collimator opening for both tube potentials, with the 90 kVp scan exhibiting slightly larger NSP and SPR. The BTF modestly modulates scatter under the phantom center, reducing the prominent off-axis lobes by factors of 1.1-1.3. The ASG reduces scatter on the central axis NSP threefold, and reduces scatter at the detectors outside the phantom shadow by factors of 25 to 500. For the phantoms with diameters of 27 and 32 cm, the scatter increases roughly three- and fourfold, respectively, demonstrating that scatter monotonically increases with phantom size, despite deployment of the ASG and BTF. In the absence of a scan subject, the ASG reduces the signal profile arising photons scattered by the BTF. Without ASG, the in-air scatter profile is relatively flat compared to the scatter profile when the ASG is present. For both 90 and 140 kVp photon spectra, the calculated attenuation coefficient decreases linearly with increasing collimation size. For both homogeneous and heterogeneous objects, NSPs are dominated by low spatial frequency content compared to the primary signal. However, the SPR, which quantifies the local magnitude of nonlinear detector response and is dominated by the high frequency content of the primary profile, can contribute strongly to high-spatial frequency streaking artifacts near high-density structures in reconstructed image artifacts. CONCLUSION: Public-domain Monte Carlo codes, Geant-4 in particular, is a feasible method for characterizing CT detector response to scattered- and off-focal radiation. Our study demonstrates that the ASG substantially reduces the scatter radiation and reshapes scatter-radiation profiles and affects the accuracy with which the detector array can measure narrow-beam attenuation due its inability to distinguish between true uncollided primary and narrow-angle coherently scattered photons. Hence, incorporating the impact of detector array collimation into the forward-projection signal formation models used by iterative reconstruction algorithms is necessary to use CT for accurately characterizing material properties. While tissue heterogeneities exercise a modest influence on local NPS shape and magnitude, they do not add significant high spatial frequency content.

Feasibility of proton FLASH irradiation using a synchrocyclotron for preclinical studies.

PURPOSE: It has been recently shown that radiotherapy at ultrahigh dose rates (>40 Gy/s, FLASH) has a potential advantage in sparing healthy organs compared to that at conventional dose rates. The purpose of this work is to show the feasibility of proton FLASH irradiation using a gantry-mounted synchrocyclotron as a first step toward implementing an experimental setup for preclinical studies. METHODS: A clinical Mevion HYPERSCAN® synchrocyclotron was modified to deliver ultrahigh dose rates. Pulse widths of protons with 230 MeV energy were manipulated from 1 to 20 µs to deliver in conventional and ultrahigh dose rate. A boron carbide absorber was placed in the beam for range modulation. A Faraday cup was used to determine the number of protons per pulse at various dose rates. Dose rate was determined by the dose measured with a plane-parallel ionization chamber with respect to the actual delivery time. The integral depth dose (IDD) was measured with a Bragg ionization chamber. Monte Carlo simulation was performed in TOPAS as the secondary check for the measurements. RESULTS: Maximum protons charge per pulse, measured with the Faraday cup, was 54.6 pC at 20 µs pulse width. The measured IDD agreed well with the Monte Carlo simulation. The average dose rate measured using the ionization chamber showed 101 Gy/s at the entrance and 216 Gy/s at the Bragg peak with a full width at half maximum field size of 1.2 cm. CONCLUSIONS: It is feasible to deliver protons at 100 and 200 Gy/s average dose rate at the plateau and the Bragg peak, respectively, in a small ~1 cm2 field using a gantry-mounted synchrocyclotron.

Concurrent chemoradiation for cervical cancer: Comparison of LDR and HDR brachytherapy.

PURPOSE: To compare clinical outcomes between low-dose-rate (LDR) brachytherapy and high-dose-rate (HDR) brachytherapy for cervical cancer patients. METHODS AND MATERIALS: All consecutive newly diagnosed cervical cancer patients undergoing pretreatment 18-fluorodeoxyglucose positron emission tomography imaging and treated with curative-intent definitive chemoradiation from 1997 to 2016 at a U.S. academic center were included. Brachytherapy boost was LDR or HDR 2D treatment planning from 1997 to 2005 and HDR with MR-based 3D planning from 2005 to 2016. Local control (LC), cancer-specific survival (CSS), and late bowel/bladder complications were evaluated. RESULTS: Tumor stages were International Federation of Gynecology and Obstetrics IB1-IIB (n = 457; 75%) and III-IVA (n = 152; 25%). Brachytherapy was LDR for 104 patients and HDR for 505 patients. Concurrent weekly cisplatin was administered to 536 patients (88%). With median followup of 9.4 years, there was no difference in LC (p = 0.24) or CSS (p = 0.50) between LDR and HDR brachytherapy. Cox multivariable regression showed that only International Federation of Gynecology and Obstetrics stage III-IVA (HR=2.4, p = 0.004) was associated with worse LC. A propensity-matched cohort (90 LDR vs. 90 HDR) was created, and the 5-year LC rates were 88% LDR and 82% HDR, p = 0.26; 5-year CSS rates were 66% LDR and 58% HDR, p = 0.19; 5-year grade ≥3 bowel/bladder toxicities were 23% LDR and 16% HDR, p = 0.44. For all patients, the 5-year late toxicity in stage III-IVA patients was higher with LDR 47% vs. HDR 15%, p = 0.03, with no difference in LC, 86% and 75%, respectively (p = 0.09). CONCLUSIONS: There was no difference in LC with either LDR or HDR brachytherapy. The late complication rate was reduced with HDR and 3D-planned brachytherapy compared to LDR and 2D-planned brachytherapy.

Experimental implementation of a joint statistical image reconstruction method for proton stopping power mapping from dual-energy CT data.

PURPOSE: To experimentally commission a dual-energy CT (DECT) joint statistical image reconstruction (JSIR) method, which is built on a linear basis vector model (BVM) of material characterization, for proton stopping power ratio (SPR) estimation. METHODS: The JSIR-BVM method builds on the relationship between the energy-dependent photon attenuation coefficients and the proton stopping power via a pair of BVM component weights. The two BVM component images are simultaneously reconstructed from the acquired DECT sinograms and then used to predict the electron density and mean excitation energy (I-value), which are required by the Bethe equation for SPR computation. A post-reconstruction image-based DECT method, which utilizes the two separate CT images reconstructed via the scanner's software, was implemented for comparison. The DECT measurement data were acquired on a Philips Brilliance scanner at 90 and 140 kVp for two phantoms of different sizes. Each phantom contains 12 different soft and bony tissue surrogates with known compositions. The SPR estimation results were compared to the reference values computed from the known compositions. The difference of the computed water equivalent path lengths (WEPL) across the phantoms between the two methods was also compared. RESULTS: The overall root-mean-square (RMS) of SPR estimation error of the JSIR-BVM method are 0.33% and 0.37% for the head- and body-sized phantoms, respectively, and all SPR estimates of the test samples are within 0.7% of the reference ground truth. The image-based method achieves overall RMS errors of 2.35% and 2.50% for the head- and body-sized phantoms, respectively. The JSIR-BVM method also reduces the pixel-wise random variation by 4-fold to 6-fold within homogeneous regions compared to the image-based method. The average differences between the JSIR-BVM method and the image-based method are 0.54% and 1.02% for the head- and body-sized phantoms, respectively. CONCLUSION: By taking advantage of an accurate polychromatic CT data model and a model-based DECT statistical reconstruction algorithm, the JSIR-BVM method accounts for both systematic bias and random noise in the acquired DECT measurement data. Therefore, the JSIR-BVM method achieves good accuracy and precision on proton SPR estimation for various tissue surrogates and object sizes. In contrast, the experimentally achievable accuracy of the image-based method may be limited by the uncertainties in the image formation process. The result suggests that the JSIR-BVM method has the potential for more accurate SPR prediction compared to post-reconstruction image-based methods in clinical settings.

Impact of joint statistical dual-energy CT reconstruction of proton stopping power images: Comparison to image- and sinogram-domain material decomposition approaches.

PURPOSE: The purpose of this study was to assess the performance of a novel dual-energy CT (DECT) approach for proton stopping power ratio (SPR) mapping that integrates image reconstruction and material characterization using a joint statistical image reconstruction (JSIR) method based on a linear basis vector model (BVM). A systematic comparison between the JSIR-BVM method and previously described DECT image- and sinogram-domain decomposition approaches is also carried out on synthetic data. METHODS: The JSIR-BVM method was implemented to estimate the electron densities and mean excitation energies (I-values) required by the Bethe equation for SPR mapping. In addition, image- and sinogram-domain DECT methods based on three available SPR models including BVM were implemented for comparison. The intrinsic SPR modeling accuracy of the three models was first validated. Synthetic DECT transmission sinograms of two 330 mm diameter phantoms each containing 17 soft and bony tissues (for a total of 34) of known composition were then generated with spectra of 90 and 140 kVp. The estimation accuracy of the reconstructed SPR images were evaluated for the seven investigated methods. The impact of phantom size and insert location on SPR estimation accuracy was also investigated. RESULTS: All three selected DECT-SPR models predict the SPR of all tissue types with less than 0.2% RMS errors under idealized conditions with no reconstruction uncertainties. When applied to synthetic sinograms, the JSIR-BVM method achieves the best performance with mean and RMS-average errors of less than 0.05% and 0.3%, respectively, for all noise levels, while the image- and sinogram-domain decomposition methods show increasing mean and RMS-average errors with increasing noise level. The JSIR-BVM method also reduces statistical SPR variation by sixfold compared to other methods. A 25% phantom diameter change causes up to 4% SPR differences for the image-domain decomposition approach, while the JSIR-BVM method and sinogram-domain decomposition methods are insensitive to size change. CONCLUSION: Among all the investigated methods, the JSIR-BVM method achieves the best performance for SPR estimation in our simulation phantom study. This novel method is robust with respect to sinogram noise and residual beam-hardening effects, yielding SPR estimation errors comparable to intrinsic BVM modeling error. In contrast, the achievable SPR estimation accuracy of the image- and sinogram-domain decomposition methods is dominated by the CT image intensity uncertainties introduced by the reconstruction and decomposition processes.

RECORDS: improved Reporting of montE CarlO RaDiation transport Studies: Report of the AAPM Research Committee Task Group 268.

Studies involving Monte Carlo simulations are common in both diagnostic and therapy medical physics research, as well as other fields of basic and applied science. As with all experimental studies, the conditions and parameters used for Monte Carlo simulations impact their scope, validity, limitations, and generalizability. Unfortunately, many published peer-reviewed articles involving Monte Carlo simulations do not provide the level of detail needed for the reader to be able to properly assess the quality of the simulations. The American Association of Physicists in Medicine Task Group #268 developed guidelines to improve reporting of Monte Carlo studies in medical physics research. By following these guidelines, manuscripts submitted for peer-review will include a level of relevant detail that will increase the transparency, the ability to reproduce results, and the overall scientific value of these studies. The guidelines include a checklist of the items that should be included in the Methods, Results, and Discussion sections of manuscripts submitted for peer-review. These guidelines do not attempt to replace the journal reviewer, but rather to be a tool during the writing and review process. Given the varied nature of Monte Carlo studies, it is up to the authors and the reviewers to use this checklist appropriately, being conscious of how the different items apply to each particular scenario. It is envisioned that this list will be useful both for authors and for reviewers, to help ensure the adequate description of Monte Carlo studies in the medical physics literature.

Multi-GPU Acceleration of Branchless Distance Driven Projection and Backprojection for Clinical Helical CT.

Model-based image reconstruction (MBIR) techniques have the potential to generate high quality images from noisy measurements and a small number of projections which can reduce the x-ray dose in patients. These MBIR techniques rely on projection and backprojection to refine an image estimate. One of the widely used projectors for these modern MBIR based technique is called branchless distance driven (DD) projection and backprojection. While this method produces superior quality images, the computational cost of iterative updates keeps it from being ubiquitous in clinical applications. In this paper, we provide several new parallelization ideas for concurrent execution of the DD projectors in multi-GPU systems using CUDA programming tools. We have introduced some novel schemes for dividing the projection data and image voxels over multiple GPUs to avoid runtime overhead and inter-device synchronization issues. We have also reduced the complexity of overlap calculation of the algorithm by eliminating the common projection plane and directly projecting the detector boundaries onto image voxel boundaries. To reduce the time required for calculating the overlap between the detector edges and image voxel boundaries, we have proposed a pre-accumulation technique to accumulate image intensities in perpendicular 2D image slabs (from a 3D image) before projection and after backprojection to ensure our DD kernels run faster in parallel GPU threads. For the implementation of our iterative MBIR technique we use a parallel multi-GPU version of the alternating minimization (AM) algorithm with penalized likelihood update. The time performance using our proposed reconstruction method with Siemens Sensation 16 patient scan data shows an average of 24 times speedup using a single TITAN X GPU and 74 times speedup using 3 TITAN X GPUs in parallel for combined projection and backprojection.

Technical Note: On the accuracy of parametric two-parameter photon cross-section models in dual-energy CT applications.

PURPOSE: To evaluate and compare the theoretically achievable accuracy of two families of two-parameter photon cross-section models: basis vector model (BVM) and modified parametric fit model (mPFM). METHOD: The modified PFM assumes that photoelectric absorption and scattering cross-sections can be accurately represented by power functions in effective atomic number and/or energy plus the Klein-Nishina cross-section, along with empirical corrections that enforce exact prediction of elemental cross-sections. Two mPFM variants were investigated: the widely used Torikoshi model (tPFM) and a more complex "VCU" variant (vPFM). For 43 standard soft and bony tissues and phantom materials, all consisting of elements with atomic number less than 20 (except iodine), we evaluated the theoretically achievable accuracy of tPFM and vPFM for predicting linear attenuation, photoelectric absorption, and energy-absorption coefficients, and we compared it to a previously investigated separable, linear two-parameter model, BVM. RESULTS: For an idealized dual-energy computed tomography (DECT) imaging scenario, the cross-section mapping process demonstrates that BVM more accurately predicts photon cross-sections of biological mixtures than either tPFM or vPFM. Maximum linear attenuation coefficient prediction errors were 15% and 5% for tPFM and BVM, respectively. The root-mean-square (RMS) prediction errors of total linear attenuation over the 20 keV to 1000 keV energy range of tPFM and BVM were 0.93% (tPFM) and 0.1% (BVM) for adipose tissue, 0.8% (tPFM) and 0.2% (BVM) for muscle tissue, and 1.6% (tPFM) and 0.2% (BVM) for cortical bone tissue. With exception of the thyroid and Teflon, the RMS error for photoelectric absorption and scattering coefficient was within 4% for the tPFM and 2% for the BVM. Neither model predicts the photon cross-sections of thyroid tissue accurately, exhibiting relative errors as large as 20%. For the energy-absorption coefficients prediction error, RMS errors for the BVM were less than 1.5%, while for the tPFM, the RMS errors were as large as 16%. CONCLUSION: Compared to modified PFMs, BVM shows superior potential to support dual-energy CT cross-section mapping. In addition, the linear, separable BVM can be more efficiently deployed by iterative model-based DECT image-reconstruction algorithms.

A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer.

PURPOSE: To describe in detail a dataset consisting of serial four-dimensional computed tomography (4DCT) and 4D cone beam CT (4DCBCT) images acquired during chemoradiotherapy of 20 locally advanced, nonsmall cell lung cancer patients we have collected at our institution and shared publicly with the research community. ACQUISITION AND VALIDATION METHODS: As part of an NCI-sponsored research study 82 4DCT and 507 4DCBCT images were acquired in a population of 20 locally advanced nonsmall cell lung cancer patients undergoing radiation therapy. All subjects underwent concurrent radiochemotherapy to a total dose of 59.4-70.2 Gy using daily 1.8 or 2 Gy fractions. Audio-visual biofeedback was used to minimize breathing irregularity during all fractions, including acquisition of all 4DCT and 4DCBCT acquisitions in all subjects. Target, organs at risk, and implanted fiducial markers were delineated by a physician in the 4DCT images. Image coordinate system origins between 4DCT and 4DCBCT were manipulated in such a way that the images can be used to simulate initial patient setup in the treatment position. 4DCT images were acquired on a 16-slice helical CT simulator with 10 breathing phases and 3 mm slice thickness during simulation. In 13 of the 20 subjects, 4DCTs were also acquired on the same scanner weekly during therapy. Every day, 4DCBCT images were acquired on a commercial onboard CBCT scanner. An optically tracked external surrogate was synchronized with CBCT acquisition so that each CBCT projection was time stamped with the surrogate respiratory signal through in-house software and hardware tools. Approximately 2500 projections were acquired over a period of 8-10 minutes in half-fan mode with the half bow-tie filter. Using the external surrogate, the CBCT projections were sorted into 10 breathing phases and reconstructed with an in-house FDK reconstruction algorithm. Errors in respiration sorting, reconstruction, and acquisition were carefully identified and corrected. DATA FORMAT AND USAGE NOTES: 4DCT and 4DCBCT images are available in DICOM format and structures through DICOM-RT RTSTRUCT format. All data are stored in the Cancer Imaging Archive (TCIA, http://www.cancerimagingarchive.net/) as collection 4D-Lung and are publicly available. DISCUSSION: Due to high temporal frequency sampling, redundant (4DCT and 4DCBCT) data at similar timepoints, oversampled 4DCBCT, and fiducial markers, this dataset can support studies in image-guided and image-guided adaptive radiotherapy, assessment of 4D voxel trajectory variability, and development and validation of new tools for image registration and motion management.