Modeling Lung Architecture in the XCAT Series of Phantoms: Physiologically Based Airways, Arteries and Veins

AAPM Truth-based CT (TrueCT) reconstruction grand challenge

BACKGROUND

This Special Report summarizes the 2022, AAPM grand challenge on Truth-based CT image reconstruction.

PURPOSE

To provide an objective framework for evaluating CT reconstruction methods using virtual imaging resources consisting of a library of simulated CT projection images of a population of human models with various diseases.

METHODS

Two hundred unique anthropomorphic, computational models were created with varied diseases consisting of 67 emphysema, 67 lung lesions, and 66 liver lesions. The organs were modeled based on clinical CT images of real patients. The emphysematous regions were modeled using segmentations from patient CT cases in the COPDGene Phase I dataset. For the lung and liver lesion cases, 1-6 malignant lesions were created and inserted into the human models, with lesion diameters ranging from 5.6 to 21.9 mm for lung lesions and 3.9 to 14.9 mm for liver lesions. The contrast defined between the liver lesions and liver parenchyma was 82 ± 12 HU, ranging from 50 to 110 HU. Similarly, the contrast between the lung lesions and the lung parenchyma was defined as 781 ± 11 HU, ranging from 725 to 805 HU. For the emphysematous regions, the defined HU values were -950 ± 17 HU ranging from -918 to -979 HU. The developed human models were imaged with a validated CT simulator. The resulting CT sinograms were shared with the participants. The participants reconstructed CT images from the sinograms and sent back their reconstructed images. The reconstructed images were then scored by comparing the results against the corresponding ground truth values. The scores included both task-generic (root mean square error [RMSE] and structural similarity matrix [SSIM]), and task-specific (detectability index [d'] and lesion volume accuracy) metrics. For the cases with multiple lesions, the measured metric was averaged across all the lesions. To combine the metrics with each other, each metric was normalized to a range of 0 to 1 per disease type, with "0" and "1" being the worst and best measured values across all cases of the disease type for all received reconstructions.

RESULTS

The True-CT challenge attracted 52 participants, out of which 5 successfully completed the challenge and submitted the requested 200 reconstructions. Across all participants and disease types, SSIM absolute values ranged from 0.22 to 0.90, RMSE from 77.6 to 490.5 HU, d' from 0.1 to 64.6, and volume accuracy ranged from 1.2 to 753.1 mm3. The overall scores demonstrated that participant "A" had the best performance in all categories, except for the metrics of d' for lung lesions and RMSE for liver lesions. Participant "A" had an average normalized score of 0.41 ± 0.22, 0.48 ± 0.32, and 0.42 ± 0.33 for the emphysema, lung lesion, and liver lesion cases, respectively.

CONCLUSIONS

The True-CT challenge successfully enabled objective assessment of CT reconstructions with the unique advantage of access to a diverse population of diseased human models with known ground truth. This study highlights the significant potential of virtual imaging trials in objective assessment of medical imaging technologies.

A framework to model charge sharing and pulse pileup for virtual imaging trials of photon-counting CT

Objective.This study describes the development, validation, and integration of a detector response model that accounts for the combined effects of x-ray crosstalk, charge sharing, and pulse pileup in photon-counting detectors.Approach.The x-ray photon transport was simulated using Geant4, followed by analytical charge sharing simulation in MATLAB. The analytical simulation models charge clouds with Gaussian-distributed charge densities, which are projected on a 3×3 pixel neighborhood of interaction location to compute detected counts. For pulse pileup, a prior analytical method for redistribution of energy-binned counts was implemented for delta pulses. The x-ray photon transport and charge sharing components were validated using experimental data acquired on the CdTe-based Pixirad-1/Pixie-III detector using monoenergetic beams at 26, 33, 37, and 50 keV. The pulse pileup implementation was verified with a comparable Monte Carlo simulation. The model output without pulse pileup was used to generate spatio-energetic response matrices for efficient simulation of scanner-specific photon-counting CT (PCCT) images with DukeSim, with pulse pileup modeled as a post-processing step on simulated projections. For analysis, images for the Gammex multi-energy phantom and the XCAT chest phantom were simulated at 120 kV, both with and without pulse pileup for a range of doses (27-1344 mAs). The XCAT images were evaluated qualitatively at 120 mAs, while images for the Gammex phantom were evaluated quantitatively for all doses using measurements of attenuation coefficients and Calcium concentrations.Main results.Reasonable agreement was observed between simulated and experimental spectra with Mean Absolute Percentage Error Values (MAPE) between 10%and 31%across all incident energies and detector modes. The increased pulse pileup from increased dose affected attenuation coefficients and calcium concentrations, with an effect on calcium quantification as high as MAPE of 28%.Significance.The presented approach demonstrates the viability of the model for enabling VITs to assess and optimize the clinical performance of PCCT.

Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs

We aim to provide an overview of technical and clinical unmet needs in deep learning (DL) applications for quantitative and qualitative PET in PET/MR, with a focus on attenuation correction, image enhancement, motion correction, kinetic modeling, and simulated data generation. (1) DL-based attenuation correction (DLAC) remains an area of limited exploration for pediatric whole-body PET/MR and lung-specific DLAC due to data shortages and technical limitations. (2) DL-based image enhancement approximating MR-guided regularized reconstruction with a high-resolution MR prior has shown promise in enhancing PET image quality. However, its clinical value has not been thoroughly evaluated across various radiotracers, and applications outside the head may pose challenges due to motion artifacts. (3) Robust training for DL-based motion correction requires pairs of motion-corrupted and motion-corrected PET/MR data. However, these pairs are rare. (4) DL-based approaches can address the limitations of dynamic PET, such as long scan durations that may cause patient discomfort and motion, providing new research opportunities. (5) Monte-Carlo simulations using anthropomorphic digital phantoms can provide extensive datasets to address the shortage of clinical data. This summary of technical/clinical challenges and potential solutions may provide research opportunities for the research community towards the clinical translation of DL solutions.

A Physics-informed Deep Neural Network for Harmonization of CT Images

Computed Tomography (CT) quantification is affected by the variability in image acquisition and rendition. This paper aimed to reduce this variability by harmonizing the images utilizing physics-based deep neural networks (DNNs).

METHODS

An adversarial generative network was trained on virtual CT images acquired under various imaging conditions using a virtual imaging platform with 40 computational patient models. These models featured anthropomorphic lungs with different levels of pulmonary diseases, including nodules and emphysema. Imaging was conducted using a validated CT simulator at two dose levels and varying reconstruction kernels. The trained model was tested on an independent virtual test dataset and two clinical datasets.

RESULTS

On the virtual test set, the harmonizer improved the structural similarity index from 79.3 ±16.4% to 95.8 ±1.7%, normalized mean squared error from 16.7 ±9.7% to 9.2 ±1.7%, and peak signal-to-noise ratio from 27.7 ±3.7 dB to 32.2 ±1.6 dB. Moreover, the harmonized images yielded more precise quantification of emphysema-based imaging biomarkers for lung attenuation, LAA -950 from 5.6 ±8.7% to 0.23 ±0.16%, Perc 15 from 43.4 ±45.4 HU to 20.0 ±7.5 HU, and Lung Mass from 0.3 ±0.3 g to 0.1 ±0.2 g. In clinical data, the harmonizer reduced biomarker variability by an average of 70%. For lung nodules, harmonized images improved the detectability index by 6.5-fold and DNN-based precision by 6%.

CONCLUSION

The proposed harmonizer significantly enhances image quality and quantification accuracy in CT imaging.

SIGNIFICANCE

The study demonstrated the potential utility of image harmonization for consistent CT image quality and reliable quantification, which is crucial for clinical applications and patient management.

A systematic assessment and optimization of photon-counting CT for lung density quantifications

BACKGROUND

Photon-counting computed tomography (PCCT) has recently emerged into clinical use; however, its optimum imaging protocols and added benefits remains unknown in terms of providing more accurate lung density quantification compared to energy-integrating computed tomography (EICT) scanners.

PURPOSE

To systematically assess the performance of a clinical PCCT scanner for lung density quantifications and compare it against EICT.

METHODS

This cross-sectional study involved a retrospective analysis of subjects scanned (August-December 2021) using a clinical PCCT system. The influence of altering reconstruction parameters was studied (reconstruction kernel, pixel size, slice thickness). A virtual CT dataset of anthropomorphic virtual subjects was acquired to demonstrate the correspondence of findings to clinical dataset, and to perform systematic imaging experiments, not possible using human subjects. The virtual subjects were imaged using a validated, scanner-specific CT simulator of a PCCT and two EICT (defined as EICT A and B) scanners. The images were evaluated using mean absolute error (MAE) of lung and emphysema density against their corresponding ground truth.

RESULTS

Clinical and virtual PCCT datasets showed similar trends, with sharper kernels and smaller voxel sizes increasing percentage of low-attenuation areas below -950 HU (LAA-950) by up to 15.7 ± 6.9% and 11.8 ± 5.5%, respectively. Under the conditions studied, higher doses, thinner slices, smaller pixel sizes, iterative reconstructions, and quantitative kernels with medium sharpness resulted in lower lung MAE values. While using these settings for PCCT, changes in the dose level (13 to 1.3 mGy), slice thickness (0.4 to 1.5 mm), pixel size (0.49 to 0.98 mm), reconstruction technique (70 keV-VMI to wFBP), and kernel (Qr48 to Qr60) increased lung MAE by 15.3 ± 2.0, 1.4 ± 0.6, 2.2 ± 0.3, 4.2 ± 0.8, and 9.1 ± 1.6 HU, respectively. At the optimum settings identified per scanner, PCCT images exhibited lower lung and emphysema MAE than those of EICT scanners (by 2.6 ± 1.0 and 9.6 ± 3.4 HU, compared to EICT A, and by 4.8 ± 0.8 and 7.4 ± 2.3 HU, compared to EICT B). The accuracy of lung density measurements was correlated with subjects' mean lung density (p < 0.05), measured by PCCT at optimum setting under the conditions studied.

CONCLUSION

Photon-counting CT demonstrated superior performance in density quantifications, with its influences of imaging parameters in line with energy-integrating CT scanners. The technology offers improvement in lung quantifications, thus demonstrating potential toward more objective assessment of respiratory conditions.

Development of physiologically-informed computational coronary artery plaques for use in virtual imaging trials

BACKGROUND

As a leading cause of death, worldwide, cardiovascular disease is of great clinical importance. Among cardiovascular diseases, coronary artery disease (CAD) is a key contributor, and it is the attributed cause of death for 10% of all deaths annually. The prevalence of CAD is commensurate with the rise in new medical imaging technologies intended to aid in its diagnosis and treatment. The necessary clinical trials required to validate and optimize these technologies require a large cohort of carefully controlled patients, considerable time to complete, and can be prohibitively expensive. A safer, faster, less expensive alternative is using virtual imaging trials (VITs), utilizing virtual patients or phantoms combined with accurate computer models of imaging devices.

PURPOSE

In this work, we develop realistic, physiologically-informed models for coronary plaques for application in cardiac imaging VITs.

METHODS

Histology images of plaques at micron-level resolution were used to train a deep convolutional generative adversarial network (DC-GAN) to create a library of anatomically variable plaque models with clinical anatomical realism. The stability of each plaque was evaluated by finite element analysis (FEA) in which plaque components and vessels were meshed as volumes, modeled as specialized tissues, and subjected to the range of normal coronary blood pressures. To demonstrate the utility of the plaque models, we combined them with the whole-body XCAT computational phantom to perform initial simulations comparing standard energy-integrating detector (EID) CT with photon-counting detector (PCD) CT.

RESULTS

Our results show the network is capable of generating realistic, anatomically variable plaques. Our simulation results provide an initial demonstration of the utility of the generated plaque models as targets to compare different imaging devices.

CONCLUSIONS

Vast, realistic, and variable CAD pathologies can be generated to incorporate into computational phantoms for VITs. There they can serve as a known truth from which to optimize and evaluate cardiac imaging technologies quantitatively.

Quantitative accuracy of lung function measurement using parametric response mapping: A virtual imaging study

Parametric response mapping (PRM) is a voxel-based quantitative CT imaging biomarker that measures the severity of chronic obstructive pulmonary disease (COPD) by analyzing both inspiratory and expiratory CT scans. Although PRM-derived measurements have been shown to predict disease severity and phenotyping, their quantitative accuracy is impacted by the variability of scanner settings and patient conditions. The aim of this study was to evaluate the variability of PRM-based measurements due to the changes in the scanner types and configurations. We developed 10 human chest models with emphysema and air-trapping at end-inspiration and end-expiration states. These models were virtually imaged using a scanner-specific CT simulator (DukeSim) to create CT images at different acquisition settings for energy-integrating and photon-counting CT systems. The CT images were used to estimate PRM maps. The quantified measurements were compared with ground truth values to evaluate the deviations in the measurements. Results showed that PRM measurements varied with scanner type and configurations. The emphysema volume was overestimated by 3 ± 9.5 % (mean ± standard deviation) of the lung volume, and the functional small airway disease (fSAD) volume was underestimated by 7.5±19 % of the lung volume. PRM measurements were more accurate and precise when the acquired settings were photon-counting CT, higher dose, smoother kernel, and larger pixel size. This study demonstrates the development and utility of virtual imaging tools for systematic assessment of a quantitative biomarker accuracy.

Deep silicon photon-counting CT: A first simulation-based study for assessing perceptual benefits across diverse anatomies

OBJECTIVES

To assess perceptual benefits provided by the improved spatial resolution and noise performance of deep silicon photon-counting CT (Si-PCCT) over conventional energy-integrating CT (ECT) using polychromatic images for various clinical tasks and anatomical regions.

MATERIALS AND METHODS

Anthropomorphic, computational models were developed for lungs, liver, inner ear, and head-and-neck (H&N) anatomies. These regions included specific abnormalities such as lesions in the lungs and liver, and calcified plaques in the carotid arteries. The anatomical models were imaged using a scanner-specific CT simulation platform (DukeSim) modeling a Si-PCCT prototype and a conventional ECT system at matched dose levels. The simulated polychromatic projections were reconstructed with matched in-plane resolutions using manufacturer-specific software. The reconstructed pairs of images were scored by radiologists to gauge the task-specific perceptual benefits provided by Si-PCCT compared to ECT based on visualization of anatomical and image quality features. The scores were standardized as z-scores for minimizing inter-observer variability and compared between the systems for evidence of statistically significant improvement (one-sided Wilcoxon rank-sum test with a significance level of 0.05) in perceptual performance for Si-PCCT.

RESULTS

Si-PCCT offered favorable image quality and improved visualization capabilities, leading to mean improvements in task-specific perceptual performance over ECT for most tasks. The improvements for Si-PCCT were statistically significant for the visualization of lung lesion (0.08 ± 0.89 vs. 0.90 ± 0.48), liver lesion (-0.64 ± 0.37 vs. 0.95 ± 0.55), and soft tissue structures (-0.47 ± 0.90 vs. 0.33 ± 1.24) and cochlea (-0.47 ± 0.80 vs. 0.38 ± 0.62) in inner ear.

CONCLUSIONS

Si-PCCT exhibited mean improvements in task-specific perceptual performance over ECT for most clinical tasks considered in this study, with statistically significant improvement for 6/20 tasks. The perceptual performance of Si-PCCT is expected to improve further with availability of spectral information and reconstruction kernels optimized for high resolution provided by smaller pixel size of Si-PCCT. The outcomes of this study indicate the positive potential of Si-PCCT for benefiting routine clinical practice through improved image quality and visualization capabilities.

Development and Application of a Virtual Imaging Trial Framework for Longitudinal Quantification of Emphysema in CT

Pulmonary emphysema is a progressive lung disease that requires accurate evaluation for optimal management. This task, possible using quantitative CT, is particularly challenging as scanner and patient attributes change over time, negatively impacting the CT-derived quantitative measures. Efforts to minimize such variations have been limited by the absence of ground truth in clinical data, thus necessitating reliance on clinical surrogates, which may not have one-to-one correspondence to CT-based findings. This study aimed to develop the first suite of human models with emphysema at multiple time points, enabling longitudinal assessment of disease progression with access to ground truth. A total of 14 virtual subjects were modeled across three time points. Each human model was virtually imaged using a validated imaging simulator (DukeSim), modeling an energy-integrating CT scanner. The models were scanned at two dose levels and reconstructed with two reconstruction kernels, slice thicknesses, and pixel sizes. The developed longitudinal models were further utilized to demonstrate utility in algorithm testing and development. Two previously developed image processing algorithms (CT-HARMONICA, EmphysemaSeg) were evaluated. The results demonstrated the efficacy of both algorithms in improving the accuracy and precision of longitudinal quantifications, from 6.1±6.3% to 1.1±1.1% and 1.6±2.2% across years 0-5. Further investigation in EmphysemaSeg identified that baseline emphysema severity, defined as >5% emphysema at year 0, contributed to its reduced performance. This finding highlights the value of virtual imaging trials in enhancing the explainability of algorithms. Overall, the developed longitudinal human models enabled ground-truth based assessment of image processing algorithms for lung quantifications.

Technical note: Towards more realistic 4DCT(MRI) numerical lung phantoms

BACKGROUND

Numerical 4D phantoms, together with associated ground truth motion, offer a flexible and comprehensive data set for realistic simulations in radiotherapy and radiology in target sites affected by respiratory motion.

PURPOSE

We present an openly available upgrade to previously reported methods for generating realistic 4DCT lung numerical phantoms, which now incorporate respiratory ribcage motion and improved lung density representation throughout the breathing cycle.

METHODS

Density information of reference CTs, toget her with motion from multiple breathing cycle 4DMRIs have been combined to generate synthetic 4DCTs (4DCT(MRI)s). Inter-subject correspondence between the CT and MRI anatomy was first established via deformable image registration (DIR) of binary masks of the lungs and ribcage. Ribcage and lung motions were extracted independently from the 4DMRIs using DIR and applied to the corresponding locations in the CT after post-processing to preserve sliding organ motion. In addition, based on the Jacobian determinant of the resulting deformation vector fields, lung densities were scaled on a voxel-wise basis to more accurately represent changes in local lung density. For validating this process, synthetic 4DCTs, referred to as 4DCT(CT)s, were compared to the originating 4DCTs using motion extracted from the latter, and the dosimetric impact of the new features of ribcage motion and density correction were analyzed using pencil beam scanned proton 4D dose calculations.

RESULTS

Lung density scaling led to a reduction of maximum mean lung Hounsfield units (HU) differences from 45 to 12 HU when comparing simulated 4DCT(CT)s to their originating 4DCTs. Comparing 4D dose distributions calculated on the enhanced 4DCT(CT)s to those on the original 4DCTs yielded 2%/2 mm gamma pass rates above 97% with an average improvement of 1.4% compared to previously reported phantoms.

CONCLUSIONS

A previously reported 4DCT(MRI) workflow has been successfully improved and the resulting numerical phantoms exhibit more accurate lung density representations and realistic ribcage motion.

Surface-based anthropomorphic bone structures for use in high-resolution simulated medical imaging

Objective.Virtual imaging trials enable efficient assessment and optimization of medical image devices and techniques via simulation rather than physical studies. These studies require realistic, detailed ground-truth models or phantoms of the relevant anatomy or physiology. Anatomical structures within computational phantoms are typically based on medical imaging data; however, for small and intricate structures (e.g. trabecular bone), it is not reasonable to use existing clinical data as the spatial resolution of the scans is insufficient. In this study, we develop a mathematical method to generate arbitrary-resolution bone structures within virtual patient models (XCAT phantoms) to model the appearance of CT-imaged trabecular bone.Approach. Given surface definitions of a bone, an algorithm was implemented to generate stochastic bicontinuous microstructures to form a network to define the trabecular bone structure with geometric and topological properties indicative of the bone. For an example adult male XCAT phantom (50th percentile in height and weight), the method was used to generate the trabecular structure of 46 chest bones. The produced models were validated in comparison with published properties of bones. The utility of the method was demonstrated with pilot CT and photon-counting CT simulations performed using the accurate DukeSim CT simulator on the XCAT phantom containing the detailed bone models.Main results. The method successfully generated the inner trabecular structure for the different bones of the chest, having quantiative measures similar to published values. The pilot simulations showed the ability of photon-counting CT to better resolve the trabecular detail emphasizing the necessity for high-resolution bone models.Significance.As demonstrated, the developed tools have great potential to provide ground truth simulations to access the ability of existing and emerging CT imaging technology to provide quantitative information about bone structures.

Development and validation of a 3D anthropomorphic phantom for dental CBCT imaging research

BACKGROUND

Optimization of dental cone beam computed tomography (CBCT) imaging is still in a preliminary stage and should be addressed using task-based methods. Dedicated models containing relevant clinical tasks for image quality studies have yet to be developed.

PURPOSE

To present a methodology to develop and validate a virtual adult anthropomorphic voxel phantom for use in task-based image quality optimization studies in dental CBCT imaging research, focusing on root fracture (RF) detection tasks in the presence of metal artefacts.

METHODS

The phantom was developed from a CBCT scan with an isotropic voxel size of 0.2 mm, from which the main dental structures, mandible and maxilla were segmented. The missing large anatomical structures, including the spine, skull and remaining soft tissues, were segmented from a lower resolution full skull scan. Anatomical abnormalities were absent in the areas of interest. Fine detailed dental structures, that could not be segmented due to the limited resolution and noise in the clinical data, were modelled using a-priori anatomical knowledge. Model resolution of the teeth was therefore increased to 0.05 mm. Models of RFs as well as dental restorations to create the artefacts, were developed, and could be inserted in the phantom in any desired configuration. Simulated CBCT images of the models were generated using a newly developed multi-resolution simulation framework that incorporated the geometry, beam quality, noise and spatial resolution characteristics of a real dental CBCT scanner. Ray-tracing and Monte Carlo techniques were used to create the projection images, which were reconstructed using the classical FDK algorithm. Validation of the models was assessed by measurements of different tooth lengths, the pulp volume and the mandible, and comparison with reference values. Additionally, the simulated images were used in a reader study in which two oral radiologists had to score the realism level of the model's normal anatomy, as well as the modelled RFs and restorations.

RESULTS

A model of an adult head, as well as models of RFs and different types of dental restorations were created. Anatomical measurements were consistent with ranges reported in literature. For the tooth length measurements, the deviations from the mean reference values were less than 20%. In 77% of all the measurements, the deviations were within 10.1%. The pulp volumes, and mandible measurements were within one standard deviation of the reference values. Regarding the normal anatomy, both readers considered the realism level of the dental structures to be good. Background structures received a lower realism score due to the lack of detailed enough trabecular bone structure, which was expected but not the focus of this study. All modelled RFs were scored at least adequate by at least one of the readers, both in appearance and position. The realism level of the modelled restorations was considered to be good.

CONCLUSIONS

A methodology was proposed to develop and validate an anthropomorphic voxel phantom for image quality optimization studies in dental CBCT imaging, with a main focus on RF detection tasks. The methodology can be extended further to create more models representative of the clinical population.

In silico simulation of hepatic arteries: An open-source algorithm for efficient synthetic data generation

BACKGROUND

In silico testing of novel image reconstruction and quantitative algorithms designed for interventional imaging requires realistic high-resolution modeling of arterial trees with contrast dynamics. Furthermore, data synthesis for training of deep learning algorithms requires that an arterial tree generation algorithm be computationally efficient and sufficiently random.

PURPOSE

The purpose of this paper is to provide a method for anatomically and physiologically motivated, computationally efficient, random hepatic arterial tree generation.

METHODS

The vessel generation algorithm uses a constrained constructive optimization approach with a volume minimization-based cost function. The optimization is constrained by the Couinaud liver classification system to assure a main feeding artery to each Couinaud segment. An intersection check is included to guarantee non-intersecting vasculature and cubic polynomial fits are used to optimize bifurcation angles and to generate smoothly curved segments. Furthermore, an approach to simulate contrast dynamics and respiratory and cardiac motion is also presented.

RESULTS

The proposed algorithm can generate a synthetic hepatic arterial tree with 40 000 branches in 11 s. The high-resolution arterial trees have realistic morphological features such as branching angles (MAD with Murray's law= 1.2 ± 1 . 2 o $ = \;1.2 \pm {1.2^o}$ ), radii (median Murray deviation= 0.08 $ = \;0.08$ ), and smoothly curved, non-intersecting vessels. Furthermore, the algorithm assures a main feeding artery to each Couinaud segment and is random (variability = 0.98 ± 0.01).

CONCLUSIONS

This method facilitates the generation of large datasets of high-resolution, unique hepatic angiograms for the training of deep learning algorithms and initial testing of novel 3D reconstruction and quantitative algorithms designed for interventional imaging.

Can Photon-Counting CT Improve Estimation Accuracy of Morphological Radiomics Features? A Simulation Study for Assessing the Quantitative Benefits from Improved Spatial Resolution in Deep Silicon-Based Photon-Counting CT

RATIONALE AND OBJECTIVES

Deep silicon-based photon-counting CT (Si-PCCT) is an emerging detector technology that provides improved spatial resolution by virtue of its reduced pixel sizes. This article reports the outcomes of the first simulation study evaluating the impact of this advantage over energy-integrating CT (ECT) for estimation of morphological radiomics features in lung lesions.

MATERIALS AND METHODS

A dynamic nutrient-access-based stochastic model was utilized to generate three distinct morphologies for lung lesions. The lesions were inserted into the lung parenchyma of an anthropomorphic phantom (XCAT - 50th percentile BMI) at 50, 70, and 90 mm from isocenter. The phantom was virtually imaged with an imaging simulator (DukeSim) modeling a Si-PCCT and a conventional ECT system using varying imaging conditions (dose, reconstruction kernel, and pixel size). The imaged lesions were segmented using a commercial segmentation tool (AutoContour, Advantage Workstation Server 3.2, GE Healthcare) followed by extraction of morphological radiomics features using an open-source radiomics package (pyradiomics). The estimation errors for both systems were computed as percent differences from corresponding feature values estimated for the ground-truth lesions.

RESULTS

Compared to ECT, the mean estimation error was lower for Si-PCCT (independent features: 35.9% vs. 54.0%, all features: 54.5% vs. 68.1%) with statistically significant reductions in errors for 8/14 features. For both systems, the estimation accuracy was minimally affected by dose and distance from the isocenter while reconstruction kernel and pixel size were observed to have a relatively stronger effect.

CONCLUSION

For all lesions and imaging conditions considered, Si-PCCT exhibited improved estimation accuracy for morphological radiomics features over a conventional ECT system, demonstrating the potential of this technology for improved quantitative imaging.

Emphysema Quantifications With CT Scan: Assessing the Effects of Acquisition Protocols and Imaging Parameters Using Virtual Imaging Trials

BACKGROUND

CT scan has notable potential to quantify the severity and progression of emphysema in patients. Such quantification should ideally reflect the true attributes and pathologic conditions of subjects, not scanner parameters. To achieve such an objective, the effects of the scanner conditions need to be understood so the influence can be mitigated.

RESEARCH QUESTION

How do CT scan imaging parameters affect the accuracy of emphysema-based quantifications and biomarkers?

STUDY DESIGN AND METHODS

Twenty anthropomorphic digital phantoms were developed with diverse anatomic attributes and emphysema abnormalities informed by a real COPD cohort. The phantoms were input to a validated CT scan simulator (DukeSim), modeling a commercial scanner (Siemens Flash). Virtual images were acquired under various clinical conditions of dose levels, tube current modulations (TCM), and reconstruction techniques and kernels. The images were analyzed to evaluate the effects of imaging parameters on the accuracy of density-based quantifications (percent of lung voxels with HU < -950 [LAA-950] and 15th percentile of lung histogram HU [Perc15]) across varied subjects. Paired t tests were performed to explore statistical differences between any two imaging conditions.

RESULTS

The most accurate imaging condition corresponded to the highest acquired dose (100 mAs) and iterative reconstruction (SAFIRE) with the smooth kernel of I31, where the measurement errors (difference between measurement and ground truth) were 35 ± 3 Hounsfield Units (HU), -4% ± 5%, and 26 ± 10 HU (average ± SD), for the mean lung HU, LAA-950, and Perc15, respectively. Without TCM and at the I31 kernel, increase of dose (20 to 100 mAs) improved the lung mean absolute error (MAE) by 4.2 ± 2.3 HU (average ± SD). TCM did not contribute to a systematic improvement of lung MAE.

INTERPRETATION

The results highlight that although CT scan quantification is possible, its reliability is impacted by the choice of imaging parameters. The developed virtual imaging trial platform in this study enables comprehensive evaluation of CT scan methods in reliable quantifications, an effort that cannot be readily made with patient images or simplistic physical phantoms.

Development and Application of a Virtual Imaging Trial framework for Airway Quantifications via CT

Chronic obstructive pulmonary disease (COPD) is one of the top three causes of death worldwide, characterized by emphysema and bronchitis. Airway measurements reflect the severity of bronchitis and other airway-related diseases. Airway structures can be objectively evaluated with quantitative computed tomography (CT). The accuracy of such quantifications is limited by the spatial resolution and image noise characteristics of the imaging system and can be potentially improved with the emerging photon-counting CT (PCCT) technology. This study evaluated the quantitative performance of PCCT against energy-integrating CT (EICT) systems for airway measurements, and further identified optimum CT imaging parameters for such quantifications. The study was performed using a novel virtual imaging framework by developing the first library of virtual patients with bronchitis. These virtual patients were developed based on CT images of confirmed COPD patients with varied bronchitis severity. The human models were virtually imaged at 6.3 and 12.6 mGy dose levels using a scanner-specific simulator (DukeSim), synthesizing clinical PCCT and EICT scanners (NAEOTOM Alpha, FLASH, Siemens). The projections were reconstructed with two algorithms and kernels at different matrix sizes and slice thicknesses. The CT images were used to quantify clinically relevant airway measurements ("Pi10" and "WA%") and compared against their ground truth values. Compared to EICT, PCCT provided more accurate Pi10 and WA% measurements by 63.1% and 68.2%, respectively. For both technologies, sharper kernels and larger matrix sizes led to more reliable bronchitis quantifications. This study highlights the potential advantages of PCCT against EICT in characterizing bronchitis utilizing a virtual imaging platform.

Segars W, Samei E, M. Hertz H. Phase-contrast virtual chest radiography

Respiratory X-ray imaging enhanced by phase contrast has shown improved airway visualization in animal models. Limitations in current X-ray technology have nevertheless hindered clinical translation, leaving the potential clinical impact an open question. Here, we explore phase-contrast chest radiography in a realistic in silico framework. Specifically, we use preprocessed virtual patients to generate in silico chest radiographs by Fresnel-diffraction simulations of X-ray wave propagation. Following a reader study conducted with clinical radiologists, we predict that phase-contrast edge enhancement will have a negligible impact on improving solitary pulmonary nodule detection (6 to 20 mm). However, edge enhancement of bronchial walls visualizes small airways (< 2 mm), which are invisible in conventional radiography. Our results show that phase-contrast chest radiography could play a future role in observing small-airway obstruction (e.g., relevant for asthma or early-stage chronic obstructive pulmonary disease), which cannot be directly visualized using current clinical methods, thereby motivating the experimental development needed for clinical translation. Finally, we discuss quantitative requirements on distances and X-ray source/detector specifications for clinical implementation of phase-contrast chest radiography.

Anatomically and physiologically informed computational model of hepatic contrast perfusion for virtual imaging trials

PURPOSE

Virtual (in silico) imaging trials (VITs), involving computerized phantoms and models of the imaging process, provide a modern alternative to clinical imaging trials. VITs are faster, safer, and enable otherwise-impossible investigations. Current phantoms used in VITs are limited in their ability to model functional behavior such as contrast perfusion which is an important determinant of dose and image quality in CT imaging. In our prior work with the XCAT computational phantoms, we determined and modeled inter-organ (organ to organ) intravenous contrast concentration as a function of time from injection. However, intra-organ concentration, heterogeneous distribution within a given organ, was not pursued. We extend our methods in this work to model intra-organ concentration within the XCAT phantom with a specific focus on the liver.

METHODS

Intra-organ contrast perfusion depends on the organ's vessel network. We modeled the intricate vascular structures of the liver, informed by empirical and theoretical observations of anatomy and physiology. The developed vessel generation algorithm modeled a dual-input-single-output vascular network as a series of bifurcating surfaces to optimally deliver flow within the bounding surface of a given XCAT liver. Using this network, contrast perfusion was simulated within voxelized versions of the phantom by using knowledge of the blood velocities in each vascular structure, vessel diameters and length, and the time since the contrast entered the hepatic artery. The utility of the enhanced phantom was demonstrated through a simulation study with the phantom voxelized prior to CT simulation with the relevant liver vasculature prepared to represent blood and iodinated contrast media. The spatial extent of the blood-contrast mixture was compared to clinical data.

RESULTS

The vascular structures of the liver were generated with size and orientation which resulted in minimal energy expenditure required to maintain blood flow. Intravenous contrast was simulated as having known concentration and known total volume in the liver as calibrated from time-concentration curves. Measurements of simulated CT ROIs were found to agree with clinically observed values of early arterial phase contrast enhancement of the parenchyma (∼ 5 $ \sim 5$ HU). Similarly, early enhancement in the hepatic artery was found to agree with average clinical enhancement( 180 $(180$ HU).

CONCLUSIONS

The computational methods presented here furthered the development of the XCAT phantoms allowing for multi-timepoint contrast perfusion simulations, enabling more anthropomorphic virtual clinical trials intended for optimization of current clinical imaging technologies and applications.

Synthetic 4DCT(MRI) lung phantom generation for 4D radiotherapy and image guidance investigations

Purpose

Respiratory motion is one of the major challenges in radiotherapy. In this work, a comprehensive and clinically plausible set of 4D numerical phantoms, together with their corresponding “ground truths,” have been developed and validated for 4D radiotherapy applications.

Methods

The phantoms are based on CTs providing density information and motion from multi‐breathing‐cycle 4D Magnetic Resonance imagings (MRIs). Deformable image registration (DIR) has been utilized to extract motion fields from 4DMRIs and to establish inter‐subject correspondence by registering binary lung masks between Computer Tomography (CT) and MRI. The established correspondence is then used to warp the CT according to the 4DMRI motion. The resulting synthetic 4DCTs are called 4DCT(MRI)s. Validation of the 4DCT(MRI) workflow was conducted by directly comparing conventional 4DCTs to derived synthetic 4D images using the motion of the 4DCTs themselves (referred to as 4DCT(CT)s). Digitally reconstructed radiographs (DRRs) as well as 4D pencil beam scanned (PBS) proton dose calculations were used for validation.

Results

Based on the CT image appearance of 13 lung cancer patients and deformable motion of five volunteer 4DMRIs, synthetic 4DCT(MRI)s with a total of 871 different breathing cycles have been generated. The 4DCT(MRI)s exhibit an average superior–inferior tumor motion amplitude of 7 ± 5 mm (min: 0.5 mm, max: 22.7 mm). The relative change of the DRR image intensities of the conventional 4DCTs and the corresponding synthetic 4DCT(CT)s inside the body is smaller than 5% for at least 81% of the pixels for all studied cases. Comparison of 4D dose distributions calculated on 4DCTs and the synthetic 4DCT(CT)s using the same motion achieved similar dose distributions with an average 2%/2 mm gamma pass rate of 90.8% (min: 77.8%, max: 97.2%).

Conclusion

We developed a series of numerical 4D lung phantoms based on real imaging and motion data, which give realistic representations of both anatomy and motion scenarios and the accessible “ground truth” deformation vector fields of each 4DCT(MRI). The open‐source code and motion data allow foreseen users to generate further 4D data by themselves. These numeric 4D phantoms can be used for the development of new 4D treatment strategies, 4D dose calculations, DIR algorithm validations, as well as simulations of motion mitigation and different online image guidance techniques for both proton and photon radiation therapy.

A truth-based primal-dual learning approach to reconstruct CT images utilizing the virtual imaging trial platform

Inherent to Computed tomography (CT) is image reconstruction, constructing 3D voxel values from noisy projection data. Modeling this inverse operation is not straightforward. Given the ill-posed nature of inverse problem in CT reconstruction, data-driven methods need regularization to enhance the accuracy of the reconstructed images. Besides, generalization of the results hinges upon the availability of large training datasets with access to ground truth. This paper offers a new strategy to reconstruct CT images with the advantage of ground truth accessible through a virtual imaging trial (VIT) platform. A learned primal-dual deep neural network (LPD-DNN) employed the forward model and its adjoint as a surrogate of the imaging's geometry and physics. VIT offered simulated CT projections paired with ground truth labels from anthropomorphic human models without image noise and resolution degradation. The models included a library of anthropomorphic, computational patient models (XCAT). The DukeSim simulator was utilized to form realistic projection data emulating the impact of the physics and geometry of a commercial-equivalent CT scanner. The resultant noisy sinogram data associated with each slice was thus generated for training. Corresponding linear attenuation coefficients of phantoms' materials at the effective energy of the x-ray spectrum were used as the ground truth labels. The LPD-DNN was deployed to learn the complex operators and hyper-parameters in the proximal primal-dual optimization. The obtained validation results showed a 12% normalized root mean square error with respect to the ground truth labels, a peak signal-to-noise ratio of 32 dB, a signal-to-noise ratio of 1.5, and a structural similarity index of 96%. These results were highly favorable compared to standard filtered-back projection reconstruction (65%, 17 dB, 1.0, 26%).

Development and Clinical Applications of a Virtual Imaging Framework for Optimizing Photon-counting CT

The purpose of this study was to develop a virtual imaging framework that simulates a new photon-counting CT (PCCT) system (NAEOTOM Alpha, Siemens). The PCCT simulator was built upon the DukeSim platform, which generates projection images of computational phantoms given the geometry and physics of the scanner and imaging parameters. DukeSim was adapted to account for the geometry of the PCCT prototype. To model the photon-counting detection process, we utilized a Monte Carlo-based detector model with the known properties of the detectors. We validated the simulation platform against experimental measurements. The images were acquired at four dose levels (CTDI_vol of 1.5, 3.0, 6.0, and 12.0 mGy) and reconstructed with three kernels (Br36, Br40, Br48). The experimental acquisitions were replicated using our developed simulation platform. The real and simulated images were quantitatively compared in terms of image quality metrics (HU values, noise magnitude, noise power spectrum, and modulation transfer function). The clinical utility of our framework was demonstrated by conducting two clinical applications (COPD quantifications and lung nodule radiomics). The phantoms with relevant pathologies were imaged with DukeSim modeling the PCCT systems. Different imaging parameters (e.g., dose, reconstruction techniques, pixel size, and slice thickness) were altered to investigate their effects on task-based quantifications. We successfully implemented the acquisition and physics attributes of the PCCT prototype into the DukeSim platform. The discrepancy between the real and simulated data was on average about 2 HU in terms of noise magnitude, 0.002 mm^-1 in terms of noise power spectrum peak frequency and 0.005 mm^-1 in terms of the frequency at 50% MTF. Analysis suggested that lung lesion radiomics to be more accurate with reduced pixel size and slice thickness. For COPD quantifications, higher doses, thinner slices, and softer kernels yielded more accurate quantification of density-based biomarkers. Our developed virtual imaging platform enables systematic comparison of new PCCT technologies as well as optimization of the imaging parameters for specific clinical tasks.

Photon-counting CT versus conventional CT for COPD quantifications: intra-scanner optimization and inter-scanner assessments using virtual imaging trials

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease and a major cause of death and disability worldwide. Quantitative CT is a powerful tool to better understand the heterogeneity and severity of this disease. Quantitative CT is being increasingly used in COPD research, and the recent advancements in CT technology have made it even more encouraging. One recent advancement has been the development of photon-counting detectors, offering higher spatial resolution, higher image contrast, and lower noise levels in the images. However, the quantification performance of this new technology compared to conventional scanners remains unknown. Additionally, different protocol settings (e.g., different dose levels, slice thicknesses, reconstruction kernels and algorithms) affect quantifications in an unsimilar fashion. This study investigates the potential advantages of photon-counting CT (PCCT) against conventional energy-integrating detector (EID) CT and explores the effects of protocol settings on lung density quantifications in COPD patients. This study was made possible using a virtual imaging platform, taking advantage of anthropomorphic phantoms with COPD (COPD-XCAT) and a scanner-specific CT simulator (DukeSim). Having the physical and geometrical properties of three Siemens commercial scanners (Flash, Force for EID and NAEOTOM Alpha for PCCT) modeled, we simulated CT images of ten COPD-XCAT phantoms at 0.63 and 3.17 mGy dose levels and reconstructed at three levels of kernel sharpness. The simulated CT images were quantified in terms of "Lung mean absolute error (MAE)," "LAA -950," "Perc 15," "Lung mass" imaging biomarkers and compared against the ground truth values of the phantoms. The intra-scanner assessment demonstrated the superior qualitative and quantitative performance of the PCCT scanner over the conventional scanners (21.01% and 22.74% mean lung MAE improvement, and 53.97% and 68.13% mean LAA -950 error improvement compared to Flash and Force). The results also showed that higher mAs, thinner slices, smoother kernels, and iterative reconstruction could lead to more accurate and precise quantification scores. This study underscored the qualitative and quantitative benefits of PCCT against conventional EID scanners as well as the importance of optimal protocol choice within scanners for more accurate quantifications.

An update on computational anthropomorphic anatomical models

The prevalent availability of high-performance computing coupled with validated computerized simulation platforms as open-source packages have motivated progress in the development of realistic anthropomorphic computational models of the human anatomy. The main application of these advanced tools focused on imaging physics and computational internal/external radiation dosimetry research. This paper provides an updated review of state-of-the-art developments and recent advances in the design of sophisticated computational models of the human anatomy with a particular focus on their use in radiation dosimetry calculations. The consolidation of flexible and realistic computational models with biological data and accurate radiation transport modeling tools enables the capability to produce dosimetric data reflecting actual setup in clinical setting. These simulation methodologies and results are helpful resources for the medical physics and medical imaging communities and are expected to impact the fields of medical imaging and dosimetry calculations profoundly.

A scanner-specific framework for simulating CT images with tube current modulation

Although tube current modulation (TCM) is routinely implemented in modern computed tomography (CT) scans, no existing CT simulator is capable of generating realistic images with TCM. The goal of this study was to develop such a framework to (1) facilitate patient-specific optimization of TCM parameters and (2) enable future virtual imaging trials (VITs) with more clinically realistic image quality and x-ray flux distributions. The framework was created by developing a TCM module and integrating it with an existing CT simulator (DukeSim). The developed module utilizes scanner-calibrated TCM parameters and two localizer radiographs to compute the mAs for each simulated CT projection. This simulation pipeline was validated in two parts. First, DukeSim was validated in the context of a commercial scanner with TCM (SOMATOM Force, Siemens Healthineers) by imaging a physical CT phantom (Mercury, Sun Nuclear) and its computational analogue. Second, the TCM module was validated by imaging a computational anthropomorphic phantom (ATOM, CIRS) using DukeSim with real and module-generated TCM profiles. The validation demonstrated DukeSim's realism in terms of noise magnitude, noise texture, spatial resolution, and image contrast (with average differences of 0.38%, 6.31%, 0.43%, and -9 HU, respectively). It also demonstrated the TCM module's realism in terms of projection-level mAs and resulting noise magnitude (2.86% and -2.60%, respectively). Finally, the framework was applied to a pilot VIT simulating images of three computational anthropomorphic phantoms (XCAT, with body mass indices (BMIs) of 24.3, 28.2, and 33.0) under five different TCM settings. The optimal TCM for each phantom was characterized based on various criteria, such as minimizing mAs or maximizing image quality. 'Very Weak' TCM minimized noise for the 24.3 BMI phantom, while 'Very Strong' TCM minimized noise for the 33.0 BMI phantom. This illustrates the utility of the developed framework for future optimization studies of TCM parameters and, more broadly, large-scale VITs with scanner-specific TCM.

Development and validation of an automated methodology to assess perceptual in vivo noise texture in liver CT

Purpose: Developing, validating, and evaluating a method for measuring noise texture directly from patient liver CT images (i.e., in vivo). Approach: The method identifies target regions within patient scans that are least likely to have major contribution of patient anatomy, detrends them locally, and measures noise power spectrum (NPS) there using a previously phantom-validated technique targeting perceptual noise-non-anatomical fluctuations in the image that may interfere with the detection of focal lesions. Method development and validation used scanner-specific CT simulations of computational, anthropomorphic phantom (XCAT phantom, three phases of contrast-enhancement) with known ground truth of the NPS. Simulations were based on a clinical scanner (Definition Flash, Siemens) and clinically relevant settings (tube voltage of 120 kV at three dose levels). Images were reconstructed with filtered backprojection (kernel: B31, B41, and B50) and Sinogram Affirmed Iterative Reconstruction (kernel: I31, I41, and I50) using a manufacturer-specific reconstruction software (ReconCT, Siemens). All NPS measurements were made in the liver. Ground-truth NPS were taken as the sum of (1) a measurement in parenchymal regions of anatomy-subtracted (i.e., noise only) scans, and (2) a measurement in the same region of noise-free (pre-noise-insertion) images. To assess in vivo NPS performance, correlation of NPS average frequency (f avg ), was reported. Sensitivity of accuracy [root-mean-square-error (RMSE)] to number of pixels included in measurement was conducted via bootstrapped pixel-dropout. Sensitivity of NPS to dose and reconstruction kernel was assessed to confirm that ground truth NPS similarities were maintained in patient-specific measurements. Results: Pearson and Spearman correlation coefficients 0.97 and 0.96 forf avg indicated good correlation. Results suggested accurate NPS measurements (within 5% total RMSE) could be acquired with ∼ 10 6    pixels . Conclusions: Relationships of similar NPS due to reconstruction kernel and dose were preserved between gold standard and observed in vivo estimations. The NPS estimation method was further deployed on clinical cases to demonstrate the feasibility of clinical analysis.

A GPU-accelerated framework for rapid estimation of scanner-specific scatter in CT for virtual imaging trials

Virtual imaging trials (VITs), defined as the process of conducting clinical imaging trials using computer simulations, offer a time- and cost-effective alternative to traditional imaging trials for CT. The clinical potential of VITs hinges on the realism of simulations modeling the image acquisition process, where the accurate scanner-specific simulation of scatter in a time-feasible manner poses a particular challenge. To meet this need, this study proposes, develops, and validates a rapid scatter estimation framework, based on GPU-accelerated Monte Carlo (MC) simulations and denoising methods, for estimating scatter in single source, dual-source, and photon-counting CT. A CT simulator was developed to incorporate parametric models for an anti-scatter grid and a curved energy integrating detector with an energy-dependent response. The scatter estimates from the simulator were validated using physical measurements acquired on a clinical CT system using the standard single-blocker method. The MC simulator was further extended to incorporate a pre-validated model for a PCD and an additional source-detector pair to model cross scatter in dual-source configurations. To estimate scatter with desirable levels of statistical noise using a manageable computational load, two denoising methods using a (1) convolutional neural network and an (2) optimized Gaussian filter were further deployed. The viability of this framework for clinical VITs was assessed by integrating it with a scanner-specific ray-tracer program to simulate images for an image quality (Mercury) and an anthropomorphic phantom (XCAT). The simulated scatter-to-primary ratios agreed with physical measurements within 4.4% ± 10.8% across all projection angles and kVs. The differences of ∼121 HU between images with and without scatter, signifying the importance of scatter for simulating clinical images. The denoising methods preserved the magnitudes and trends observed in the reference scatter distributions, with an averaged rRMSE value of 0.91 and 0.97 for the two methods, respectively. The execution time of ∼30 s for simulating scatter in a single projection with a desirable level of statistical noise indicates a major improvement in performance, making our tool an eligible candidate for conducting extensive VITs spanning multiple patients and scan protocols.

Pathological lung segmentation in chest CT images based on improved random walker

BACKGROUND AND OBJECTIVE

Pathological lung segmentation as a pretreatment step in the diagnosis of lung diseases has been widely explored. Because of the complexity of pathological lung structures and gray blur of the border, accurate lung segmentation in clinical 3D computed tomography images is a challenging task. In view of the current situation, the work proposes a fast and accurate pathological lung segmentation method. The following contributions have been made: First, the edge weights introduce spatial information and clustering information, so that walkers can use more image information during walking. Second, a Gaussian Distribution of seed point set is established to further expand the possibility of selection between fake seed points and real seed points. Finally, the pre-parameter is calculated using original seed points, and the final results are fitted with new seed points.

METHODS

This study proposes a segmentation method based on an improved random walker algorithm. The proposed method consists of the following steps: First, a gray value is used as the sample distribution. Gaussian mixture model is used to obtain the clustering probability of an image. Thus, the spatial distance and clustering result are added as new weights, and the new edge weights are used to construct a random walker map. Second, a large number of marked points are automatically selected, and the intermediate results are obtained from the newly constructed map and retained only as pre-parameters. When new seed points are introduced, the probability value of the walker is quickly calculated from the new parameters and pre-parameters, and the final segmentation result can be obtained.

RESULTS

The proposed method was tested on 65 sets of CT cases. Quantitative evaluation with different methods confirms the high accuracy on our dataset (98.55%) and LOLA11 dataset (97.41%). Similarly, the average segmentation time (10.5s) is faster than random walker (1,332.5s).

CONCLUSIONS

The comparison of the experimental results show that the proposed method can accurately and quickly obtain pathological lung processing results. Therefore, it has potential clinical applications.

Integrative Computational Models of Lung Structure-Function Interactions

Anatomically based integrative models of the lung and their interaction with other key components of the respiratory system provide unique capabilities for investigating both normal and abnormal lung function. There is substantial regional variability in both structure and function within the normal lung, yet it remains capable of relatively efficient gas exchange by providing close matching of air delivery (ventilation) and blood delivery (perfusion) to regions of gas exchange tissue from the scale of the whole organ to the smallest continuous gas exchange units. This is despite remarkably different mechanisms of air and blood delivery, different fluid properties, and unique scale-dependent anatomical structures through which the blood and air are transported. This inherent heterogeneity can be exacerbated in the presence of disease or when the body is under stress. Current computational power and data availability allow for the construction of sophisticated data-driven integrative models that can mimic respiratory system structure, function, and response to intervention. Computational models do not have the same technical and ethical issues that can limit experimental studies and biomedical imaging, and if they are solidly grounded in physiology and physics they facilitate investigation of the underlying interaction between mechanisms that determine respiratory function and dysfunction, and to estimate otherwise difficult-to-access measures. © 2021 American Physiological Society. Compr Physiol 11:1501-1530, 2021.

Virtual Imaging Trials for Coronavirus Disease (COVID-19)

OBJECTIVE. The virtual imaging trial is a unique framework that can greatly facilitate the assessment and optimization of imaging methods by emulating the imaging experiment using representative computational models of patients and validated imaging simulators. The purpose of this study was to show how virtual imaging trials can be adapted for imaging studies of coronavirus disease (COVID-19), enabling effective assessment and optimization of CT and radiography acquisitions and analysis tools for reliable imaging and management of COVID-19. MATERIALS AND METHODS. We developed the first computational models of patients with COVID-19 and as a proof of principle showed how they can be combined with imaging simulators for COVID-19 imaging studies. For the body habitus of the models, we used the 4D extended cardiac-torso (XCAT) model that was developed at Duke University. The morphologic features of COVID-19 abnormalities were segmented from 20 CT images of patients who had been confirmed to have COVID-19 and incorporated into XCAT models. Within a given disease area, the texture and material of the lung parenchyma in the XCAT were modified to match the properties observed in the clinical images. To show the utility, three developed COVID-19 computational phantoms were virtually imaged using a scanner-specific CT and radiography simulator. RESULTS. Subjectively, the simulated abnormalities were realistic in terms of shape and texture. Results showed that the contrast-to-noise ratios in the abnormal regions were 1.6, 3.0, and 3.6 for 5-, 25-, and 50-mAs images, respectively. CONCLUSION. The developed toolsets in this study provide the foundation for use of virtual imaging trials in effective assessment and optimization of CT and radiography acquisitions and analysis tools to help manage the COVID-19 pandemic.

Imposing implicit feasibility constraints on deformable image registration using a statistical generative model

Purpose: Deformable registration problems are conventionally posed in a regularized optimization framework, where balance between fidelity and prescribed regularization usually needs to be tuned for each case. Even so, using a single weight to control regularization strength may be insufficient to reflect spatially variant tissue properties and limit registration performance. In this study, we proposed to incorporate a spatially variant deformation prior into image registration framework using a statistical generative model. Approach: A generator network is trained in an unsupervised setting to maximize the likelihood of observing the moving and fixed image pairs, using an alternating back-propagation approach. The trained model imposes constraints on deformation and serves as an effective low-dimensional deformation parametrization. During registration, optimization is performed over this learned parametrization, eliminating the need for explicit regularization and tuning. The proposed method was tested against SimpleElastix, DIRNet, and Voxelmorph. Results: Experiments with synthetic images and simulated CTs showed that our method yielded registration errors significantly lower than SimpleElastix and DIRNet. Experiments with cardiac magnetic resonance images showed that the method encouraged physical and physiological feasibility of deformation. Evaluation with left ventricle contours showed that our method achieved a dice of ( 0.93 ± 0.03 ) with significant improvement over all SimpleElastix options, DIRNet, and VoxelMorph. Mean average surface distance was on millimeter level, comparable to the best SimpleElastix setting. The average 3D registration time was 12.78 s, faster than 24.70 s in SimpleElastix. Conclusions: The learned implicit parametrization could be an efficacious alternative to regularized B-spline model, more flexible in admitting spatial heterogeneity.

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

Virtual clinical trial for quantifying the effects of beam collimation and pitch on image quality in computed tomography

Purpose: To utilize a virtual clinical trial (VCT) construct to investigate the effects of beam collimation and pitch on image quality (IQ) in computed tomography (CT) under different respiratory and cardiac motion rates. Approach: A computational human model [extended cardiac-torso (XCAT) phantom] with added lung lesions was used to simulate seven different rates of cardiac and respiratory motions. A validated CT simulator (DukeSim) was used in this study. A supplemental validation was done to ensure the accuracy of DukeSim across different pitches and beam collimations. Each XCAT phantom was imaged using the CT simulator at multiple pitches (0.5 to 1.5) and beam collimations (19.2 to 57.6 mm) at a constant dose level. The images were compared against the ground truth using three task-generic IQ metrics in the lungs. Additionally, the bias and variability in radiomics (morphological) feature measurements were quantified for task-specific lung lesion quantification across the studied imaging conditions. Results: All task-generic metrics degraded by 1.6% to 13.3% with increasing pitch. When imaged with motion, increasing pitch reduced motion artifacts. The IQ slightly degraded (1.3%) with changes in the studied beam collimations. Patient motion exhibited negative effects (within 7%) on the IQ. Among all features across all imaging conditions studies, compactness2 and elongation showed the largest ( - 26.5 % , 7.8%) and smallest ( - 0.8 % , 2.7%) relative bias and variability. The radiomics results were robust across the motion profiles studied. Conclusions: While high pitch and large beam collimations can negatively affect the quality of CT images, they are desirable for fast imaging. Further, our results showed no major adverse effects in morphology quantification of lung lesions with the increase in pitch or beam collimation. VCTs, such as the one demonstrated in this study, represent a viable methodology for experiments in CT.

Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN)

Develop a machine learning-based method to generate multi-contrast anatomical textures in the 4D extended cardiac-torso (XCAT) phantom for more realistic imaging simulations. As a pilot study, we synthesize CT and CBCT textures in the chest region. For training purposes, major organs and gross tumor volumes (GTVs) in chest region were segmented from real patient images and assigned to different HU values to generate organ maps, which resemble the XCAT images. A dual-discriminator conditional-generative adversarial network (D-CGAN) was developed to synthesize anatomical textures in the corresponding organ maps. The D-CGAN was uniquely designed with two discriminators, one trained for the body and the other for the tumor. Various XCAT phantoms were input to the D-CGAN to generate textured XCAT phantoms. The D-CGAN model was trained separately using 62 CT and 63 CBCT images from lung SBRT patients to generate multi-contrast textured XCAT (MT-XCAT). The MT-XCAT phantoms were evaluated by comparing the intensity histograms and radiomic features with those from real patient images using Wilcoxon rank-sum test. The visual examination demonstrated that the MT-XCAT phantoms presented similar general contrast and anatomical textures as CT and CBCT images. The mean HU of the MT-XCAT-CT and MT-XCAT-CBCT were [Formula: see text] and [Formula: see text], compared with that of real CT ([Formula: see text]) and CBCT ([Formula: see text]). The majority of radiomic features from the MT-XCAT phantoms followed the same distribution as the real images according to the Wilcoxon rank-sum test, except for limited second-order features. The study demonstrated the feasibility of generating realistic MT-XCAT phantoms using D-CGAN. The MT-XCAT phantoms can be further expanded to include other modalities (MRI, PET, ultrasound, etc) under the same scheme. This crucial development greatly enhances the value of the phantom for various clinical applications, including testing and optimizing novel imaging techniques, validation of radiomics analysis methods, and virtual clinical trials.

Considerations for Using the Vasculature as a Coordinate System to Map All the Cells in the Human Body

Several ongoing international efforts are developing methods of localizing single cells within organs or mapping the entire human body at the single cell level, including the Chan Zuckerberg Initiative's Human Cell Atlas (HCA), and the Knut and Allice Wallenberg Foundation's Human Protein Atlas (HPA), and the National Institutes of Health's Human BioMolecular Atlas Program (HuBMAP). Their goals are to understand cell specialization, interactions, spatial organization in their natural context, and ultimately the function of every cell within the body. In the same way that the Human Genome Project had to assemble sequence data from different people to construct a complete sequence, multiple centers around the world are collecting tissue specimens from diverse populations that vary in age, race, sex, and body size. A challenge will be combining these heterogeneous tissue samples into a 3D reference map that will enable multiscale, multidimensional Google Maps-like exploration of the human body. Key to making alignment of tissue samples work is identifying and using a coordinate system called a Common Coordinate Framework (CCF), which defines the positions, or "addresses," in a reference body, from whole organs down to functional tissue units and individual cells. In this perspective, we examine the concept of a CCF based on the vasculature and describe why it would be an attractive choice for mapping the human body.

Development of a scanner-specific simulation framework for photon-counting computed tomography

The aim of this study was to develop and validate a simulation platform that generates photon-counting CT images of voxelized phantoms with detailed modeling of manufacturer-specific components including the geometry and physics of the x-ray source, source filtrations, anti-scatter grids, and photon-counting detectors. The simulator generates projection images accounting for both primary and scattered photons using a computational phantom, scanner configuration, and imaging settings. Beam hardening artifacts are corrected using a spectrum and threshold dependent water correction algorithm. Physical and computational versions of a clinical phantom (ACR) were used for validation purposes. The physical phantom was imaged using a research prototype photon-counting CT (Siemens Healthcare) with standard (macro) mode, at four dose levels and with two energy thresholds. The computational phantom was imaged with the developed simulator with the same parameters and settings used in the actual acquisition. Images from both the real and simulated acquisitions were reconstructed using a reconstruction software (FreeCT). Primary image quality metrics such as noise magnitude, noise ratio, noise correlation coefficients, noise power spectrum, CT number, in-plane modulation transfer function, and slice sensitivity profiles were extracted from both real and simulated data and compared. The simulator was further evaluated for imaging contrast materials (bismuth, iodine, and gadolinium) at three concentration levels and six energy thresholds. Qualitatively, the simulated images showed similar appearance to the real ones. Quantitatively, the average relative error in image quality measurements were all less than 4% across all the measurements. The developed simulator will enable systematic optimization and evaluation of the emerging photon-counting computed tomography technology.

DukeSim: A Realistic, Rapid, and Scanner-Specific Simulation Framework in Computed Tomography

The purpose of this study was to develop a CT simulation platform that is: 1) compatible with voxel-based computational phantoms; 2) capable of modeling the geometry and physics of commercial CT scanners; and 3) computationally efficient. Such a simulation platform is designed to enable the virtual evaluation and optimization of CT protocols and parameters for achieving a targeted image quality while reducing radiation dose. Given a voxelized computational phantom and a parameter file describing the desired scanner and protocol, the developed platform DukeSim calculates projection images using a combination of ray-tracing and Monte Carlo techniques. DukeSim includes detailed models for the detector quantum efficiency, quantum and electronic noise, detector crosstalk, subsampling of the detector and focal spot areas, focal spot wobbling, and the bowtie filter. DukeSim was accelerated using GPU computing. The platform was validated using physical and computational versions of a phantom (Mercury phantom). Clinical and simulated CT scans of the phantom were acquired at multiple dose levels using a commercial CT scanner (Somatom Definition Flash; Siemens Healthcare). The real and simulated images were compared in terms of image contrast, noise magnitude, noise texture, and spatial resolution. The relative error between the clinical and simulated images was less than 1.4%, 0.5%, 2.6%, and 3%, for image contrast, noise magnitude, noise texture, and spatial resolution, respectively, demonstrating the high realism of DukeSim. The runtime, dependent on the imaging task and the hardware, was approximately 2-3 minutes per rotation in our study using a computer with 4 GPUs. DukeSim, when combined with realistic human phantoms, provides the necessary toolset with which to perform large-scale and realistic virtual clinical trials in a patient and scanner-specific manner.

Accuracy of registration algorithms in subtraction CT of the lungs: A digital phantom study

Purpose

The purpose of this study was to assess, using an anthropomorphic digital phantom, the accuracy of algorithms in registering precontrast and contrast‐enhanced computed tomography (CT) chest images for generation of iodine maps of the pulmonary parenchyma via temporal subtraction.

Materials and methods

The XCAT phantom, with enhanced airway and pulmonary vessel structures, was used to simulate precontrast and contrast‐enhanced chest images at various inspiration levels and added CT simulation for realistic system noise. Differences in diaphragm position were varied between 0 and 20 mm, with the maximum chosen to exceed the 95th percentile found in a dataset of 100 clinical subtraction CTs. In addition, the influence of whole body movement, degree of iodine enhancement, beam hardening artifacts, presence of nodules and perfusion defects in the pulmonary parenchyma, and variation in noise on the registration were also investigated. Registration was performed using three lung registration algorithms — a commercial (algorithm A) and a prototype (algorithm B) version from Canon Medical Systems and an algorithm from the MEVIS Fraunhofer institute (algorithm C). For each algorithm, we calculated the voxel‐by‐voxel difference between the true deformation and the algorithm‐estimated deformation in the lungs.

Results

The median absolute residual error for all three algorithms was smaller than the voxel size (1.0 × 1.0 × 1.0 mm³) for up to an 8 mm diaphragm difference, which is the average difference in diaphragm levels found clinically, and increased with increasing difference in diaphragm position. At 20 mm diaphragm displacement, the median absolute residual error after registration was 0.85 mm (interquartile range, 0.51–1.47 mm) for algorithm A, 0.82 mm (0.50–1.40 mm) for algorithm B, and 0.91 mm (0.54–1.52 mm) for algorithm C. The largest errors were seen in the paracardiac regions and close to the diaphragm. The impact of all other evaluated conditions on the residual error varied, resulting in an increase in the median residual error lower than 0.1 mm for all algorithms, except in the case of whole body displacements for algorithm B, and with increased noise for algorithm C.

Conclusion

Motion correction software can compensate for respiratory and cardiac motion with a median residual error below 1 mm, which was smaller than the voxel size, with small differences among the tested registration algorithms for different conditions. Perfusion defects above 50 mm will be visible with the commercially available subtraction CT software, even in poorly registered areas, where the median residual error in that area was 7.7 mm.

Advances in Computational Human Phantoms and Their Applications in Biomedical Engineering - A Topical Review

Over the past decades, significant improvements have been made in the field of computational human phantoms (CHPs) and their applications in biomedical engineering. Their sophistication has dramatically increased. The very first CHPs were composed of simple geometric volumes, e.g., cylinders and spheres, while current CHPs have a high resolution, cover a substantial range of the patient population, have high anatomical accuracy, are poseable, morphable, and are augmented with various details to perform functionalized computations. Advances in imaging techniques and semi-automated segmentation tools allow fast and personalized development of CHPs. These advances open the door to quickly develop personalized CHPs, inherently including the disease of the patient. Because many of these CHPs are increasingly providing data for regulatory submissions of various medical devices, the validity, anatomical accuracy, and availability to cover the entire patient population is of utmost importance. The article is organized into two main sections: the first section reviews the different modeling techniques used to create CHPs, whereas the second section discusses various applications of CHPs in biomedical engineering. Each topic gives an overview, a brief history, recent developments, and an outlook into the future.

Modeling "Textured" Bones in Virtual Human Phantoms

The purpose of this study was to develop detailed and realistic models of the cortical and trabecular bones in the spine, ribs, and sternum and incorporate them into the library of virtual human phantoms (XCAT). Cortical bone was modeled by 3D morphological erosion of XCAT homogenously defined bones with an average thickness measured from the CT dataset upon which each individual XCAT phantom was based. The trabecular texture was modeled using a power law synthesis algorithm where the parameters were tuned using high-resolution anatomical images of the Human Visible Female. The synthesized bone textures were added into the XCAT phantoms. To qualitatively evaluate the improved realism of the bone modeling, CT simulations of the XCAT phantoms were acquired with and without the textured bone modeling. The 3D power spectrum of the anatomical images exhibited a power law behavior (R² = 0.84), as expected in fractal and porous textures. The proposed texture synthesis algorithm was able to synthesize textures emulating real anatomical images, with the simulated CT images with the prototyped bones were more realistic than those simulated with the original XCAT models. Incorporating intra-organ structures, the "textured" phantoms are envisioned to be used to conduct virtual clinical trials in the context of medical imaging in cases where the actual trials are infeasible due to the lack of ground truth, cost, or potential risks to the patients.