Multivariate signal-to-noise ratio as a metric for characterizing spectral computed tomography.

Objective.With the introduction of spectral CT techniques into the clinic, the imaging capacities of CT were expanded to multiple energy levels. Due to a variety of factors, the acquired signal in spectral CT datasets is shared between these images. Conventional image quality metrics assume independence between images which is not preserved within spectral CT datasets, limiting their utility for characterizing energy selective images. The purpose of this work was to develop a metrology to characterize energy selective images by incorporating the shared information between images within a spectral CT dataset.Approach.The signal-to-noise ratio (SNR) was extended into a multivariate space where each image within a spectral CT dataset was treated as a separate information channel. The general definition was applied to the specific case of contrast to define a multivariate contrast-to-noise ratio (CNR). The matrix contained two types of terms: a conventional CNR term which characterized image quality within each image in the spectral CT dataset and covariance weighted CNR (Covar-CNR) which characterized the contrast in each image relative to the covariance between images. Experimental data from an investigational photon-counting CT scanner was used to demonstrate the insight of this metrology. A cylindrical water phantom containing vials of iodine and gadolinium (2, 4, and 8 mg ml-1) was imaged under conditions of variable tube current, tube voltage, and energy threshold. Two image series (threshold and bin images) containing two images each were defined based upon the contribution of photons to reconstructed images. Analysis of variance (ANOVA) was calculated between CNR terms and image acquisition variables. A multivariate regression was then fitted to experimental data.Main Results.Image type had a major difference on how Covar-CNR values were distributed. Bin images had a slightly higher mean and wider standard deviation (Covar-CNRlo: 3.38 ±17.25, Covar-CNRhi: 5.77 ± 30.64) compared to threshold images (Covar-CNRlo: 2.08 ±1.89, Covar-CNRhi: 3.45 ± 2.49) across all conditions. ANOVA found that each acquisition variable had a significant relationship with both Covar-CNR terms. The multivariate regression model suggested that material concentration had the largest impact on all CNR terms.Signficance.In this work, we described a theoretical framework to extend the SNR to a multivariate form that is able to characterize images independently and also provide insight regarding the relationship between images. Experimental data was used to demonstrate the insight that this metrology provides about image formation factors in spectral CT.

Estimation of threshold thickness of residual normal tissue in lung dysfunction detectable by dynamic chest radiography: A virtual imaging trial.

BACKGROUND: Dynamic chest radiography (DCR) is a recently developed functional x-ray imaging technique that detects pulmonary ventilation impairment as a decrease in changes in lung density during respiration. However, the diagnostic performance of DCR is uncertain owing to an insufficient number of clinical cases. One solution is virtual imaging trials (VITs), which is an emerging alternative method for efficiently evaluating medical imaging technology via computer simulation techniques. PURPOSE: This study aimed to estimate the typical threshold thickness of residual normal tissue below which the presence of emphysema may be detected by DCR via VITs using virtual patients with different physiques and a user-defined ground truth. METHODS: Twenty extended cardiac-torso (XCAT) phantoms that exhibited changes in lung density during respiration were generated to simulate virtual patients. To simulate a locally collapsed lung, an air sphere was inserted into each lung regions in the phantom. The XCAT phantom was virtually projected using an x-ray simulator. The respiratory changes in pixel value (ΔPV) were measured on the projected air spheres (simulated lesions) to calculate the percentage of decrease (ΔPV%) relative to ΔPVexp-ins in the absence of an air sphere. The relationship between the amount of residual normal tissue and ΔPV% was fitted to a cubic approximation curve (hereafter, performance curve), and the threshold at which the ΔPV% began to decrease (normal-tissuethre) was determined. The goodness of fit for each performance curve was evaluated according to the coefficient of determination (R2) and the 95% confidence interval derived from the standard errors between the measured and theoretical values corresponding to each performance curve. The ΔPV% was also visualized as a color scaling to validate the results of the VITs in both virtual and clinical patients. RESULTS: For each lung region in all body sizes, the ΔPV% decreased as the amount of residual normal tissue decreased and could be defined as a function of the amount of residual normal tissue in front of and behind the simulated lesions with high R2 values. Meanwhile, the difference between the measured and theoretical values corresponding to each performance curve was only partially included in the 95% confidence interval. The normal-tissuethre values were 146.0, 179.5, and 170.9 mm for the upper, middle, and lower lungs, respectively, which were demonstrated in virtual patients and one real patient, where the value of the residual normal tissue was less than that of normal-tissuethre; any reduction in the residual normal tissue was reflected as a reduced ΔPV and depicted as a reduced color intensity. CONCLUSIONS: The performance of DCR-based pulmonary impairment assessment depends on the amount of residual normal tissue in front of and behind the lesion rather than on the lesion size. The performance curve can be defined as a function of the amount of residual normal tissue in each lung region with a specific threshold of normal tissue remaining where lesions become detectable, shown as a decrease in ΔPV. The results of VITs are expected to accelerate future clinical trials for DCR-based pulmonary function assessment.

Development of a separability index for task specific characterization of spectral computed tomography.

PURPOSE: In this work, we define a signal detection based metrology to characterize the separability of two different multi-dimensional signals in spectral CT acquisitions. METHOD: Signal response was modelled as a random process with a deterministic signal and stochastic noise component. A linear Hotelling observer was used to estimate a scalar test statistic distribution that predicts the likelihood of an intensity value belonging to a signal. Two distributions were estimated for two materials of interest and used to derive two metrics separability: a separability index (s') and the area under the curve of the test statistic distributions. Experimental and simulated data of photon-counting CT scanners were used to evaluate each metric. Experimentally, vials of iodine and gadolinium (2, 4, 8 mg/mL) were scanned at multiple tube voltages, tube currents and energy thresholds. Additionally, a simulated dataset with low tube current (10-150 mAs) and material concentrations (0.25-4 mg/mL) was generated. RESULTS: Experimental data showed that conditions favorable for low noise and expression of k-edge signal produced the highest separability. Material concentration had the greatest impact on separability. The simulated data showed that under more difficult separation conditions, difference in material concentration still had the greatest impact on separability. CONCLUSION: The results demonstrate the utility of a task specific metrology to measure the overlap in signal between different materials in spectral CT. Using experimental and simulated data, the separability index was shown to describe the relationship between image formation factors and the signal responses of material.

VLST: Virtual Lung Screening Trial for Lung Cancer Detection Using Virtual Imaging Trial.

Importance: The efficacy of lung cancer screening can be significantly impacted by the imaging modality used. This Virtual Lung Screening Trial (VLST) addresses the critical need for precision in lung cancer diagnostics and the potential for reducing unnecessary radiation exposure in clinical settings. Objectives: To establish a virtual imaging trial (VIT) platform that accurately simulates real-world lung screening trials (LSTs) to assess the diagnostic accuracy of CT and CXR modalities. Design Setting and Participants: Utilizing computational models and machine learning algorithms, we created a diverse virtual patient population. The cohort, designed to mirror real-world demographics, was assessed using virtual imaging techniques that reflect historical imaging technologies. Main Outcomes and Measures: The primary outcome was the difference in the Area Under the Curve (AUC) for CT and CXR modalities across lesion types and sizes. Results: The study analyzed 298 CT and 313 CXR simulated images from 313 virtual patients, with a lesion-level AUC of 0.81 (95% CI: 0.78-0.84) for CT and 0.55 (95% CI: 0.53-0.56) for CXR. At the patient level, CT demonstrated an AUC of 0.85 (95% CI: 0.80-0.89), compared to 0.53 (95% CI: 0.47-0.60) for CXR. Subgroup analyses indicated CT's superior performance in detecting homogeneous lesions (AUC of 0.97 for lesion-level) and heterogeneous lesions (AUC of 0.71 for lesion-level) as well as in identifying larger nodules (AUC of 0.98 for nodules > 8 mm). Conclusion and Relevance: The VIT platform validated the superior diagnostic accuracy of CT over CXR, especially for smaller nodules, underscoring its potential to replicate real clinical imaging trials. These findings advocate for the integration of virtual trials in the evaluation and improvement of imaging-based diagnostic tools.

Proceedings Virtual Imaging Trials in Medicine 2024.

This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday.

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.

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.

Technology Characterization Through Diverse Evaluation Methodologies: Application to Thoracic Imaging in Photon-Counting Computed Tomography.

OBJECTIVE: Different methods can be used to condition imaging systems for clinical use. The purpose of this study was to assess how these methods complement one another in evaluating a system for clinical integration of an emerging technology, photon-counting computed tomography (PCCT), for thoracic imaging. METHODS: Four methods were used to assess a clinical PCCT system (NAEOTOM Alpha; Siemens Healthineers, Forchheim, Germany) across 3 reconstruction kernels (Br40f, Br48f, and Br56f). First, a phantom evaluation was performed using a computed tomography quality control phantom to characterize noise magnitude, spatial resolution, and detectability. Second, clinical images acquired using conventional and PCCT systems were used for a multi-institutional reader study where readers from 2 institutions were asked to rank their preference of images. Third, the clinical images were assessed in terms of in vivo image quality characterization of global noise index and detectability. Fourth, a virtual imaging trial was conducted using a validated simulation platform (DukeSim) that models PCCT and a virtual patient model (XCAT) with embedded lung lesions imaged under differing conditions of respiratory phase and positional displacement. Using known ground truth of the patient model, images were evaluated for quantitative biomarkers of lung intensity histograms and lesion morphology metrics. RESULTS: For the physical phantom study, the Br56f kernel was shown to have the highest resolution despite having the highest noise and lowest detectability. Readers across both institutions preferred the Br56f kernel (71% first rank) with a high interclass correlation (0.990). In vivo assessments found superior detectability for PCCT compared with conventional computed tomography but higher noise and reduced detectability with increased kernel sharpness. For the virtual imaging trial, Br40f was shown to have the best performance for histogram measures, whereas Br56f was shown to have the most precise and accurate morphology metrics. CONCLUSION: The 4 evaluation methods each have their strengths and limitations and bring complementary insight to the evaluation of PCCT. Although no method offers a complete answer, concordant findings between methods offer affirmatory confidence in a decision, whereas discordant ones offer insight for added perspective. Aggregating our findings, we concluded the Br56f kernel best for high-resolution tasks and Br40f for contrast-dependent tasks.

Recurrent medical imaging exposures for the care of patients: one way forward.

Medical imaging is both valuable and essential in the care of patients. Much of this imaging depends on ionizing radiation with attendant responsibilities for judicious use when performing an examination. This responsibility applies in settings of both individual as well as multiple (recurrent) imaging with associated repeated radiation exposures. In addressing the roles and responsibilities of the medical communities in the paradigm of recurrent imaging, both the International Atomic Energy Agency (IAEA) and the American Association of Physicists in Medicine (AAPM) have issued position statements, each affirmed by other organizations. The apparent difference in focus and approach has resulted in a lack of clarity and continued debate. Aiming towards a coherent approach in dealing with radiation exposure in recurrent imaging, the IAEA convened a panel of experts, the purpose of which was to identify common ground and reconcile divergent perspectives. The effort has led to clarifying recommendations for radiation exposure aspects of recurrent imaging, including the relevance of patient agency and the provider-patient covenant in clinical decision-making. CLINICAL RELEVANCE STATEMENT: An increasing awareness, generating some lack of clarity and divergence in perspectives, with patients receiving relatively high radiation doses (e.g., ≥ 100 mSv) from recurrent imaging warrants a multi-stakeholder accord for the benefit of patients, providers, and the imaging community. KEY POINTS: • Recurrent medical imaging can result in an accumulation of exposures which exceeds 100 milli Sieverts. • Professional organizations have different perspectives on roles and responsibilities for recurrent imaging. • An expert panel reconciles differing perspectives for addressing radiation exposure from recurrent medical imaging.

Protocol selection formalism for minimizing detectable differences in morphological radiomics features of lung lesions in repeated CT acquisitions.

Background: The accuracy of morphological radiomic features (MRFs) can be affected by various acquisition settings and imaging conditions. To ensure that clinically irrelevant changes do not reduce sensitivity to capture the radiomics changes between successive acquisitions, it is essential to determine the optimal imaging systems and protocols to use. Purpose: The main goal of our study was to optimize CT protocols and minimize the minimum detectable difference (MDD) in successive acquisitions of MRFs. Method: MDDs were derived based on the previous research involving 15 realizations of nodule models at two different sizes. Our study involved simulations of two consecutive acquisitions using 297 different imaging conditions, representing variations in scanners' reconstruction kernels, dose levels, and slice thicknesses. Parametric polynomial models were developed to establish correlations between imaging system characteristics, lesion size, and MDDs. Additionally, polynomial models were used to model the correlation of the imaging system parameters. Optimization problems were formulated for each MRF to minimize the approximated function. Feature importance was determined for each MRF through permutation feature analysis. The proposed method was compared to the recommended guidelines by the quantitative imaging biomarkers alliance (QIBA). Results: The feature importance analysis showed that lesion size is the most influential parameter to estimate the MDDs in most of the MRFs. Our study revealed that thinner slices and higher doses had a measurable impact on reducing the MDDs. Higher spatial resolution and lower noise magnitude were identified as the most suitable or noninferior acquisition settings. Compared to QIBA, the proposed protocol selection guideline demonstrated a reduced coefficient of variation, with values decreasing from 1.49 to 1.11 for large lesions and from 1.68 to 1.12 for small lesions. Conclusion: The protocol optimization framework provides means to assess and optimize protocols to minimize the MDD to increase the sensitivity of the measurements in lung cancer screening.

Characterizing imaging radiation risk in a population of 8918 patients with recurrent imaging for a better effective dose.

An updated extension of effective dose was recently introduced, namely relative effective dose ( E r ), incorporating age and sex factors. In this study we extended E r application to a population of about 9000 patients who underwent multiple CT imaging exams, and we compared it with other commonly used radiation protection metrics in terms of their correlation with radiation risk. Using Monte Carlo methods, E r , dose-length-product based effective dose ( E DLP ), organ-dose based effective dose ( E OD ), and organ-dose based risk index ( RI ) were calculated for each patient. Each metric's dependency to RI was assessed in terms of its sensitivity and specificity. E r showed the best sensitivity, specificity, and agreement with RI (R2 = 0.97); while E DLP yielded the lowest specificity and, along with E OD , the lowest sensitivity. Compared to other metrics, E r provided a closer representation of patient and group risk also incorporating age and sex factors within the established framework of effective dose.

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.

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.

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.

Myelography Using Energy-Integrating Detector CT Versus Photon-Counting Detector CT for Detection of CSF-Venous Fistulas in Patients With Spontaneous Intracranial Hypotension.

BACKGROUND. CSF-venous fistulas (CVFs), which are an increasingly recognized cause of spontaneous intracranial hypotension (SIH), are often diminutive in size and exceedingly difficult to detect by conventional imaging. OBJECTIVE. This purpose of this study was to compare energy-integrating detector (EID) CT myelography and photon-counting detector (PCD) CT myelography in terms of image quality and diagnostic performance for detecting CVFs in patients with SIH. METHODS. This retrospective study included 38 patients (15 men and 23 women; mean age, 55 ± 10 [SD] years) with SIH who underwent both clinically indicated EID CT myelography (slice thickness, 0.625 mm) and PCD CT myelography (slice thickness, 0.2 mm; performed in ultrahigh-resolution mode) to assess for CSF leak. Three blinded radiologists reviewed examinations in random order, assessing image noise, discernibility of spinal nerve root sleeves, and overall image quality (each assessed using a scale of 0-100, with 100 denoting highest quality) and recording locations of the CVFs. Definite CVFs were defined as CVFs described in CT myelography reports using unequivocal language and having an attenuation value greater than 70 HU. RESULTS. For all readers, PCD CT myelography, in comparison with EID CT myelography, showed higher mean image noise (reader 1: 69.9 ± 18.5 [SD] vs 37.6 ± 15.2; reader 2: 59.5 ± 8.7 vs 49.3 ± 12.7; and reader 3: 57.6 ± 13.2 vs 42.1 ± 16.6), higher mean nerve root sleeve discernibility (reader 1: 81.6 ± 21.7 [SD] vs 30.4 ± 13.6; reader 2: 83.6 ± 10 vs 70.1 ± 18.9; and reader 3: 59.6 ± 13.5 vs 50.5 ± 14.4), and higher mean overall image quality (reader 1: 83.2 ± 20.0 [SD] vs 38.1 ± 13.5; reader 2: 80.1 ± 10.1 vs 72.4 ± 19.8; and reader 3: 57.8 ± 11.2 vs 51.9 ± 13.6) (all p < .05). Eleven patients had a definite CVF. Sensitivity and specificity of EID CT myelography and PCD CT myelography for the detection of definite CVF were 45% and 96% versus 64% and 85%, respectively, for reader 1; 36% and 100% versus 55% and 96%, respectively, for reader 2; and 57% and 100% versus 55% and 93%, respectively, for reader 3. The sensitivity was significantly higher for PCD CT myelography than for EID CT myelography for reader 1 and reader 2 (both p < .05) and was not significantly different between the two techniques for reader 3 (p = .45); for all three readers, specificity was not significantly different between the two modalities (all p > .05). CONCLUSION. In comparison with EID CT myelography, PCD CT myelography yielded significantly improved image quality with significantly higher sensitivity for CVFs (for two of three readers), without significant loss of specificity. CLINICAL IMPACT. The findings support a potential role for PCD CT myelography in facilitating earlier diagnosis and targeted treatment of SIH, avoiding high morbidity during potentially prolonged diagnostic workups.

A systematic task-based image quality assessment of photon-counting and energy integrating CT as a function of reconstruction kernel and phantom size.

BACKGROUND: Image quality of photon-counting and energy integrating CT scanners changes with object size, dose to the object, and kernel selection. PURPOSE: To comprehensively compare task-generic image quality of photon-counting CT (PCCT) and energy integrating CT (EICT) systems as a function of phantom size, dose, and reconstruction kernel. METHODS: A size-variant phantom (Mercury Phantom 3.0) was used to characterize the image quality of PCCT and EICT systems as a function of object size. The phantom contained five cylinders attached by slanted tapered sections. Each cylinder contained two sections: one uniform for noise, and the other with inserts for spatial resolution and contrast measurements. The phantom was scanned on Siemens' SOMATOM Force and NAEOTOM Alpha at 1.18 and 7.51 mGy without tube current modulation. CTDIvol was matched across two systems by setting the required tube currents, else, all other acquisition settings were fixed. CT sinograms were reconstructed using FBP and iterative (ADMIRE2 - Force; QIR2 - Alpha) algorithms with Body regular (Br) kernels. Noise Power Spectrum (NPS), Task Transfer Function (TTF), contrast-to-noise ratio (CNR), and detectability index (d') for a task of identifying 2-mm disk were evaluated based on AAPM TG-233 metrology using imQuest, an open-source software package. Averaged noise frequency (fav ) and 50% cut-off frequency for TTF (f50 ) were used as scalar metrics to quantify noise texture and spatial resolution, respectively. The difference between image quality metrics' measurements was calculated as IQPCCT - IQEICT . RESULTS: From Br40 (soft) to Br64 (sharp), f50 for air insert increased from 0.35 mm-1  ± 0.04 (standard deviation) to 0.76 mm-1  ± 0.17, 0.34 mm-1  ± 0.04 to 0.77 mm-1  ± 0.17, 0.37 mm-1  ± 0.02 to 0.95 mm-1  ± 0.17 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively, when averaged over all sizes and dose levels. Similarly, from Br40 to Br64, noise magnitude increased from 10.86 HU ± 7.12 to 38.61 HU ± 18.84, 10.94 HU ± 7.08 to 38.82 HU ± 18.70, 13.74 HU ± 11.02 to 52.11 HU ± 26.22 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively. The difference in fav was consistent across all sizes and dose levels. PCCT-70keV-VMI showed better consistency in contrast measurements for iodine and bone inserts than PCCT-T3D and EICT; however, PCCT-T3D had higher contrast for both inserts. From Br40 to Br64, considering all sizes and dose levels, CNR for iodine insert decreased from 52.30 ± 46.44 to 12.18 ± 10.07, 40.42 ± 33.42 to 9.48 ± 7.16, 39.94 ± 37.60 to 7.84 ± 6.67 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively. CONCLUSIONS: Both PCCT image types, T3D and 70-keV-VMI exhibited similar or better noise, contrast, CNR than EICT when comparing kernels with similar names. For 512 × 512 matrix, PCCT's sharp kernels had lower resolution than EICT's sharp kernels. For all image quality metrics, except extreme low, every dose condition had a similar set of IQ-matching kernels. It suggests that considering patient size and dose level to determine IQ-matching kernel pairs across PCCT and EICT systems may not be imperative while translating protocols, except when the signal to the detector is extremely low.

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.

Coronary stenosis quantification in cardiac computed tomography angiography: multi-factorial optimization of image quality and radiation dose.

Background: The accuracy and variability of quantification in computed tomography angiography (CTA) are affected by the interplay of imaging parameters and patient attributes. The assessment of these combined effects has been an open engineering challenge. Purpose: In this study, we developed a framework that optimizes imaging parameters for accurate and consistent coronary stenosis quantification in cardiac CTA while accounting for patient-specific variables. Methods: The framework utilizes a task-specific image quality index, the estimability index (e'), approximated by a surrogate estimability polynomial function (EPF) capable of finding the optimal protocol that (1) maximizes image quality with an upper bound for desired radiation dose or (2) minimizes the dose level with a lower bound of acceptable image quality. The optimization process was formulated with the decision variables being subject to a set of constraints. The methodology was verified using CTA data from a prior clinical trial (prospective multi-center imaging study for evaluation of chest pain) by assessing the concordance of its prediction with the trial results. Further, the framework was used to derive an optimum protocol for each case based on the patient attributes, gauging how much improvement would have been possible if the derived optimized protocol would have been deployed. Results: The framework produced results consistent with imaging physics principles with approximated EPFs of 97% accuracy. The feature importance evaluation demonstrated a close match with earlier studies. The verification study found e' scores closely predicting the cardiologist scores to within 95% in terms of the area under the receiver operating characteristic curve and predicting potential for either an average of fourfold increase in e' within a targeted dose or a reduction in radiation dose by an average of 57% without reducing the image quality. Conclusions: The protocol optimization framework provides means to assess and optimize CTA in terms of either image quality or radiation dose objectives with its results predicting prior clinical trial findings.

Pulsed wave Doppler ultrasound: Accuracy, variability, and impact of acquisition parameters on flow measurements.

BACKGROUND: Pulsed wave Doppler ultrasound is a useful modality for assessing vascular health as it quantifies blood flow characteristics. To facilitate accurate diagnosis, accuracy and consistency of this modality should be assessed through Doppler quality assurance (QA). PURPOSE: The purpose of this study was to characterize the accuracy, reproducibility, and inter-scanner variability of ultrasound flow velocity measurements via a flow phantom, with a focus on the effect of systematic acquisition parameters on measured flow velocity accuracy. METHODS: Using a manufacturer-calibrated flow phantom, pulsed wave measurements were acquired on five clinical systems (iU22, Philips) with three models of transducers, including both linear and curvilinear models. The peak and mean flow velocities were estimated by vendor-supplied spectral analysis tools. To investigate intra- and inter-scanner variability, measurements were repeated using each scanner-transducer pair under a standardized set of conditions. Inter-scanner variability was assessed using ANOVA. Flow velocity accuracy was investigated by mean absolute percentage error. The impacts of receive gain, measurement depth, and beam steering on measured flow velocity accuracy were examined by varying each parameter over its available range and comparing to the ground truth flow velocity. RESULTS: Inter-scanner variability was statistically significant for peak flow measurements made using both linear and curvilinear transducers, though absolute differences in measured velocity were small. Inter-scanner variability was not statistically significant for mean flow velocity. Receive gain, measurement depth, and beam steering were all found to impact the accuracy of measured flow characteristics for linear transducers. Accuracy of the flow measurements made with the curvilinear transducer demonstrated high consistency to changes in receive gain at a constant depth, though were impacted by increasing the measurement depth. CONCLUSIONS: Carefully and consistently selected acquisition and set-up parameters are essential in order to establish a reliable and meaningful QA program.

Influence of CT dose reduction on AI-driven malignancy estimation of incidental pulmonary nodules.

OBJECTIVES: The purpose of this study was to determine the influence of dose reduction on a commercially available lung cancer prediction convolutional neuronal network (LCP-CNN). METHODS: CT scans from a cohort provided by the local lung cancer center (n = 218) with confirmed pulmonary malignancies and their corresponding reduced dose simulations (25% and 5% dose) were subjected to the LCP-CNN. The resulting LCP scores (scale 1-10, increasing malignancy risk) and the proportion of correctly classified nodules were compared. The cohort was divided into a low-, medium-, and high-risk group based on the respective LCP scores; shifts between the groups were studied to evaluate the potential impact on nodule management. Two different malignancy risk score thresholds were analyzed: a higher threshold of ≥ 9 ("rule-in" approach) and a lower threshold of > 4 ("rule-out" approach). RESULTS: In total, 169 patients with 196 nodules could be included (mean age ± SD, 64.5 ± 9.2 year; 49% females). Mean LCP scores for original, 25% and 5% dose levels were 8.5 ± 1.7, 8.4 ± 1.7 (p > 0.05 vs. original dose) and 8.2 ± 1.9 (p < 0.05 vs. original dose), respectively. The proportion of correctly classified nodules with the "rule-in" approach decreased with simulated dose reduction from 58.2 to 56.1% (p = 0.34) and to 52.0% for the respective dose levels (p = 0.01). For the "rule-out" approach the respective values were 95.9%, 96.4%, and 94.4% (p = 0.12). When reducing the original dose to 25%/5%, eight/twenty-two nodules shifted to a lower, five/seven nodules to a higher malignancy risk group. CONCLUSION: CT dose reduction may affect the analyzed LCP-CNN regarding the classification of pulmonary malignancies and potentially alter pulmonary nodule management. CLINICAL RELEVANCE STATEMENT: Utilization of a "rule-out" approach with a lower malignancy risk threshold prevents underestimation of the nodule malignancy risk for the analyzed software, especially in high-risk cohorts. KEY POINTS: • LCP-CNN may be affected by CT image parameters such as noise resulting from low-dose CT acquisitions. • CT dose reduction can alter pulmonary nodule management recommendations by affecting the outcome of the LCP-CNN. • Utilization of a lower malignancy risk threshold prevents underestimation of pulmonary malignancies in high-risk cohorts.