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.

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.

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.

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.

Harmonizing CT Images via Physics-based Deep Neural Networks.

The rendition of medical images influences the accuracy and precision of quantifications. Image variations or biases make measuring imaging biomarkers challenging. The objective of this paper is to reduce the variability of computed tomography (CT) quantifications for radiomics and biomarkers using physics-based deep neural networks (DNNs). With the proposed framework, it is possible to harmonize the different renditions of a single CT scan (with variations in reconstruction kernel and dose) into an image that is in close agreement with the ground truth. To this end, a generative adversarial network (GAN) model was developed where the generator is informed by the scanner's modulation transfer function (MTF). To train the network, a virtual imaging trial (VIT) platform was used to acquire CT images, from a set of forty computational models (XCAT) serving as the patient model. Phantoms with varying levels of pulmonary disease, such as lung nodules and emphysema, were used. We scanned the patient models with a validated CT simulator (DukeSim) modeling a commercial CT scanner at 20 and 100 mAs dose levels and then reconstructed the images by twelve kernels representing smooth to sharp kernels. An evaluation of the harmonized virtual images was conducted in four different ways: 1) visual quality of the images, 2) bias and variation in density-based biomarkers, 3) bias and variation in morphological-based biomarkers, and 4) Noise Power Spectrum (NPS) and lung histogram. The trained model harmonized the test set images with a structural similarity index of 0.95±0.1, a normalized mean squared error of 10.2±1.5%, and a peak signal-to-noise ratio of 31.8±1.5 dB. Moreover, emphysema-based imaging biomarkers of LAA-950 (-1.5±1.8), Perc15 (13.65±9.3), and Lung mass (0.1±0.3) had more precise quantifications.

A systematic assessment of photon-counting CT for bone mineral density and microarchitecture quantifications.

Photon-counting CT (PCCT) is an emerging imaging technology with potential improvements in quantification and rendition of micro-structures due to its smaller detector sizes. The aim of this study was to assess the performance of a new PCCT scanner (NAEOTOM Alpha, Siemens) in quantifying clinically relevant bone imaging biomarkers for characterization of common bone diseases. We evaluated the ability of PCCT in quantifying microarchitecture in bones compared to conventional energy-integrating CT. The quantifications were done through virtual imaging trials, using a 50 percentile BMI male virtual patient, with a detailed model of trabecular bone with varied bone densities in the lumbar spine. The virtual patient was imaged using a validated CT simulator (DukeSim) at CTDIvol of 20 and 40 mGy for three scan modes: ultra-high-resolution PCCT (UHR-PCCT), high-resolution PCCT (HR-PCCT), and a conventional energy-integrating CT (EICT) (FORCE, Siemens). Further, each scan mode was reconstructed with varying parameters to evaluate their effect on quantification. Bone mineral density (BMD), trabecular volume to total bone volume (BV/TV), and radiomics texture features were calculated in each vertebra. The most accurate BMD measurements relative to the ground truth were UHR-PCCT images (error: 3.3% ± 1.5%), compared to HR-PCCT (error: 5.3% ± 2.0%) and EICT (error: 7.1% ± 2.0%). UHR-PCCT images outperformed EICT and HR-PCCT. In BV/TV quantifications, UHR-PCCT (errors of 29.7% ± 11.8%) outperformed HR-PCCT (error: 80.6% ± 31.4%) and EICT (error: 67.3% ± 64.3). UHR-PCCT and HR-PCCT texture features were sensitive to anatomical changes using the sharpest kernel. Conversely, the texture radiomics showed no clear trend to reflect the progression of the disease in EICT. This study demonstrated the potential utility of PCCT technology in improved performance of bone quantifications leading to more accurate characterization of bone diseases.

Image quality of photon counting and energy integrating chest CT - Prospective head-to-head comparison on same patients.

PURPOSE: To prospectively compare the image quality of high-resolution, low-dose photon-counting detector CT (PCD-CT) with standard energy-integrating-detector CT (EID) on the same patients. METHOD: IRB-approved, prospective study; patients received same-day non-contrast CT on EID and PCD-CT (NAEOTOM Alpha, blinded) with clinical protocols. Four blinded radiologists evaluated subsegmental bronchial wall definition, noise, and overall image quality in randomized order (0 = worst; 100 = best). Cases were quantitatively compared using the average Global-Noise-Index (GNI), Noise-Power-Spectrum average frequency (fav), NPS frequency-peak (fpeak), Task-Transfer-Function-10%-frequency (f10) an adjusted detectability index (d'adj), and applied output radiation doses (CTDIvol). RESULTS: Sixty patients were prospectively imaged (27 men, mean age 67 ± 10 years, mean BMI 27.9 ± 6.5, 15.9-49.4 kg/m2). Subsegmental wall definition was rated significantly better for PCD-CT than EID (mean 71 [56-87] vs 60 [45-76]; P < 0.001), noise was rated higher for PCD-CT (48 [26-69] vs 34 [13-56]; P < 0.001). Overall image quality was rated significantly higher for PCD-CT than EID (66 [48-85] vs 61 [42-79], P = 0.008). Automated image quality measures showed similar differences for PCD-CT vs EID (mean GNI 70 ± 19 HU vs 26 ± 8 HU, fav 0.35 ± 0.02 vs 0.25 ± 0.02 mm-1, fpeak 0.07 ± 0.01 vs 0.09 ± 0.03 mm-1, f10 0.7 ± 0.08 vs 0.6 ± 0.1 mm-1, all p-values < 0.001). PCD-CT showed a 10% average d'adj increase (-49% min, 233% max). PCD-CT studies were acquired at significantly lower radiation doses than EID (mean CTDIvol 4.5 ± 2.1 vs 7.7 ± 3.2 mGy, P < 0.01). CONCLUSION: Though PCD-CT had higher measured and perceived noise, it offered equivalent or better diagnostic quality compared to EID at lower radiation doses, due to its improved resolution.

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 (CTDIvol 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.