Leng, Shuai

Abstract Objective. Four-dimensional CT (4DCT) enables visualization of dynamic wrist joint pathology but may be compromised by motion artifacts. Deep learning (DL)-assisted motion correction has the potential to enhance wrist 4DCT quality, but model development is hindered by scarce training data. To address this, we developed a model-based motion simulation framework that generated realistic 4DCT data with paired static ground truth images to demonstrate wrist motion correction using a physics-informed DL model trained with simulated data. Approach. Wrist motion was simulated by synchronizing rotational and translational movements of carpal bones and connective tissues. Elastic deformation guided by analytically-derived motion vector fields was applied to static wrist scans at neutral positions to create artifact-free images at multiple angular positions. Virtual motion-corrupted 4DCT images were produced via forward projection and reconstruction with synchronized gantry and wrist motion. Experimental data from 4DCT cadaver scans were collected to validate simulation quality. Low-intensity region score (LIRS), positivity, and entropy were used to assess simulated motion artifacts. A physics-informed motion correction network was adapted exclusively using simulated training data and subsequently deployed on simulated, real cadaveric, and in vivo 4DCT scans. LIRS and structural similarity index (SSIM) were used to evaluate motion correction quality. Wilcoxon signed-rank tests were conducted ( α = 0.05). Results. The simulation method created realistic motion artifacts compared to real scan reference ( p > 0.05). Motion artifacts were effectively mitigated across testing cases demonstrating generalizability. In simulated scans, LIRS improved from 0.55 ± 0.12–0.75 ± 0.04 and SSIM from 0.87 ± 0.08–0.95 ± 0.03 at bone regions (each p < 0.01). In experimental cadaveric scans, LIRS improved from 0.05 ± 0.01–0.31 ± 0.17 in marker regions and from 0.58 ± 0.19–0.77 ± 0.14 in bone regions (each p < 0.01). Significance. Our simulation method facilitates training DL-assisted motion correction algorithms by providing high-quality training data, effectively supporting 4DCT image quality enhancement.

Abstract Background/Purpose Diagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). Methods Personalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. Results Average area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. Conclusion This study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.

Abstract Photon-counting detector CT (PCD-CT) directly detects individual photons and measures their energies, enabling the acquisition of multi-energy data from a single x-ray source with higher spatial resolution, thereby allowing visualization of smaller arteries and veins and other important imaging features not seen at conventional CT. It also substantially reduces calcium and stent blooming artifacts. Appropriate patient selection and task-based optimization of acquisition, reconstruction, and interpretation are critical to realize these benefits. Vascular imaging examinations for which early evidence shows that PCD-CT demonstrates higher diagnostic performance compared to conventional CT include coronary CT angiography (CTA) for evaluation of partially calcified plaques or stents, peripheral runoff CTA, detection of cerebrospinal fluid-venous fistulas, and head CTA for evaluation of small intracranial vessels and aneurysms. Current limitations include excessive image noise with the sharpest reconstruction kernels and challenges when adapting conventional CT protocols (eg, requiring sharper kernels, thinner slices, higher matrices, tailored scan modes) to achieve time-efficient image reconstruction and hanging protocols. New solutions and directions will include deep-learning-based image noise reduction, improved spectral separation, strategic triage of specific patient populations to PCD-CT systems, and subspecialty clinical practice recommendations for this new technology.

A deep learning–based neural network enabled virtual monoenergetic imaging at energy-integrating detector CT, reduced blooming artifacts, and improved stenosis assessment in heavily calcified coronary plaques, with performance comparable to ultrahigh-resolution photon-counting detector CT.

Abstract Objectives Determine differences in quantitative stenosis severity measurements for ultra-high-resolution (UHR) photon-counting detector (PCD) coronary CT angiography (cCTA) relative to energy-integrating detector (EID) cCTA in a large patient cohort. Methods Adult participants seen between November 2022 and March 2023 underwent a clinical dual-source EID-CT cCTA and a research dual-source PCD-CT cCTA on the same day. Percent diameter stenosis (PDS) was measured, and stenosis severity was assigned based on the PDS of the most severe lesion per patient to determine a coronary artery disease reporting and data system (CAD-RADS) score. Agreement between EID-CT and PCD-CT for PDS and CAD-RADS was determined. Results A total of 112 participants were enrolled, yielding 82 subjects with at least 1 stenosis at PCD-CT (mean age, 68.0 ± 10.8 years; 69.5% [57/82] males). A total of 177 paired stenoses were quantified. The percent decrease in mean PDS from PCD-CT (24.3%) to EID-CT (29.4%) was 17.3% (median difference in PDS: −5.0%, 95% CI: −5.7% to −4.0%). The assigned CAD-RADS score changed in 31/82 subjects. In 11/31 subjects, the most severe stenosis was missed at EID-CT due to partial volume averaging of a small calcification, yet PCD clearly identified stenosis at those locations. In 20/31 subjects, the CAD-RADS score decreased at PCD-CT due to the decreased calcium blooming resulting from the improved spatial resolution. Conclusions UHR PCD cCTA decreases quantitative measures of stenosis severity when a stenosis is identified at EID, leading to decreases in CAD-RADS assignments. Advances in knowledge With its improved spatial resolution, PCD-CT identifies stenoses missed on EID-CT.

Abstract Quantitative computed tomography (CT) enables the measurement of biophysical processes characterized by morphology, composition, flow, and/or motion to aid in clinical diagnosis and intervention. Since its initial application for determining bone mineral density for skeletal fragility assessment, quantitative CT has continued to evolve alongside CT’s technological advancements. A key challenge facing quantitative CT is the lack of standardization pertinent to image acquisition, reconstruction, and image analysis. With the introduction of spectral CT involving dual-energy approaches and photon-counting detectors (PCD), we are now able to obtain detailed information regarding mass densities of endogenous and exogeneous materials. Furthermore, energy-resolved CT yields spectral image types (eg, virtual monoenergetic image) that are, in principle, independent of tube potential and patient size. This paves the way for workflow standardization and helps improve the consistency and reproducibility of CT-derived measurements. In this article, we review clinical applications of quantitative CT, discuss key challenges associated with quantitative CT and its adoption into routine practice, and outline the unique benefits ushered by new CT technologies such as PCD-CT towards improving quantitative imaging.

Abstract Objectives To assess quantitative and qualitative image characteristics in phantoms and patients using a simultaneous ultra-high resolution (UHR) and multi-energy (ME) dual-source cardiac scan mode in a photon counting detector (PCD) CT scanner. Methods UHR-ME cardiac scans were performed to determine the spatial resolution and multi-energy performance. UHR reconstructions were completed to measure transverse and axial spatial resolution in terms of modulation transfer function (MTF) and slice sensitivity profile (SSP). Virtual monoenergetic images (VMIs), virtual non-contrast (VNC) images, and iodine concentration images were created using moderately sharp reconstruction kernels to assess ME performance. With IRB approval, coronary CT angiograms were acquired in 19 patients using the UHR-ME mode, from which iodine contrast-to-noise ratio (CNR) was measured. Various ME post-processing tasks were performed to show the utility of the spectral capabilities. Results VMIs showed a mean CT number error of 6.0%, while iodine accuracy was 0.71 mg/cc (root-mean-square-error). Spatial resolution matched well with a standard UHR mode. Patient images showed a trend of higher CNR at lower keV, while higher keV images showed decreased calcium blooming. ME techniques to separate iodine and calcium were used successfully. Conclusions The combination of UHR-ME cardiac mode retains UHR image quality while providing flexibility for ME post-processing. Advances in knowledge Cardiac UHR-ME PCD-CT scanning offers simultaneous UHR and ME imaging without loss of spatial or temporal resolution or quantitative accuracy.