Leng, Shuai

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.

Abstract Background Accurate low‐dose CT simulation is required to efficiently assess reconstruction and dose reduction techniques. Projection domain noise insertion requires proprietary information from manufacturers. Analytic image domain noise insertion methods are successful for linear reconstruction algorithms, however extending them to non‐linear algorithms remains challenging. Emerging, deep‐learning‐based image domain noise insertion methods have potential, but few approaches have explicitly incorporated physics information and a texture‐synthesis model to guide the generation of locally and globally correlated noise texture. Purpose We proposed a p hysics‐informed model‐based gener a tive neura l network for simulating scann e r‐ and algori t hm‐specific low‐dose C T e xams (PALETTE). It is expected to provide an alternative to projection domain noise insertion methods in the absence of manufacturers’ proprietary information and tools. Methods PALETTE integrated a physics‐based noise prior generation process, a Noise2Noisier sub‐network, and a noise texture synthesis sub‐network. The Noise2Noisier sub‐network provided a bias prior, which, combined with the noise prior, served as the inputs to noise texture synthesis sub‐network. Explicit regularizations in spatial and frequency domains were developed to account for noise spatial correlation and frequency characteristics. For proof‐of‐concept, PALETTE was trained and validated for a commercial iterative reconstruction algorithm (SAFIRE, Siemens Healthineers), using the paired routine and 25% dose images from CT phantoms (lateral size 30–40 cm; three training and four testing phantoms) and open‐access patient cases (10 training and 20 testing cases). In phantom validation, noise power spectra (NPS) were compared in water background and tissue‐mimicking inserts, using peak frequency and mean‐absolute‐error (MAE). In patient case evaluation, visual inspection and quantitative assessment were conducted on axial, coronal, and sagittal planes. Local and global noise texture were visually inspected in low‐dose CT images and the difference images between routine and low dose. Noise levels in liver and fat were measured. Local and global 2D Fourier magnitude spectra of the difference images and the corresponding radial mean profiles were used to assess similarity in noise frequency components within tissues and entire field‐of‐view, using spectral correlation mapper (SCM) and spectral angle mapper (SAM). Several baseline neural network models (e.g., GAN) were included in the evaluation. Statistical significance was tested using a t ‐test for related samples. Results PALETTE‐derived NPS showed accurate noise peak frequency (PALETTE/reference: water 1.40/1.40 lp/cm; inserts 1.7/1.7lp/cm) and small MAE (≤0.65 HU 2 cm 2 ). PALETTE created anatomy‐dependent noise texture, showing realistic local and global granularity and streaks. No statistically significant difference was observed in noise levels ( p > 0.05). Noise range was comparable across 3D image volume (PALETTE / reference):liver – [18.0, 53.4] / [19.3, 50.0] HU; fat – [11.7, 42.4] / [12.1, 41.3] HU. Percent absolute difference of local noise was small (mean ± standard deviation): liver 4.1%±3.1%, fat 4.6%±3.1%. Noise frequency distribution was close to the reference (mean per case): SCM ≥ 0.92, SAM ≤ 0.22. Additionally, PALETTE outperformed all baseline models in visual inspection and quantitative comparison. Conclusion PALETTE can provide high‐quality image domain noise insertion for simulating accurate low‐dose CT images created with a commercial non‐linear reconstruction algorithm.

Abstract Background Multi‐energy CT (MECT) enables quantification of material concentrations by measuring linear attenuation coefficient line integrals with multiple x‐ray spectra. Photon counting detector (PCD)‐CT utilizes a detector‐based approach for MECT that can suffer from substantial spectral overlap, resulting in amplified material quantification noise. Dual‐source dual‐kV approaches for MECT are currently utilized in some energy‐integrating detector (EID)‐CT systems and can potentially be utilized with PCD‐CT for improved spectral separation. Purpose To evaluate the iodine quantification performance of a novel dual‐source (DS)‐PCD‐CT scan mode and compare to single‐source (SS)‐PCD‐CT and DS‐EID‐CT. Materials and Methods A 30 cm × 40 cm solid water phantom with four iodine inserts (2, 5, 10, and 15 mg/mL) was scanned with the three spectral modalities: SS‐PCD‐CT with two energy thresholds, DS‐PCD‐CT (90/Sn150 kV), and DS‐EID‐CT (90/Sn150 kV). For each modality, full‐dose (12 mGy) and half‐dose scans were acquired, and images were reconstructed with filtered back‐projection using a quantitative (Qr40) kernel. When scanning in a DS configuration, the total radiation dose budget is split between two detectors, increasing the strength of a signal‐dependent filter compared to a SS acquisition. To account for this effect, the modulation transfer function (MTF) for each modality was measured from a 0.05 mm tungsten wire. A linear spatial filter was applied to the SS images to match their MTF to that of the DS images. The resulting high‐ and low‐energy images were input into an image‐domain least squares material decomposition algorithm with iodine and water as the two basis materials. Iodine quantification accuracy and noise measured from the iodine basis images were used as figures of merit, and t‐tests used to compare between modalities. Results The 10% MTF cutoffs were 0.56, 0.57, 0.60, and 0.57 lp/mm for DS‐EID‐CT, DS‐PCD‐CT, SS‐PCD‐CT, and SS‐PCD‐CT after MTF‐matching, respectively, with the SS‐PCD‐CT MTF cutoff dropping to 0.58 lp/mm at half‐dose. Without accounting for the signal‐dependent filter by matching the MTFs, the noise in iodine material basis images from SS‐PCD‐CT was 10% higher than that of DS‐EID‐CT. After matching the MTFs, the noise in the SS‐PCD‐CT iodine image was 9%–22% lower than that of the DS‐EID‐CT. The lowest iodine image noise was from the DS‐PCD‐CT, which was 39%–41% lower than the DS‐EID‐CT. The DS‐PCD‐CT noise magnitude was significantly different from the other modalities. Mean iodine quantification accuracy across all measured concentrations was within 5% for all modalities at full dose, but was only below 5% for the DS‐PCD‐CT at half‐dose. Conclusions SS‐PCD‐CT with two energy thresholds outperformed DS‐EID‐CT in terms of image noise in iodine basis images when spatial resolution was matched. DS‐PCD‐CT gave the lowest noise due to the combination of PCD technology and improved spectral separation from the different x‐ray spectra.

AbstractBackgroundPhoton counting X‐ray detectors (PCDs) provide better spatial resolution than energy integrating X‐ray detectors, but even higher resolution is desired in some applications. Certain charge sharing compensation techniques such as coincidence counting preferentially detect photons that arrive at the boundary between pixels, and this could be used for subpixel localization of incident photons and enhanced spatial resolution.PurposeTo estimate improvements to spatial resolution and detective quantum efficiency that are possible when using coincidence counting for high‐resolution, non‐spectral imaging.MethodsThe modulation transfer function (MTF), noise power spectrum (NPS), and detective quantum efficiency (DQE) were estimated using numerical simulations of a two‐dimensional parallel‐beam CT system. Coincidence counters were modeled either geometrically or with Monte Carlo simulations. The geometric model consisted of narrow coincidence counters that interlaced with wider ordinary pixels, and showed that using standard filtered backprojection decreases low‐frequency DQE, but that a frequency weighting technique could be used to restore DQE. The Monte Carlo simulations were used to estimate the possible improvements that could be expected from real systems. The pixel pitch was 0.25 mm and the source apertures considered were 0, 0.125, and 0.5 mm. A numerical stent phantom was also used to illustrate possible improvements.ResultsAssuming a 0.125 mm source aperture and the Monte Carlo model, the limiting MTF (at 10%) increased from 20 to 40 lp/cm using coincidence counters. This can be explained by the increased sampling (and higher Nyquist limit) possible from coincidence counters. In the geometric model, coincidence counters are compared to conventional double sampling techniques such as in‐plane flying focal spot, and the MTF at 38 lp/cm increased from 5% to 53%. Without the frequency weighting technique, low‐frequency DQE was reduced by about 20%, but these losses are recovered with frequency weighting. Improvements are much more modest with the 0.25 mm source aperture because the system becomes source‐limited.ConclusionsCoincidence counting could be used to increase spatial resolution in PCDs. The increases in system resolution could be large if a high‐resolution X‐ray source were available.

AbstractBackgroundCochlear implants (CIs) are used to rehabilitate moderate to profound sensorineural hearing loss and stimulate the auditory system via a multichannel electrode array positioned in the scala tympani. Imaging evaluation of CIs is substantially degraded by metallic artifacts. Photon‐counting detector (PCD)‐CT has the potential to minimize such artifacts, though this has not been convincingly established in prior studies. This study compared the visualization of CI electrodes on PCD‐CT to conventional energy integrating detector (EID)‐CT.Materials and MethodsFive patients with CIs were prospectively recruited to undergo temporal bone imaging on both EID‐CT and PCD‐CT scanners yielding 2D and 3D Volume Rendering Technique (VRT) images. Three nonblinded neuroradiologists reviewed 2D and 3D VRT images for the ability to resolve CI electrode leads using a Likert scale. The average Likert scores of PCD‐CT versus EID‐CT were calculated for each reader for both 2D and 3D VRT images. (Likert scores 1–5 based on inferior to superior rating of PCD‐CT compared to EID‐CT). Inter‐rater agreement was assessed using Krippendorff's alpha with ordinal weights. ResultsFive patients underwent temporal bone CT for CI array evaluation. As evidenced by 5‐point Likert scales that were independently used by the three reviewers, this study demonstrated superior resolution of CI electrode array using PCD‐CT as compared to EID‐CT with an average score of 4.2 ± 0.8 for 2D images and 4.9 ± 0.4 for 3D VRT images.ConclusionsPCD‐CT is superior to EID‐CT in the visualization of CI electrodes, particularly in 3D VRT imaging.

AbstractBackground and PurposeTo compare the performance of the photon‐counting detector (PCD)‐CT against a cutting‐edge energy‐integrated detector (EID)‐CT to visualize different segments of the chorda tympani nerve.Materials and MethodsAn IRB‐approved protocol prospectively enrolled patients for temporal bone CT on both EID‐CT and PCD‐CT scanners. Fellowship‐trained neuroradiologists compared visualization of chorda tympani segments on each scanner using a 5‐point Likert scale (1 indicating EID is far superior to PCD, 3 indicating they are equivalent, and 5 indicating PCD is far superior to EID). Scores were averaged across segments and tested using a one‐sample Wilcoxon signed‐rank test with a one‐sided alternative hypothesis.ResultsForty temporal bones were evaluated. Average Likert scores for visualization of chorda tympani segments‐chorda tympani canal/proximal segment, middle ear/posterior tympanic segment, and petrotympanic fissure/distal segment‐ on the PCD compared to EID scanner were 4.2 (SD = 0.4), 4.4 (0.5), and 4 (0.0), respectively. The anterior tympanic segment of the chorda tympani was not well visualized on either platform or therefore not scored. PCD compared to the EID scanner had better overall performance (Median = 4.2, 95% CI: [4.1, 5.0], p‐value < .001).ConclusionPCD‐CT improves visualization of the petrous and intratympanic chorda tympani nerve segments, chorda tympani canaliculus, and petrotympanic fissure compared to comparable EID‐CT examinations. The improved visualization of the chorda tympani is important for the characterization of middle ear pathology involving this structure and presurgical planning to determine the size of the facial recess for cochlear implantation and to estimate the risk of nerve injury.

AbstractMulti‐energy computed tomography (MECT) offers the opportunity for advanced visualization, detection, and quantification of select elements (e.g., iodine) or materials (e.g., fat) beyond the capability of standard single‐energy computed tomography (CT). However, the use of MECT requires careful consideration as substantially different hardware and software approaches have been used by manufacturers, including different sets of user‐selected or hidden parameters that affect the performance and radiation dose of MECT. Another important consideration when designing MECT protocols is appreciation of the specific tasks being performed; for instance, differentiating between two different materials or quantifying a specific element. For a given task, it is imperative to consider both the radiation dose and task‐specific image quality requirements. Development of a quality control (QC) program is essential to ensure the accuracy and reproducibility of these MECT applications. Although standard QC procedures have been well established for conventional single‐energy CT, the substantial differences between single‐energy CT and MECT in terms of system implementations, imaging protocols, and clinical tasks warrant QC tests specific to MECT. This task group was therefore charged with developing a systematic QC program designed to meet the needs of MECT applications. In this report, we review the various MECT approaches that are commercially available, including information about hardware implementation, MECT image types, image reconstruction, and postprocessing techniques that are unique to MECT. We address the requirements for MECT phantoms, review representative commercial MECT phantoms, and offer guidance regarding homemade MECT phantoms. We discuss the development of MECT protocols, which must be designed carefully with proper consideration of MECT technology, imaging task, and radiation dose. We then outline specific recommended QC tests in terms of general image quality, radiation dose, differentiation and quantification tasks, and diagnostic and therapeutic applications.

Abstract The scaphotrapeziotrapezoid (STT) joint is involved in load transmission between the wrist and thumb. A quantitative description of baseline STT joint morphometrics is needed to capture the variation of normal anatomy as well as to guide staging of osteoarthritis. Statistical shape modeling (SSM) techniques quantify variations in three‐dimensional shapes and relative positions. The objectives of this study are to describe the morphology of the STT joint using a multi‐domain SSM. We asked: (1) What are the dominant modes of variation that impact bone and articulation morphology at the STT joint, and (2) what are the morphometrics of SSM‐generated STT joints? Thirty adult participants were recruited to a computed tomography study of normal wrist imaging and biomechanics. Segmentations of the carpus were converted to three‐dimensional triangular surface meshes. A multi‐domain, particle‐based entropy system SSM was used to quantify variation in carpal bone shape and position as well as articulation morphology. Articular surface areas and interosseous proximity distributions were calculated between mesh vertex pairs on adjacent bones within distance (2.0 mm) and surface‐normal angular (35°) thresholds. In the SSM, the first five modes of variation captured 76.2% of shape variation and contributed to factors such as bone scale, articular geometries, and carpal tilt. Median interosseous proximities—a proxy for joint space—were 1.39 mm (scaphotrapezium), 1.42 mm (scaphotrapezoid), and 0.61 mm (trapeziotrapezoid). This study quantifies morphological and articular variations at the STT joint, presenting a range of normative anatomy. The range of estimated interosseous proximities may guide interpretation of imaging‐derived STT joint space.