Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network.

Purpose: We aim to reduce image noise in high-resolution (HR) virtual monoenergetic images (VMIs) from photon-counting detector (PCD) CT scans by developing a prior knowledge-aware iterative denoising neural network (PKAID-Net) that efficiently exploits the unique noise characteristics of VMIs at different energy (keV) levels. Approach: PKAID-Net offers two major features: first, it utilizes a lower-noise VMI (e.g., 70 keV) as a prior input; second, it iteratively constructs a refined training dataset to improve the neural network's denoising performance. In each iteration, the denoised image from the previous module serves as an updated target image, which is included in the dataset for the subsequent training iteration. Our study includes 10 patient coronary CT angiography exams acquired on a clinical dual-source PCD-CT (NAEOTOM Alpha, Siemens Healthineers). The HR VMIs were reconstructed at 50, 70, and 100 keV, using a sharp vascular kernel (Bv68) and thin (0.6 mm) slice thickness (0.3 mm increment). PKAID-Net's performance was evaluated in terms of image noise, spatial detail preservation, and quantitative accuracy. Results: PKAID-Net achieved a noise reduction of 96% compared to filtered back projection and 65% relative to iterative reconstruction, all while preserving spatial and spectral fidelity and maintaining a natural noise texture. The iterative refinement of PCD-CT data during the training process substantially enhanced the robustness of deep learning-based denoising compared to the original method, which resulted in some spatial detail loss. Conclusions: The PKAID-Net provides substantial noise reduction while maintaining spatial and spectral fidelity of the HR VMIs from PCD-CT.

Beckwith-Widemann Macroglossia: The Role of Surgical Tongue Reduction.

Objective: This review was conducted to define the natural history of unoperated Beckwith-Wiedemann syndrome (BWS) macroglossia and the effect of tongue reduction surgery upon breathing, eating, speaking and dentoskeletal development in individuals having BWS. Design: This is a retrospective study of medical records. SETTING: All patients were evaluated and treated in one of two Children's Hospitals by an ACPA approved Craniofacial Team. PATIENTS/PARTICIPANTS: Medical records were reviewed of 526 individuals having a diagnosis of BWS and evaluated in-person by a single craniofacial surgeon between 1986 and 2014 in conjunction with a series of multi-disciplinary craniofacial team colleagues. 28 individuals were excluded having had multiple tongue reductions elsewhere. 498 individuals comprise the "pre tongue-reduction group". The "post tongue-reduction group" consists of 391 individuals who underwent surgical tongue reduction by one surgeon using one technique between 1986 and 2014. MAIN OUTCOME MEASURES: The primary outcome measure was change in anterior dental occlusion following tongue reduction surgery. Tongue reduction surgery was performed on the assumption that it would improve dentoskeletal relationships. Secondary outcome measures were: breathing, feeding/swallowing, and speech. Results: A significant difference (p<0.001) over time between the two groups was found with less anterior occlusal abnormality in the tongue reduction group. Tongue reduction surgery had no mortality and minimal morbidity for breathing, feeding/swallowing, and speech and can ameliorate obstructive sleep apnea. Conclusions: Surgical tongue reduction for BWS macroglossia is recommended for the infant or child in primary dentition with a grossly abnormal anterior tooth/jaw relationship and/or obstructive sleep apnea.

Characterization of single- and multi-energy CT performance of an oral dark borosilicate contrast media using a clinical photon-counting-detector CT platform.

BACKGROUND: The feasibility of oral dark contrast media is under exploration in abdominal computed tomography (CT) applications. One of the experimental contrast media in this class is dark borosilicate contrast media (DBCM), which has a CT attenuation lower than that of intra-abdominal fat. PURPOSE: To evaluate the performances of DBCM using single- and multi-energy CT imaging on a clinical photon-counting-detector CT (PCD-CT). METHODS: Five vials, three with iodinated contrast agent (5, 10, and 20 mg/mL; Omnipaque 350) and two with DBCM (6% and 12%; Nextrast, Inc.), and one solid-water rod (neutral contrast agent) were inserted into two multi-energy CT phantoms, and scanned on a clinical PCD-CT system (NAEOTOM Alpha) at 90, 120, 140, Sn100, and Sn140 kV (Sn: tin filter) in multi-energy mode. CARE keV IQ level was 180 (CTDIvol: 3.0 and 12.0 mGy for the small and large phantoms, respectively). Low-energy threshold images were reconstructed with a quantitative kernel (Qr40, iterative reconstruction strength 2) and slice thickness/increment of 2.0/2.0 mm. Virtual monoenergetic images (VMIs) were reconstructed from 40 to 140 keV at 10 keV increments. On all images, average CT numbers for each vial/rod were measured using circular region-of-interests and averaged over eight slices. The contrast-to-noise ratio (CNR) of iodine (5 mg/mL) against DBCM was calculated and plotted against tube potential and VMI energy level, and compared to the CNR of iodine against water. Similar analyses were performed on iodine maps and VNC images derived from the multi-energy scan at 120 kV. RESULTS: With increasing kV or VMI keV, the negative HU of DBCM decreased only slightly, whereas the positive HU of iodine decreased across all contrast concentrations and phantom sizes. CT numbers for DBCM decreased from -178.5 ± 9.6 to -194.4 ± 6.3 HU (small phantom) and from -181.7 ± 15.7 to -192.1 ± 11.9 HU (large phantom) for DBCM-12% from 90 to Sn140 kV; on VMIs, the CT numbers for DBCM decreased minimally from -147.1 ± 15.7 to -185.1 ± 9.2 HU (small phantom) and -158.8 ± 28.6 to -188.9 ± 14.7 HU (large phantom) from 40 to 70 keV, but remained stable from 80 to 140 keV. The highest iodine CNR against DBCM in low-energy threshold images was seen at 90 or Sn140 kV for the small phantom, whereas all CNR values from low-energy threshold images for the large phantom were comparable. The CNR values of iodine against DBCM computed on VMIs were highest at 40 or 70 keV depending on iodine and DBCM concentrations. The CNR values of iodine against DBCM were consistently higher than iodine to water (up to 460% higher dependent on energy level). Further, the CNR of iodine compared to DBCM is less affected by VMI energy level than the identical comparison between iodine and water: CNR values at 140 keV were reduced by 46.6% (small phantom) or 42.6% (large phantom) compared to 40 keV; CNR values for iodine compared to water were reduced by 86.3% and 83.8% for similar phantom sizes, respectively. Compared to 70 keV VMI, the iodine CNR against DBCM was 13%-79% lower on iodine maps and VNC. CONCLUSIONS: When evaluated at different tube potentials and VMI energy levels using a clinical PCD-CT system, DBCM showed consistently higher CNR compared to iodine versus water (a neutral contrast).

Technical note: Exploring the detectability of coronary calcification using ultra-high-resolution photon-counting-detector CT.

BACKGROUND: Coronary calcification is a strong indicator of coronary artery disease, and patients with a "zero" coronary calcification score have a much lower risk of future cardiac events than those with even small amounts of calcium. However, false-negative (incorrect zero scores) may occur if small calcifications are missed at CT due to limited spatial resolution. PURPOSE: To demonstrate lower limits of detection for coronary calcification using an ultra-high-resolution (UHR) mode on a clinical photon-counting-detector CT (PCD-CT), compared to a conventional energy-integrating-detector CT (EID-CT). METHODS: Chicken eggshell fragments (0.4-0.8 mm) mimicking coronary calcifications were scanned on a clinical PCD-CT (NAEOTOM Alpha) in UHR mode and a conventional EID-CT (SOMATOM Force) with matched tube potential and radiation dose levels to the PCD-CT. PCD-CT images were reconstructed with a sharp kernel (Qr68) and a quantum iterative algorithm (QIR-3). Two sets of EID-CT images were reconstructed: routine clinical kernel (Qr36, ADMIRE-3) and a sharper kernel (Qr54) with similar noise to PCD-CT images. With institutional review board approval, in vivo exams performed with the PCD-CT in UHR mode were compared against patients' clinical EID-CT exams. The visibility of calcifications on PCD-CT and EID-CT images was assessed and compared qualitatively. RESULTS: PCD-CT images visualized all calcified fragments, while EID-CT failed to detect those below 0.6 mm using a routine protocol. EID-CT with Qr54 improved visibility but distorted boundaries. Calcifications were less visible on EID-CT than PCD-CT as phantom sizes increased. 0.6- and 0.7-mm calcified fragments were barely visible on 35- and 40-cm phantom EID-CT images. Patient cases showed small calcifications missed on EID-CT but detected on PCD-CT. CONCLUSION: At matched radiation dose, PCD-CT in UHR mode provided higher spatial resolution and improved the detectability of small calcified fragments for different phantom/patient sizes in comparison to EID-CT.

Ex vivo coronary calcium volume quantification using a high-spatial-resolution clinical photon-counting-detector computed tomography.

Purpose: Coronary artery calcification (CAC) is an important indicator of coronary disease. Accurate volume quantification of CAC is challenging using computed tomography (CT) due to calcium blooming, which is a consequence of limited spatial resolution. Ex vivo coronary specimens were scanned on an ultra-high-resolution (UHR) clinical photon-counting detector (PCD) CT scanner, and the accuracy of CAC volume estimation was compared with a state-of-the-art conventional energy-integrating detector (EID) CT, a previous-generation investigational PCD-CT, and micro-CT. Approach: CAC specimens (n=13) were scanned on EID-CT and PCD-CT using matched parameters (120 kV, 9.3 mGy CTDIvol). EID-CT images were reconstructed using our institutional routine clinical protocol for CAC quantification. UHR PCD-CT data were reconstructed using a sharper kernel. An image-based denoising algorithm was applied to the PCD-CT images to achieve similar noise levels as EID-CT. Micro-CT images served as the volume reference standard. Calcification images were segmented, and their volume estimates were compared. The CT data were further compared with previous work using an investigational PCD-CT. Results: Compared with micro-CT, CT volume estimates had a mean absolute percent error of 24.1%±25.6% for clinical PCD-CT, 60.1%±48.2% for EID-CT, and 51.1%±41.7% for previous-generation PCD-CT. Clinical PCD-CT absolute percent error was significantly (p<0.01) lower than both EID-CT and previous generation PCD-CT. The mean calcification CT number and contrast-to-noise ratio were both significantly (p<0.01) higher in clinical PCD-CT relative to EID-CT. Conclusions: UHR clinical PCD-CT showed reduced calcium blooming artifacts and further enabled improved accuracy of CAC quantification beyond that of conventional EID-CT and previous generation PCD-CT systems.

PKAID-Net: Prior Knowledge Aware Iterative Denoising Neural Network for Photon Counting Detector CT.

An important feature enabled by Photon-Counting Detector (PCD) CT is the simultaneous acquisition of multi-energy data, which can produce virtual monoenergetic images (VMIs) at a high spatial resolution. However, noise levels observed in the high-resolution (HR) VMIs are markedly increased. Recent work involving deep learning methods has shown great potential in CT image denoising. Many CNN applications involve training using spatially co-registered low- and high-dose CT images featuring high and low image noise, respectively. However, this is implausible in routine clinical practice. Further, typical denoising methods treat each VMI energy level independently, without consideration of the valuable information in the spectral domain. To overcome these obstacles, we propose a prior knowledge-aware iterative denoising neural network (PKAID-Net). PKAID-Net offers two major benefits: first, it utilizes spectral information by including a lower-noise VMI as a prior input; and second, it iteratively constructs refined datasets for neural network training to improve the denoising performance. This study includes 10 patient coronary CT angiography (CTA) exams acquired on a clinical HR PCD-CT (NAEOTOM Alpha, Siemens Healthineers). The HR VMIs were reconstructed at 50 and 70 keV, using a sharp kernel (Bv68) and thin (0.6 mm, 0.3 mm increment) slice thickness. Results showed that the PKAID-Net provided a noise reduction of 96% and 70% relative to FBP and iterative reconstruction, respectively while maintaining spatial and spectral fidelity and a natural noise texture. These results demonstrate the noise reduction capacity of PKAID-Net as applied to cutting-edge PCD-CT data to enable HR, multi-energy cardiac CT imaging.

Pie-Net: Prior-information-enabled deep learning noise reduction for coronary CT angiography acquired with a photon counting detector CT.

BACKGROUND: Photon-counting-detector CT (PCD-CT) enables the production of virtual monoenergetic images (VMIs) at a high spatial resolution (HR) via simultaneous acquisition of multi-energy data. However, noise levels in these HR VMIs are markedly increased. PURPOSE: To develop a deep learning technique that utilizes a lower noise VMI as prior information to reduce image noise in HR, PCD-CT coronary CT angiography (CTA). METHODS: Coronary CTA exams of 10 patients were acquired using PCD-CT (NAEOTOM Alpha, Siemens Healthineers). A prior-information-enabled neural network (Pie-Net) was developed, treating one lower-noise VMI (e.g., 70 keV) as a prior input and one noisy VMI (e.g., 50 keV or 100 keV) as another. For data preprocessing, noisy VMIs were reconstructed by filtered back-projection (FBP) and iterative reconstruction (IR), which were then subtracted to generate "noise-only" images. Spatial decoupling was applied to the noise-only images to mitigate overfitting and improve randomization. Thicker slice averaging was used for the IR and prior images. The final training inputs for the convolutional neural network (CNN) inside the Pie-Net consisted of thicker-slice signal images with the reinsertion of spatially decoupled noise-only images and the thicker-slice prior images. The CNN training labels consisted of the corresponding thicker-slice label images without noise insertion. Pie-Net's performance was evaluated in terms of image noise, spatial detail preservation, and quantitative accuracy, and compared to a U-net-based method that did not include prior information. RESULTS: Pie-Net provided strong noise reduction, by 95 ± 1% relative to FBP and by 60 ± 8% relative to IR. For HR VMIs at different keV (e.g., 50 keV or 100 keV), Pie-Net maintained spatial and spectral fidelity. The inclusion of prior information from the PCD-CT data in the spectral domain was able to improve a robust deep learning-based denoising performance compared to the U-net-based method, which caused some loss of spatial detail and introduced some artifacts. CONCLUSION: The proposed Pie-Net achieved substantial noise reduction while preserving HR VMI's spatial and spectral properties.

Measurement of enhanced vasa vasorum density in a porcine carotid model using photon counting detector CT.

Purpose: The onset of atherosclerosis is preceded by changes in blood perfusion within the arterial wall due to localized proliferation of the vasa vasorum. The purpose of this study was to quantify these changes in spatial density of the vasa vasorum using a research whole-body photon-counting detector CT (PCD-CT) scanner and a porcine model. Approach: Vasa vasorum angiogenesis was stimulated in the left carotid artery wall of anesthetized pigs ( n = 5 ) while the right carotid served as a control. After a 6-week recovery period, the animals were scanned on the PCD-CT prior to and after injection of iodinated contrast. Annular regions of interest were used to measure wall enhancement in the injured and control arteries. The exact Wilcoxon-signed rank test was used to determine whether a significant difference in contrast enhancement existed between the injured and control arterial walls. Results: The greatest arterial wall enhancement was observed following contrast recirculation. The wall enhancement measurements made over these time points revealed that the enhancement was greater in the injured artery for 13/16 scanned arterial regions. Using an exact Wilcoxon-signed rank test, a significantly increased enhancement ratio was found in injured arteries compared with control arteries ( p = 0.013 ). Vasa vasorum angiogenesis was confirmed in micro-CT scans of excised arteries. Conclusions: Whole-body PCD-CT scanners can be used to detect and quantify the increased perfusion occurring within the porcine carotid arterial wall resulting from an increased density of vasa vasorum.

Accuracy of Nodule Volume and Airway Wall Thickness Measurement Using Low-Dose Chest CT on a Photon-Counting Detector CT Scanner.

OBJECTIVES: A comparison of high-resolution photon-counting detector computed tomography (PCD-CT) versus energy-integrating detector (EID) CT via a phantom study using low-dose chest CT to evaluate nodule volume and airway wall thickness quantification. MATERIALS AND METHODS: Twelve solid and ground-glass lung nodule phantoms with 3 diameters (5 mm, 8 mm, and 10 mm) and 2 shapes (spherical and star-shaped) and 12 airway tube phantoms (wall thicknesses, 0.27-1.54 mm) were placed in an anthropomorphic chest phantom. The phantom was scanned with EID-CT and PCD-CT at 5 dose levels (CTDI vol = 0.1-0.8 mGy at Sn-100 kV, 7.35 mGy at 120 kV). All images were iteratively reconstructed using matched kernels for EID-CT and medium-sharp kernel (MK) PCD-CT and an ultra-sharp kernel (USK) PCD-CT kernel, and image noise at each dose level was quantified. Nodule volumes were measured using semiautomated segmentation software, and the accuracy was expressed as the percentage error between segmented and reference volumes. Airway wall thicknesses were measured, and the root-mean-square error across all tubes was evaluated. RESULTS: MK PCD-CT images had the lowest noise. At 0.1 mGy, the mean volume accuracy for the solid and ground-glass nodules was improved in USK PCD-CT (3.1% and 3.3% error) compared with MK PCD-CT (9.9% and 10.2% error) and EID-CT images (11.4% and 9.2% error), respectively. At 0.2 mGy and 0.8 mGy, the wall thickness root-mean-square error values were 0.42 mm and 0.41 mm for EID-CT, 0.54 mm and 0.49 mm for MK PCD-CT, and 0.23 mm and 0.16 mm for USK PCD-CT. CONCLUSIONS: USK PCD-CT provided more accurate lung nodule volume and airway wall thickness quantification at lower radiation dose compared with MK PCD-CT and EID-CT.

Quantification of Coronary Calcification using High-Resolution Photon-Counting-Detector CT and an Image Domain Denoising Algorithm.

Coronary artery calcification (CAC) is an important indicator of coronary disease. Accurate volume quantification of coronary calcification, especially calcifications smaller than a few mm, using computed tomography (CT) is challenging due to calcium blooming, which is a consequence of limited spatial resolution. In this study, ex-vivo coronary specimens were scanned on a clinical photon-counting detector (PCD) CT scanner and the estimated coronary calcification volume were compared with a conventional energy-integrating detector (EID) CT. Scans were performed using the same tube potential and radiation dose (120 kV, 9.3 mGy CTDIvol). EID-CT images were reconstructed using our routine clinical protocol for CAC quantification. PCD-CT images were reconstructed using a sharper reconstruction kernel than that was supported by the EID-CT scanner, resulting in improved resolution but higher image noise levels. An image-based denoising algorithm was applied to the PCD-CT images to achieve similar noise levels as the EID-CT images. Calcifications were segmented to estimate the volume. Micro-CT images of the same calcifications were acquired and served as the reference standard. PCD-CT images showed reduced calcium blooming artifacts compared to EID-CT. Calcification volume estimates were found to overestimate the micro-CT volumes by 9 ± 12% for PCD-CT data, and 24 ± 18% for the EID-CT data. Volume quantification accuracy of the current PCD-CT system was also found to be superior to a previous-generation investigational PCD-CT scanner with larger detector pixels.

Full field-of-view, high-resolution, photon-counting detector CT: technical assessment and initial patient experience.

We report a comprehensive evaluation of a full field-of-view (FOV) photon-counting detector (PCD) computed tomography (CT) system using phantoms, and qualitatively assess image quality in patient examples. A whole-body PCD-CT system with 50 cm FOV, 5.76 cm z-detector coverage and two acquisition modes (standard: 144 × 0.4 mm collimation and ultra-high resolution (UHR): 120 × 0.2 mm collimation) was used in this study. Phantoms were scanned to assess image uniformity, CT number accuracy, noise power spectrum, spatial resolution, material decomposition and virtual monoenergetic imaging (VMI) performance. Four patients were scanned on the PCD-CT system with matched or lower radiation dose than their prior clinical CT scans performed using energy-integrating detector (EID) CT, and the potential clinical impact of PCD-CT was qualitatively evaluated. Phantom results showed water CT numbers within ±5 HU, and image uniformity measured between peripheral and central regions-of-interests to be within ±5 HU. For the UHR mode using a dedicated sharp kernel, the cut-off spatial frequency was 40 line-pairs cm-1, which corresponds to a 125µm limiting in-plane spatial resolution. The full-width-at-half-maximum for the section sensitivity profile was 0.33 mm for the smallest slice thickness (0.2 mm) using the UHR mode. Material decomposition in a multi-energy CT phantom showed accurate material classification, with a root-mean-squared-error of 0.3 mg cc-1for iodine concentrations (2-15 mg cc-1) and 14.2 mg cc-1for hydroxyapatite concentrations (200 and 400 mg cc-1). The average percent error for CT numbers corresponding to the iodine concentrations in VMI (40-70 keV) was 2.75%. Patient PCD-CT images demonstrated better delineation of anatomy for chest and temporal bone exams performed with the UHR mode, which allowed the use of very sharp kernels not possible with EID-CT. VMI and virtual non-contrast images generated from a patient head CT angiography exam using the standard acquisition mode demonstrated the multi-energy capability of the PCD-CT system.

Oxidized sulfur-rich arc magmas formed porphyry Cu deposits by 1.88 Ga.

Most known porphyry Cu deposits formed in the Phanerozoic and are exclusively associated with moderately oxidized, sulfur-rich, hydrous arc-related magmas derived from partial melting of the asthenospheric mantle metasomatized by slab-derived fluids. Yet, whether similar metallogenic processes also operated in the Precambrian remains obscure. Here we address the issue by investigating the origin, fO2, and S contents of calc-alkaline plutonic rocks associated with the Haib porphyry Cu deposit in the Paleoproterozoic Richtersveld Magmatic Arc (southern Namibia), an interpreted mature island-arc setting. We show that the ca. 1886-1881 Ma ore-forming magmas, originated from a mantle-dominated source with minor crustal contributions, were relatively oxidized (1‒2 log units above the fayalite-magnetite-quartz redox buffer) and sulfur-rich. These results indicate that moderately oxidized, sulfur-rich arc magma associated with porphyry Cu mineralization already existed in the late Paleoproterozoic, probably as a result of recycling of sulfate-rich seawater or sediments from the subducted oceanic lithosphere at that time.

Deep-learning-based direct synthesis of low-energy virtual monoenergetic images with multi-energy CT.

Purpose: We developed a deep learning method to reduce noise and beam-hardening artifact in virtual monoenergetic image (VMI) at low x-ray energy levels. Approach: An encoder-decoder type convolutional neural network was implemented with customized inception modules and in-house-designed training loss (denoted as Incept-net), to directly estimate VMI from multi-energy CT images. Images of an abdomen-sized water phantom with varying insert materials were acquired from a research photon-counting-detector CT. The Incept-net was trained with image patches ( 64 × 64 pixels ) extracted from the phantom data, as well as synthesized, random-shaped numerical insert materials. The whole CT images ( 512 × 512 pixels ) with the remaining real insert materials that were unseen in network training were used for testing. Seven contrast-enhanced abdominal CT exams were used for preliminary evaluation of Incept-net generalizability over anatomical background. Mean absolute percentage error (MAPE) was used to evaluate CT number accuracy. Results: Compared to commercial VMI software, Incept-net largely suppressed beam-hardening artifact and reduced noise (53%) in phantom study. Incept-net presented comparable CT number accuracy at higher-density ( P -value [0.0625, 0.999]) and improved it at lower-density inserts ( P - value = 0.0313 ) with overall MAPE: Incept-net [2.9%, 4.6%]; commercial-VMI [6.7%, 10.9%]. In patient images, Incept-net suppressed beam-hardening artifact and reduced noise (up to 50%, P - value = 0.0156 ). Conclusion: In this preliminary study, Incept-net presented the potential to improve low-energy VMI quality.

Improved coronary calcification quantification using photon-counting-detector CT: an ex vivo study in cadaveric specimens.

OBJECTIVES: To compare the accuracy of coronary calcium quantification of cadaveric specimens imaged from a photon-counting detector (PCD)-CT and an energy-integrating detector (EID)-CT. METHODS: Excised coronary specimens were scanned on a PCD-CT scanner, using both the PCD and EID subsystems. The scanning and reconstruction parameters for EID-CT and PCD-CT were matched: 120 kV, 9.3-9.4 mGy CTDIvol, and a quantitative kernel (D50). PCD-CT images were also reconstructed using a sharper kernel (D60). Scanning the same specimens using micro-CT served as a reference standard for calcified volumes. Calcifications were segmented with a half-maximum thresholding technique. Segmented calcified volume differences were analyzed using the Friedman test and post hoc pairwise Wilcoxon signed rank test with the Bonferroni correction. Image noise measurements were compared between EID-CT and PCD-CT with a repeated-measures ANOVA test and post hoc pairwise comparison with the Bonferroni correction. A p < 0.05 was considered statistically significant. RESULTS: The volume measurements in 12/13 calcifications followed a similar trend: EID-D50 > PCD-D50 > PCD-D60 > micro-CT. The median calcified volumes in EID-D50, PCD-D50, PCD-D60, and micro-CT were 22.1 (IQR 10.2-64.8), 21.0 (IQR 9.0-56.5), 18.2 (IQR 8.3-49.3), and 14.6 (IQR 5.1-42.4) mm3, respectively (p < 0.05 for all pairwise comparisons). The average image noise in EID-D50, PCD-D50, and PCD-D60 was 60.4 (± 3.5), 56.0 (± 4.2), and 113.6 (± 8.5) HU, respectively (p < 0.01 for all pairwise comparisons). CONCLUSION: The PCT-CT system quantified coronary calcifications more accurately than EID-CT, and a sharp PCD-CT kernel further improved the accuracy. The PCD-CT images exhibited lower noise than the EID-CT images. KEY POINTS: • High spatial resolution offered by PCD-CT reduces partial volume averaging and consequently leads to better morphological depiction of coronary calcifications. • Improved quantitative accuracy for coronary calcification volumes could be achieved using high-resolution PCD-CT compared to conventional EID-CT. • PCD-CT images exhibit lower image noise than conventional EID-CT at matched radiation dose and reconstruction kernel.

Deep-learning lesion and noise insertion for virtual clinical trial in Chest CT.

Accurate and objective image quality assessment is essential for the task of radiation dose optimization in clinical CT. Standard method relies on multi-reader multi-case (MRMC) studies in which radiologists are tasked to interpret diagnostic image quality of many carefully-collected positive and negative cases. The efficiency of such MRMC studies is frequently challenged by the lengthy and expensive procedure of case collection and the establishment of clinical reference standard. To address this challenge, multiple methods of virtual clinical trial to synthesize patient cases at different conditions have been proposed. Projection-domain lesion- / noise-insertion methods require the access to patient raw data and vendor-specific proprietary tools which are frequently not accessible to most users. The conventional image-domain noise-insertion methods are often challenged by the over-simplified lesion models and CT system models which may not represent conditions in real scans. In this work, we developed deep-learning lesion and noise insertion techniques that can synthesize chest CT images at different dose levels with and without lung nodules using existing patient cases. The proposed method involved a nodule-insertion convolutional neural network (CNN) and a noise-insertion CNN. Both CNNs demonstrated comparable quality to our previously-validated projection domain lesion- / noise-insertion techniques: mean structural similarity index (SSIM) of inserted nodules 0.94 (routine dose), and mean percent noise difference ~5% (50% of routine dose). The proposed deep-learning techniques for chest CT virtual clinical trial operate directly on image domain, which is more widely applicable than projection-domain techniques.

Multi-energy CT imaging for large patients using dual-source photon-counting detector CT.

Multi-energy CT imaging of large patients with conventional dual-energy (DE)-CT using an energy-integrating-detector (EID) is challenging due to photon starvation-induced image artifacts, especially in lower tube potential (80-100 kV) images. Here, we performed phantom experiments to investigate the performance of DECT for morbidly obese patients, using an iodine and water material decomposition task as an example, on an emulated dual-source (DS)-photon-counting-detector (PCD)-CT, and compared its performance with a clinical DS-EID-CT. An abdominal CT phantom with iodine inserts of different concentrations was wrapped with tissue-equivalent gel layers to emulate a large patient (50 cm lateral size). The phantom was scanned on a research whole-body single-source (SS)-PCD-CT (140 kV tube potential), a DS-PCD-CT (100/Sn140 kV; Sn140 indicates 140 kV with Sn filter), and a clinical DS-EID-CT (100/Sn140 kV) with the same radiation dose. Phantom scans were repeated five times on each system. The DS-PCD-CT acquisition was emulated by scanning twice on the SS-PCD-CT using different tube potentials. The multi-energy CT images acquired on each system were then reconstructed, and iodine- and water-specific images were generated using material decomposition. The root-mean-square-error (RMSE) between true and measured iodine concentrations were calculated for each system and compared. The images acquired on the DS-EID-CT showed severe artifacts, including ringing, reduced uniformity, and photon starvation artifacts, especially for low-energy images. These were largely reduced in DS-PCD-CT images. The CT number difference that was measured using regions-of-interest across field-of-view were reduced from 20.3 ± 0.9 (DS-EID-CT) to 2.5 ± 0.4 HU on DS-PCD-CT, showing improved image uniformity using DS-PCD-CT. Iodine RMSE was reduced from 3.42 ± 0.03 mg ml-1 (SS-PCD-CT) and 2.90 ± 0.03 mg ml-1 (DS-EID-CT) to 2.39 ± 0.05 mg ml-1 using DS-PCD-CT. DS-PCD-CT out-performed a clinical DS-EID-CT for iodine and water-based material decomposition on phantom emulating obese patients by reducing image artifacts and improving iodine quantification (RMSE reduced by 20%). With DS-PCD-CT, multi-energy CT can be performed on large patients that cannot be accommodated with current DECT.

A Blooming correction technique for improved vasa vasorum detection using an ultra-high-resolution photon-counting detector CT.

Proliferation of vasa vasorum, the microvasculature within artery walls, is an early marker of atherosclerosis. Detection of subtle changes in the spatial density of vasa vasorum using contrast-enhanced CT is challenging due to the limited spatial resolution and blooming effects. We report a forward model-based blooming correction technique to improve vasa vasorum detection in a porcine model imaged using an ultra-high resolution photon-counting detector CT. Six weeks preceding the CT study the animal received autologous blood injections in its left carotid artery to stimulate vasa vasorum proliferation within the arterial wall (right carotid served as control). The forward model predicted radial extent and magnitude of the luminal blooming affecting the wall signal by using prior data acquired with a vessel phantom of known dimensions. The predicted contamination from blooming was then subtracted from the original wall signal measurement to recover the obscured vasa vasorum signal. Attenuation measurements made on a testing vessel phantom before and after blooming corrections revealed a reduction in mean squared error by ~99.9% when compared to the ground truth. Applying corrections to contrast-enhanced carotid arteries from in vivo scan data demonstrated consistent reductions of blooming contamination within the vessel walls. An unpaired student t-test applied to measurements from the uncorrected porcine scan data revealed no significant difference between the vessel walls (p=0.26). However, after employing blooming correction, the mean enhancement was significantly greater in the injured vessel wall (p=0.0006).

Evaluation of a photon counting Medipix3RX cadmium zinc telluride spectral x-ray detector.

We assess the performance of a cadmium zinc telluride (CZT)-based Medipix3RX energy-resolving and photon-counting x-ray detector as a candidate for spectral microcomputed tomography (micro-CT) imaging. It features an array of 128 × 128 , 110 - µ m 2 pixels, each with four simultaneous threshold counters that utilize real-time charge summing. Each pixel's response is assessed by imaging with a range of incident x-ray intensities and detector integration times. Energy-related assessments are made by exposing the detector to the emission from an I-125 radioisotope brachytherapy seed. Long-term stability is assessed by repeating identical exposures over the course of 1 h. The high yield of properly functioning pixels (98.8%), long-term stability (linear regression of whole-chip response over 1 h of acquisitions: y = - 0.0038 x + 2284 ; standard deviation: 3.7 counts), and energy resolution [2.5 keV full-width half-maximum (FWHM) (single pixel), 3.7 keV FWHM (across the full image)] make this device suitable for spectral micro-CT.

Adult Quality of Life Post Cleft Palate Repair: A Comparison of Two Techniques.

BACKGROUND AND PURPOSE: In 1989, the Cleft Palate-Craniofacial Journal published the first randomized prospective cleft surgery study, comparing the Kriens intravelar veloplasty (IVV) with a non-IVV 2-flap repair. Results in that and follow-up publications yielded no difference between the 2 groups for need for secondary velopharyngeal management. The subjects have now reached adulthood. This study was designed to ask: Is there any difference between the groups in the outcomes that multidisciplinary team care addresses: speech intelligibility, facial growth, breathing while awake and asleep, attainment of education, and long-term socioeconomic status? METHODS: Enrollees from the original published study were invited to participate in a survey. Subjects responded to questions about speech therapy and speech satisfaction, additional surgery, breathing patterns, sleep quality/sleep disorder, and dental occlusion. Demographic information, information on education level, profession, and socio-economic status were queried. Student t test and Fisher exact test were used to compare results. RESULTS: Forty-two of the original 200 patients (20 Kriens IVV and 19 non-IVV) chose to participate. Average age at survey was 25 ±â€Š3 years. Analysis yielded no difference between the 2 respondent groups for need for secondary velopharyngeal management. There were no differences in speech outcome and satisfaction (8 questions, 0.30 < P < 0.97), sleep concerns (3 questions, 0.16 < P < 0.39), and dental occlusion (P = 0.69). Equivalent proportions of the 2 groups had been in speech therapy (P = 0.22). There was no difference in education attainment of the 2 groups (P = 0.26). CONCLUSIONS: The original randomized prospective trial suggested that there was no difference between the 2 surgery types in need for secondary velopharyngeal management. This long-term survey study on the same group of patients suggests that in young adulthood, the 2 groups have similar outcomes in terms of education, career choice, speech satisfaction, dental occlusion, and sleep disorder.

Progressive Tightening of the Levator Veli Palatini Muscle Improves Velopharyngeal Dysfunction in Early Outcomes of Primary Palatoplasty.

BACKGROUND: Management of the levator veli palatini with intravelar veloplasty has been shown to improve speech resonance. The senior author has introduced a more aggressive procedure where the levator is separately dissected, overlapped, and tightened. This study compares resonance results from four levator management protocols: non-intravelar veloplasty, Kriens intravelar veloplasty, radical intravelar veloplasty, and overlapping intravelar veloplasty. METHODS: Retrospective chart review was conducted on 252 patients who underwent primary palatoplasty with speech follow-up at 3 years of age. Velopharyngeal function was evaluated with perceptual speech examinations, and subjects were scored on a four-point scale (0 = normal resonance; 1 = occasional hypernasality/nasal emission/turbulence/grimacing, no further assessment warranted; 2 = mild hypernasality/intermittent nasal turbulence/grimacing, velopharyngeal imaging suggested; and 3 = severe hypernasality, surgical intervention recommended). Fisher's exact test was used to compare outcomes. RESULTS: A single surgeon performed all the non-intravelar veloplasty (n = 92), Kriens intravelar veloplasty (n = 103), and radical intravelar veloplasty (n = 31), whereas the senior author performed the overlapping intravelar veloplasty (n = 26). Cleft severity proportions were equivalent across the four methods (p = 0.28). Patients who underwent overlapping intravelar veloplasty demonstrated significantly better velopharyngeal function, and none required further imaging or secondary surgery compared with the other three procedures (p < 0.001). CONCLUSIONS: Speech resonance outcomes at 3 years of age are improved and need for secondary velopharyngeal dysfunction management is reduced with more aggressive levator dissection and reconstruction during primary one-stage palatoplasty. Results were best when the muscle was overlapped. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.