PurposeWe 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.ApproachPKAID-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.ResultsPKAID-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.ConclusionsThe PKAID-Net provides substantial noise reduction while maintaining spatial and spectral fidelity of the HR VMIs from PCD-CT.
PurposeCoronary 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.ApproachCAC 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.ResultsCompared 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.ConclusionsUHR 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.
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.
PurposeThe 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.ApproachVasa 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.ResultsThe 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.ConclusionsWhole-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.
Coronary artery calcification is an important indicator of coronary disease. Accurate volume quantification of coronary calcification using computed tomography (CT) is challenging due to calcium blooming. In this study, ex-vivo coronary specimens were scanned on an investigational photon-counting detector (PCD) CT scanner and the estimated coronary calcification volume were compared with a conventional energy-integrating detector (EID) CT. An image-based denoising algorithm was applied to the PCD-CT images to achieve similar noise levels as EID-CT. Calcifications were segmented to estimate the volume, with micro-CT images of the same calcifications serving as reference. PCD-CT images showed reduced calcium blooming artifacts.
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.
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.
Computed tomography (CT) using photon-counting detectors (PCD) offers dose-efficient ultra-high-resolution imaging, high iodine contrast-to-noise ratio, multi-energy and material decomposition capabilities. We have previously demonstrated the potential benefits of PCD-CT using phantoms, cadavers, and human studies on a prototype PCD-CT system. This system, however, had several limitations in terms of scan field-of-view (FOV) and longitudinal coverage. Recently, a full FOV (50 cm) PCD-CT system with wider longitudinal coverage and higher spatial resolution (0.15 mm detector pixels) has been installed in our lab capable of human scanning at clinical dose and dose rate. In this work, we share our initial experience of the new PCD-CT system and compare its performance with a state-of-the-art 3rd generation dual-source CT scanner. Basic image quality was assessed using an ACR CT accreditation phantom, high-resolution performance using an anthropomorphic head phantom, and multi-energy and material decomposition performance using a multi-energy CT phantom containing various concentrations of iodine and hydroxyapatite. Finally, we demonstrate the feasibility of high-resolution, full FOV PCD-CT imaging for improved delineation of anatomical and pathological features in a patient with pulmonary nodules.
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).
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-μm2 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 = − 0.0038x + 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.
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