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.