Determination of Optimal Image Type and Lowest Detectable Concentration for Iodine Detection on a Photon Counting Detector-Based Multi-Energy CT System.

Photon counting detector (PCD) based multi-energy CT is able to generate different types of images such as virtual monoenergetic images (VMIs) and material specific images (e.g., iodine maps) in addition to the conventional single energy images. The purpose of this study is to determine the image type that has optimal iodine detection and to determine the lowest detectable iodine concentration using a PCD-CT system. A 35 cm body phantom with iodine inserts of 4 concentrations and 2 sizes was scanned on a research PCD-CT system. For each iodine concentration, 80 repeated scans were performed and images were reconstructed for each energy threshold. In addition, VMIs at different keVs and iodine maps were also generated. CNR was measured for each type of images. A channelized Hotelling observer was used to assess iodine detectability after being validated with human observer studies, with area under the ROC curve (AUC) as a figure of merit. The agreement between model and human observer performance indicated that model observer could serve as an effective approach to determine optimal image type for the clinical practice and to determine the lowest detectable iodine concentration. Results demonstrated that for all size and concentration combinations, VMI at 70 keV had similar performance as that of threshold low images, both of which outperformed the iodine map images. At the AUC value of 0.8, iodine concentration as low as 0.2 mgI/cc could be detected for an 8 mm object and 0.5 mgI/cc for a 4 mm object with a 5 mm slice thickness.

Impact of Photon Counting Detector Technology on kV Selection and Diagnostic Workflow in CT.

The purpose of this study is to determine the optimal iodine contrast-to-noise ratio (CNR) achievable for different patient sizes using virtual-monoenergetic-images (VMIs) and a universal acquisition protocol on photon-counting-detector CT (PCD-CT), and to compare results to those from single-energy (SE) and dual-source-dual-energy (DSDE) CT. Vials containing 3 concentrations of iodine were placed in torso-shaped water phantoms of 5 sizes and scanned on a 2nd generation DSDE scanner with both SE and DE modes. Tube current was automatically adjusted based on phantom size with CTDIvol ranging from 5.1 to 22.3 mGy. PCD-CT scans were performed at 140 kV, 25 and 75 keV thresholds, with CTDIvol matched to the SE scans. DE VMIs were created and CNR was calculated for SE images and DE VMIs. The optimal kV (SE) or keV (DE VMI) was chosen at the point of highest CNR with no noticeable artifacts. For 10 mgI/cc vials in the 35 cm phantom, the optimal CNR of VMIs on PCD (22.6@50keV) was comparable to that of the best DSDE protocol (23.9@50keV) and was higher than that of the best SE protocol (19.7@80kV). In general, the difference of optimal CNR between PCD and SE increased with phantom size, with PCD 50 keV VMIs having an equivalent CNR (0.6% difference) with that of SE at the 25 cm phantom and 57% higher CNR at the 45 cm phantom. PCD-CT demonstrated comparable iodine CNR of VMIs to that of DSDE across patient sizes. Whereas SE and DSDE CT exams require use of patient-size-specific acquisitions settings, our findings point to the ability of PCD-CT to simplify protocol selection, using a single VMI keV setting (50 keV), acquisition kV (140 kV), and energy thresholds (25 and 75 keV) for all patient sizes, while achieving optimal or near optimal iodine CNR values.

Ultra-High Resolution Photon-Counting Detector CT Reconstruction using Spectral Prior Image Constrained Compressed-Sensing (UHR-SPICCS).

Photon-counting detector based CT (PCD-CT) enables dose efficient high resolution imaging, in addition to providing multi-energy information. This allows better delineation of anatomical structures crucial for several clinical applications ranging from temporal bone imaging to pulmonary nodule visualization. Due to the smaller detector pixel sizes required for high resolution imaging, the PCD-CT images suffer from higher noise levels. The image quality is further degraded in narrow energy bins as a consequence of low photon counts. This limits the potential benefits that high-resolution PCD-CT could offer. Conventional reconstruction techniques such as the filtered back projection (FBP) have poor performance when reconstructing noisy CT projection data. To enable low noise multi-energy reconstructions, we employed a spectral prior image constrained compressed sensing (SPICCS) framework that exploits the spatio-spectral redundancy in the multi-energy acquisitions. We demonstrated noise reduction in narrow energy bins without losing energy-specific attenuation information and spatial resolution. We scanned an anthropomorphic head phantom, and a euthanized pig using our whole-body prototype PCD-CT system in the ultra-high resolution mode at 120kV. Image reconstructions were performed using SPICCS and compared with conventional FBP. Noise reduction of 18 to 46% was noticed in narrow energy bins corresponding to 25 - 65 keV and 65 - 12 keV, while the mean CT number was preserved. Spatial resolution measurement showed similar modulation transfer function (MTF) values between FBP and SPICCS, demonstrating preservation of spatial resolution.

Measuring arterial wall perfusion using photon-counting computed tomography (CT): improving CT number accuracy of artery wall using image deconvolution.

Changes in arterial wall perfusion mark the onset of atherosclerosis. A characteristic change is the increased spatial density of vasa vasorum (VV), the microvessels in the arterial walls. Measuring this increased VV (IVV) density using contrast-enhanced computed tomography (CT) has had limited success due to blooming effects from contrast media. If the system point-spread function (PSF) is known, then the blooming effect can be modeled as a convolution between the true signal and the PSF. We report the application of image deconvolution to improve the CT number accuracy in the arterial wall of a phantom and in a porcine model of IVV density, both scanned using a whole-body research photon-counting CT scanner. A 3D-printed carotid phantom filled with three concentrations of iodinated contrast material was scanned to assess blooming and its effect on wall CT number accuracy. The results showed a reduction in blooming effects following image deconvolution, and, consequently, a better delineation between lumen and wall was achieved. Results from the animal experiment showed improved CT number difference between the carotid with IVV density and the normal carotid artery after deconvolution, enabling the detection of VV proliferation, which may serve as an early indicator of atherosclerosis.

Detection of increased vasa vasorum in artery walls: Improving CT number accuracy using image deconvolution.

Changes in arterial wall perfusion are an indicator of early atherosclerosis. This is characterized by an increased spatial density of vasa vasorum (VV), the micro-vessels that supply oxygen and nutrients to the arterial wall. Detection of increased VV during contrast-enhanced computed tomography (CT) imaging is limited due to contamination from blooming effect from the contrast-enhanced lumen. We report the application of an image deconvolution technique using a measured system point-spread function, on CT data obtained from a photon-counting CT system to reduce blooming and to improve the CT number accuracy of arterial wall, which enhances detection of increased VV. A phantom study was performed to assess the accuracy of the deconvolution technique. A porcine model was created with enhanced VV in one carotid artery; the other carotid artery served as a control. CT images at an energy range of 25-120 keV were reconstructed. CT numbers were measured for multiple locations in the carotid walls and for multiple time points, pre and post contrast injection. The mean CT number in the carotid wall was compared between the left (increased VV) and right (control) carotid arteries. Prior to deconvolution, results showed similar mean CT numbers in the left and right carotid wall due to the contamination from blooming effect, limiting the detection of increased VV in the left carotid artery. After deconvolution, the mean CT number difference between the left and right carotid arteries was substantially increased at all the time points, enabling detection of the increased VV in the artery wall.