Citations

AbstractBackgroundPhoton‐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.PurposeTo 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).MethodsCoronary 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.ResultsPie‐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.ConclusionThe proposed Pie‐Net achieved substantial noise reduction while preserving HR VMI's spatial and spectral properties.

Objectives The aim of this study was to evaluate the high-helical pitch, multienergy (ME) scanning mode of a clinical dual-source photon-counting detector (PCD) computed tomography (CT) and the benefit of virtual monoenergetic images (VMIs) for low-contrast-dose coronary CT angiography (CTA). Materials and Methods High-pitch (3.2) ME coronary CTA was performed in PCD-CT in 27 patients using low contrast dose (30 mL of iohexol 350 mg/mL) and in 26 patients at routine contrast dose (60 mL). Low-energy-threshold 120 kV images (also known as T3D images) and 50 kiloelectron volts (50 keV) and 100 kiloelectron volts (100 keV) VMIs were reconstructed using a 1024 × 1024 matrix and 0.6-mm slices. The CT numbers, noise, and contrast-to-noise ratio (CNR) were measured in the ascending aorta (AA), left main coronary artery (LMCA), and distal left anterior descending (LAD) artery. Confidence in grading luminal stenosis with calcific plaque, noncalcific plaque, and stent was evaluated by 2 independent readers on a 0–100 scale (0 the lowest), and a CAD-RADS score was assigned. Image contrast enhancement, sharpness, noise, artifacts, and overall image quality were rated using a 5-point ordinal scale (1 the lowest). Results The radiation doses (CTDI) in low- and routine-contrast cohorts were 2.5 ± 0.6 mGy and 3.1 ± 1.7 mGy, respectively ( P = 0.12). At all measured locations, the mean CT number was >300 HU in 120 kV (LMCA 382.9 ± 76.2, distal LAD 341.0 ± 53.9, AA 399.5 ± 76.1) and 50 keV images (LMCA 667.5 ± 139.9, distal LAD 578.1 ± 121.5, AA 700.8 ± 142.5) in the low-contrast cohort, with a 96% increase in CT numbers for 50 keV over 120 kV. The CT numbers were significantly higher ( P < 0.0001) in 50 keV than 120 kV and 100 keV VMI. The CNR was also significantly ( P < 0.0001) higher in 50 keV than 120 kV and 100 keV images in all vessels. Confidence in the assessment of luminal stenosis in the presence of calcific plaque was significantly higher ( P = 0.001) with the addition of 100 keV VMI (median score, 100) than using 50 keV alone (median score, 70) and 120 kV (median score, 70) for reader 1, but no significant differences were seen for reader 2 who had same median scores of 100 for all image types. The confidence in the assessment of luminal stenosis within a stent improved with the use of 100 keV images for both readers (reader 1: median scores for 50 + 100 keV = 100, 50 keV = 82.5, 120 kV = 82.5; reader 2: 50 + 100 keV = 100, 50 keV = 90, 120 kV = 90). There were no significant differences in confidence scores for assessment of luminal stenosis from noncalcific plaques for both readers. The reader-averaged qualitative scores for vascular enhancement and overall image quality were significantly higher for 50 keV VMI than for 120 kV images in both low- and routine-contrast dose cohorts. The image sharpness was nonsignificantly higher at 50 keV VMI than 120 kV images, and the artifact score was comparable for 50 keV VMI and 120 kV images. The noise was higher in 50 keV VMI than in 120 kV images. Conclusions High-pitch ME PCD-CT mode produced diagnostic quality coronary CTA images at low radiation and iodinated contrast doses. The availability of ME VMIs significantly improved the CNR, overall image quality, and confidence in assessment of luminal stenosis in the presence of calcific plaques and stents, and resulted in change of CAD-RADS categories in 9 patients.

AbstractObjectiveTo measure the accuracy of material decomposition using a dual‐source photon‐counting‐detector (DS‐PCD) CT operated in the high‐pitch helical scanning mode and compare the results against dual‐source energy‐integrating‐detector (DS‐EID) CT, which requires use of a low‐pitch value in dual‐energy mode.MethodsA DS‐PCD CT and a DS‐EID CT were used to scan a cardiac motion phantom consisting of a 3‐mm diameter iodine cylinder. Iodine maps were reconstructed using DS‐PCD in high‐pitch mode and DS‐EID in low‐pitch mode. Image‐based circularity, diameter, and iodine concentration of the iodine cylinder were calculated and compared between the two scanners. With institutional review board approval, in vivo exams were performed with the DS‐PCD CT in high‐pitch mode. Images were qualitatively compared against patients with similar heart rates that were scanned with DS‐EID CT in low‐pitch dual‐energy mode.ResultsOn iodine maps, the mean circularity was 0.97 ± 0.02 with DS‐PCD in high‐pitch mode and 0.95 ± 0.06 with DS‐EID in low‐pitch mode. The mean diameter was 2.9 ± 0.2 mm with DS‐PCD and 3.1 ± 0.2 mm with DS‐EID, both of which are close to the 3 mm ground truth. For DS‐PCD, the mean iodine concentration was 9.6 ± 0.8 mg/ml and this was consistent with the 9.4 ± 0.6 mg/ml value obtained with the cardiac motion disabled. For DS‐EID, the concentration was 12.7 ± 1.2 mg/ml with motion enabled and 11.7 ± 0.5 mg/ml disabled. The background noise in the iodine maps was 15.1 HU with DS‐PCD and 14.4 HU with DS‐EID, whereas the volume CT dose index (CTDIvol) was 3 mGy with DS‐PCD and 11 mGy with DS‐EID. On comparison of six patients (three on PCD, three on EID) with similar heart rates, DS‐PCD provided iodine maps with well‐defined coronaries even at a high heart rate of 86 beats per minute. Meanwhile, there were substantial motion artifacts in iodine maps obtained with DS‐EID for patients with similar heart rates.ConclusionIn a cardiac motion phantom, DS‐PCD CT can perform accurate material decomposition in high‐pitch mode, providing iodine maps with excellent geometric accuracy and robustness to motion at approximately 38% of the dose for similar noise as DS‐EID CT.

AbstractBackgroundDeep artificial neural networks such as convolutional neural networks (CNNs) have been shown to be effective models for reducing noise in CT images while preserving anatomic details. A practical bottleneck for developing CNN‐based denoising models is the procurement of training data consisting of paired examples of high‐noise and low‐noise CT images. Obtaining these paired data are not practical in a clinical setting where the raw projection data is not available. This work outlines a technique to optimize CNN denoising models using methods that are available in a routine clinical setting.PurposeTo demonstrate a phantom‐based training framework for CNN noise reduction that can be efficiently implemented on any CT scanner.MethodsThe phantom‐based training framework uses supervised learning in which training data are synthesized using an image‐based noise insertion technique. Ten patient image series were used for training and validation (9:1) and noise‐only images obtained from anthropomorphic phantom scans. Phantom noise‐only images were superimposed on patient images to imitate low‐dose CT images for use in training. A modified U‐Net architecture was used with mean‐squared‐error and feature reconstruction loss. The training framework was tested for clinically indicated whole‐body‐low‐dose CT images, as well as routine abdomen‐pelvis exams for which projection data was unavailable. Performance was assessed based on root‐mean‐square error, structural similarity, line profiles, and visual assessment.ResultsWhen the CNN was tested on five reserved quarter‐dose whole‐body‐low‐dose CT images, noise was reduced by 75%, root‐mean‐square‐error reduced by 34%, and structural similarity increased by 60%. Visual analysis and line profiles indicated that the method significantly reduced noise while maintaining spatial resolution of anatomic features.ConclusionThe proposed phantom‐based training framework demonstrated strong noise reduction while preserving spatial detail. Because this method is based within the image domain, it can be easily implemented without access to projection data.

AbstractBackgroundCurrent CT quality control (QC) for low‐contrast detectability relies on visual inspection and measurement of contrast‐to‐noise ratio (CNR). However, CNR numbers become unreliable when it comes to nonlinear methods, such as iterative reconstruction (IR) and deep‐learning‐based techniques. Image quality metrics using channelized Hotelling observer (CHO) have been validated to be well correlated with human observer performance on phantom‐based and patient‐based tasks, but it has not been widely used in routine CT QC mainly because the CHO calculation typically requires a large number of repeated scans in order to provide accurate and precise estimate of index of detectability (d′).PurposeThe main goal of this work is to optimize channel filters and other CHO parameters and accurately estimate the low‐contrast detectability with minimum number of repeated scans for the widely used American College of Radiology (ACR) CT accreditation phantom so that it can become practically feasible for routine CT QC tests.MethodsTo provide a converged d′ value, an ACR phantom was repeatedly scanned 100 times at three dose levels (24, 12, and 6 mGy). Images were reconstructed with two kernels (FBP Br44 and IR Br44‐3). d′ as a function of number of repeated scans was determined for different number of background regions of interest (ROIs), different number of low‐contrast objects, different number of slices per each object, and different channel filter options. A reference d′ was established using the optimized CHO setting, and the bias of d′ was quantified using the d′ calculated from all 100 repeated scans. The variation of d′ at each condition was estimated using a resampling method combining random subsampling among 100 repeated scans and bootstrapping of the ensembles of signal and background ROIs.ResultsOptimized parameters in CHO calculation were determined: two background ROIs per object, four objects per low‐contrast object size, nine non‐overlapping slices per object, and a 4‐channel Gabor filter. The bias and uncertainty were estimated at different numbers of repeated scans using these parameters. When only one single scan was used in the CHO calculation, the bias of d′ was below 6.2% and the uncertainty 15.6‐19.6% for the 6, 5, and 4 mm objects, while with three repeated scans the bias was below 2.0% and uncertainty 8.7‐10.9% for the three object sizes.ConclusionWith optimized parameter settings in CHO, efficient and accurate measurement of low‐contrast detectability on the commonly used ACR phantom becomes feasible, which could potentially lead to adoption of CHO‐based low‐contrast evaluation in routine QC tests.

Introduction: The surgical technique for urinary stone removal is partly influenced by its fragility, as prognosticated by the clinician. This feasibility study aims to develop a linear regression model from CT-based radiomic markers to predict kidney stone comminution time in vivo with two ultrasonic lithotrites. Materials and Methods: Patients identified by urologists at our institution as eligible candidates for percutaneous nephrolithotomy were prospectively enrolled. The active engagement time of the lithotrite in breaking the stone during surgery denoted the comminution time of each stone. The comminution rate was computed as the stone volume disintegrated per minute. Stones were grouped into three fragility classes (fragile, moderate, hard), based on inverse of the comminution rates with respect to the mean. Multivariable linear regression models were trained with radiomic features extracted from clinical CT images to predict comminution times in vivo . The model with the least root mean squared error (RMSE) on comminution times and the fewest misclassification of fragility was finally selected. Results: Twenty-eight patients with 31 stones in total were included in this study. Stones in the cohort averaged 1557 (±2472) mm 3 in volume and 5.3 (±7.4) minutes in comminution time. Ten stones had nonmoderate fragility. Linear regression of stone volume alone predicted comminution time with an RMSE of 6.8 minutes and missed all 10 stones with nonmoderate fragility. A fragility model that included stone volume, internal morphology, shape-based radiomics, and device type improved RMSE to below 3.3 minutes and correctly classified 20/21 moderate and 6/10 nonmoderate stones. Conclusions: CT metrics-based fragility models may provide information to surgeons regarding kidney stone fragility and facilitate the selection of stone removal procedures.