Citations

AbstractPurposeTo demonstrate the feasibility of simultaneous dual‐contrast imaging in a large animal using a newly developed dual‐source energy‐integrating detector (EID)‐based multi‐energy computed tomography (MECT) system.MethodsTwo imaging tasks that may have potential clinical applications were investigated: head/neck (HN) CT angiography (CTA)/CT venography (CTV) with iodine and gadolinium, and small bowel imaging with iodine and bismuth in domestic swine. Dual‐source X‐ray beam configurations of 70 kV + Au120/Sn120 kV and 70 kV + Au140/Sn140 kV were used for the HN‐CTA/CTV and small bowel imaging studies, respectively. A test bolus scan was performed for each study. The regions of interest (ROIs) in the carotid artery and jugular vein for HN‐CTA/CTV imaging and abdominal aorta for small bowel imaging were used to determine the time‐attenuation curves, based on which the timing for contrast injection and the CT scan was determined. In the HN‐CTA/CTV study, an MECT scan was performed at the time point corresponding to the optimal arterial enhancement by iodine and the optimal venous enhancement by gadolinium. In the small bowel imaging study, an MECT scan was performed at the optimal time point to simultaneously capture the mesenteric arterial enhancement of iodine and the enteric enhancement of bismuth. Image‐based material decomposition was performed to decompose different materials for each study. To quantitatively characterize contrast material separation and misclassification, two ROIs on left common carotid artery and left internal jugular vein in HN‐CTA/CTV imaging and three ROIs on superior mesenteric artery, ileal lumen, and collapsed ileum (ileal wall) in small bowel imaging were placed to measure the mean concentration values and the standard deviations.ResultsIn the HN‐CTA/CTV study, common carotid arteries containing iodine and internal/external jugular veins containing gadolinium were clearly delineated from each other. Fine vessels such as cephalic veins and branches of external jugular veins were noticeable but clear visualization was hindered by image noise in gadolinium‐specific (CTV) images, as reviewed by a neuroradiologist. In the small bowel imaging study, the mesenteric arteries and collapsed bowel wall containing iodine and the small bowel loops containing bismuth were clearly distinctive from each other in the iodine‐ and bismuth‐specific images after material decomposition, as reviewed by an abdominal radiologist. Quantitative analyses showed that the misclassifications between the two contrast materials were less than 1.7 and 0.1 mg/ml for CTA/CTV and small bowel imaging studies, respectively.ConclusionsFeasibility of simultaneous CTA/CTV imaging in head and neck with iodine and gadolinium and simultaneous imaging of arterial and enteric phases of small bowel with iodine and bismuth, using a dual‐source EID‐MECT system, was demonstrated in a swine study. Compared to iodine and gadolinium in CTA/CTV, better delineation and classification of iodine and bismuth in small bowel imaging were achieved mainly due to wider separation between the corresponding two K‐edge energies.

AbstractPurposeConventional model observers (MO) in CT are often limited to a uniform background or varying background that is random and can be modeled in an analytical form. It is unclear if these conventional MOs can be readily generalized to predict human observer performance in clinical CT tasks that involve realistic anatomical background. Deep‐learning‐based model observers (DL‐MO) have recently been developed, but have not been validated for challenging low contrast diagnostic tasks in abdominal CT. We consequently sought to validate a DL‐MO for a low‐contrast hepatic metastases localization task.MethodsWe adapted our recently developed DL‐MO framework for the liver metastases localization task. Our previously‐validated projection‐domain lesion‐/noise‐insertion techniques were used to synthesize realistic positive and low‐dose abdominal CT exams, using the archived patient projection data. Ten experimental conditions were generated, which involved different lesion sizes/contrasts, radiation dose levels, and image reconstruction types. Each condition included 100 trials generated from a patient cohort of 7 cases. Each trial was presented as liver image patches (160×160×5 voxels). The DL‐MO performance was calculated for each condition and was compared with human observer performance, which was obtained by three sub‐specialized radiologists in an observer study. The performance of DL‐MO and radiologists was gauged by the area under localization receiver‐operating‐characteristic curves. The generalization performance of the DL‐MO was estimated with the repeated twofold cross‐validation method over the same set of trials used in the human observer study. A multi‐slice Channelized Hoteling Observers (CHO) was compared with the DL‐MO across the same experimental conditions.ResultsThe performance of DL‐MO was highly correlated to that of radiologists (Pearson's correlation coefficient: 0.987; 95% CI: [0.942, 0.997]). The performance level of DL‐MO was comparable to that of the grouped radiologists, that is, the mean performance difference was ‐3.3%. The CHO performance was poorer than the grouped radiologist performance, before internal noise could be added. The correlation between CHO and radiologists was weaker (Pearson's correlation coefficient: 0.812, and 95% CI: [0.378, 0.955]), and the corresponding performance bias (‐29.5%) was statistically significant.ConclusionThe presented study demonstrated the potential of using the DL‐MO for image quality assessment in patient abdominal CT tasks.

Objectives: The objectives of the study were to estimate the impact of high matrix image reconstruction on chest computed tomography (CT) compared to standard image reconstruction. Material and Methods: This retrospective study included patients with interstitial or parenchymal lung disease, airway disease, and pulmonary nodules who underwent chest CT. Chest CT images were reconstructed using high matrix (1024 × 1024) or standard matrix (512 × 512), with all other parameters matched. Two radiologists, blinded to reconstruction technique, independently examined each lung, viewing image sets side by side and rating the conspicuity of imaging findings using a 5-point relative conspicuity scale. The presence of pulmonary nodules and confidence in classification of internal attenuation was also graded. Overall image quality and subjective noise/artifacts were assessed. Results: Thirty-four patients with 68 lungs were evaluated. Relative conspicuity scores were significantly higher using high matrix image reconstruction for all imaging findings indicative of idiopathic lung fibrosis (peripheral airway visualization, interlobular septal thickening, intralobular reticular opacity, and end-stage fibrotic change; P ≤ 0.001) along with emphysema, mosaic attenuation, and fourth order bronchi for both readers (P ≤ 0.001). High matrix reconstruction did not improve confidence in the presence or classification of internal nodule attenuation for either reader. Overall image quality was increased but not subjective noise/artifacts with high matrix image reconstruction for both readers (P < 0.001). Conclusion: High matrix image reconstruction significantly improves the conspicuity of imaging findings reflecting interstitial lung disease and may be useful for diagnosis or treatment response assessment.

AbstractPurposeEye‐tracking approaches have been used to understand the visual search process in radiology. However, previous eye‐tracking work in computer tomography (CT) has been limited largely to single cross‐sectional images or video playback of the reconstructed volume, which do not accurately reflect radiologists' visual search activities and their interactivity with three‐dimensional image data at a computer workstation (e.g., scroll, pan, and zoom) for visual evaluation of diagnostic imaging targets. We have developed a platform that integrates eye‐tracking hardware with in‐house‐developed reader workstation software to allow monitoring of the visual search process and reader‐image interactions in clinically relevant reader tasks. The purpose of this work is to validate the spatial accuracy of eye‐tracking data using this platform for different eye‐tracking data acquisition modes.MethodsAn eye‐tracker was integrated with a previously developed workstation designed for reader performance studies. The integrated system captured real‐time eye movement and workstation events at 1000 Hz sampling frequency. The eye‐tracker was operated either in head‐stabilized mode or in free‐movement mode. In head‐stabilized mode, the reader positioned their head on a manufacturer‐provided chinrest. In free‐movement mode, a biofeedback tool emitted an audio cue when the head position was outside the data collection range (general biofeedback) or outside a narrower range of positions near the calibration position (strict biofeedback). Four radiologists and one resident were invited to participate in three studies to determine eye‐tracking spatial accuracy under three constraint conditions: head‐stabilized mode (i.e., with use of a chin rest), free movement with general biofeedback, and free movement with strict biofeedback. Study 1 evaluated the impact of head stabilization versus general or strict biofeedback using a cross‐hair target prior to the integration of the eye‐tracker with the image viewing workstation. In Study 2, after integration of the eye‐tracker and reader workstation, readers were asked to fixate on targets that were randomly distributed within a volumetric digital phantom. In Study 3, readers used the integrated system to scroll through volumetric patient CT angiographic images while fixating on the centerline of designated blood vessels (from the left coronary artery to dorsalis pedis artery). Spatial accuracy was quantified as the offset between the center of the intended target and the detected fixation using units of image pixels and the degree of visual angle.ResultsThe three head position constraint conditions yielded comparable accuracy in the studies using digital phantoms. For Study 1 involving the digital crosshairs, the median ± the standard deviation of offset values among readers were 15.2 ± 7.0 image pixels with the chinrest, 14.2 ± 3.6 image pixels with strict biofeedback, and 19.1 ± 6.5 image pixels with general biofeedback. For Study 2 using the random dot phantom, the median ± standard deviation offset values were 16.7 ± 28.8 pixels with use of a chinrest, 16.5 ± 24.6 pixels using strict biofeedback, and 18.0 ± 22.4 pixels using general biofeedback, which translated to a visual angle of about 0.8° for all three conditions. We found no obvious association between eye‐tracking accuracy and target size or view time. In Study 3 viewing patient images, use of the chinrest and strict biofeedback demonstrated comparable accuracy, while the use of general biofeedback demonstrated a slightly worse accuracy. The median ± standard deviation of offset values were 14.8 ± 11.4 pixels with use of a chinrest, 21.0 ± 16.2 pixels using strict biofeedback, and 29.7 ± 20.9 image pixels using general biofeedback. These corresponded to visual angles ranging from 0.7° to 1.3°.ConclusionsAn integrated eye‐tracker system to assess reader eye movement and interactive viewing in relation to imaging targets demonstrated reasonable spatial accuracy for assessment of visual fixation. The head‐free movement condition with audio biofeedback performed similarly to head‐stabilized mode.

AbstractObjectiveIn X‐raycomputed tomography (CT), many important clinical applications may benefit from a fast acquisition speed. The helical scan is the most widely used acquisition mode in clinical CT, where a fast helical pitch can improve the acquisition speed. However, on a typical single‐source helical CT (SSCT) system, the helical pitch p typically cannot exceed 1.5; otherwise, reconstruction artifacts will result from data insufficiency. The purpose of this work is to develop a deep convolutional neural network (CNN) to correct for artifacts caused by an ultra‐fast pitch, which can enable faster acquisition speed than what is currently achievable.MethodsA customized CNN (denoted as ultra‐fast‐pitch network (UFP‐net)) was developed to restore the underlying anatomical structure from the artifact‐corrupted post‐reconstruction data acquired from SSCT with ultra‐fast pitch (i.e., p ≥ 2). UFP‐net employed residual learning to capture the features of image artifacts. UFP‐net further deployed in‐house‐customized functional blocks with spatial‐domain local operators and frequency‐domain non‐local operators, to explore multi‐scale feature representation. Images of contrast‐enhanced patient exams (n = 83) with routine pitch setting (i.e., p < 1) were retrospectively collected, which were used as training and testing datasets. This patient cohort involved CT exams over different scan ranges of anatomy (chest, abdomen, and pelvis) and CT systems (Siemens Definition, Definition Flash, Definition AS+, Siemens Healthcare, Inc.), and the corresponding base CT scanning protocols used consistent settings of major scan parameters (e.g., collimation and pitch). Forward projection of the original images was calculated to synthesize helical CT scans with one regular pitch setting (p = 1) and two ultra‐fast‐pitch setting (p = 2 and 3). All patient images were reconstructed using the standard filtered‐back‐projection (FBP) algorithm. A customized multi‐stage training scheme was developed to incrementally optimize the parameters of UFP‐net, using ultra‐fast‐pitch images as network inputs and regular pitch images as labels. Visual inspection was conducted to evaluate image quality. Structural similarity index (SSIM) and relative root‐mean‐square error (rRMSE) were used as quantitative quality metrics.ResultsThe UFP‐net dramatically improved image quality over standard FBP at both ultra‐fast‐pitch settings. At p = 2, UFP‐net yielded higher mean SSIM (> 0.98) with lower mean rRMSE (< 2.9%), compared to FBP (mean SSIM < 0.93; mean rRMSE > 9.1%). Quantitative metrics at p = 3: UFP‐net—mean SSIM [0.86, 0.94] and mean rRMSE [5.0%, 8.2%]; FBP—mean SSIM [0.36, 0.61] and mean rRMSE [36.0%, 58.6%].ConclusionThe proposed UFP‐net has the potential to enable ultra‐fast data acquisition in clinical CT without sacrificing image quality. This method has demonstrated reasonable generalizability over different body parts when the corresponding CT exams involved consistent base scan parameters.

Background Due to their easy accessibility, CT scans have been increasingly used for investigation of gastrointestinal (GI) bleeding. Purpose To estimate the performance of a dual-phase, dual-energy (DE) GI bleed CT protocol in patients with overt GI bleeding in clinical practice and examine the added value of portal phase and DE images. Materials and Methods Consecutive patients with GI bleeding underwent a two-phase DE GI bleed CT protocol. Two gastroenterologists established the reference standard. Performance was estimated using clinical CT reports. Three GI radiologists rated confidence in GI bleeding in a subset of 62 examinations, evaluating first mixed kV arterial images, then after examining additional portal venous phase images, and finally after additional DE images (virtual non-contrast and virtual monoenergetic 50 keV images). Results 52 of 176 patients (29.5%) had GI bleeding by the reference standard. The overall sensitivity, specificity, and positive and negative predictive values of the CT GI bleed protocol for detecting GI bleeding were 65.4%, 89.5%, 72.3%, and 86.0%, respectively. In patients with GI bleeding, diagnostic confidence of readers increased after adding portal phase images to arterial phase images ( p = 0.002), without additional benefit from dual energy images. In patients without GI bleeding, confidence in luminal extravasation appropriately decreased after adding portal phase, and subsequently DE images ( p = 0.006, p = 0.018). Conclusion A two-phase DE GI bleed CT protocol had high specificity and negative predictive value in clinical practice. Portal venous phase images improved diagnostic confidence in comparison to arterial phase images alone. Dual-energy images further improved radiologist confidence in the absence of bleeding.

Abstract 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 125 μ m 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 −1 for iodine concentrations (2–15 mg cc −1 ) and 14.2 mg cc −1 for 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.