Citations

Abstract Objective . Photon-counting detector (PCD) CT enables routine virtual-monoenergetic image (VMI) reconstruction. We evaluated the performance of an automatic VMI energy level (keV) selection tool on a clinical PCD-CT system in comparison to an automatic tube potential (kV) selection tool from an energy-integrating-detector (EID) CT system from the same manufacturer. Approach. Four torso-shaped phantoms (20–50 cm width) containing iodine (2, 5, and 10 mg cc −1 ) and calcium (100 mg cc −1 ) were scanned on PCD-CT and EID-CT. Dose optimization techniques, task-based VMI energy level and tube-potential selection on PCD-CT (CARE keV) and task-based tube potential selection on EID-CT (CARE kV), were enabled. CT numbers, image noise, and dose-normalized contrast-to-noise ratio (CNR d ) were compared. Main results . PCD-CT produced task-specific VMIs at 70, 65, 60, and 55 keV for non-contrast, bone, soft tissue with contrast, and vascular settings, respectively. A 120 kV tube potential was automatically selected on PCD-CT for all scans. In comparison, EID-CT used x-ray tube potentials from 80 to 150 kV based on imaging task and phantom size. PCD-CT achieved consistent dose reduction at 9%, 21% and 39% for bone, soft tissue with contrast, and vascular tasks relative to the non-contrast task, independent of phantom size. On EID-CT, dose reduction factor for contrast tasks relative to the non-contrast task ranged from a 65% decrease (vascular task, 70 kV, 20 cm phantom) to a 21% increase (soft tissue with contrast task, 150 kV, 50 cm phantom) due to size-specific tube potential adaptation. PCD-CT CNR d was equivalent to or higher than those of EID-CT for all tasks and phantom sizes, except for the vascular task with 20 cm phantom, where 70 kV EID-CT CNR d outperformed 55 keV PCD-CT images. Significance . PCD-CT produced more consistent CT numbers compared to EID-CT due to standardized VMI output, which greatly benefits standardization efforts and facilitates radiation dose reduction.

AbstractBackgroundThe detectability performance of a CT scanner is difficult to precisely quantify when nonlinearities are present in reconstruction. An efficient detectability assessment method that is sensitive to small effects of dose and scanner settings is desirable. We previously proposed a method using a search challenge instrument: a phantom is embedded with hundreds of lesions at random locations, and a model observer is used to detect lesions. Preliminary tests in simulation and a prototype showed promising results.PurposeIn this work, we fabricated a full‐size search challenge phantom with design updates, including changes to lesion size, contrast, and number, and studied our implementation by comparing the lesion detectability from a nonprewhitening (NPW) model observer between different reconstructions at different exposure levels, and by estimating the instrument sensitivity to detect changes in dose.MethodsDesigned to fit into QRM anthropomorphic phantoms, our search challenge phantom is a cylindrical insert 10 cm wide and 4 cm thick, embedded with 12 000 lesions (nominal width of 0.6 mm, height of 0.8 mm, and contrast of −350 HU), and was fabricated using PixelPrint, a 3D printing technique. The insert was scanned alone at a high dose to assess printing accuracy. To evaluate lesion detectability, the insert was placed in a QRM thorax phantom and scanned from 50 to 625 mAs with increments of 25 mAs, once per exposure level, and the average of all exposure levels was used as high‐dose reference. Scans were reconstructed with three different settings: filtered‐backprojection (FBP) with Br40 and Br59, and Sinogram Affirmed Iterative Reconstruction (SAFIRE) with strength level 5 and Br59 kernel. An NPW model observer was used to search for lesions, and detection performance of different settings were compared using area under the exponential transform of free response ROC curve (AUC). Using propagation of uncertainty, the sensitivity to changes in dose was estimated by the percent change in exposure due to one standard deviation of AUC, measured from 5 repeat scans at 100, 200, 300, and 400 mAs.ResultsThe printed insert lesions had an average position error of 0.20 mm compared to printing reference. As the exposure level increases from 50 mAs to 625 mAs, the lesion detectability AUCs increase from 0.38 to 0.92, 0.42 to 0.98, and 0.41 to 0.97 for FBP Br40, FBP Br59, and SAFIRE Br59, respectively, with a lower rate of increase at higher exposure level. FBP Br59 performed best with AUC 0.01 higher than SAFIRE Br59 on average and 0.07 higher than FBP Br40 (all P < 0.001). The standard deviation of AUC was less than 0.006, and the sensitivity to detect changes in mAs was within 2% for FBP Br59.ConclusionsOur 3D‐printed search challenge phantom with 12 000 submillimeter lesions, together with an NPW model observer, provide an efficient CT detectability assessment method that is sensitive to subtle effects in reconstruction and is sensitive to small changes in dose.

Abstract Objectives. To improve quality of coronary CT angiography (CCTA) images using a generalizable motion-correction algorithm. Approach . A neural network with attention gate and spatial transformer (ATOM) was developed to correct coronary motion. Phantom and patient CCTA images (39 males, 32 females, age range 19–92, scan date 02/2020 to 10/2021) retrospectively collected from dual-source CT were used to create training, development, and testing sets corresponding to 140- and 75 ms temporal resolution, with 75 ms images as labels. To test generalizability, ATOM was deployed for locally adaptive motion-correction in both 140- and 75 ms patient images. Objective metrics were used to assess motion-corrupted and corrected phantom and patient images, including structural-similarity-index (SSIM), dice-similarity-coefficient (DSC), peak-signal-noise-ratio (PSNR), and normalized root-mean-square-error (NRMSE). In objective quality assessment, ATOM was compared with several baseline networks, including U-net, U-net plus attention gate, U-net plus spatial transformer, VDSR, and ResNet. Two cardiac radiologists independently interpreted motion-corrupted and -corrected images at 75 and 140 ms in a blinded fashion and ranked diagnostic image quality (worst to best: 1–4, no ties). Main results . ATOM improved quality metrics ( p < 0.05) before/after correction: in phantom, SSIM 0.87/0.95, DSC 0.85/0.93, PSNR 19.4/22.5, NRMSE 0.38/0.27; in patient images, SSIM 0.82/0.88, DSC 0.88/0.90, PSNR 30.0/32.0, NRMSE 0.16/0.12. ATOM provided more consistent improvement of objective image quality, compared to the presented baseline networks. The motion-corrected images received better ranks than un-corrected at the same temporal resolution ( p < 0.05): 140 ms images 1.65/2.25, and 75 ms images 3.1/3.2. The motion-corrected 75 ms images received the best rank in 65% of testing cases. A fair-to-good inter-reader agreement was observed (Kappa score 0.58). Significance . ATOM reduces motion artifacts, improving visualization of coronary arteries. This algorithm can be used to virtually improve temporal resolution in both single- and dual-source CT.

Objectives Photon-counting detector (PCD) computed tomography (CT) offers improved spatial and contrast resolution, which can impact quantitative measurements. This work aims to determine in human subjects the effect of dual-source PCD-CT on the quantitation of coronary artery calcification (CAC) compared with dual-source energy-integrating detector (EID) CT in both 1- and 3-mm images. Methods This prospective study enrolled patients receiving a clinical EID-CT CAC examination to undergo a research PCD-CT CAC examination. Axial images were reconstructed with a 512 × 512 matrix, 200-mm field of view, 3-mm section thickness/1.5-mm interval using a quantitative kernel (Qr36). Sharper kernels (Qr56/QIR strength 4 for PCD and Qr49/ADMIRE strength 5 for EID) were used to reconstruct images with 1-mm section thickness/0.5-mm interval. Pooled analysis was performed for all calcifications with nonzero values, and volume and Agatston scores were compared between EID-CT and PCD-CT. A Wilcoxon signed-rank test was performed with P < 0.05 considered statistically significant. Results In 21 subjects (median age, 58 years; range, 50–75 years; 13 male [62%]) with a total of 42 calcified arteries detected at 3 mm and 46 calcified arteries at 1-mm images, EID-CT CAC volume and Agatston scores were significantly lower than those of PCD-CT (P ≤ 0.001). At 3-mm thickness, the mean (standard deviation) volume and Agatston score for EID-CT were 55.5 (63.4) mm3 and 63.8 (76.9), respectively, and 61.5 (69.4) mm3 and 70.4 (85.3) for PCD-CT (P = 0.0001 and P = 0.0013). At 1-mm thickness, the mean (standard deviation) volume and score for EID-CT were 50.0 (56.3) mm3 and 61.1 (69.3), respectively, and 59.5 (63.9) mm3 and 72.5 (79.9) for PCD-CT (P < 0.0001 for both). The applied radiation dose (volume CT dose index) for the PCD-CT scan was 2.1 ± 0.6 mGy, which was 13% lower than for the EID-CT scan (2.4 ± 0.7 mGy, P < 0.001). Conclusions Relative to EID-CT, PCD-CT demonstrated a small but significant increase in coronary artery calcium volume and Agatston score.

AbstractBackgroundPrior implementations of the channelized Hotelling observer (CHO) model have succeeded in assessing the performance of X‐ray angiography systems under a variety of imaging conditions. However, often times these conditions do not resemble those present in routine clinical imaging scenarios, such as having complex anthropomorphic backgrounds in conjunction with moving test objects.PurposeThis work builds up on prior established CHO methods and introduces a new approach to switch from the already established “multiple‐sample” CHO implementation to a “single‐sample” technique. The proposed implementation enables the inclusion of moving test objects upon nonuniform backgrounds by allowing only a single sample to represent the test object present condition that is to be used within the statistical test to estimate the detectability index.MethodsTo assess the proposed method, two image data sets were acquired with a clinical X‐ray angiography system. The first set consisted of a uniform background in combination with static test objects while the second consisted of an anthropomorphic chest phantom in conjunction with moving test objects. The first set was used to validate the proposed approach against the multiple‐sample method while the second was used to assess the feasibility of the proposed method under a variety of imaging conditions, including seven object sizes and seven detector target dose (DTD) levels.ResultsFor the uniform background data set, considering all detectability indices greater or equal than 1, the ratio between the detectability indices of the proposed single‐sample approach versus the multiple‐sample method was 0.997 ± 0.056 (range 0.884–1.159). The average single‐direction width of the 95% confidence intervals (CIs) of the detectability index estimates for the multiple‐sample method was 0.38 ± 0.43 (range 0.03–2.20). For the single‐sample approach, the average width was 2.52 ± 0.63 (range 1.11–5.44). For the anthropomorphic background image set, the results were consistent with classical quantum‐limited signal‐to‐noise ratio (SNR) theory. The magnitude of the detectability indices varied predictably with changes in both object size and DTD, with the highest value associated with the highest dose and the largest object size. Additionally, the proposed method was able to capture differences in the imaging performance for a given test object across the field of view, which was associated with the attenuation levels provided by different features of the anthropomorphic background.ConclusionsA new single‐sample variant of the CHO model to assess the performance of X‐ray angiography imaging systems is proposed. The new approach is consistent with quantum‐limited image quality theory and with a standard implementation of the CHO model. The proposed method enables the assessment of moving test objects in combination with complex, nonuniform image backgrounds, thereby opening the possibility to assess imaging conditions which more closely resemble those used in clinical care.

AbstractBackgroundThe feasibility of oral dark contrast media is under exploration in abdominal computed tomography (CT) applications. One of the experimental contrast media in this class is dark borosilicate contrast media (DBCM), which has a CT attenuation lower than that of intra‐abdominal fat.PurposeTo evaluate the performances of DBCM using single‐ and multi‐energy CT imaging on a clinical photon‐counting‐detector CT (PCD‐CT).MethodsFive vials, three with iodinated contrast agent (5, 10, and 20 mg/mL; Omnipaque 350) and two with DBCM (6% and 12%; Nextrast, Inc.), and one solid‐water rod (neutral contrast agent) were inserted into two multi‐energy CT phantoms, and scanned on a clinical PCD‐CT system (NAEOTOM Alpha) at 90, 120, 140, Sn100, and Sn140 kV (Sn: tin filter) in multi‐energy mode. CARE keV IQ level was 180 (CTDIvol: 3.0 and 12.0 mGy for the small and large phantoms, respectively). Low‐energy threshold images were reconstructed with a quantitative kernel (Qr40, iterative reconstruction strength 2) and slice thickness/increment of 2.0/2.0 mm. Virtual monoenergetic images (VMIs) were reconstructed from 40 to 140 keV at 10 keV increments. On all images, average CT numbers for each vial/rod were measured using circular region‐of‐interests and averaged over eight slices. The contrast‐to‐noise ratio (CNR) of iodine (5 mg/mL) against DBCM was calculated and plotted against tube potential and VMI energy level, and compared to the CNR of iodine against water. Similar analyses were performed on iodine maps and VNC images derived from the multi‐energy scan at 120 kV.ResultsWith increasing kV or VMI keV, the negative HU of DBCM decreased only slightly, whereas the positive HU of iodine decreased across all contrast concentrations and phantom sizes. CT numbers for DBCM decreased from −178.5 ± 9.6 to −194.4 ± 6.3 HU (small phantom) and from −181.7 ± 15.7 to −192.1 ± 11.9 HU (large phantom) for DBCM‐12% from 90 to Sn140 kV; on VMIs, the CT numbers for DBCM decreased minimally from −147.1 ± 15.7 to −185.1 ± 9.2 HU (small phantom) and −158.8 ± 28.6 to −188.9 ± 14.7 HU (large phantom) from 40 to 70 keV, but remained stable from 80 to 140 keV. The highest iodine CNR against DBCM in low‐energy threshold images was seen at 90 or Sn140 kV for the small phantom, whereas all CNR values from low‐energy threshold images for the large phantom were comparable. The CNR values of iodine against DBCM computed on VMIs were highest at 40 or 70 keV depending on iodine and DBCM concentrations. The CNR values of iodine against DBCM were consistently higher than iodine to water (up to 460% higher dependent on energy level). Further, the CNR of iodine compared to DBCM is less affected by VMI energy level than the identical comparison between iodine and water: CNR values at 140 keV were reduced by 46.6% (small phantom) or 42.6% (large phantom) compared to 40 keV; CNR values for iodine compared to water were reduced by 86.3% and 83.8% for similar phantom sizes, respectively. Compared to 70 keV VMI, the iodine CNR against DBCM was 13%–79% lower on iodine maps and VNC.ConclusionsWhen evaluated at different tube potentials and VMI energy levels using a clinical PCD‐CT system, DBCM showed consistently higher CNR compared to iodine versus water (a neutral contrast).

AbstractBackgroundCoronary calcification is a strong indicator of coronary artery disease, and patients with a “zero” coronary calcification score have a much lower risk of future cardiac events than those with even small amounts of calcium. However, false‐negative (incorrect zero scores) may occur if small calcifications are missed at CT due to limited spatial resolution.PurposeTo demonstrate lower limits of detection for coronary calcification using an ultra‐high‐resolution (UHR) mode on a clinical photon‐counting‐detector CT (PCD‐CT), compared to a conventional energy‐integrating‐detector CT (EID‐CT).MethodsChicken eggshell fragments (0.4–0.8 mm) mimicking coronary calcifications were scanned on a clinical PCD‐CT (NAEOTOM Alpha) in UHR mode and a conventional EID‐CT (SOMATOM Force) with matched tube potential and radiation dose levels to the PCD‐CT. PCD‐CT images were reconstructed with a sharp kernel (Qr68) and a quantum iterative algorithm (QIR‐3). Two sets of EID‐CT images were reconstructed: routine clinical kernel (Qr36, ADMIRE‐3) and a sharper kernel (Qr54) with similar noise to PCD‐CT images. With institutional review board approval, in vivo exams performed with the PCD‐CT in UHR mode were compared against patients’ clinical EID‐CT exams. The visibility of calcifications on PCD‐CT and EID‐CT images was assessed and compared qualitatively.ResultsPCD‐CT images visualized all calcified fragments, while EID‐CT failed to detect those below 0.6 mm using a routine protocol. EID‐CT with Qr54 improved visibility but distorted boundaries. Calcifications were less visible on EID‐CT than PCD‐CT as phantom sizes increased. 0.6‐ and 0.7‐mm calcified fragments were barely visible on 35‐ and 40‐cm phantom EID‐CT images. Patient cases showed small calcifications missed on EID‐CT but detected on PCD‐CT.ConclusionAt matched radiation dose, PCD‐CT in UHR mode provided higher spatial resolution and improved the detectability of small calcified fragments for different phantom/patient sizes in comparison to EID‐CT.