Learned high-resolution cardiac CT imaging from ultra-high-resolution PCD-CT.

Coronary computed tomography angiography (cCTA) is a widely used non-invasive diagnostic exam for patients with coronary artery disease (CAD). However, most clinical CT scanners are limited in spatial resolution from use of energy-integrating detectors (EIDs). Radiological evaluation of CAD is challenging, as coronary arteries are small (3-4 mm diameter) and calcifications within them are highly attenuating, leading to blooming artifacts. As such, this is a task well suited for high spatial resolution. Recently, photon-counting-detector (PCD) CT became commercially available, allowing for ultra-high resolution (UHR) data acquisition. However, PCD-CTs are costly, restricting widespread accessibility. To address this problem, we propose a super resolution convolutional neural network (CNN): ILUMENATE (Improved LUMEN visualization through Artificial super-resoluTion imagEs), creating a high resolution (HR) image simulating UHR PCD-CT. The network was trained and validated using patches extracted from 8 patients with a modified U-Net architecture. Training input and labels consisted of UHR PCD-CT images reconstructed with a smooth kernel degrading resolution (LR input) and sharp kernel (HR label). The network learned the resolution difference and was tested on 5 unseen LR patients. We evaluated network performance quantitatively and qualitatively through visual inspection, line profiles to assess spatial resolution improvements, ROIs for CT number stability and noise assessment, structural similarity index (SSIM), and percent diameter luminal stenosis. Overall, ILUMENATE improved images quantitatively and qualitatively, creating sharper edges more closely resembling reconstructed HR reference images, maintained stable CT numbers with less than 4% difference, reduced noise by 28%, maintained structural similarity (average SSIM = 0.70), and reduced percent diameter stenosis with respect to input images. ILUMENATE demonstrates potential impact for CAD patient management, improving the quality of LR CT images bringing them closer to UHR PCD-CT images.

Photon-Counting Detector Computed Tomography Versus Energy-Integrating Detector Computed Tomography for Coronary Artery Calcium Quantitation.

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) mm 3 and 63.8 (76.9), respectively, and 61.5 (69.4) mm 3 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) mm 3 and 61.1 (69.3), respectively, and 59.5 (63.9) mm 3 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.

Comparison of Photon-counting Detector and Energy-integrating Detector CT for Visual Estimation of Coronary Percent Luminal Stenosis.

Background Compared with energy-integrating detector (EID) CT, the improved resolution of photon-counting detector (PCD) CT coupled with high-energy virtual monoenergetic images (VMIs) has been shown to decrease calcium blooming on images in phantoms and cadaveric specimens. Purpose To determine the impact of dual-source PCD CT on visual and quantitative estimation of percent diameter luminal stenosis compared with dual-source EID CT in patients. Materials and Methods This prospective study recruited consecutive adult patients from an outpatient facility between January and March 2022. Study participants underwent clinical dual-source EID coronary CT angiography followed by a research dual-source PCD CT examination. For PCD CT, multienergy data were used to create VMIs at 50 and 100 keV. Two readers independently reviewed EID CT images followed by PCD CT images after a washout period. Readers visually graded the most severe stenosis in terms of percent diameter luminal stenosis for the left main, left anterior descending, right, and circumflex coronary arteries, unblinded to scanner type. Quantitative measures of percent stenosis were made using commercial software. Visual and quantitative estimates of percent stenosis were compared between EID CT and PCD CT using the Wilcoxon signed-rank test. Results A total of 25 participants (median age, 59 years [range, 18-78 years]; 16 male participants) were enrolled. On EID CT images, readers 1 and 2 identified 39 and 32 luminal stenoses, respectively, with a percent diameter luminal stenosis greater than 0%. Visual estimates of percent stenosis were lower on PCD CT images than EID CT images (reader 1: median 20.6% [IQR, 8.8%-61.2%] vs 31.8% [IQR, 12.9%-69.7%], P < .001; reader 2: 6.5% [IQR, 0.4%-54.1%] vs 22.9% [IQR, 1.8%-67.4%], P = .002). No difference was observed between EID CT and PCD CT for quantitative measures of percent stenosis (median difference, -1.5% [95% CI: -3.0%, 2.5%]; P = .51). Conclusion Relative to using EID CT, using PCD CT led to decreases in visual estimates of percent stenosis. © RSNA, 2023 See also the editorial by Murphy and Donnelly in this issue.