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BackgroundPhoton‐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.

An important feature enabled by Photon-Counting Detector (PCD) CT is the simultaneous acquisition of multi-energy data, which can produce virtual monoenergetic images (VMIs) at a high spatial resolution. However, noise levels observed in the high-resolution (HR) VMIs are markedly increased. Recent work involving deep learning methods has shown great potential in CT image denoising. Many CNN applications involve training using spatially co-registered low- and high-dose CT images featuring high and low image noise, respectively. However, this is implausible in routine clinical practice. Further, typical denoising methods treat each VMI energy level independently, without consideration of the valuable information in the spectral domain. To overcome these obstacles, we propose a prior knowledge-aware iterative denoising neural network (PKAID-Net). PKAID-Net offers two major benefits: first, it utilizes spectral information by including a lower-noise VMI as a prior input; and second, it iteratively constructs refined datasets for neural network training to improve the denoising performance. This study includes 10 patient coronary CT angiography (CTA) exams acquired on a clinical HR PCD-CT (NAEOTOM Alpha, Siemens Healthineers). The HR VMIs were reconstructed at 50 and 70 keV, using a sharp kernel (Bv68) and thin (0.6 mm, 0.3 mm increment) slice thickness. Results showed that the PKAID-Net provided a noise reduction of 96% and 70% relative to FBP and iterative reconstruction, respectively while maintaining spatial and spectral fidelity and a natural noise texture. These results demonstrate the noise reduction capacity of PKAID-Net as applied to cutting-edge PCD-CT data to enable HR, multi-energy cardiac CT imaging.

Background Small coronary arteries containing stents pose a challenge in CT imaging due to metal‐induced blooming artifact. High spatial resolution imaging capability is as the presence of highly attenuating materials limits noninvasive assessment of luminal patency. Purpose The purpose of this study was to quantify the effective lumen diameter within coronary stents using a clinical photon‐counting‐detector (PCD) CT in concert with a convolutional neural network (CNN) denoising algorithm, compared to an energy‐integrating‐detector (EID) CT system. Methods Seven coronary stents of different materials and inner diameters between 3.43 and 4.72 mm were placed in plastic tubes of diameters 3.96–4.87 mm containing 20 mg/mL of iodine solution, mimicking stented contrast‐enhanced coronary arteries. Tubes were placed parallel with or perpendicular to the scanner's z‐axis in an anthropomorphic phantom emulating an average‐sized patient and scanned with a clinical EID‐CT and PCD‐CT. EID scans were performed using our standard coronary computed tomography angiography (cCTA) protocol (120 kV, 180 quality reference mAs). PCD scans were performed using the ultra‐high‐resolution (UHR) mode (120 × 0.2 mm collimation) at 120 kV with tube current adjusted so that CTDIvol was matched to that of EID scans. EID images were reconstructed per our routine clinical protocol (Br40, 0.6 mm thickness), and with the sharpest available kernel (Br69). PCD images were reconstructed at a thickness of 0.6 mm and a dedicated sharp kernel (Br89) which is only possible with the PCD UHR mode. To address increased image noise introduced by the Br89 kernel, an image‐based CNN denoising algorithm was applied to the PCD images of stents scanned parallel to the scanner's z‐axis. Stents were segmented based on full‐width half maximum thresholding and morphological operations, from which effective lumen diameter was calculated and compared to reference sizes measured with a caliper. Results Substantial blooming artifacts were observed on EID Br40 images, resulting in larger stent struts and reduced lumen diameter (effective diameter underestimated by 41% and 47% for parallel and perpendicular orientations, respectively). Blooming artifacts were observed on EID Br69 images with 19% and 31% underestimation of lumen diameter compared to the caliper for parallel and perpendicular scans, respectively. Overall image quality was substantially improved on PCD, with higher spatial resolution and reduced blooming artifacts, resulting in the clearer delineation of stent struts. Effective lumen diameters were underestimated by 9% and 19% relative to the reference for parallel and perpendicular scans, respectively. CNN reduced image noise by about 50% on PCD images without impacting lumen quantification (<0.3% difference). Conclusion The PCD UHR mode improved in‐stent lumen quantification for all seven stents as compared to EID images due to decreased blooming artifacts. Implementation of CNN denoising algorithms to PCD data substantially improved image quality.

Background: 7T MRI offers significant benefits to spatial and contrast resolution compared to lower field strengths. This superior image quality can help better delineate targets in stereotactic neurosurgical procedures; however, the potential for increased geometric distortions at 7T has impaired its widespread use for these applications. Image geometric distortions can be due to distortions of B 0 arising from tissue magnetic susceptibility effects or inherent field inhomogeneities, and nonlinearity of the magnetic field gradients. Purpose: The purpose of this study was to investigate the use of 7T MRI for neurosurgical frameless stereotactic navigation procedures. Image geometric distortions at the skin surface in 7T images were minimized and compared to results from clinical 3T frameless imaging protocols. Methods: A 3D-printed grid phantom filled with oil was designed to perform a fine calibration of the 7T imaging gradients, and an oil-filled head phantom with internal targets was used to determine ground truth (from computed tomography [CT]) positioning errors. Three volunteers and the head phantom were imaged consecutively at 3T and 7T. Ten skin-adhesive fiducial markers were placed on each subject's exposed skin surface at standard clinical placement locations for frameless procedures. Imaging sequences included MPRAGE (three bandwidths at 7T: 400, 690, and 1020 Hz/pixel, and one at 3T: 400 Hz/pixel), T2 SPACE, and T2 SPACE FLAIR acquisitions. An additional GRE field map was acquired on both scanners using a multi-echo GRE sequence. Custom Matlab code was used to perform additional distortion correction of the images using the unwrapped field maps. Fiducial localization was performed with 3D Slicer, with absolute fiducial positioning errors determined in phantom experiments following rigid registration to the CT images. For human experiments, 3T and 7T images were registered and relative differences in fiducial locations were compared using two-tailed paired t-tests. Results: Phantom measurements at 7T yielded gradient distance scaling errors of 1.1%, 2.2%, and 1.0% along the x-, y-, and z-axes, respectively. These system miscalibrations were traced back to phantom manufacturing deviations in the sphericity of the vendor's gradient calibration phantom. Correction factors along each gradient axis were applied, and afterward, geometric distortions of less than 1 mm were obtained in the 7T MR head phantom images for the 1020 Hz/pixel bandwidth MPRAGE sequence. For the human subjects, four fiducial locations were excluded from the analysis due to patient positioning differences. Differences between 3T and 7T MPRAGE with low/medium/high bandwidth were 2.2 /2.6/2.3 mm, respectively, before the correction, reducing to 1.6/1.3/1.0 mm after the correction (p < 0.001). T2 SPACE and T2 SPACE 694