Thorne, Jamison E.

A deep learning–based neural network enabled virtual monoenergetic imaging at energy-integrating detector CT, reduced blooming artifacts, and improved stenosis assessment in heavily calcified coronary plaques, with performance comparable to ultrahigh-resolution photon-counting detector CT.

Objectives: To determine the feasibility of reduced-dose chest computed tomographic angiography (CTA) with convolutional neural network (CNN) denoising for detecting pulmonary arteriovenous malformations (pAVMs) in children with hereditary hemorrhagic telangiectasia (cwHHT). Methods: Fifteen cwHHT underwent a chest CTA (ie, a controlled “study” dose). Noise was inserted to simulate a quarter dose (QD) exam. Images were reconstructed using iterative reconstruction (IR) and our self-trained CNN denoising model. For each case, 3 sets of images were created: study dose (SD)+IR, QD+IR, and QD+CNN. Two thoracic radiologists independently scored each set to assess quality, spatial resolution, artifacts, and the presence of pAVMs using 4-level ordinal scales. Quantitative assessments of image quality were performed using contrast-to-noise ratios (CNRs) with comparisons made between the experimental conditions. Results: Thirteen of the 15 patients recruited with hereditary hemorrhagic telangiectasia (mean age: 9.3±4.5 y) were positive for pAVM by transthoracic contrast echocardiography. The sensitivities using QD+CNN were 0.85 and 1.00 for readers 1 and 2, respectively. This was compared with 0.69 and 0.84 using QD+IR versus 0.85 and 0.92 for SD+IR. Inter-reader agreement for pAVM detection utilizing QD+CNN was moderate and resulted in kappa=0.59 ( P =0.012). The subjective assessments for QD+CNN were comparable to the SD technique. Regression analysis of reader scores revealed improved quality in QD+CNN versus QD+IR ( P =0.001). Similarly, the QD+CNN condition demonstrated the highest CNRs. Conclusions: Reduced-dose chest CTA with CNN denoising provides a level of sensitivity comparable to standard dose CTA and high CNRs for the detection of pAVMs in cwHHT.

Abstract Background Coronary computed tomography angiography (cCTA) is a widely used noninvasive diagnostic exam to assess patients for coronary artery disease (CAD). However, the spatial resolution of most CT scanners is limited due to the use of energy‐integrating detectors (EIDs). Purpose To develop a convolutional neural network ( I mproved LUMEN visualization through A rtificial super‐resolu T ion imag E s (ILUMENATE)) informed by photon‐counting‐detector (PCD)‐CT to improve EID‐CT image resolution and determine its impact on cCTA. Materials and methods With IRB approval, 30 patients undergoing clinically indicated cCTA were scanned with EID‐CT (SOMATOM Force, Siemens Healthineers, Forchheim, Germany) and subsequently with ultra‐high‐resolution (UHR) PCD‐CT (NAEOTOM Alpha, Siemens Healthineers) on the same day. ILUMENATE was trained on eight patient PCD‐CT datasets (67,890 patch pairs with 90% for training (61,101), 10% reserved for validation (6,789)) and applied to 22 unseen EID‐CT cases. Spatial resolution was evaluated using line profiles and percent diameter stenosis quantified with a severity score assigned. Two experienced radiologists, blinded to image type, selected preferred series and scored images for overall quality, sharpness, and noise comparing original EID‐CT and ILUMENATE output. Results Visual assessment and line profiles showed substantial resolution improvement with ILUMENATE. Percent diameter stenosis was significantly reduced (mean ± standard deviation: 4.42% ± 4.82%) using ILUMENATE ( p < 0.001) with nine lesions shifting down in severity score. Readers preferred ILUMENATE images in 22/22 cases and scored ILUMENATE superiorly for overall quality, sharpness, and noise ( p < 0.05). Conclusions ILUMENATE enhanced image resolution, resulting in improved overall image quality, reduced calcium blooming artifacts, and improved lumen visibility in cCTA exams performed using EID‐CT. This could potentially allow for improved accessibility to UHR image quality, allowing for more accurate assessment of CAD.

Background SynthesiZed Improved Resolution and Concurrent nOise reductioN (ZIRCON) is a multi-kernel synthesis method that creates a single series of thin-slice computed tomography (CT) images displaying low noise and high spatial resolution, increasing reader efficiency and minimizing partial volume averaging. Purpose To compare the diagnostic performance of a single set of ZIRCON images to two routine clinical image series using conventional CT head and bone reconstruction kernels for diagnosing intracranial findings and fractures in patients with trauma or suspected acute neurologic deficit. Material and Methods In total, 50 patients underwent clinically indicated head CT in the ER (15 normal, 35 abnormal cases). A non-reader neuroradiologist established the reference standard. Three neuroradiologists reviewed two routine clinical series (head and bone kernels) and a single ZIRCON series, detecting intracranial findings or fractures and rating confidence (0–100). Sensitivity, specificity, and jackknife free-response receiver operating characteristic (JAFROC) figure of merit (FOM) were compared (limit of non-inferiority: −0.10). Results ZIRCON and conventional images demonstrated comparable performance for fractures (sensitivity: 51.5% vs. 54.5%; specificity: 40.2% vs. 34.2%) and intracranial findings (sensitivity: 88.2% vs. 91.4%; specificity: 77.2% vs. 73.7%).The estimated difference of JAFROC FOM demonstrated ZIRCON non-inferiority for acute pathologies overall (0.003 [95% CI=−0.051–0.057]) and fractures (0.048 [95% CI=−0.050–0.145]) but not for intracranial findings alone (−0.024 [95% CI=−0.100–0.052]). Conclusion Thin-slice, low noise, and high spatial resolution images can be created to display intracranial findings and fractures replacing multiple images series in head CT with similar performance. Future studies in more patients and further algorithmic development are warranted.

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.

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.

Abstract Photon counting detector (PCD) CT represents the newest advance in CT technology, with improved radiation dose efficiency, increased spatial resolution, inherent spectral imaging capabilities, and the ability to eliminate electronic noise. Its design fundamentally differs from conventional energy integrating detector CT because photons are directly converted to electrical signal in a single step. Rather than converting X-rays to visible light and having an output signal that is a summation of energies, PCD directly counts each photon and records its individual energy information. The current commercially available PCD-CT utilizes a dual-source CT geometry, which allows 66 ms cardiac temporal resolution and high-pitch (up to 3.2) scanning. This can greatly benefit pediatric patients by facilitating high quality fast scanning to allow sedation-free imaging. The energy-resolving nature of the utilized PCDs allows “always-on” dual-energy imaging capabilities, such as the creation of virtual monoenergetic, virtual non-contrast, virtual non-calcium, and other material-specific images. These features may be combined with high-resolution imaging, made possible by the decreased size of individual detector elements and the absence of interelement septa. This work reviews the foundational concepts associated with PCD-CT and presents examples to highlight the benefits of PCD-CT in the pediatric population.

AbstractBackgroundSmall 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.PurposeThe 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.MethodsSeven 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.ResultsSubstantial 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).ConclusionThe 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.