Coronary stent imaging in photon counting computed Tomography: Optimization of reconstruction kernels in a phantom.

PURPOSE: Imaging stents and in-stent stenosis remains a challenge in coronary computed tomography angiography (CCTA). In comparison to conventional Computed Tomography, Photon Counting CT (PCCT) provides decisive clinical advantages, among other things by providing low dose ultra-high resolution imaging of coronary arteries. This work investigates the image quality in CCTA using clinically established kernels and those optimized for the imaging of cardiac stents in PCCT, both for in-vitro stent imaging in 400 µm standard resolution mode (SRM) and 200 µm Ultra High Resolution Mode (UHR). METHODS: Based on experimental scans, vascular reconstruction kernels (Bv56, Bv64, Bv72) were optimized. In an established phantom, 10 different coronary stents with 3 mm diameter were scanned in the first clinically available PCCT. Scans were reconstructed with clinically established and optimized kernels. Four readers measured visible stent lumen, performed ROI-based density measurements and rated image quality. RESULTS: Regarding the visible stent lumen, UHR is significantly superior to SRM (p < 0.001). In all levels, the optimized kernels are superior to the clinically established kernels (p < 0.001). One optimized kernel showed a significant reduction of noise compared to the clinically established kernels. Overall image quality is improved with optimized kernels. CONCLUSIONS: In a phantom study PCCT UHR with optimized kernels for stent imaging significantly improves the ability to assess the in-stent lumen of small cardiac stents. We recommend using UHR with an optimized sharp vascular reconstruction kernel (Bv72uo) for imaging of cardiac stent.

Evaluation of Accuracy and Performance of a Novel, Fully Gantry Integrated 3D Laser System for Computed Tomography Guided Needle Placement: A Phantom Study.

The purpose of this phantom study was to compare the accuracy, speed and technical performance of CT guided needle placement using a conventional technique versus a novel, gantry integrated laser guidance system for both an expert and a novice. A total of 80 needle placements were performed in an abdominal phantom using conventional CT guidance and a laser guidance system. Analysis of pooled results of expert and novice showed a significant reduction of time (277 vs. 204 s, p = 0.001) and of the number of needle corrections (3.28 vs. 1.58, p < 0.001) required when using laser guidance versus conventional technique. No significant improvement in absolute (3.81 vs. 3.41 mm, p = 0.213) or angular deviation (2.85 vs. 2.18°, p = 0.079) was found. With either approach, the expert was significantly faster (conventional guidance: 207 s vs. 346 s, p < 0.001; laser guidance: 144 s vs. 264 s, p < 0.001) and required fewer needle corrections (conventional guidance: 4 vs. 3, p = 0.027; laser guidance: 2 vs. 1, p = 0.001) than the novice. The laser guidance system helped both the expert and the novice to perform CT guided interventions in a phantom faster and with fewer needle corrections compared to the conventional technique, while achieving similar accuracy.

Diagnostic Performance in Low- and High-Contrast Tasks of an Image-Based Denoising Algorithm Applied to Radiation Dose-Reduced Multiphase Abdominal CT Examinations.

BACKGROUND. Anatomic redundancy between phases can be used to achieve denoising of multiphase CT examinations. A limitation of iterative reconstruction (IR) techniques is that they generally require use of CT projection data. A frequency-split multi-band-filtration algorithm applies denoising to the multiphase CT images themselves. This method does not require knowledge of the acquisition process or integration into the reconstruction system of the scanner, and it can be implemented as a supplement to commercially available IR algorithms. OBJECTIVE. The purpose of the present study is to compare radiologists' performance for low-contrast and high-contrast diagnostic tasks (i.e., tasks for which differences in CT attenuation between the imaging target and its anatomic background are subtle or large, respectively) evaluated on multiphase abdominal CT between routine-dose images and radiation dose-reduced images processed by a frequency-split multiband-filtration denoising algorithm. METHODS. This retrospective single-center study included 47 patients who underwent multiphase contrast-enhanced CT for known or suspected liver metastases (a low-contrast task) and 45 patients who underwent multiphase contrast-enhanced CT for pancreatic cancer staging (a high-contrast task). Radiation dose-reduced images corresponding to dose reduction of 50% or more were created using a validated noise insertion technique and then underwent denoising using the frequency-split multi-band-filtration algorithm. Images were independently evaluated in multiple sessions by different groups of abdominal radiologists for each task (three readers in the low-contrast arm and four readers in the high-contrast arm). The noninferiority of denoised radiation dose-reduced images to routine-dose images was assessed using the jackknife alternative free-response ROC (JAFROC) figure-of-merit (FOM; limit of noninferiority, -0.10) for liver metastases detection and using the Cohen kappa statistic and reader confidence scores (100-point scale) for pancreatic cancer vascular invasion. RESULTS. For liver metastases detection, the JAFROC FOM for denoised radiation dose-reduced images was 0.644 (95% CI, 0.510-0.778), and that for routine-dose images was 0.668 (95% CI, 0.543-0.792; estimated difference, -0.024 [95% CI, -0.084 to 0.037]). Intraobserver agreement for pancreatic cancer vascular invasion was substantial to near perfect when the two image sets were compared (κ = 0.53-1.00); the 95% CIs of all differences in confidence scores between image sets contained zero. CONCLUSION. Multiphase contrast-enhanced abdominal CT images with a radiation dose reduction of 50% or greater that undergo denoising by a frequency-split multiband-filtration algorithm yield performance similar to that of routine-dose images for detection of liver metastases and vascular staging of pancreatic cancer. CLINICAL IMPACT. The image-based denoising algorithm facilitates radiation dose reduction of multiphase examinations for both low- and high-contrast diagnostic tasks without requiring manufacturer-specific hardware or software.

Ultra-High-Resolution Coronary CT Angiography With Photon-Counting Detector CT: Feasibility and Image Characterization.

OBJECTIVES: The aim of this study was to evaluate the feasibility and quality of ultra-high-resolution coronary computed tomography angiography (CCTA) with dual-source photon-counting detector CT (PCD-CT) in patients with a high coronary calcium load, including an analysis of the optimal reconstruction kernel and matrix size. MATERIALS AND METHODS: In this institutional review board-approved study, 20 patients (6 women; mean age, 79 ± 10 years; mean body mass index, 25.6 ± 4.3 kg/m 2 ) undergoing PCD-CCTA in the ultra-high-resolution mode were included. Ultra-high-resolution CCTA was acquired in an electrocardiography-gated dual-source spiral mode at a tube voltage of 120 kV and collimation of 120 × 0.2 mm. The field of view (FOV) and matrix sizes were adjusted to the resolution properties of the individual reconstruction kernels using a FOV of 200 × 200 mm 2 or 150 × 150 mm 2 and a matrix size of 512 × 512 pixels or 1024 × 1024 pixels, respectively. Images were reconstructed using vascular kernels of 8 sharpness levels (Bv40, Bv44, Bv56, Bv60, Bv64, Bv72, Bv80, and Bv89), using quantum iterative reconstruction (QIR) at a strength level of 4, and a slice thickness of 0.2 mm. Images with the Bv40 kernel, QIR at a strength level of 4, and a slice thickness of 0.6 mm served as the reference. Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), vessel sharpness, and blooming artifacts were quantified. For subjective image quality, 2 blinded readers evaluated image noise and delineation of coronary artery plaques and the adjacent vessel lumen using a 5-point discrete visual scale. A phantom scan served to characterize image noise texture by calculating the noise power spectrum for every reconstruction kernel. RESULTS: Maximum spatial frequency (f peak ) gradually shifted to higher values for reconstructions with the Bv40 to Bv64 kernel (0.15 to 0.56 mm -1 ), but not for reconstructions with the Bv72 to Bv89 kernel. Ultra-high-resolution CCTA was feasible in all patients (median calcium score, 479). In patients, reconstructions with the Bv40 kernel and a slice thickness of 0.6 mm showed largest blooming artifacts (55.2% ± 9.8%) and lowest vessel sharpness (477.1 ± 73.6 ΔHU/mm) while achieving highest SNR (27.4 ± 5.6) and CNR (32.9 ± 6.6) and lowest noise (17.1 ± 2.2 HU). Considering reconstructions with a slice thickness of 0.2 mm, image noise, SNR, CNR, vessel sharpness, and blooming artifacts significantly differed across kernels (all P 's < 0.001). With higher kernel sharpness, SNR and CNR continuously decreased, whereas image noise and vessel sharpness increased, with highest sharpness for the Bv89 kernel (2383.4 ± 787.1 ΔHU/mm). Blooming artifacts continuously decreased for reconstructions with the Bv40 (slice thickness, 0.2 mm; 52.8% ± 9.2%) to the Bv72 kernel (39.7% ± 9.1%). Subjective noise was perceived by both readers in agreement with the objective measurements. Considering delineation of coronary artery plaques and the adjacent vessel lumen, reconstructions with the Bv64 and Bv72 kernel (for both, median score of 5) were favored by the readers providing an excellent anatomic delineation of plaque characteristics and vessel lumen. CONCLUSIONS: Ultra-high-resolution CCTA with PCD-CT is feasible and enables the visualization of calcified coronaries with an excellent image quality, high sharpness, and reduced blooming. Coronary plaque characterization and delineation of the adjacent vessel lumen are possible with an optimal quality using Bv64 kernel, a FOV of 200 × 200 mm 2 , and a matrix size of 512 × 512 pixels.

Deep learning-based extended field of view computed tomography image reconstruction: influence of network design on image estimation outside the scan field of view.

The problem of data truncation in Computed Tomography (CT) is caused by the missing data when the patient exceeds the Scan Field of View (SFOV) of a CT scanner. The reconstruction of a truncated scan produces severe truncation artifacts both inside and outside the SFOV. We have employed a deep learning-based approach to extend the field of view and suppress truncation artifacts. Thereby, our aim is to generate a good estimate of the real patient data and not to provide a perfect and diagnostic image even in regions beyond the SFOV of the CT scanner. This estimate could then be used as an input to higher order reconstruction algorithms [1]. To evaluate the influence of the network structure and layout on the results, three convolutional neural networks (CNNs), in particular a general CNN called ConvNet, an autoencoder, and the U-Net architecture have been investigated in this paper. Additionally, the impact of L1, L2, structural dissimilarity and perceptual loss functions on the neural network's learning have been assessed and evaluated. The evaluation of data set comprising 12 truncated test patients demonstrated that the U-Net in combination with the structural dissimilarity loss showed the best performance in terms of image restoration in regions beyond the SFOV of the CT scanner. Moreover, this network produced the best mean absolute error, L1, L2, and structural dissimilarity evaluation measures on the test set compared to other applied networks. Therefore, it is possible to achieve truncation artifact removal using deep learning techniques.

Evaluation of novel AI-based extended field-of-view CT reconstructions.

PURPOSE: Modern computed tomography (CT) scanners have an extended field-of-view (eFoV) for reconstructing images up to the bore size, which is relevant for patients with higher BMI or non-isocentric positioning due to fixation devices. However, the accuracy of the image reconstruction in eFoV is not well known since truncated data are used. This study introduces a new deep learning-based algorithm for extended field-of-view reconstruction and evaluates the accuracy of the eFoV reconstruction focusing on aspects relevant for radiotherapy. METHODS: A life-size three-dimensional (3D) printed thorax phantom, based on a patient CT for which eFoV was necessary, was manufactured and used as reference. The phantom has holes allowing the placement of tissue mimicking inserts used to evaluate the Hounsfield unit (HU) accuracy. CT images of the phantom were acquired using different configurations aiming to evaluate geometric and HU accuracy in the eFoV. Image reconstruction was performed using a state-of-the-art reconstruction algorithm (HDFoV), commercially available, and the novel deep learning-based approach (HDeepFoV). Five patient cases were selected to evaluate the performance of both algorithms on patient data. There is no ground truth for patients so the reconstructions were qualitatively evaluated by five physicians and five medical physicists. RESULTS: The phantom geometry reconstructed with HDFoV showed boundary deviations from 1.0 to 2.5 cm depending on the volume of the phantom outside the regular scan field of view. HDeepFoV showed a superior performance regardless of the volume of the phantom within eFOV with a maximum boundary deviation below 1.0 cm. The maximum HU (absolute) difference for soft issue inserts is below 79 and 41 HU for HDFoV and HDeepFoV, respectively. HDeepFoV has a maximum deviation of -18 HU for an inhaled lung insert while HDFoV reached a 229 HU difference. The qualitative evaluation of patient cases shows that the novel deep learning approach produces images that look more realistic and have fewer artifacts. CONCLUSION: To be able to reconstruct images outside the sFoV of the CT scanner there is no alternative than to use some kind of extrapolated data. In our study, we proposed and investigated a new deep learning-based algorithm and compared it to a commercial solution for eFoV reconstruction. The deep learning-based algorithm showed superior performance in quantitative evaluations based on phantom data and in qualitative assessments of patient data.