An effective noise reduction method for multi-energy CT images that exploit spatio-spectral features

CT image denoising methods for image quality improvement and radiation dose reduction

With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low-dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)-based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL-based models.

Evaluation of the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients

OBJECTIVES

Evaluate a novel algorithm for noise reduction in obese patients using dual-source dual-energy (DE) CT imaging.

METHODS

Seventy-nine patients with contrast-enhanced abdominal imaging (54 women; age: 58 ± 14 years; BMI: 39 ± 5 kg/m2, range: 35-62 kg/m2) from seven DECT (SOMATOM Flash or Force) were retrospectively included (01/2019-12/2020). Image domain data were reconstructed with the standard clinical algorithm (ADMIRE/SAFIRE 2), and denoised with a comparison (ME-NLM) and a test algorithm (rank-sparse kernel regression). Contrast-to-noise ratio (CNR) was calculated. Four blinded readers evaluated the same original and denoised images (0 (worst)-100 (best)) in randomized order for perceived image noise, quality, and their comfort making a diagnosis from a table of 80 options. Comparisons between algorithms were performed using paired t-tests and mixed-effects linear modeling.

RESULTS

Average CNR was 5.0 ± 1.9 (original), 31.1 ± 10.3 (comparison; p < 0.001), and 8.9 ± 2.9 (test; p < 0.001). Readers were in good to moderate agreement over perceived image noise (ICC: 0.83), image quality (ICC: 0.71), and diagnostic comfort (ICC: 0.6). Diagnostic accuracy was low across algorithms (accuracy: 66, 63, and 67% (original, comparison, test)). The noise received a mean score of 54, 84, and 66 (p < 0.05); image quality 59, 61, and 65; and the diagnostic comfort 63, 68, and 68, respectively. Quality and comfort scores were not statistically significantly different between algorithms.

CONCLUSIONS

The test algorithm produces quantitatively higher image quality than current standard and existing denoising algorithms in obese patients imaged with DECT and readers show a preference for it.

CLINICAL RELEVANCE STATEMENT

Accurate diagnosis on CT imaging of obese patients is challenging and denoising algorithms can increase the diagnostic comfort and quantitative image quality. This could lead to better clinical reads.

KEY POINTS

• Improving image quality in DECT imaging of obese patients is important for accurate and confident clinical reads, which may be aided by novel denoising algorithms using image domain data. • Accurate diagnosis on CT imaging of obese patients is especially challenging and denoising algorithms can increase quantitative and qualitative image quality. • Image domain algorithms can generalize well and can be implemented at other institutions.

A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images

Photon-counting CT (PCCT) is powerful for spectral imaging and material decomposition but produces noisy weighted filtered backprojection (wFBP) reconstructions. Although iterative reconstruction effectively denoises these images, it requires extensive computation time. To overcome this limitation, we propose a deep learning (DL) model, UnetU, which quickly estimates iterative reconstruction from wFBP. Utilizing a 2D U-net convolutional neural network (CNN) with a custom loss function and transformation of wFBP, UnetU promotes accurate material decomposition across various photon-counting detector (PCD) energy threshold settings. UnetU outperformed multi-energy non-local means (ME NLM) and a conventional denoising CNN called UnetwFBP in terms of root mean square error (RMSE) in test set reconstructions and their respective matrix inversion material decompositions. Qualitative results in reconstruction and material decomposition domains revealed that UnetU is the best approximation of iterative reconstruction. In reconstructions with varying undersampling factors from a high dose ex vivo scan, UnetU consistently gave higher structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) to the fully sampled iterative reconstruction than ME NLM and UnetwFBP. This research demonstrates UnetU's potential as a fast (i.e., 15 times faster than iterative reconstruction) and generalizable approach for PCCT denoising, holding promise for advancing preclinical PCCT research.

Accuracy of Nodule Volume and Airway Wall Thickness Measurement Using Low-Dose Chest CT on a Photon-Counting Detector CT Scanner

OBJECTIVES

A comparison of high-resolution photon-counting detector computed tomography (PCD-CT) versus energy-integrating detector (EID) CT via a phantom study using low-dose chest CT to evaluate nodule volume and airway wall thickness quantification.

MATERIALS AND METHODS

Twelve solid and ground-glass lung nodule phantoms with 3 diameters (5 mm, 8 mm, and 10 mm) and 2 shapes (spherical and star-shaped) and 12 airway tube phantoms (wall thicknesses, 0.27-1.54 mm) were placed in an anthropomorphic chest phantom. The phantom was scanned with EID-CT and PCD-CT at 5 dose levels (CTDI vol = 0.1-0.8 mGy at Sn-100 kV, 7.35 mGy at 120 kV). All images were iteratively reconstructed using matched kernels for EID-CT and medium-sharp kernel (MK) PCD-CT and an ultra-sharp kernel (USK) PCD-CT kernel, and image noise at each dose level was quantified. Nodule volumes were measured using semiautomated segmentation software, and the accuracy was expressed as the percentage error between segmented and reference volumes. Airway wall thicknesses were measured, and the root-mean-square error across all tubes was evaluated.

RESULTS

MK PCD-CT images had the lowest noise. At 0.1 mGy, the mean volume accuracy for the solid and ground-glass nodules was improved in USK PCD-CT (3.1% and 3.3% error) compared with MK PCD-CT (9.9% and 10.2% error) and EID-CT images (11.4% and 9.2% error), respectively. At 0.2 mGy and 0.8 mGy, the wall thickness root-mean-square error values were 0.42 mm and 0.41 mm for EID-CT, 0.54 mm and 0.49 mm for MK PCD-CT, and 0.23 mm and 0.16 mm for USK PCD-CT.

CONCLUSIONS

USK PCD-CT provided more accurate lung nodule volume and airway wall thickness quantification at lower radiation dose compared with MK PCD-CT and EID-CT.

Spectral CT reconstruction via low-rank representation and structure preserving regularization

Objective:With the development of computed tomography (CT) imaging technology, it is possible to acquire multi-energy data by spectral CT. Being different from conventional CT, the X-ray energy spectrum of spectral CT is cut into several narrow bins which leads to the result that only a part of photon can be collected in each individual energy channel.This can severely degrade the image qualities. To address this problem, we propose a spectral CT reconstruction algorithm based on low-rank representation and structure preserving regularization in this paper.Approach:To make full use of the prior knowledge about both the inter-channel correlation and the sparsity in gradient domain of inner-channel data, this paper combines a low-rank correlation descriptor with a structure extraction operator as priori regularization terms for spectral CT reconstruction. Furthermore, a split-Bregman based iterative algorithm is developed to solve the reconstruction model. Finally, we propose a multi-channel adaptive parameters generation strategy according to CT values of each individual energy channel.Main results: Experimental results on numerical simulations and real mouse data indicate that the proposed algorithm achieves higher accuracy on both reconstruction and material decomposition than the methods based on simultaneous algebraic reconstruction technique (SART), total variation minimization (TVM), total variation with low-rank (LRTV), and spatial-spectral cube matching frame (SSCMF). Compared with SART, our algorithm improves the feature similarity (FSIM) by 40.4% on average for numerical simulation reconstruction, whereas TVM, LRTV, and SSCMF correspond to 26.1%, 28.2%, and 29.5%, respectively.Significance: We outline a multi-channel reconstruction algorithm tailored for spectral CT. The qualitative and quantitative comparisons present a significant improvement of image quality, indicating its promising potential in spectral CT imaging.

Measurement of enhanced vasa vasorum density in a porcine carotid model using photon counting detector CT

Purpose

The onset of atherosclerosis is preceded by changes in blood perfusion within the arterial wall due to localized proliferation of the vasa vasorum. The purpose of this study was to quantify these changes in spatial density of the vasa vasorum using a research whole-body photon-counting detector CT (PCD-CT) scanner and a porcine model.

Approach

Vasa vasorum angiogenesis was stimulated in the left carotid artery wall of anesthetized pigs ( n = 5 ) while the right carotid served as a control. After a 6-week recovery period, the animals were scanned on the PCD-CT prior to and after injection of iodinated contrast. Annular regions of interest were used to measure wall enhancement in the injured and control arteries. The exact Wilcoxon-signed rank test was used to determine whether a significant difference in contrast enhancement existed between the injured and control arterial walls.

Results

The greatest arterial wall enhancement was observed following contrast recirculation. The wall enhancement measurements made over these time points revealed that the enhancement was greater in the injured artery for 13/16 scanned arterial regions. Using an exact Wilcoxon-signed rank test, a significantly increased enhancement ratio was found in injured arteries compared with control arteries ( p = 0.013 ). Vasa vasorum angiogenesis was confirmed in micro-CT scans of excised arteries.

Conclusions

Whole-body PCD-CT scanners can be used to detect and quantify the increased perfusion occurring within the porcine carotid arterial wall resulting from an increased density of vasa vasorum.

Spectral CT reconstruction via Spectral-Image Tensor and Bidirectional Image-gradient minimization

It is challenging to obtain good image quality in spectral computed tomography (CT) as the photon-number for the photon-counting detectors is limited for each narrow energy bin. This results in a lower signal to noise ratio (SNR) for the projections. To handle this issue, we first formulate the weight bidirectional image gradient with L₀-norm constraint of spectral CT image. Then, as a new regularizer, bidirectional image gradient with L₀-norm constraint is introduced into the tensor decomposition model, generating the Spectral-Image Tensor and Bidirectional Image-gradient Minimization (SITBIM) algorithm. Finally, the split-Bregman method is employed to optimize the proposed SITBIM mathematical model. The experiments on the numerical mouse phantom and real mouse experiments are designed to validate and evaluate the SITBIM method. The results demonstrate that the SITBIM can outperform other state-of-the-art methods (including TVM, TV + LR, SSCMF and NLCTF). INDEX TERMS: -spectral CT, image reconstruction, tensor decomposition, unidirectional image gradient, image similarity.

Multi-material spectral photon-counting micro-CT with minimum residual decomposition and self-supervised deep denoising

Spectral micro-CT imaging with direct-detection energy discriminating photon counting detectors having small pixel size (< 100×100 µm²) is mainly hampered by: i) the limited energy resolution of the imaging device due to charge sharing effects and ii) the unavoidable noise amplification in the images resulting from basis material decomposition. In this work, we present a cone-beam micro-CT setup that includes a CdTe photon counting detector implementing a charge summing hardware solution to correct for the charge-sharing issue and an innovative image processing pipeline based on accurate modeling of the spectral response of the imaging system, an improved basis material decomposition (BMD) algorithm named minimum-residual BMD (MR-BMD), and self-supervised deep convolutional denoising. Experimental tomographic projections having a pixel size of 45×45 µm² of a plastinated mouse sample including I, Ba, and Gd small cuvettes were acquired. Results demonstrate the capability of the combined hardware and software tools to sharply discriminate even between materials having their K-Edge separated by a few keV, such as e.g., I and Ba. By evaluating the quality of the reconstructed decomposed images (water, bone, I, Ba, and Gd), the quantitative performances of the spectral system are here assessed and discussed.

Deep learning based spectral CT imaging

Spectral computed tomography (CT) has attracted much attention in radiation dose reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray energy spectrum is divided into several bins, each energy-bin-specific projection has a low signal-noise-ratio (SNR) than the current-integrating counterpart, which makes image reconstruction a unique challenge. Traditional wisdom is to use prior knowledge based iterative methods. However, this kind of methods demands a great computational cost. Inspired by deep learning, here we first develop a deep learning based reconstruction method; i.e., U-net with L_p^p-norm, Total variation, Residual learning, and Anisotropic adaption (ULTRA). Specifically, we emphasize the various multi-scale feature fusion and multichannel filtering enhancement with a denser connection encoding architecture for residual learning and feature fusion. To address the image deblurring problem associated with the L₂²- loss, we propose a general L_p^p-loss, p>0. Furthermore, the images from different energy bins share similar structures of the same object, the regularization characterizing correlations of different energy bins is incorporated into the L_p^p- loss function, which helps unify the deep learning based methods with traditional compressed sensing based methods. Finally, the anisotropically weighted total variation is employed to characterize the sparsity in the spatial-spectral domain to regularize the proposed network In particular, we validate our ULTRA networks on three large-scale spectral CT datasets, and obtain excellent results relative to the competing algorithms. In conclusion, our quantitative and qualitative results in numerical simulation and preclinical experiments demonstrate that our proposed approach is accurate, efficient and robust for high-quality spectral CT image reconstruction.

Spectral Photon Counting CT: Imaging Algorithms and Performance Assessment

Photon counting x-ray detectors (PCDs) with spectral capabilities have the potential to revolutionize computed tomography (CT) for medical imaging. The ideal PCD provides accurate energy information for each incident x-ray, and at high spatial resolution. This information enables material-specific imaging, enhanced radiation dose efficiency, and improved spatial resolution in CT images. In practice, PCDs are affected by non-idealities, including limited energy resolution, pulse pileup, and cross talk due to charge sharing, K-fluorescence, and Compton scattering. In order to maximize their performance, PCDs must be carefully designed to reduce these effects and then later account for them during correction and post-acquisition steps. This review article examines algorithms for using PCDs in spectral CT applications, including how non-idealities impact image quality. Performance assessment metrics that account for spatial resolution and noise such as the detective quantum efficiency (DQE) can be used to compare different PCD designs, as well as compare PCDs with conventional energy integrating detectors (EIDs). These methods play an important role in enhancing spectral CT images and assessing the overall performance of PCDs.

Photon Counting CT: Clinical Applications and Future Developments

The use of a photon counting detector in CT (PCD CT) is currently the subject of intense investigation and development. In this review article, we will describe potential clinical applications of this technology with a particular focus on the experience of our own institution with a prototype PCD CT scanner. PCDs have three primary advantages over conventional, energy integrating detectors (EIDs): they provide spectral information without need for a dedicated dual energy protocol; they are immune to electronic noise; and they can be made very high resolution without significant compromises to quantum efficiency. These advantages translate into several clinical applications. Metal artifacts, beam hardening artifacts, and noise streaks from photon starvation can be better mitigated using PCD CT. Certain incidental findings can be better characterized using the spectral information from PCD CT. High-contrast, high-resolution structures such as the temporal bone can be better visualized using PCD CT and at greatly reduced dose. We also discuss new possibilities on the horizon, including new contrast agents, and how anticipated improvements in PCD CT will translate to performance in these applications.

Inpainting-filtering for metal artifact reduction (IMIF-MAR) in computed tomography

The reduction of metal artifacts remains a challenge in computed tomography because they decrease image quality, and consequently might affect the medical diagnosis. The objective of this study is to present a novel method to correct metal artifacts based solely on the CT-slices. The proposed method consists of four steps. First, metal implants in the original CT-slice are segmented using an entropy based method, producing a metal image. Second, a prior image is acquired using three transformations: Gaussian filter, Parisotto and Schoenlieb inpainting method with the Mumford-Shah image model and L0 Gradient Minimization method (L0GM). Next, based on the projections from the original CT-slice, prior image and metal image, the sinogram is corrected in the traces affected by metal in the process called normalization and denormalization. Finally, the reconstructed image is obtained by FBP and a Nonlocal Means (NLM) filtering. The efficacy of the algorithm is evaluated by comparing five image quality metrics of the images and by inspecting regions of interest (ROI). Phantom data as well as clinical datasets are included. The proposed method is compared with three established metal artifact reduction (MAR) methods. The results from a phantom and clinical dataset show the visible reduction of artifacts. The conclusion is that IMIF-MAR method can reduce streak metal artifacts effectively and avoid new artifacts around metal implants, while preserving the anatomical structures. Considering both clinical and phantom studies, the proposed MAR algorithm improves the quality of clinical images affected by metal artifacts, and could be integrated in clinical setting.

Improved coronary calcification quantification using photon-counting-detector CT: an ex vivo study in cadaveric specimens

OBJECTIVES

To compare the accuracy of coronary calcium quantification of cadaveric specimens imaged from a photon-counting detector (PCD)-CT and an energy-integrating detector (EID)-CT.

METHODS

Excised coronary specimens were scanned on a PCD-CT scanner, using both the PCD and EID subsystems. The scanning and reconstruction parameters for EID-CT and PCD-CT were matched: 120 kV, 9.3-9.4 mGy CTDI_vol, and a quantitative kernel (D50). PCD-CT images were also reconstructed using a sharper kernel (D60). Scanning the same specimens using micro-CT served as a reference standard for calcified volumes. Calcifications were segmented with a half-maximum thresholding technique. Segmented calcified volume differences were analyzed using the Friedman test and post hoc pairwise Wilcoxon signed rank test with the Bonferroni correction. Image noise measurements were compared between EID-CT and PCD-CT with a repeated-measures ANOVA test and post hoc pairwise comparison with the Bonferroni correction. A p < 0.05 was considered statistically significant.

RESULTS

The volume measurements in 12/13 calcifications followed a similar trend: EID-D50 > PCD-D50 > PCD-D60 > micro-CT. The median calcified volumes in EID-D50, PCD-D50, PCD-D60, and micro-CT were 22.1 (IQR 10.2-64.8), 21.0 (IQR 9.0-56.5), 18.2 (IQR 8.3-49.3), and 14.6 (IQR 5.1-42.4) mm³, respectively (p < 0.05 for all pairwise comparisons). The average image noise in EID-D50, PCD-D50, and PCD-D60 was 60.4 (± 3.5), 56.0 (± 4.2), and 113.6 (± 8.5) HU, respectively (p < 0.01 for all pairwise comparisons).

CONCLUSION

The PCT-CT system quantified coronary calcifications more accurately than EID-CT, and a sharp PCD-CT kernel further improved the accuracy. The PCD-CT images exhibited lower noise than the EID-CT images.

KEY POINTS

• High spatial resolution offered by PCD-CT reduces partial volume averaging and consequently leads to better morphological depiction of coronary calcifications. • Improved quantitative accuracy for coronary calcification volumes could be achieved using high-resolution PCD-CT compared to conventional EID-CT. • PCD-CT images exhibit lower image noise than conventional EID-CT at matched radiation dose and reconstruction kernel.

A quantitative assessment of dual energy computed tomography-based material decomposition for imaging bone marrow edema associated with acute knee injury

PURPOSE

This study developed methods to quantify and improve the accuracy of dual-energy CT (DECT)-based bone marrow edema imaging using a clinical CT system. Objectives were: (a) to quantitatively compare DECT with gold-standard, fluid-sensitive MRI for imaging of edema-like marrow signal intensity (EMSI) and (b) to identify image analysis parameters that improve delineation of EMSI associated with acute knee injury on DECT images.

METHODS

DECT images from ten participants with acute knee injury were decomposed into estimated fractions of bone, healthy marrow, and edema based on energy-dependent differences in tissue attenuation. Fluid-sensitive MR images were registered to DECT for quantitative, voxel-by-voxel comparison between the two modalities. An optimization scheme was developed to find attenuation coefficients for healthy marrow and edema that improved EMSI delineation, compared to MRI. DECT method accuracy was evaluated by measuring dice coefficients, mutual information, and normalized cross correlation between the DECT result and registered MRI.

RESULTS

When applying the optimized three-material decomposition method, dice coefficients for EMSI identified through DECT vs MRI were 0.32 at the tibia and 0.13 at the femur. Optimization of attenuation coefficients improved dice coefficient, mutual information, and cross-correlation between DECT and gold-standard MRI by 48%-107% compared to three-material decomposition using non-optimized parameters, and improved mutual information and cross-correlation by 39%-58% compared to the manufacturer-provided two-material decomposition.

CONCLUSIONS

This study quantitatively evaluated the performance of DECT in imaging knee injury-associated EMSI and identified a method to optimize DECT-based visualization of complex tissues (marrow and edema) whose attenuation parameters cannot be easily characterized. Further studies are needed to improve DECT-based EMSI imaging at the femur.

Photon-counting x-ray detectors for CT

The introduction of photon-counting detectors is expected to be the next major breakthrough in clinical x-ray computed tomography (CT). During the last decade, there has been considerable research activity in the field of photon-counting CT, in terms of both hardware development and theoretical understanding of the factors affecting image quality. In this article, we review the recent progress in this field with the intent of highlighting the relationship between detector design considerations and the resulting image quality. We discuss detector design choices such as converter material, pixel size, and readout electronics design, and then elucidate their impact on detector performance in terms of dose efficiency, spatial resolution, and energy resolution. Furthermore, we give an overview of data processing, reconstruction methods and metrics of imaging performance; outline clinical applications; and discuss potential future developments.

Noise reduction in CT image using prior knowledge aware iterative denoising

The clinical demand for low image noise often limits the slice thickness used in many CT applications. However, a thick-slice image is more susceptible to longitudinal partial volume effects, which can blur key anatomic structures and pathologies of interest. In this work, we develop a prior-knowledge-aware iterative denoising (PKAID) framework that utilizes spatial data redundancy in the slice increment direction to generate low-noise, thin-slice images, and demonstrate its application in non-contrast head CT exams. The proposed technique takes advantage of the low-noise of thicker images and exploits the structural similarity between the thick- and thin-slice images to reduce noise in the thin-slice image. Phantom data and patient cases (n=3) of head CT were used to assess performance of this method. Images were reconstructed at clinically-utilized slice thickness (5 mm) and thinner slice thickness (2 mm). PKAID was used to reduce image noise in 2 mm images using the 5 mm images as low-noise prior. Noise amplitude, noise power spectra (NPS), modulation transfer function (MTF), and slice sensitivity profiles (SSP) of images before/after denoising were analyzed. The NPS and MTF analysis showed that PKAID preserved noise texture and resolution of the original thin-slice image, while reducing noise to the level of thick-slice image. The SSP analysis showed that the slice thickness of the original thin-slice image was retained. Patient examples demonstrated that PKAID-processed, thin-slice images better delineated brain structures and key pathologies such as subdural hematoma compared to the clinical 5 mm images, while additionally reducing image noise. To test an alternative PKAID utilization for dose reduction, a head exam with 40% dose reduction was simulated using projection-domain noise insertion. The image of 5 mm slice thickness was then denoised using PKAID. The results showed that the PKAID-processed reduced-dose images maintained similar noise and image quality compared to the full-dose images.

Overcoming calcium blooming and improving the quantification accuracy of percent area luminal stenosis by material decomposition of multi-energy computed tomography datasets

Purpose: Conventional stenosis quantification from single-energy computed tomography (SECT) images relies on segmentation of lumen boundaries, which suffers from partial volume averaging and calcium blooming effects. We present and evaluate a method for quantifying percent area stenosis using multienergy CT (MECT) images. Approach: We utilize material decomposition of MECT images to measure stenosis based on the ratio of iodine mass between vessel locations with and without a stenosis, thereby eliminating the requirement for segmentation of iodinated lumen. The method was first assessed using simulated MECT images created with different spatial resolutions. To experimentally assess this method, four phantoms with different stenosis severity (30% to 51%), vessel diameters (5.5 to 14 mm), and calcification densities (700 to 1100    mgHA / cc ) were fabricated. Conventional SECT images were acquired using a commercial CT system and were analyzed with commercial software. MECT images were acquired using a commercial dual-energy CT (DECT) system and also from a research photon-counting detector CT (PCD-CT) system. Three-material-decomposition was performed on MECT data, and iodine density maps were used to quantify stenosis. Clinical radiation doses were used for all data acquisitions. Results: Computer simulation verified that this method reduced partial volume and blooming effects, resulting in consistent stenosis measurements. Phantom experiments showed accurate and reproducible stenosis measurements from MECT images. For DECT and two-threshold PCD-CT images, the estimation errors were 4.0% to 7.0%, 2.0% to 9.0%, 10.0% to 18.0%, and - 1.0 % to - 5.0 % (ground truth: 51%, 51%, 51%, and 30%). For four-threshold PCD-CT images, the errors were 1.0% to 3.0%, 4.0% to 6.0%, - 1.0 % to 9.0%, and 0.0% to 6.0%. Errors using SECT were much larger, ranging from 4.4% to 46%, and were especially worse in the presence of dense calcifications. Conclusions: The proposed approach was shown to be insensitive to acquisition parameters, demonstrating the potential to improve the accuracy and precision of stenosis measurements in clinical practice.

Photon-counting Detector CT: System Design and Clinical Applications of an Emerging Technology

Photon-counting detector (PCD) CT is an emerging technology that has shown tremendous progress in the last decade. Various types of PCD CT systems have been developed to investigate the benefits of this technology, which include reduced electronic noise, increased contrast-to-noise ratio with iodinated contrast material and radiation dose efficiency, reduced beam-hardening and metal artifacts, extremely high spatial resolution (33 line pairs per centimeter), simultaneous multienergy data acquisition, and the ability to image with and differentiate among multiple CT contrast agents. PCD technology is described and compared with conventional CT detector technology. With the use of a whole-body research PCD CT system as an example, PCD technology and its use for in vivo high-spatial-resolution multienergy CT imaging is discussed. The potential clinical applications, diagnostic benefits, and challenges associated with this technology are then discussed, and examples with phantom, animal, and patient studies are provided. ^©RSNA, 2019.

A Blooming correction technique for improved vasa vasorum detection using an ultra-high-resolution photon-counting detector CT

Proliferation of vasa vasorum, the microvasculature within artery walls, is an early marker of atherosclerosis. Detection of subtle changes in the spatial density of vasa vasorum using contrast-enhanced CT is challenging due to the limited spatial resolution and blooming effects. We report a forward model-based blooming correction technique to improve vasa vasorum detection in a porcine model imaged using an ultra-high resolution photon-counting detector CT. Six weeks preceding the CT study the animal received autologous blood injections in its left carotid artery to stimulate vasa vasorum proliferation within the arterial wall (right carotid served as control). The forward model predicted radial extent and magnitude of the luminal blooming affecting the wall signal by using prior data acquired with a vessel phantom of known dimensions. The predicted contamination from blooming was then subtracted from the original wall signal measurement to recover the obscured vasa vasorum signal. Attenuation measurements made on a testing vessel phantom before and after blooming corrections revealed a reduction in mean squared error by ~99.9% when compared to the ground truth. Applying corrections to contrast-enhanced carotid arteries from in vivo scan data demonstrated consistent reductions of blooming contamination within the vessel walls. An unpaired student t-test applied to measurements from the uncorrected porcine scan data revealed no significant difference between the vessel walls (p=0.26). However, after employing blooming correction, the mean enhancement was significantly greater in the injured vessel wall (p=0.0006).

FORCE dual-energy CT in pathological grading of clear cell renal cell carcinoma

The aim of the present study was to examine the value of FORCE dual-energy CT in grading the clear cell renal cell carcinoma (ccRCC). A total of 35 cases of ccRCC were included. Hematoxylin and eosin staining was performed, and the cases were divided into low- (Fuhrman I-II) and high-grade (Fuhrman III-IV) groups. FORCE dual-energy CT parameters, including virtual network computing CT value (VNCV), iodine overlay value (IOV), mixed energy CT value (MEV), iodine concentration (IC), normalized iodine concentration (NIC), NIC based on aorta (NICA), NIC based on cortex (NICC) and NIC based on medulla (NICM), were analyzed and compared. Receiver operating characteristic analysis was also performed. There were significant differences in the arterial phase IOV, MEV and IC, and the venous phase IOV and IC between the low- and high-grade groups. No significant differences were observed in VNCV and MEV between the low -and high-grade groups in the venous phase. Significant differences were observed in the NICA and NICC between these two groups, however no difference was observed in NICM. There were significant differences in the tumor CT values for the arterial phase at the 40, 60, 80 and 100 kiloelectron volt (keV) between the low- and high-grade groups, while no significant differences were observed at the 120-140 keV levels. The k-slope for the low-grade group was significantly higher than the high-grade group. In addition, the area under curve for the arterial phase IOV, arterial phase MEV, arterial phase IC, aortic NIC, cortical NIC, venous phase IOV, venous phase IC and curve slope K of mono-energy CT value suggested high value in diagnosis of low- and high-grade ccRCC cases.

Dose Efficiency of Quarter-Millimeter Photon-Counting Computed Tomography: First-in-Human Results

PURPOSE

The aim of this study was to assess the clinical feasibility, image quality, and radiation dose implications of 0.25-mm imaging mode in a cohort of humans, achieved by dividing the photon-counting detector (PCD) size in half compared with standard-resolution photon-counting computed tomography (CT) (0.5 mm).

METHODS

In this technical feasibility study, a whole-body prototype PCD-CT scanner was studied in the 0.25 mm detector mode (measured at isocenter). A high-resolution PCD-CT protocol was first tested in phantom and canine studies in terms of image noise and spatial resolution. Then, 8 human subjects (mean age, 58 ± 8 years; 2 men) underwent axial PCD 0.25-mm scans of the brain, the thorax, and at the level of the upper left kidney. Filtered backprojection reconstruction was performed with a sharp kernel (B70) for standard-resolution and high-resolution data at 0.5-mm isotropic image voxel. High-resolution data, in addition, were reconstructed with an ultrasharp kernel (U70) at 0.25-mm isotropic voxels.

RESULTS

Image reconstructions from the PCD 0.25-mm detector system led to an improvement in resolution from 9 to 18 line pairs/cm in a line pair phantom. Modulation transfer function improved from 9.5 to 15.8 line pairs/cm at 10% modulation transfer function. When fully exploiting this improvement, image noise increased by 75% compared with dose-matched 0.5-mm slice PCD standard-resolution acquisition. However, when comparing with standard-resolution data at same in-plane resolution and slice thickness, the PCD 0.25-mm detector mode showed 19% less image noise in phantom, animal, and human scans.

CONCLUSION

High-resolution photon-counting CT in humans showed improved image quality in terms of spatial resolution and image noise compared with standard-resolution photon-counting.

Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction

Spectral computed tomography (CT) reconstructs material-dependent attenuation images from the projections of multiple narrow energy windows, which is meaningful for material identification and decomposition. Unfortunately, the multi-energy projection datasets usually have lower signal-noise ratios (SNR). Very recently, a spatial-spectral cube matching frame (SSCMF) was proposed to explore the non-local spatial-spectral similarities for spectral CT. This method constructs a group by clustering up a series of non-local spatial-spectral cubes. The small size of spatial patches for such a group makes the SSCMF fail to fully encode the sparsity and low-rank properties. The hard-thresholding and collaboration filtering in the SSCMF also cause difficulty in recovering the image features and spatial edges. While all the steps are operated on 4-D group, the huge computational cost and memory load might not be affordable in practice. To avoid the above limitations and further improve the image quality, we first formulate a non-local cube-based tensor instead of group to encode the sparsity and low-rank properties. Then, as a new regularizer, the Kronecker-basis-representation tensor factorization is employed into a basic spectral CT reconstruction model to enhance the capability of image feature extraction and spatial edge preservation, generating a non-local low-rank cube-based tensor factorization (NLCTF) method. Finally, the split-Bregman method is adopted to solve the NLCTF model. Both numerical simulations and preclinical mouse studies are performed to validate and evaluate the NLCTF algorithm. The results show that the NLCTF method outperforms the other state-of-the-art competing algorithms.

Iterative quality enhancement via residual-artifact learning networks for low-dose CT

Radiation exposure and the associated risk of cancer for patients in computed tomography (CT) scans have been major clinical concerns. The radiation exposure can be reduced effectively via lowering the x-ray tube current (mA). However, this strategy may lead to excessive noise and streak artifacts in the conventional filtered back-projection reconstructed images. To address this issue, some deep convolutional neural network (ConvNet) based approaches have been developed for low-dose CT imaging inspired by the recent development of machine learning. Nevertheless, some of the image textures reconstructed by the ConvNet could be corrupted by the severe streaks, especially in ultra-low-dose cases, which could be close to prostheses and hamper diagnosis. Therefore, in this work, we propose an iterative residual-artifact learning ConvNet (IRLNet) approach to improve the reconstruction performance over the ConvNet based approaches. Specifically, the proposed IRLNet estimates the high-frequency details within the noise and then removes them iteratively; after eliminating severe streaks in the low-dose CT images, the residual low-frequency details can be processed through the conventional network. Moreover, the proposed IRLNet scheme can be extended for robust handling of quantitative dual energy CT/cerebral perfusion CT imaging, and statistical iterative reconstruction. Real patient data are used to evaluate the proposed IRLNet, and the experimental results demonstrate that the proposed IRLNet approach outperforms the previous ConvNet based approaches in reducing the image noise and streak artifacts efficiently at the same time as preserving edge details well, suggesting that the proposed IRLNet approach can be used to improve the CT image quality, especially in ultra-low-dose cases.

Quarter-millimeter spectral coronary stent imaging with photon-counting CT: Initial experience

PURPOSE

To evaluate the performance and clinical feasibility of 0.25 mm resolution mode of a dual-energy photon-counting detector (PCD) computed tomography (CT) system for coronary stent imaging and to compare the results to state-of-the-art dual-energy energy-integrating detector (EID) CT.

MATERIALS AND METHODS

Coronary stents with different diameters (2.0-4.0 mm) were examined inside a coronary artery phantom consisting of plastic tubes filled with iodine-based and gadolinium-based contrast material diluted to approximate clinical concentrations (n = 18). EID images were acquired using 2nd and 3rd generation dual-source CT systems (SOMATOM Flash and SOMATOM Force, Siemens Healthcare) at 0.60 mm (defined as standard-resolution (SR)) isotropic voxel size. Radiation-dose matched PCD images were acquired using a human prototype PCD system (Siemens Healthcare) at 0.50 mm (SR) and 0.25 mm (HR) imaging modes. Images were reconstructed using optimized convolution kernels.

RESULTS

Dual-energy HR PCD images significantly better stent lumen visualization (median: 69.5%, IQR: 61.2-78.9%) over dual-energy EID, and standard-resolution PCD images (median: 53.2-57.4%, all P < 0.01). HR PCD acquisitions reconstructed at SR image voxel size showed 25.3% lower image noise compared to SR PCD acquisitions (P < 0.001). High-resolution iodine and gadolinium maps, as well as virtual monoenergetic images, were calculated from the PCD data and enabled estimation of contrast agent concentration in the lumen without interference from the coronary stent.

CONCLUSION

HR spectral PCD imaging significantly improves coronary stent lumen visibility over dual-energy EID. When the PCD-HR data was reconstructed into standard voxel sizes (0.5 mm isotropic) the image noise decreased by 25% compared to SR acquisition of PCD. Both dual-energy systems were consistent in estimating contrast agent concentrations.

Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction

PURPOSE

Photon counting detectors (PCDs) are being introduced in advanced x-ray computed tomography (CT) scanners. From a single PCD-CT acquisition, multiple images can be reconstructed, each based on only a part of the original x-ray spectrum. In this study, we investigated whether PCD-CT can be used to estimate stopping power ratios (SPRs) for proton therapy treatment planning, both by comparing to other SPR methods proposed for single energy CT (SECT) and dual energy CT (DECT) as well as to experimental measurements.

METHODS

A previously developed DECT-based SPR estimation method was adapted to PCD-CT data, by adjusting the estimation equations to allow for more energy spectra. The method was calibrated directly on noisy data to increase the robustness toward image noise. The new PCD SPR estimation method was tested in theoretical calculations as well as in an experimental setup, using both four and two energy bin PCD-CT images, and through comparison to two other SPR methods proposed for SECT and DECT. These two methods were also evaluated on PCD-CT images, full spectrum (one-bin) or two-bin images, respectively. In a theoretical framework, we evaluated the effect of patient-specific tissue variations (density and elemental composition) and image noise on the SPR accuracy; the latter effect was assessed by applying three different noise levels (low, medium, and high noise). SPR estimates derived using real PCD-CT images were compared to experimentally measured SPRs in nine organic tissue samples, including fat, muscle, and bone tissues.

RESULTS

For the theoretical calculations, the root-mean-square error (RMSE) of the SPR estimation was 0.1% for the new PCD method using both two and four energy bins, compared to 0.2% and 0.7% for the DECT- and SECT-based method, respectively. The PCD method was found to be very robust toward CT image noise, with a RMSE of 2.7% when high noise was added to the CT numbers. Introducing tissue variations, the RMSE only increased to 0.5%; even when adding high image noise to the changed tissues, the RMSE stayed within 3.1%. In the experimental measurements, the RMSE over the nine tissue samples was 0.8% when using two energy bins, and 1.0% for the four-bin images.

CONCLUSIONS

In all tested cases, the new PCD method produced similar or better results than the SECT- and DECT-based methods, showing an overall improvement of the SPR accuracy. This study thus demonstrated that PCD-CT scans will be a qualified candidate for SPR estimations.

Material elemental decomposition in dual and multi-energy CT via a sparsity-dictionary approach for proton stopping power ratio calculation

PURPOSE

Accurate calculation of proton stopping power ratio (SPR) relative to water is crucial to proton therapy treatment planning, since SPR affects prediction of beam range. Current standard practice derives SPR using a single CT scan. Recent studies showed that dual-energy CT (DECT) offers advantages to accurately determine SPR. One method to further improve accuracy is to incorporate prior knowledge on human tissue composition through a dictionary approach. In addition, it is also suggested that using CT images with multiple (more than two) energy channels, i.e., multi-energy CT (MECT), can further improve accuracy. In this paper, we proposed a sparse dictionary-based method to convert CT numbers of DECT or MECT to elemental composition (EC) and relative electron density (rED) for SPR computation.

METHOD

A dictionary was constructed to include materials generated based on human tissues of known compositions. For a voxel with CT numbers of different energy channels, its EC and rED are determined subject to a constraint that the resulting EC is a linear non-negative combination of only a few tissues in the dictionary. We formulated this as a non-convex optimization problem. A novel algorithm was designed to solve the problem. The proposed method has a unified structure to handle both DECT and MECT with different number of channels. We tested our method in both simulation and experimental studies.

RESULTS

Average errors of SPR in experimental studies were 0.70% in DECT, 0.53% in MECT with three energy channels, and 0.45% in MECT with four channels. We also studied the impact of parameter values and established appropriate parameter values for our method.

CONCLUSION

The proposed method can accurately calculate SPR using DECT and MECT. The results suggest that using more energy channels may improve the SPR estimation accuracy.