Improving Calibration of Photon Counting Detector CT Using Redundant Sampling

Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT. To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images. Specifically, we employ a feature-sharing encoder to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details. We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods. In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts.

DuDo-RAC: Dual-domain optimization for ring artifact correction in photon counting CT.

In Computed Tomography (CT) imaging, the ring artifacts caused by the inconsistent detector response can significantly degrade the reconstructed images, having negative impacts on the subsequent applications. The new generation of CT systems based on photon-counting detectors are affected by ring artifacts more severely. The flexibility and variety of detector responses make it difficult to build a well-defined model to characterize the ring artifacts. In this context, this study proposes the global dependency-enhanced dual-domain parallel neural network for Ring Artifact Removal (RAR). First, based on the fact that the features of ring artifacts are different in Cartesian and Polar coordinates, the parallel architecture is adopted to construct the deep neural network so that it can extract and exploit the latent features from different domains to improve the performance of ring artifact removal. Besides, the ring artifacts are globally relevant whether in Cartesian or Polar coordinate systems, but convolutional neural networks show inherent shortcomings in modeling long-range dependency. To tackle this problem, this study introduces the novel Mamba mechanism to achieve a global receptive field without incurring high computational complexity. It enables effective capture of the long-range dependency, thereby enhancing the model performance in image restoration and artifact reduction. The experiments on the simulated data validate the effectiveness of the dual-domain parallel neural network and the Mamba mechanism, and the results on two unseen real datasets demonstrate the promising performance of the proposed RAR algorithm in eliminating ring artifacts and recovering image details.

Ring artifacts usually appear in photon counting computed tomography (PCCT) images, which may compromise image quality and cause non-uniformity bias. This study proposed a fast ring artifacts removal method by exploring the correlation from projections with different views for PCCT. This method has three advantages. First, our method only employ mean projection of current scan to correct projections without additional scans. Second, our method can correct the inconsistency of all detector pixels simultaneously without locating the inconsistent response pixels. Third, it can preserve reconstructed image details well without extra computational cost. Both numerical and preclinical experiments demonstrate the proposed method can suppress the ring artifacts very well than the competitors.

Purpose: Defective pixels in a flat panel detector may be characterized by a nonlinear signal response drastically different from those in the neighboring pixels. They could lead to ring artifacts in cone beam CT. In this presentation, we will describe and demonstrate the use of a filter based calibration technique to detect these pixels. In addition, we will employ this technique as a flat field correction method to correct nonlinear signal response. Methods and Materials: To force the signal responses of all pixels to vary smoothly without sudden changes, we acquired images at fixed mAs but with various filtrations. Each filtered image is fitted to a smooth surface whose values are close to those of normal pixels but vary smoothly in the image. The ratios of the surface fit values to the original values were then computed on a pixel‐by‐pixel basis and used to map pixel values during subsequent image acquisition. This mapping would compensate for the nonlinear signal response associated with the defective pixels thus eliminating the ring artifacts. Using the surface fits as the reference, defective pixels were detected by automatic thresholding. Results: Using filter based calibration, defective (∼0.035 %) pixels were successfully detected and corrected for. Therefore, ring artifacts were largely eliminated in cone beam CT images. A reduction in patterned noise artifacts in the projection images was also observed. The automated surface fitting procedure was found to be robust. Conclusions: Although the conventional flat field correction addresses non‐uniform response across the detector, artifacts may still form as the pixel response varies with the beam quality and signal intensity. The filter based calibration procedure was successfully used to detect and correct for these artifacts.This work was supported in part by a research grant EB00117 from the NIH‐NIBIB and a research grant CA104759 from the NIH‐NCI.

CT images often have some ring artefacts caused by the inconsistent response of detector pixels and other factors. Removing ring artefacts effectively without impairing the image quality is still a hard problem although some methods have been proposed. Given that total variation minimisation is widely used for image denoising, a unidirectional relative variation model is proposed and performs it on the reconstructed images transformed into polar coordinates to reduce the artefacts. The proposed method is tested on both simulated data and real CT images, and the qualitative and quantitative results show that it is effective.

Raw-data–based material decomposition in spectral CT using photon–counting energy–selective detectors relies on a precise forward model that predicts a count–rate given intersection lengths for each material. This re- quires extensive system–specific measurements or calibration techniques. Existing calibrations either estimate a detected spectrum and are able to account for spectrally distorted assumptions or correct the predicted count rate using a correction function and can accommodate for count rate–dependent effects such as pulse pileup. We propose a calibration method that uses transmission measurements to optimize a correction function that, unlike existing methods, depends both on the photon energy and the count rate. It is thus able to correct for both kinds of distortions. In a simulated material decomposition into water and iodine, the error was reduced by 96 % compared to the best performing reference method if only pulse pileup was present and reduced by 23 % if additionally spectral distortions were taken into account. In phantom measurements using a Dectris SANTIS prototype detector, the proposed method allowed to reduce the error by 29 % compared to the best performing reference method. Artifacts were below the noise level for the proposed method, while the reference methods either showed an offset in the water region or ring artifacts.

Spectral Photon Counting Computed Tomography (SPCCT), a ground-breaking development in CT technology, has immense potential to address the persistent problem of metal artefacts in CT images. This study aims to evaluate the potential of Mars photon-counting CT technology in reducing metal artefacts. It focuses on identifying and quantifying clinically significant materials in the presence of metal objects. A multi-material phantom was used, containing inserts of varying concentrations of hydroxyapatite (a mineral present in teeth, bones, and calcified plaque), iodine (used as a contrast agent), CT water (to mimic soft tissue), and adipose (as a fat substitute). Three sets of scans were acquired: with aluminium, with stainless steel, and without a metal insert as a reference dataset. Data acquisition was performed using a Mars SPCCT scanner (Microlab 5×120); operated at 118 kVp and 80 μA. The images were subsequently reconstructed into five energy bins: 7-40, 40-50, 50-60, 60-79, and 79-118 keV. Evaluation metrics including signal-to-noise ratio (SNR), linearity of attenuation profiles, root mean square error (RMSE), and area under the curve (AUC) were employed to assess the energy and material-density images with and without metal inserts. Results show decreased metal artefacts and a better signal-to-noise ratio (up to 25%) with increased energy bins as compared to reference data. The attenuation profile also demonstrated high linearity (R2 >0.95) and lower RMSE across all material concentrations, even in the presence of aluminium and steel. Material identification accuracy for iodine and hydroxyapatite (with and without metal inserts) remained consistent, minimally impacting AUC values. For demonstration purposes, the biological sample was also scanned with the stainless steel volar implant and cortical bone screw, and the images were objectively assessed to indicate the potential effectiveness of SPCCT in replicating real-world clinical scenarios.

Introduction: The Varian Ethos v 1.1 system (product version 4.0) is equipped with the new HyperSight flat-panel detector (86 × 43 cm2, cesium iodine scintillator detectors). Improvements in its design and iterative reconstruction algorithm allow for almost CT-like image quality. In the Ethos, the kV-CBCT HyperSight imaging modality plays a crucial role in adaptive radiation therapy. Any kV-CBCT image artifacts can potentially impact the precision of adaptive treatment and must be identified by the QA program. This study investigates the occurrence of ring artifacts in kV-CBCT patient scans and explores their presence using various phantoms for quality assurance (QA) testing. Materials and Methods: The study involves the following approaches: 1. Review of the kV-CBCT scans to assess the presence of ring artifacts. 2. Analysis of monthly image QA test data for the kV-CBCT modality using the Quart Phantom, evaluating Hounsfield Unit (HU) constancy, homogeneity, noise levels, image scaling and artifact presence. 3. Recalibration kV, kV-CBCT and another activity performed by the service team. 4. Evaluation of artifacts with the additional phantoms (such as Catphan 604 and an anthropomorphic phantom (Alderson-Rando phantom)). The phantoms were imaged using the Varian HyperSight imaging platform (pelvic protocols with different acquisition parameters). Results: Ring artifacts were observed in the kV-CBCT scans of the patients. However, the routine QA tests with the Quart phantom did not detect them, showing image uniformity, noise levels, scaling, and HU constancy, to be within the acceptable limits. Tests performed using the Catphan phantom also did not show any artifacts. The detection of these artifacts was only possible by using an anthropomorphic phantom that reflects the patient geometry. Recalibration of the flat panel total detector with PVA procedures performed in Service Mode had minimal effect on permanent artifact reduction. Other Varian maintenance procedures also did not produce lasting results. Ultimately, replacing the flat detector significantly improved image quality. Conclusions: The observed artifacts make the segmentation process and the evaluation of deformation fusion by the physician more difficult during on-site adaptation. At the same time, at this stage the user is not able to determine their impact on dose distribution. Such artifacts should certainly be eliminated from the images as soon as possible. This study underscores the phantom-dependent nature of the ring artifacts in Ethos kV-CBCT imaging. The presence of such artifacts in the patient scans and the scans obtained for the anthropomorphic phantom, and their absence when the standard QA phantoms are applied, suggests that certain material properties or scatter conditions may be of importance in contrast-visible artifact formation. It is worthwhile to include the use of anthropomorphic phantoms in QA protocols to better represent clinical imaging conditions. Furthermore, the study highlights the importance of collaboration and communication between radiation therapists, physicians, medical physicists and manufacturer service to optimize adaptive radiation therapy procedures.

In practice, mis-calibrated detector pixels give rise to wide and faint ring artifacts in the reconstruction image of the In-line phase-contrast computed tomography (IL-PC-CT). Ring artifacts correction is essential in IL-PC-CT. In this study, a novel method of wide and faint ring artifacts correction was presented based on combining TV-L1 model with guided image filtering (GIF) in the reconstruction image domain. The new correction method includes two main steps namely, the GIF step and the TV-L1 step. To validate the performance of this method, simulation data and real experimental synchrotron data are provided. The results demonstrate that TV-L1 model with GIF step can effectively correct the wide and faint ring artifacts for IL-PC-CT.

BACKGROUND. Photon-counting detector (PCD) CT may allow lower radiation doses than used for conventional energy-integrating detector (EID) CT, with preserved image quality. OBJECTIVE. The purpose of this study was to compare PCD CT and EID CT, reconstructed with and without a denoising tool, in terms of image quality of the osseous pelvis in a phantom, with attention to low radiation doses. METHODS. A pelvic phantom comprising human bones in acrylic material mimicking soft tissue underwent PCD CT and EID CT at various tube potentials and radiation doses ranging from 0.05 to 5.00 mGy. Additional denoised reconstructions were generated using a commercial tool. Noise was measured in the acrylic material. Two readers performed independent qualitative assessments that entailed determining the denoised EID CT reconstruction with the lowest acceptable dose and then comparing this reference reconstruction with PCD CT reconstructions without and with denoising, using subjective Likert scales. RESULTS. Noise was lower for PCD CT than for EID CT. For instance, at 0.05 mGy and 100 kV with tin filter, noise was 38.4 HU for PCD CT versus 48.8 HU for EID CT. Denoising further reduced noise; for example, for PCD CT at 100 kV with tin filter at 0.25 mGy, noise was 19.9 HU without denoising versus 9.7 HU with denoising. For both readers, lowest acceptable dose for EID CT was 0.10 mGy (total score, 11 of 15 for both readers). Both readers somewhat agreed that PCD CT without denoising at 0.10 mGy (reflecting reference reconstruction dose) was relatively better than the reference reconstruction in terms of osseous structures, artifacts, and image quality. Both readers also somewhat agreed that denoised PCD CT reconstructions at 0.10 mGy and 0.05 mGy (reflecting matched and lower doses, respectively, with respect to reference reconstruction dose) were relatively better than the reference reconstruction for the image quality measures. CONCLUSION. PCD CT showed better-quality images than EID CT when performed at the lowest acceptable radiation dose for EID CT. PCD CT with denoising yielded better-quality images at a dose lower than lowest acceptable dose for EID CT. CLINICAL IMPACT. PCD CT with denoising could facilitate lower radiation doses for pelvic imaging.

Photon counting detectors (PCDs) provide multiple energy-dependent measurements for estimating basis line-integrals. However, the measured spectrum is distorted from the spectral response effect (SRE) via charge sharing, K-fluorescence emission, and so on. Thus, in order to avoid bias and artifacts in images, the SRE needs to be compensated. For this purpose, we recently developed a computationally efficient three-step algorithm for PCD-CT without contrast agents by approximating smooth X-ray transmittance using low-order polynomial bases. It compensated the SRE by incorporating the SRE model in a linearized estimation process and achieved nearly the minimum variance and unbiased (MVU) estimator. In this paper, we extend the three-step algorithm to K-edge imaging applications by designing optimal bases using a low-rank approximation to model X-ray transmittances with arbitrary shapes (i.e., smooth without the K-edge or discontinuous with the K-edge). The bases can be used to approximate the X-ray transmittance and to linearize the PCD measurement modeling and then the three-step estimator can be derived as in the previous approach: estimating the x-ray transmittance in the first step, estimating basis line-integrals including that of the contrast agent in the second step, and correcting for a bias in the third step. We demonstrate that the proposed method is more accurate and stable than the low-order polynomial-based approaches with extensive simulation studies using gadolinium for the K-edge imaging application. We also demonstrate that the proposed method achieves nearly MVU estimator, and is more stable than the conventional maximum likelihood estimator in high attenuation cases with fewer photon counts.

To assess the image quality and delineation value of compressed sensing (CS)-accelerated 3D T2W turbo-spin-echo (TSE) sequence for radiotherapy treatment planning (RTP) of prostate cancer. An optimized CS-accelerated 3D-T2W-TSE was determined by volunteer imaging and applied for clinical RTP-MRI. This optimized CS-accelerated planning MRI and the standardized adaptive MRI acquired at 1.5T were retrospectively analyzed in 26 prostate cancer patients who were to receive MR-guided radiotherapy. Signal-to-noise ratio (SNR) and relative contrast ratio (CR) were quantitatively assessed. Image quality and artifacts were qualitatively assessed using a five-point scale rating. Delineation value in the prostate and organs-at-risk (OARs) was also rated and compared. Wilcoxon signed-rank test was used for SNR, relative CR, and rating comparisons. The interobserver rating agreement was evaluated by percent agreement. Significantly better SNR and relative CR in the prostate, rectum, bowel, penis, and penile bulb, while significantly worse in the cauda equina, were observed on the planning MRI. Significantly better ratings of image quality and artifacts were given to the planning MRI, with much less Gibbs ringing and reconstruction artifacts. Significantly better delineation value rating was achieved on the planning MRI in the prostate, seminal vesicle, rectum, penis, penile bulb, and testes, while significantly worse in the cauda equina. A strong to almost perfect interobserver rating agreement was obtained. This study suggested that CS acceleration is applicable and valuable in prostate RTP-MRI. CS-accelerated 3D-T2W-TSE images should benefit the delineation of prostate and many OARs.

The presence of high density objects leads to significant artifacts in CT images. These artifacts impact the quantitative as well as qualitative accuracy of CT images. These artifacts are caused due to factors such as beam hardening, scatter, photon starvation. The ramp filter prior to standard back-projection enhances some of the artifacts. The artifacts can be reduced by using better data acquisition such as dual energy imaging, higher kVp imaging. Several software based techniques have been proposed to reduce metal artifacts that can be classified into model-based algorithms and sinogram in-painting methods. We propose an improved metal artifact correction algorithm that belongs to the category of sinogram inpainting. In the prior art, the prior images used to generate the inpainted data is created by segmenting the original or the first pass metal artifact reduced (MAR) images. We propose a multi-band filter design to generate the prior image. The original image and the first pass MAR image possess complimentary information and are combined using a multi-band filter. The combined image is then segmented to generate the final prior image. It is shown that the new approach leads to a prior that is more consistent to the original image compared to the conventional prior and hence improved inpainted data. The proposed approach is demonstrated to be superior to the conventional approach using clinical datasets. We further compare two different inpainting algorithms to replace the original corrupted sinogram samples with the forward projection of the prior, also defined as the prior data. The first approach is based on the linear baseline shift algorithm, while in the second approach the replacement step used in the normalized metal artifact correction algorithm (NMAR) is used. Both the approaches are validated using both phantom and clinical data and is demonstrated to be superior to standard interpolation based techniques.

To evaluate the performance and reliability of the single-energy metal artifact reduction (SEMAR) algorithm in patients with different orthopedic hardware at the hips. A total of 153 patients with hip instrumentation who had undergone CT with adaptive iterative dose reduction (AIDR) 3D and SEMAR algorithms between February 2015 and October 2019 were included retrospectively. Patients were divided into 5 groups by the hardware type. Two readers with 21 and 13years of experience blindly reviewed all image sets and graded the extent of artifacts and imaging quality using 5-point scales. To evaluate reliability, the mean densities and image noises were measured at the urinary bladder, veins, and fat in images with artifacts and the reference images. No significant differences were found in the mean densities of the urinary bladder, veins, and fat between the SEMAR images with artifacts (7.57 ± 9.49, 40.29 ± 23.07, 86.78 ± 38.34) and the reference images (7.77 ± 6.2, 40.27 ± 8.66, 89.10 ± 20.70) (P = .860, .994, .392). Image noises of the urinary bladder in the SEMAR images with artifacts (14.25 ± 4.50) and the SEMAR reference images (9.69 ± 1.29) were significantly higher than those in the AIDR 3D reference images (9.11 ± 1.12) (P < .001; P < .001). All AIDR 3D images were non-diagnostic (overall quality ≤ 3) and less than a quarter of the SEMAR images were non-diagnostic (16.7-23.7%), mainly in patients with prostheses [reader 1: 91.7% (22/24); reader 2: 92.6% (25/27)]. The SEMAR algorithm significantly reduces metal artifacts in CT images, more in patients with internal fixations than in patients with prostheses, and provides reliable attenuation of soft tissues.