Assessment of material identification and quantification in the presence of metals using spectral photon counting CT

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.

Unsharp Structure Guided Filtering for Self-Supervised Low-Dose CT Imaging

Low-dose computed tomography (LDCT) imaging faces great challenges. Although supervised learning has revealed great potential, it requires sufficient and high-quality references for network training. Therefore, existing deep learning methods have been sparingly applied in clinical practice. To this end, this paper presents a novel Unsharp Structure Guided Filtering (USGF) method, which can reconstruct high-quality CT images directly from low-dose projections without clean references. Specifically, we first employ low-pass filters to estimate the structure priors from the input LDCT images. Then, inspired by classical structure transfer techniques, deep convolutional networks are adopted to implement our imaging method which combines guided filtering and structure transfer. Finally, the structure priors serve as the guidance images to alleviate over-smoothing, as they can transfer specific structural characteristics to the generated images. Furthermore, we incorporate traditional FBP algorithms into self-supervised training to enable the transformation of projection domain data to the image domain. Extensive comparisons and analyses on three datasets demonstrate that the proposed USGF has achieved superior performance in terms of noise suppression and edge preservation, and could have a significant impact on LDCT imaging in the future.

Components of carotid atherosclerotic plaque in spectral photon-counting CT with histopathologic comparison

OBJECTIVES

This study aimed to demonstrate the effectiveness of spectral photon-counting CT (SPCCT) in quantifying fibrous cap (FC) thickness, FC area, and lipid-rich necrotic core (LRNC) area, in excised carotid atherosclerotic plaques by comparing it with histopathological measurements.

METHODS

This is a single-center ex vivo cross-sectional observational study. Excised plaques of 20 patients (71 +/- 6 years; 13 men), obtained from carotid endarterectomy were scanned with SPCCT using standardized acquisition settings (120k Vp/19 μA; 7-18 keV, 18-30 keV, 30-45 keV, 45-75 keV, and 75-118 keV). FC thickness, FC area, and LRNC area were quantified and compared between high-resolution 3D multi-energy CT images and histopathology using the Wilcoxon signed-ranks test and Bland-Altman analysis. Images were interpreted twice by two radiologists separately, blinded to the histopathology; inter- and intra-rater reliability were assessed with the intra-class correlation coefficients (ICC).

RESULTS

FC thickness and FC area did not show significant differences between the SPCCT-derived radiological measurements versus the histopathological measurements (p value range 0.15-0.51 for FC thickness and 0.053-0.30 for FC area). For the LRNC area, the p value was statistically non-significant for reader 1 (range 0.36-0.81). The Bland-Altman analysis showed mean difference and 95% confidence interval for FC thickness, FC area, and LRNC area, 0.04 (-0.36 to 0.12) square root mm, -0.18 (-0.34 to -0.02) log₁₀ mm² and 0.10 (-0.088. to 0.009) log₁₀ mm² respectively.

CONCLUSION

The result demonstrated a viable technique for quantifying FC thickness, FC area, and LRNC area due to the combined effect of high spatial and energy resolution of SPCCT.

KEY POINTS

• SPCCT can identify and quantify different components of carotid atherosclerotic plaque in ex vivo study. • Components of atherosclerotic plaque did not show significant differences between the SPCCT-derived radiological measurements versus the histopathological measurements.

Masked Joint Bilateral Filtering via Deep Image Prior for Digital X-ray Image Denoising

Medical image denoising faces great challenges. Although deep learning methods have shown great potential, their efficiency is severely affected by millions of trainable parameters. The non-linearity of neural networks also makes them difficult to be understood. Therefore, existing deep learning methods have been sparingly applied to clinical tasks. To this end, we integrate known filtering operators into deep learning and propose a novel Masked Joint Bilateral Filtering (MJBF) via deep image prior for digital X-ray image denoising. Specifically, MJBF consists of a deep image prior generator and an iterative filtering block. The deep image prior generator produces plentiful image priors by a multi-scale fusion network. The generated image priors serve as the guidance for the iterative filtering block, which is utilized for the actual edge-preserving denoising. The iterative filtering block contains three trainable Joint Bilateral Filters (JBFs), each with only 18 trainable parameters. Moreover, a masking strategy is introduced to reduce redundancy and improve the understanding of the proposed network. Experimental results on the ChestX-ray14 dataset and real data show that the proposed MJBF has achieved superior performance in terms of noise suppression and edge preservation. Tests on the portability of the proposed method demonstrate that this denoising modality is simple yet effective, and could have a clinical impact on medical imaging in the future.

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.

Spectral CT imaging of human osteoarthritic cartilage via quantitative assessment of glycosaminoglycan content using multiple contrast agents

Detection of early osteoarthritis to stabilize or reverse the damage to articular cartilage would improve patient function, reduce disability, and limit the need for joint replacement. In this study, we investigated nondestructive photon-processing spectral computed tomography (CT) for the quantitative measurement of the glycosaminoglycan (GAG) content compared to destructive histological and biochemical assay techniques in normal and osteoarthritic tissues. Cartilage-bone cores from healthy bovine stifles were incubated in 50% ioxaglate (Hexabrix^®) or 100% gadobenate dimeglumine (MultiHance^®). A photon-processing spectral CT (MARS) scanner with a CdTe-Medipix3RX detector imaged samples. Calibration phantoms of ioxaglate and gadobenate dimeglumine were used to determine iodine and gadolinium concentrations from photon-processing spectral CT images to correlate with the GAG content measured using a dimethylmethylene blue assay. The zonal distribution of GAG was compared between photon-processing spectral CT images and histological sections. Furthermore, discrimination and quantification of GAG in osteoarthritic human tibial plateau tissue using the same contrast agents were demonstrated. Contrast agent concentrations were inversely related to the GAG content. The GAG concentration increased from 25 μg/ml (85 mg/ml iodine or 43 mg/ml gadolinium) in the superficial layer to 75 μg/ml (65 mg/ml iodine or 37 mg/ml gadolinium) in the deep layer of healthy bovine cartilage. Deep zone articular cartilage could be distinguished from subchondral bone by utilizing the material decomposition technique. Photon-processing spectral CT images correlated with histological sections in healthy and osteoarthritic tissues. Post-imaging material decomposition was able to quantify the GAG content and distribution throughout healthy and osteoarthritic cartilage using Hexabrix^® and MultiHance^® while differentiating the underlying subchondral bone.