Feasibility Study of Radiation Dose Reduction in Adult Female Pelvic CT Scan with Low Tube-Voltage and Adaptive Statistical Iterative Reconstruction

Hybrid plug-and-play CT image restoration using nonconvex low-rank group sparsity and deep denoiser priors

Objective. Low-dose computed tomography (LDCT) is an imaging technique that can effectively help patients reduce radiation dose, which has attracted increasing interest from researchers in the field of medical imaging. Nevertheless, LDCT imaging is often affected by a large amount of noise, making it difficult to clearly display subtle abnormalities or lesions. Therefore, this paper proposes a multiple complementary priors CT image reconstruction method by simultaneously considering both the internal prior and external image information of CT images, thereby enhancing the reconstruction quality of CT images.Approach. Specifically, we propose a CT image reconstruction method based on weighted nonconvex low-rank regularized group sparse and deep image priors under hybrid plug-and-play framework by utilizing the weighted nonconvex low rankness and group sparsity of dictionary domain coefficients of each group of similar patches, and a convolutional neural network denoiser. To make the proposed reconstruction problem easier to tackle, we utilize the alternate direction method of multipliers for optimization.Main results. To verify the performance of the proposed method, we conduct detailed simulation experiments on the images of the abdominal, pelvic, and thoracic at projection views of 45, 65, and 85, and at noise levels of1×105and1×106, respectively. A large number of qualitative and quantitative experimental results indicate that the proposed method has achieved better results in texture preservation and noise suppression compared to several existing iterative reconstruction methods.Significance. The proposed method fully considers the internal nonlocal low rankness and sparsity, as well as the external local information of CT images, providing a more effective solution for CT image reconstruction. Consequently, this method enables doctors to diagnose and treat diseases more accurately by reconstructing high-quality CT images.

Value of deep-learning image reconstruction at submillisievert CT for evaluation of the female pelvis

AIM

To assess the value of deep-learning reconstruction (DLR) at submillisievert computed tomography (CT) for the evaluation of the female pelvis, with standard dose (SD) hybrid iterative reconstruction (IR) images as reference.

MATERIALS AND METHODS

The present study enrolled 50 female patients consecutively who underwent contrast-enhanced abdominopelvic CT for clinically indicated reasons. Submillisievert pelvic images were acquired using a noise index of 15 for low-dose (LD) scans, which were reconstructed with DLR (body and body sharp), hybrid-IR, and model-based IR (MBIR). Additionally, SD scans were reconstructed with a noise index of 7.5 using hybrid-IR. Radiation dose, quantitative image quality, overall image quality, image appearance using a five-point Likert scale (1-5: worst to best), and lesion evaluation in both SD and LD images were analysed and compared.

RESULTS

The submillisievert pelvic CT examinations showed a 61.09 ± 4.13% reduction in the CT dose index volume compared to SD examinations. Among the LD images, DLR (body sharp) had the highest quantitative quality, followed by DLR (body), MBIR, and hybrid-IR. LD DLR (body) had overall image quality comparable to the reference (p=0.084) and favourable image appearance (p=0.209). In total, 40 pelvic lesions were detected in both SD and LD images. LD DLR (body and body sharp) exhibited similar diagnostic confidence (p=0.317 and 0.096) compared with SD hybrid-IR.

CONCLUSION

DLR algorithms, providing comparable image quality and diagnostic confidence, are feasible in submillisievert abdominopelvic CT. The DLR (body) algorithm with favourable image appearance is recommended in clinical settings.

Image quality and radiologists' subjective acceptance using model-based iterative and deep learning reconstructions as adjuncts to ultrahigh-resolution CT in low-dose contrast-enhanced abdominopelvic CT: phantom and clinical pilot studies

Purpose

In contrast-enhanced abdominopelvic CT (CE-APCT) for oncologic follow-up, ultrahigh-resolution CT (UHRCT) may improve depiction of fine lesions and low-dose scans are desirable for minimizing the potential adverse effects by ionizing radiation. We compared image quality and radiologists’ acceptance of model-based iterative (MBIR) and deep learning (DLR) reconstructions of low-dose CE-APCT by UHRCT.

Methods

Using our high-resolution (matrix size: 1024) and low-dose (tube voltage 100 kV; noise index: 20–40 HU) protocol, we scanned phantoms to compare the modulation transfer function and noise power spectrum between MBIR and DLR and assessed findings in 36 consecutive patients who underwent CE-APCT (noise index: 35 HU; mean CTDI_vol: 4.2 ± 1.6 mGy) by UHRCT. We used paired t-test to compare objective noise and contrast-to-noise ratio (CNR) and Wilcoxon signed-rank test to compare radiologists’ subjective acceptance regarding noise, image texture and appearance, and diagnostic confidence between MBIR and DLR using our routine protocol (matrix size: 512; tube voltage: 120 kV; noise index: 15 HU) for reference.

Results

Phantom studies demonstrated higher spatial resolution and lower low-frequency noise by DLR than MBIR at equal doses. Clinical studies indicated significantly worse objective noise, CNR, and subjective noise by DLR than MBIR, but other subjective characteristics were better (P < 0.001 for all). Compared with the routine protocol, subjective noise was similar or better by DLR, and other subjective characteristics were similar or worse by MBIR.

Conclusion

Image quality, except regarding noise characteristics, and acceptance by radiologists were better by DLR than MBIR in low-dose CE-APCT by UHRCT.

Graphical abstract

Noise suppression-guided image filtering for low-SNR CT reconstruction

In practical computed tomography (CT) applications, projections with low signal-to-noise ratio (SNR) are often encountered due to the reduction of radiation dose or device limitations. In these situations, classical reconstruction algorithms, like simultaneous algebraic reconstruction technique (SART), cannot reconstruct high-quality CT images. Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression-guided image filtering reconstruction (NSGIFR) algorithm. In each iteration of NSGIFR, the output image of SART reserves more details and is used as input image of GIF, while the image denoised by BM3D serves as guidance image of GIF. Experimental results indicate that the proposed algorithm displays outstanding performance on preserving structures and suppressing noise for low-SNR CT reconstruction. NSGIFR can achieve more superior image quality than SART, POCS-TV and POCS-BM3D in terms of visual effect and quantitative analysis. Graphical abstract Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression-guided image filtering reconstruction (NSGIFR) algorithm.

The impact of adaptive iterative dose reduction 3D on the improvement of shoulder image quality in head and neck CTA

OBJECTIVE

The aim of this study was to determine the impact of adaptive iterative dose reduction 3 D (AIDR3D) on the improvement of shoulder image quality in low-radiographic dose head and neck CT angiography (CTA).

MATERIALS AND METHODS

Ninety patients who underwent CTA examination were randomly divided into two groups, namely group A (n = 45) and B (n = 45). Patients in group A were scanned under 120 kV and 300 mA, with images reconstructed by filtered back projection (FBP), and patients in group B were scanned under 80 kV and auto mA with images reconstructed by AIDR3D. Image quality was accessed by two experienced radiologists. The noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of common carotid artery (CCA) at C7 level, and radiation dosage were compared between the two groups.

RESULTS

The score of CCA in group B was significantly higher than group A (p < 0.05), and there were no significant differences in the scores of carotid sinus and internal carotid artery between the two groups (p > 0.05). The score of intracranial artery in group B was lower than that of group A, however, the image quality in group B can meet the requirement of clinical diagnosis. The noise value of CCA at C7 level in group B was significantly lower than that of group A (p < 0.05). SNR and CNR values of CCA at C7 level in group B were significantly higher than those of group A (p < 0.05). Effective radiation dose in group B was significantly decreased compared with group A (p < 0.05).

CONCLUSION

AIDR3D remarkably improved image quality in low-radiographic dose head and neck CTA over FBP, which made the low-dose CTA images meet the requirement of clinical diagnosis.

Low-Tube-Voltage CT Urography Using Low-Concentration-Iodine Contrast Media and Iterative Reconstruction: A Multi-Institutional Randomized Controlled Trial for Comparison with Conventional CT Urography

OBJECTIVE

To compare the image quality of low-tube-voltage and low-iodine-concentration-contrast-medium (LVLC) computed tomography urography (CTU) with iterative reconstruction (IR) with that of conventional CTU.

MATERIALS AND METHODS

This prospective, multi-institutional, randomized controlled trial was performed at 16 hospitals using CT scanners from various vendors. Patients were randomly assigned to the following groups: 1) the LVLC-CTU (80 kVp and 240 mgI/mL) with IR group and 2) the conventional CTU (120 kVp and 350 mgI/mL) with filtered-back projection group. The overall diagnostic acceptability, sharpness, and noise were assessed. Additionally, the mean attenuation, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and figure of merit (FOM) in the urinary tract were evaluated.

RESULTS

The study included 299 patients (LVLC-CTU group: 150 patients; conventional CTU group: 149 patients). The LVLC-CTU group had a significantly lower effective radiation dose (5.73 ± 4.04 vs. 8.43 ± 4.38 mSv) compared to the conventional CTU group. LVLC-CTU showed at least standard diagnostic acceptability (score ≥ 3), but it was non-inferior when compared to conventional CTU. The mean attenuation value, mean SNR, CNR, and FOM in all pre-defined segments of the urinary tract were significantly higher in the LVLC-CTU group than in the conventional CTU group.

CONCLUSION

The diagnostic acceptability and quantitative image quality of LVLC-CTU with IR are not inferior to those of conventional CTU. Additionally, LVLC-CTU with IR is beneficial because both radiation exposure and total iodine load are reduced.

Noise indices adjusted to body mass index and an iterative reconstruction algorithm maintain image quality on low-dose contrast-enhanced liver CT

OBJECTIVES

Since body mass index (BMI) affects medical imaging quality or noise due to penetration of the radiation through bodies with varying sizes, this study aims to investigate and determine the optimal BMI-adjusted noise index (NI) setting on the contrast-enhanced liver CT scans obtained using 3D Smart mA technology with adaptive statistical iterative reconstruction (ASIR 2.0) algorithm.

MATERIALS AND METHODS

A total of 320 patients who had contrast-enhanced liver CT scans were divided into two equal-sized groups: A (18.5 kg/m2≤BMI<24.9 kg/m2) and B (24.9 kg/m2 ≤ BMI ≤34.9 kg/m2). The two groups were randomly divided into four subgroups with an NI of 11, 13, 15, and 17. All images were reconstructed with 50% ASIR 2.0. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated after the late arterial, portal venous, and equilibrium phases were completed. Images were evaluated by two radiologists using a subjective 0 -5 scale. Mean CT dose index of volume, dose-length product, and effective dose (ED) were calculated and compared using one-way ANOVA.

RESULTS

In group A, the best-quality images obtained at the lowest ED were scanned at an NI of 15 in the late arterial phase, and at an NI of 17 in the portal venous and equilibrium phases. In group B, the best results were obtained at an NI of 13 in the late arterial phase, and at an NI of 15 in the portal venous and equilibrium phases.

CONCLUSION

Adjusting NI and iterative reconstruction algorithm based on body mass index can help improve image quality on contrast-enhanced liver CT scans, even at low radiation dose.