Abdominal CT: Comparison of Low-Dose CT With Adaptive Statistical Iterative Reconstruction and Routine-Dose CT With Filtered Back Projection in 53 Patients

A comparative study of low voltage, low contrast cerebral computed tomography angiography with iterative reconstruction and conventional cerebral computed tomography angiography

BACKGROUND

Cerebral computed tomography angiography (CTA) has revolutionized the diagnosis of neurovascular emergencies. Strategies to reduce radiation, a concern for cancer, involve tube voltage and current reduction but with increased noise and inferior image quality. Hence, the objective of the study was to evaluate the quality of images obtained through low-dose radiation and low-contrast volume CTA with an iterative reconstruction (IR) technique versus standard CTA without IR.

METHODS

This prospective trial involved 100 adults requiring cerebral CTA for cerebrovascular diseases. They were split into two groups: one with 120 kVp tube voltage and 80 mL contrast using filtered back projection, and the other with 80 kVp and 30 mL contrast with IR. Evaluation criteria included attenuation values, signal-to-noise ratio, contrast-to-noise ratio, and subjective assessments.

RESULTS

Compared to 120 kVp, 80 kVp showed higher vessel attenuation in the internal (272.91 ± 30.59 vs 405.52 ± 53.08; p < .001) and middle cerebral artery (247.55 ± 29.84 vs 372.55 ± 49.02; p < .001) regions. Brain parenchymal attenuation at the centrum semiovale was lower with 80 kVp (29.12 ± 1.87 vs 24.78 ± 2.94; p < .001), accompanied by higher noise. Signal-to-noise ratio (p < .001) and contrast-to-noise ratio (p < .05) were lower at 80 kVp. Image quality didn't significantly differ, and radiation exposure reduced significantly by 70% in the 80 kVp group, suggesting its diagnostic feasibility.

CONCLUSIONS

The 80 kVp protocol for CTA of the cerebral vessels combined with lower contrast volume produces images with similar image quality with significant radiation effective dose and total iodine dose reduction. The 80 kVp protocol holds significant promise for replacing the standard 120 kVp protocol in cerebral CTA.

Feasibility of Low-Dose and Low-Contrast Media Volume Approach in Computed Tomography Cardiovascular Imaging Reconstructed with Model-Based Algorithm

Aim: To evaluate the dose reduction and image quality of low-dose, low-contrast media volume in computed tomography (CT) examinations reconstructed with the model-based iterative reconstruction (MBIR) algorithm in comparison with the hybrid iterative (HIR) one. Methods: We prospectively enrolled a total of 401 patients referred for cardiovascular CT, evaluated with a 256-MDCT scan with a low kVp (80 kVp) reconstructed with an MBIR (study group) or a standard HIR protocol (100 kVp-control group) after injection of a fixed dose of contrast medium volume. Vessel contrast enhancement and image noise were measured by placing the region of interest (ROI) in the left ventricle, ascending aorta; left, right and circumflex coronary arteries; main, right and left pulmonary arteries; aortic arch; and abdominal aorta. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were computed. Subjective image quality obtained by consensus was assessed by using a 4-point Likert scale. Radiation dose exposure was recorded. Results: HU values of the proximal tract of all coronary arteries; main, right and left pulmonary arteries; and of the aorta were significantly higher in the study group than in the control group (p < 0.05), while the noise was significantly lower (p < 0.05). SNR and CNR values in all anatomic districts were significantly higher in the study group (p < 0.05). MBIR subjective image quality was significantly higher than HIR in CCTA and CTPA protocols (p < 0.05). Radiation dose was significantly lower in the study group (p < 0.05). Conclusions: The MBIR algorithm combined with low-kVp can help reduce radiation dose exposure, reduce noise, and increase objective and subjective image quality.

Design and Validation of a Deep Learning Model for Renal Stone Detection and Segmentation on Kidney-Ureter-Bladder Images

Kidney-ureter-bladder (KUB) imaging is used as a frontline investigation for patients with suspected renal stones. In this study, we designed a computer-aided diagnostic system for KUB imaging to assist clinicians in accurately diagnosing urinary tract stones. The image dataset used for training and testing the model comprised 485 images provided by Kaohsiung Chang Gung Memorial Hospital. The proposed system was divided into two subsystems, 1 and 2. Subsystem 1 used Inception-ResNetV2 to train a deep learning model on preprocessed KUB images to verify the improvement in diagnostic accuracy with image preprocessing. Subsystem 2 trained an image segmentation model using the ResNet hybrid, U-net, to accurately identify the contours of renal stones. The performance was evaluated using a confusion matrix for the classification model. We conclude that the model can assist clinicians in accurately diagnosing renal stones via KUB imaging. Therefore, the proposed system can assist doctors in diagnosis, reduce patients' waiting time for CT scans, and minimize the radiation dose absorbed by the body.

Iterative Reconstruction: State-of-the-Art and Future Perspectives

ABSTRACT

Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented various technical advances, such as automatic noise reduction filters, automatic exposure control, and refined imaging reconstruction algorithms.Focusing on imaging reconstruction, filtered back-projection has represented the standard reconstruction algorithm for over 3 decades, obtaining adequate image quality at standard radiation dose exposures. To overcome filtered back-projection reconstruction flaws in low-dose CT data sets, advanced iterative reconstruction algorithms consisting of either backward projection or both backward and forward projections have been developed, with the goal to enable low-dose CT acquisitions with high image quality. Iterative reconstruction techniques play a key role in routine workflow implementation (eg, screening protocols, vascular and pediatric applications), in quantitative CT imaging applications, and in dose exposure limitation in oncologic patients.Therefore, this review aims to provide an overview of the technical principles and the main clinical application of iterative reconstruction algorithms, focusing on the strengths and weaknesses, in addition to integrating future perspectives in the new era of artificial intelligence.

Quality Assessment of Computed Tomography Images using a Channelized Hoteling Observer: Optimization of Protocols in Clinical Practice

Background

This study investigated the feasibility of channelized hoteling observer (CHO) model in computed tomography (CT) protocol optimization regarding the image quality and patient exposure. While the utility of using model observers such as to optimize the clinical protocol is evident, the pitfalls associated with the use of this method in practice require investigation.

Materials and Methods

This study was performed using variable tube current and adaptive statistical iterative reconstruction (ASIR) level (ASIR 10% to ASIR 100%). Various criteria including noise, high-contrast spatial resolution, CHOs model were used to compare image quality at different captured levels. For the implementation of CHO, we first tuned the model in a restricted dataset and then it to the evaluation of a large dataset of images obtained with different reconstruction ASIR and filtered back projection (FBP) levels.

Results

The results were promising in terms of CHO use for the stated purposes. Comparisons of the noise of reconstructed images with 30% ASIR and higher levels of noise in rebuilding images using the FBP approach showed a significant difference (P < 0.05). The spatial resolution obtained using various ASIR levels and tube currents were 0.8 pairs of lines per millimeter, which did not differ significantly from the FBP method (P > 0.05).

Conclusions

Based on the results, using 80% ASIR can reduce the radiation dose on lungs, abdomen, and pelvis CT scans while maintaining image quality. Furthermore using ASIR 60% only for the reconstruction of lungs, abdomen, and pelvis images at standard radiation dose leads to optimal image quality.

Deep Learning Model for Computer-Aided Diagnosis of Urolithiasis Detection from Kidney-Ureter-Bladder Images

Kidney-ureter-bladder (KUB) imaging is a radiological examination with a low cost, low radiation, and convenience. Although emergency room clinicians can arrange KUB images easily as a first-line examination for patients with suspicious urolithiasis, interpreting the KUB images correctly is difficult for inexperienced clinicians. Obtaining a formal radiology report immediately after a KUB imaging examination can also be challenging. Recently, artificial-intelligence-based computer-aided diagnosis (CAD) systems have been developed to help clinicians who are not experts make correct diagnoses for further treatment more effectively. Therefore, in this study, we proposed a CAD system for KUB imaging based on a deep learning model designed to help first-line emergency room clinicians diagnose urolithiasis accurately. A total of 355 KUB images were retrospectively collected from 104 patients who were diagnosed with urolithiasis at Kaohsiung Chang Gung Memorial Hospital. Then, we trained a deep learning model with a ResNet architecture to classify KUB images in terms of the presence or absence of kidney stones with this dataset of pre-processed images. Finally, we tuned the parameters and tested the model experimentally. The results show that the accuracy, sensitivity, specificity, and F1-measure of the model were 0.977, 0.953, 1, and 0.976 on the validation set and 0.982, 0.964, 1, and 0.982 on the testing set, respectively. Moreover, the results demonstrate that the proposed model performed well compared to the existing CNN-based methods and was able to detect urolithiasis in KUB images successfully. We expect the proposed approach to help emergency room clinicians make accurate diagnoses and reduce unnecessary radiation exposure from computed tomography (CT) scans, along with the associated medical costs.

Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program

We propose a deep learning-assisted overscan decision algorithm in chest low-dose computed tomography (LDCT) applicable to the lung cancer screening. The algorithm reflects the radiologists’ subjective evaluation criteria according to the Korea institute for accreditation of medical imaging (KIAMI) guidelines, where it judges whether a scan range is beyond landmarks’ criterion. The algorithm consists of three stages: deep learning-based landmark segmentation, rule-based logical operations, and overscan determination. A total of 210 cases from a single institution (internal data) and 50 cases from 47 institutions (external data) were utilized for performance evaluation. Area under the receiver operating characteristic (AUROC), accuracy, sensitivity, specificity, and Cohen’s kappa were used as evaluation metrics. Fisher’s exact test was performed to present statistical significance for the overscan detectability, and univariate logistic regression analyses were performed for validation. Furthermore, an excessive effective dose was estimated by employing the amount of overscan and the absorbed dose to effective dose conversion factor. The algorithm presented AUROC values of 0.976 (95% confidence interval [CI]: 0.925–0.987) and 0.997 (95% CI: 0.800–0.999) for internal and external dataset, respectively. All metrics showed average performance scores greater than 90% in each evaluation dataset. The AI-assisted overscan decision and the radiologist’s manual evaluation showed a statistically significance showing a p-value less than 0.001 in Fisher’s exact test. In the logistic regression analysis, demographics (age and sex), data source, CT vendor, and slice thickness showed no statistical significance on the algorithm (each p-value > 0.05). Furthermore, the estimated excessive effective doses were 0.02 ± 0.01 mSv and 0.03 ± 0.05 mSv for each dataset, not a concern within slight deviations from an acceptable scan range. We hope that our proposed overscan decision algorithm enables the retrospective scan range monitoring in LDCT for lung cancer screening program, and follows an as low as reasonably achievable (ALARA) principle.

A Comparative Study between Image- and Projection-Domain Self-Supervised Learning for Ultra Low-Dose CBCT

We compare image domain and projection domain denoising approaches with self-supervised learning for ultra low-dose cone-beam CT (CBCT), where number of detected x-ray photons is significantly low. For image-domain self-supervised denoising, we first reconstruct CBCT images with the standard filtered backprojection. For model training, we use blind-spot filtering to partially blind images and recover the blind spots. For projection-domain self-supervised denoising, we regard the post-log projections as training examples of convolutional neural network. From experimental results with various low-dose CBCT settings, the projection-domain denoiser outperforms the image-domain denoiser both in image quality and accuracy for ultra low-dose CBCT.

Assessment of low-dose paranasal sinus CT imaging using a new deep learning image reconstruction technique in children compared to adaptive statistical iterative reconstruction V (ASiR-V)

Purpose

To compare the effects of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASiR-V) on image quality in low-dose computed tomography (CT) of paranasal sinuses in children.

Methods

Low-dose CT scans of the paranasal sinuses in 25 pediatric patients were retrospectively evaluated. The raw data were reconstructed with three levels of DLIR (high, H; medium, M; and low, L), filtered back projection (FBP), and ASiR-V (30% and 50%). Image noise was measured in both soft tissue and bone windows, and the signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the images were calculated. Subjective image quality at the ethmoid sinus and nasal cavity levels of the six groups of reconstructed images was assessed by two doctors using a five-point Likert scale in a double-blind manner.

Results

The patients’ mean dose-length product and effective dose were 36.65 ± 2.44 mGy·cm and 0.17 ± 0.03 mSv, respectively. (1) Objective evaluation: 1. Soft tissue window: The difference among groups in each parameter was significant (P < 0.05). Pairwise comparisons showed that the H group’ s parameters were significantly better (P < 0.05) than those of the 50% post-ASiR-V group. 2. Bone window: No significant between-group differences were found in the noise of the petrous portion of the temporal bone or its SNR or in the noise of the pterygoid processes of the sphenoids or their SNRs (P > 0.05). Significant differences were observed in the background noise and CNR (P < 0.05). As the DLIR intensity increased, image noise decreased and the CNR improved. The H group exhibited the best image quality. (2) Subjective evaluation: Scores for images of the ethmoid sinuses were not significantly different among groups (P > 0.05). Scores for images of the nasal cavity were significantly different among groups (P < 0.05) and were ranked in descending order as follows: H, M, L, 50% post-ASiR-V, 30% post-ASiR-V, and FBP.

Conclusion

DLIR was superior to FBP and post-ASiR-V in low-dose CT scans of pediatric paranasal sinuses. At high intensity (H), DLIR provided the best reconstruction effects.

Texture-Aware Dual Domain Mapping Model for Low Dose CT Reconstruction

BACKGROUND

Remarkable progress has been made for low-dose CT reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researchers to further improve the effect of low dose CT reconstruction. Purpose Most of the existing deep learning-based low dose CT reconstruction methods are derived from popular frameworks, and most models focus on the image domain. Even few existing methods start with dual domains (sinogram and image) by considering the processing of the data itself, the final performances are limited due to the lack of perception of textures. With this in mind, we propose a method for texture perception on dual domains, so that the reconstruction process can be uniformly driven by visual effects.

METHODS

The proposed method involves the processing of two domains: the sinogram domain and the image domain. For the sinogram domain, we have designed a novel dilated residual network (S-DRN) which aims to increase the receptive field to obtain multi-scale information. For the image domain, we propose a self attention (SA) residual encoder&decoder network (SRED-Net) as the denoising network for obtaining much acceptable edges and textures. In addition, the composite loss function composed of the feature loss constructed by the proposed boundary and texture feature aware network (BTFAN) and the mean square error (MSE) can obtain a higher image quality while retaining more details and fewer artifacts, thereby obtaining better visual image quality.

RESULTS

The proposed method was validated using both the AAPM-Mayo clinic low-dose CT datasets and a real clinic data. Experimental results demonstrated that the new method has achieved the state-of-the-art performance on objective indicators and visual metrics in terms of denoising and texture restoration.

CONCLUSIONS

Compared with single-domain or existing dual-domain processing strategies, the proposed texture-aware dual domain mapping network(TADDM-Net) can much better improve the visual effect of reconstructed CT images. Meantime, we also provide much intuitive evidence in terms of model interpretability. This article is protected by copyright. All rights reserved.

Unenhanced abdominal low-dose CT reconstructed with deep learning-based image reconstruction: image quality and anatomical structure depiction

Purpose

To evaluate the utility of deep learning-based image reconstruction (DLIR) algorithm in unenhanced abdominal low-dose CT (LDCT).

Materials and methods

Two patient groups were included in this prospective study: 58 consecutive patients who underwent unenhanced abdominal standard-dose CT reconstructed with hybrid iterative reconstruction (SDCT group) and 48 consecutive patients who underwent unenhanced abdominal LDCT reconstructed with high strength level of DLIR (LDCT group). The background noise and signal-to-noise ratio (SNR) of the liver, pancreas, spleen, kidney, abdominal aorta, inferior vena cava, and portal vein were calculated. Two radiologists qualitatively assessed the overall image noise, overall image quality, and abdominal anatomical structures depiction. Quantitative and qualitative parameters and size-specific dose estimates (SSDE) were compared between SDCT and LDCT groups.

Results

The background noise was lower in LDCT group than in SDCT group (P = 0.02). SNRs were higher in LDCT group than in SDCT group (P < 0.001–0.004) except for the liver. Overall image noise was superior in LDCT group than in SDCT group (P < 0.001). Overall image quality was not different between SDCT and LDCT groups (P = 0.25–0.26). Depiction of almost all abdominal anatomical structures was equal to or better in LDCT group than in SDCT group (P < 0.001–0.88). The SSDE was lower in LDCT group (4.0 mGy) than in SDCT group (20.6 mGy) (P < 0.001).

Conclusions

DLIR facilitates substantial radiation dose reduction of > 75% and significantly reduces background noise. DLIR can maintain image quality and anatomical structure depiction in unenhanced abdominal LDCT.

In Vivo Comparison of Micro-Balloon Interventions (MBI) Advantage: A Retrospective Cohort Study of DEB-TACE Versus b-TACE and of SIRT Versus b-SIRT

PURPOSE

The purpose of this study was to evaluate in vivo the role of the micro-balloon by comparing trans-arterial chemoembolization (DEB-TACE) and selective internal radiotherapy (SIRT) procedures performed with and without balloon micro-catheter (b-DEB-TACE and DEB-TACE/SIRT and b-SIRT) for the treatment of hepatocellular carcinoma (HCC).

METHODS

The impact of a balloon micro-catheter on trans-arterial loco-regional treatment was analyzed using non-enhanced post-procedural cone-beam CT (Ne-CBCT) by comparing the attenuation values in the embolized area and the surrounding liver tissue before and after DEB-TACE versus b-DEB-TACE and by comparing 2D/3D dosimetry in single-photon emission computed tomography after SIRT versus b-SIRT, and by comparing the histological count of the beads following orthotopic liver transplantation in the DEB-TACE versus b-DEB-TACE subgroup.

RESULTS

We treated 84 HCC patients using trans-arterial loco-regional therapy. Fifty-three patients (26 DEB-TACE and 27 b-DEB-TACE) were analyzed in the TACE group. Contrast, signal-to-noise ratio, and contrast-to-noise ratio were all significantly higher in b-DEB-TACE subgroup than DEB-TACE (182.33 HU [CI95% 160.3-273.5] vs. 124 HU [CI95% 80.6-163.6]; 8.3 [CI95% 5.7-10.1] vs. 4.5 [CI95% 3.7-6.0]; 6.9 [CI95% 4.3-7.8] vs. 3.1 [CI95% 2.2-5.0] p < 0.05). Thirty-one patients (24 SIRT and 7 b-SIRT) were analyzed in the SIRT group. 2D dosimetry profile evaluation showed an activity intensity peak significantly higher in the b-SIRT than in the SIRT subgroup (987.5 ± 393.8 vs. 567.7 ± 302.2, p = 0.005). Regarding 3D dose analysis, the mean dose administered to the treated lesions was significantly higher in the b-SIRT than in the SIRT group (151.6 Gy ± 53.2 vs. 100.1 Gy ± 43.4, p = 0.01). In histological explanted liver analysis, there was a trend for higher intra-tumoral localization of embolic microspheres for b-DEB-TACE in comparison with DEB-TACE.

CONCLUSIONS

Due to the use of three different methods, the results of this study demonstrate in vivo, a better embolization profile of oncological intra-arterial interventions performed with balloon micro-catheter regardless of the embolic agent employed.

Image quality and diagnostic accuracy of reduced-dose computed tomography enterography with model-based iterative reconstruction in pediatric Crohn's disease patients

This study assessed the image quality and diagnostic accuracy in determining disease activity of the terminal ileum of the reduced-dose computed tomography enterography using model-based iterative reconstruction in pediatric patients with Crohn's disease (CD). Eighteen patients were prospectively enrolled and allocated to the standard-dose (SD) and reduced-dose (RD) computed tomography enterography (CTE) groups (n = 9 per group). Image quality, reader confidence in interpreting bowel findings, accuracy in determining active CD in the terminal ileum, and radiation dose were evaluated. Objective image quality did not show intergroup differences, except for image sharpness. Although reader confidence in detecting mural stratification, ulcer, and perienteric fat stranding of the RD-CTE were inferior to SD-CTE, RD-CTE correctly diagnosed active disease in all patients. The mean values of radiation dose metrics (SD-CTE vs. RD-CTE) were 4.3 versus 0.74 mGy, 6.1 versus 1.1 mGy, 211.9 versus 34.5 mGy∙cm, and 4.4 versus 0.7 mSv mGy∙cm for CTDI_vol, size-specific dose estimation, dose-length product, and effective dose, respectively. RD-CTE showed comparable diagnostic accuracy to SD-CTE in determining active disease of the terminal ileum in pediatric CD patients. However, image quality and reader confidence in detecting ulcer and perienteric fat stranding was compromised.

Making computed tomography safer for patients with Crohn's disease

Background and aims: Computed tomography (CT), often more accessible than magnetic resonance imaging (MRI), remains widely used though radiation exposure is an obvious disadvantage. We previously showed that modern CT technology can achieve over 70% reduction in radiation-dose without loss of accuracy. Here, we compare low- versus conventional-dose CT in patients with known Crohn's disease to assess clinical confidence and accuracy of the low-dose procedure in the semi-acute setting.Methods: A comparative study of low-dose CT with full iterative reconstruction (IR) versus conventional-dose CT was conducted in 50 consecutive outpatients with Crohn's disease. Clinicians were provided with the low-dose images and reports, whereas conventional-dose images were reviewed after 4 weeks.Results: The clinical question was adequately addressed with low-dose IR imaging in all cases. Complications of Crohn's were detected in 37/50 (74%) with no disagreement between low- and conventional-dose imaging. The effective radiation dose reduction was 76.5% (low-dose mean 2.15 mSv versus conventional-dose CT 6.99 mSv).Conclusion: Low-dose IR CT is safe and accurate for evaluating distribution and complications of known Crohn's disease in the outpatient setting. We propose that low-dose radiation imaging should be adopted as standard-of-care for the evaluation of Crohn's disease and an acceptable alternative to MR particularly in the acute setting. ClinicalTrials.gov: NCT03140306.

Effects of Adaptive Statistical Iterative Reconstruction-V Technology on the Image Quality and Radiation Dose of Unenhanced and Enhanced CT Scans of the Piglet Abdomen

To investigate the optimal pre- and post-adaptive statistical iterative reconstruction-V (ASiR-V) levels in pediatric abdominal computed tomography (CT) to minimize radiation exposure and maintain image quality using an animal model. A total of 10 standard piglets were selected and scanned to obtain unenhanced and enhanced images under different pre-ASiR-V conditions. The corresponding images were obtained using ASiR-V algorithm at different post-ASiR-V levels. CT value, signal-to-noise ratio (SNR), contrast noise ratio (CNR) of abdominal tissues, subjective image score, and radiation dose of unenhanced and enhanced scans were analyzed. With the increase of pre-ASiR-V level, the radiation dose in piglets gradually decreased (P < 0.05). Within the same group of pre-ASiR-V, the image noise was decreased (P < 0.05) by increasing post-ASiR-V level. There was no statistical difference between SNR and CNR values. In unenhanced CT, the subjective score of the images with the combination of 40% pre- and 60% post-ASiR-V levels had no statistical difference compared to the combination of 0% pre- and 60% post-ASiR-V levels, while the radiation dose decreased by 31.6%. In the enhanced CT, the subjective image score with the 60% pre- and 60% post-ASiR-V combination had no statistical difference compared to the 0% pre- and 60% post-ASiR-V combination, while the radiation dose was reduced by 48.9%. The combined use of pre- and post-ASiR-V maintains image quality at the reduced radiation dose. The optimal level for unenhanced CT is 40% pre-combined with 60% post-ASiR-V, while that for enhanced CT is 60% pre-combined with 60% post-ASiR-V in pediatric abdominal CT.

Simulated Annealing-Based Image Reconstruction for Patients With COVID-19 as a Model for Ultralow-Dose Computed Tomography

The proposed algorithm of inverse problem of computed tomography (CT), using limited views, is based on stochastic techniques, namely simulated annealing (SA). The selection of an optimal cost function for SA-based image reconstruction is of prime importance. It can reduce annealing time, and also X-ray dose rate accompanying better image quality. In this paper, effectiveness of various cost functions, namely universal image quality index (UIQI), root-mean-squared error (RMSE), structural similarity index measure (SSIM), mean absolute error (MAE), relative squared error (RSE), relative absolute error (RAE), and root-mean-squared logarithmic error (RMSLE), has been critically analyzed and evaluated for ultralow-dose X-ray CT of patients with COVID-19. For sensitivity analysis of this ill-posed problem, the stochastically estimated images of lung phantom have been reconstructed. The cost function analysis in terms of computational and spatial complexity has been performed using image quality measures, namely peak signal-to-noise ratio (PSNR), Euclidean error (EuE), and weighted peak signal-to-noise ratio (WPSNR). It has been generalized for cost functions that RMSLE exhibits WPSNR of 64.33 ± 3.98 dB and 63.41 ± 2.88 dB for 8 × 8 and 16 × 16 lung phantoms, respectively, and it has been applied for actual CT-based image reconstruction of patients with COVID-19. We successfully reconstructed chest CT images of patients with COVID-19 using RMSLE with eighteen projections, a 10-fold reduction in radiation dose exposure. This approach will be suitable for accurate diagnosis of patients with COVID-19 having less immunity and sensitive to radiation dose.

Self-supervised Projection Denoising for Low-Dose Cone-Beam CT

We consider the problem of denoising low-dose xray projections for cone-beam CT, where x-ray measurements are typically modeled as signal corrupted by Poisson noise. Since each projection view is a 2D image, we regard the lowdose projection views as examples to train a convolutional neural network. For self-supervised training without ground truth, we partially blind noisy projections and train the denoising model to recover the blind spots of projection views. From the projection views denoised by the learned model, we can reconstruct a high-quality 3D volume with a reconstruction algorithm such as the standard filtered backprojection. Through a series of phantom experiments, our self-supervised denoising approach simultaneously reduces noise level and restores structural information in cone-beam CT images.

Tailored versus fixed scan delay in contrast-enhanced abdominal multi-detector CT: An intra-patient comparison of image quality

PURPOSE

To perform anintra-patient comparison betweena single-pass protocol (SP) and a portal venous phase (PVP) by means ofboth quantitative and qualitative analysis of image quality.

METHODS

Forty patients (31 M; 9F; aged 20-77 years; BMI 23 ± 4 Kg/m²) underwent both a SP and a PVP using a 64-rows multi-detector CT with a median interval time of 56 days (range5-903). All patients underwent i.v. bolus injection (2.0 cc/sec) of 1.7 cc/Kg of a non ionic iodinated contrast-media (370 mgI/ml) with scan delays of 67 ± 8 and 90 s for the SP and the PVP, respectively. Signal- (SNR) and contrast-to-noise ratios (CNR) were calculated for most visceral organs and for both abdominal aorta (AA) and main portal vein (MPV). For qualitative analysis, reproduction of abdominal viscera and vascular structures was blindly evaluated and inter-observer agreement calculated by the weighted Cohen k-analysis.

RESULTS

Attenuation values (H.U.) of AA (232 ± 53vs180 ± 36) and MPV (215 ± 39vs187 ± 42) were significantly (p < 0.001) higher in the SP than in PVP, respectively. At qualitative analysis, reproduction of mostabdominal viscerawas also significantly sharper (p < 0.001) with the SP than the PVPwith inter-observer agreement scores (k)ranging from 0.60 to 0.88 for all but one imaging criteria.

CONCLUSIONS

As the SP resulted in a significantly higher vascular enhancement and in a sharper reproduction of most abdominal viscera, it may be better suited than a PVP for the CT evaluation of non traumatic acute abdomen.

Reader Performance as a Function of Patient Size for the Detection of Hepatic Metastases

OBJECTIVE

To investigate reader performance as a function of patient size for the detection of hepatic metastases when an automatic exposure control (AEC) system is used, which varies image noise as a function of patient size.

METHODS

Abdominal computed tomograhy examinations across 100, 120, 160, and 200 quality reference tube current-time product were collected, involving a cohort of 83 patients. Three radiologists identified hepatic metastases across all dose levels. Partial Spearman rank correlation and multivariate logistic regression were used to evaluate correlations between reader performance and patient size and lesion size/contrast while accounting for potential confounding effects. Analyses were repeated on an emulated less-variable noise AEC.

RESULTS

No statistically significant correlation was observed between patient size and radiologist performance (for variable-noise AEC: range of partial Spearman ρ, -0.157 to -0.035]; range of adjusted odds ratios, 0.987, 1.006).

CONCLUSIONS

Reader performance was independent of patient size, suggesting that variable-noise AEC provides better modulation for larger patients than constant-noise AEC.

The potential for reduced radiation dose from deep learning-based CT image reconstruction: A comparison with filtered back projection and hybrid iterative reconstruction using a phantom

The purpose of this phantom study is to compare radiation dose and image quality of abdominal computed tomography (CT) scanned with different tube voltages and tube currents, reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (IR) and deep learning image reconstruction (DLIR) algorithms.A total of 15 CT scans of whole body phantoms were taken with 3 different tube voltages and 5 different tube currents. The images were reconstructed with FBP, 30% and 50% hybrid IR adaptive statistical iterative reconstruction (ASIR-V), and low, medium and high strength DLIR algorithms. The image scanned with tube voltage/tube current of 120 kV/ 200 mA and reconstructed with FBP algorithm was chosen as the reference image. Five radiologists independently analyzed the images individually and also compared it with the reference image in overall, using the visual grading analysis. The mean score of each image was calculated and compared.Using DLIR algorithms, the radiation dose was reduced by 65.5% to 68.1% compared with the dose used in the reference image, while maintaining comparable image quality. Using the DLIR algorithm of medium strength, the image quality was even better than the reference image with a reduced radiation dose up to 36.2% to 50.0%. The DLIR algorithms generated better quality images than ASIR-V algorithms in all the data sets. In addition, among the data sets reconstructed with DLIR algorithms, image quality was the best at the medium strength level, followed by low and high.This phantom study suggests that DLIR algorithms may be considered as a new reconstruction technique by reducing radiation dose while maintaining the image quality of abdominal CTs.

Comparison of conventional chest x ray with a novel projection technique for ultra-low dose CT

PURPOSE

To compare a novel thick-slab projection technique for ultra-low dose computed tomography (CT; thoracic tomogram) with conventional chest x ray with respect to 13 diagnostic categories.

METHODS

With the approval of the institutional ethics board, a dataset was retrospectively collected of 22 consecutive patients who had undergone a clinically requested emergency room conventional chest x ray (CXR) and a same-day standard-of-care non-contrast CT. Scanner specific noise was added to the CT images to simulate a target dose of 0.18 mSv. A novel algorithm was used to post-process CT images as coronal isotropic reformats by applying a voxel-based, locally normalized weighted-intensity projection to generate 2 cm thick slabs with 1 cm overlap. Three chest radiologists with no prior training for the study reviewed the CXR and thoracic tomogram for each case and assessed each diagnostic category (pneumonic infiltrates, pulmonary edema, interstitial lung disease, nodules > 5 mm, nodules < 5 mm, pleural effusion, pericardial effusion, heart size, acute bone fractures, foreign bodies, pneumothorax, mediastinal vessel diameter, free abdominal air) on a Likert scale from -4 (definitely absent/normal) to +4 (definitely present/abnormal). MRMC ROC curves were generated for each category. Time for interpretation and subjective image quality score (0-10) were also assessed.

RESULTS

For focal lung disease (pneumonic infiltrates, nodules < 5 mm, nodules > 5mm), the area under the ROC curve (AUC) was significantly higher for thoracic tomograms than CXR (0.803 vs 0.648, respectively, P = 0.02). For non-focal lung disease (pulmonary edema, interstitial lung disease) and effusions (pulmonary, pericardial), the AUC was larger for thoracic tomograms than CXR but the difference did not reach significance (0.870 vs 0.833, P = 0.141; and 0.823 vs 0.752, P = 0.296, respectively). For acute bone fractures and foreign bodies, the AUC was smaller for thoracic tomograms than CXR, the difference was however not significant (0.491 vs 0.532, P = 0.42; and 0.871 vs 0.971, P = 0.39, respectively). Other diagnostic categories had no true positive cases in the dataset. The mean time for interpretation for each was 36.9 and 24.0 s with standard deviations of 0.857 and 5.977. The image quality score for each was 8.2 and 7.8 with standard deviations of 0.970 and 1.614.

CONCLUSION

Thoracic tomograms were found to be diagnostically superior to CXR for focal lung disease, at no increased radiation dose. The thoracic tomogram presents an opportunity to improve the standard-of-care for patients who would otherwise receive a conventional CXR.

Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction

OBJECTIVE

To evaluate the effect of a commercial deep learning algorithm on the image quality of chest CT, focusing on the upper abdomen.

METHODS

One hundred consecutive patients who simultaneously underwent contrast-enhanced chest and abdominal CT were collected. The radiation dose was optimized for each scan (mean CTDI_vol: chest CT, 3.19 ± 1.53 mGy; abdominal CT, 7.10 ± 1.88 mGy). Three image sets were collected: chest CT reconstructed with an adaptive statistical iterative reconstruction (ASiR-CHT; 50% blending), chest CT with a deep learning algorithm (DLIR-CHT), and abdominal CT with ASiR (ASiR-ABD; 40% blending). Afterwards, the images covering the upper abdomen were extracted, and image noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured. For subjective evaluation, three radiologists independently assessed noise, spatial resolution, presence of artifacts, and overall image quality. Additionally, readers selected the most preferable reconstruction technique among three image sets for each case.

RESULTS

The average measured noise for DLIR-CHT, ASiR-CHT, and ASiR-ABD was 8.01 ± 2.81, 14.8 ± 2.56, and 12.3 ± 2.28, respectively (p < .001). Deep learning-based image reconstruction (DLIR) also showed the best SNR and CNR (p < .001). However, in the subjective analysis, ASiR-ABD showed less subjective noise than DLIR (2.94 ± 0.23 vs. 2.87 ± 0.26; p < .001), while DLIR showed better spatial resolution (2.60 ± 0.34 vs. 2.44 ± 0.31; p = .02). ASiR-ABD showed a better overall image quality (p = .001), but two of the three readers preferred DLIR more frequently.

CONCLUSION

With < 50% of the radiation dose, DLIR chest CT showed comparable image quality in the upper abdomen to that of dedicated abdominal CT and was preferred by most readers.

KEY POINTS

• With < 50% radiation dose, a deep learning algorithm applied to contrast-enhanced chest CT exhibited better image noise and signal-to-noise ratio than standard abdominal CT with the ASiR technique. • Pooled readers mostly preferred deep learning algorithm-reconstructed contrast-enhanced chest CT reconstructed using a standard ASiR-reconstructed abdominal CT. • Reconstruction algorithm-induced distortion artifacts were more frequently observed on deep learning algorithm-reconstructed images, but diagnostic difficulty was reported in only 0.3% of cases.

A feasibility study of realizing low-dose abdominal CT using deep learning image reconstruction algorithm

OBJECTIVES

To explore the feasibility of achieving diagnostic images in low-dose abdominal CT using a Deep Learning Image Reconstruction (DLIR) algorithm.

METHODS

Prospectively enrolled 47 patients requiring contrast-enhanced abdominal CT scans. The late-arterial phase scan was added and acquired using lower-dose mode (tube current range, 175-545 mA; 80 kVp for patients with BMI ≤24 kg/m2 and 100 kVp for patients with BMI > 24 kg/m2) and reconstructed with DLIR at medium setting (DLIR-M) and high setting (DLIR-H), ASIR-V at 0% (FBP), 40% and 80% strength. Both the quantitative measurement and qualitative analysis of the five types of reconstruction methods were compared. In addition, radiation dose and image quality between the early-arterial phase ASIR-V images using standard-dose and the late-arterial phase DLIR images using low-dose were compared.

RESULTS

For the late-arterial phase, all five reconstructions had similar CT value (P > 0.05). DLIR-H, DLIR-M and ASIR-V80% images significantly reduced the image noise and improved the image contrast noise ratio, compared with the standard ASIR-V40% images (P < 0.05). ASIR-V80% images had undesirable image characteristics with obvious "waxy" artifacts, while DLIR-H images maintained high spatial resolution and had the highest subjective image quality. Compared with the early-arterial scans, the late-arterial phase scans significantly reduced the radiation dose (P < 0.05), while the DLIR-H images exhibited lower image noise and good display of the specific image details of lesions.

CONCLUSIONS

DLIR algorithm improves image quality under low-dose scan condition and may be used to reduce the radiation dose without adversely affecting the image quality.

Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm

Objective

To compare the image quality of low-dose (LD) computed tomography (CT) obtained using a deep learning-based denoising algorithm (DLA) with LD CT images reconstructed with a filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE).

Materials and Methods

One hundred routine-dose (RD) abdominal CT studies reconstructed using FBP were used to train the DLA. Simulated CT images were made at dose levels of 13%, 25%, and 50% of the RD (DLA-1, -2, and -3) and reconstructed using FBP. We trained DLAs using the simulated CT images as input data and the RD CT images as ground truth. To test the DLA, the American College of Radiology CT phantom was used together with 18 patients who underwent abdominal LD CT. LD CT images of the phantom and patients were processed using FBP, ADMIRE, and DLAs (LD-FBP, LD-ADMIRE, and LD-DLA images, respectively). To compare the image quality, we measured the noise power spectrum and modulation transfer function (MTF) of phantom images. For patient data, we measured the mean image noise and performed qualitative image analysis. We evaluated the presence of additional artifacts in the LD-DLA images.

Results

LD-DLAs achieved lower noise levels than LD-FBP and LD-ADMIRE for both phantom and patient data (all p < 0.001). LD-DLAs trained with a lower radiation dose showed less image noise. However, the MTFs of the LD-DLAs were lower than those of LD-ADMIRE and LD-FBP (all p < 0.001) and decreased with decreasing training image dose. In the qualitative image analysis, the overall image quality of LD-DLAs was best for DLA-3 (50% simulated radiation dose) and not significantly different from LD-ADMIRE. There were no additional artifacts in LD-DLA images.

Conclusion

DLAs achieved less noise than FBP and ADMIRE in LD CT images, but did not maintain spatial resolution. The DLA trained with 50% simulated radiation dose showed the best overall image quality.

[Effects of Iterative Reconstruction on Image Quality of Pediatric Body Computed Tomography Images]

This study evaluated the effects of three types of hybrid iterative reconstruction (IR) on image quality of pediatric body computed tomography images. The image quality components evaluated were noise power spectrum (NPS), task-based modulation transfer function (TTF), and system performance function (SPF). As the IR strength was increased while reducing the radiation dose, the NPS increased in a low-frequency range and the TTF decreased in low-contrast regions. In the low-contrast regions, the calculated SPF decreased over the entire frequency range. Alternatively, in the high-contrast regions, the SPF decreased in the low-frequency regions and increased in the high-frequency regions. The radiation dose reduction using the hybrid IR resulted in the deterioration of the image quality in the low-contrast regions and changes in the spatial frequency characteristics in the high-contrast regions.

THE USE OF LOW-DOSE CT WITH ADAPTIVE STATISTICAL ITERATIVE RECONSTRUCTION FOR THE DIAGNOSIS OF URINARY CALCULI

This study aimed to explore the diagnosis of urinary calculi through utilisation of low-dose computed tomography (LDCT) with adaptive statistical iterative reconstruction (ASIR). Data from 140 patients that had undergone pathological or operative diagnosis with urinary calculi were analysed. Patients were divided into two groups based on whether they received conventional-dose computed tomography (CDCT) or LDCT, respectively, followed by filtered back projection or ASIR. Average CDCT radiation doses were roughly 4-fold higher than LDCT doses. Despite this difference, there were no significant differences between groups with respect to stone size or location, image quality (P = 0.261), image noise (P = 0.153) and diagnostic efficacy (P = 0.371). LDCT is an effective approach to urinary calculi diagnosis, performing to ASIR while decreasing the effective radiation dose, improving the safety of this procedure.

Influence of acquisition settings and radiation exposure on CT lung densitometry-An anthropomorphic ex vivo phantom study

Objectives

To systematically evaluate the influence of acquisition settings in conjunction with raw-data based iterative image reconstruction (IR) on lung densitometry based on multi-row detector computed tomography (CT) in an anthropomorphic chest phantom.

Materials and methods

Ten porcine heart-lung explants were mounted in an ex vivo chest phantom shell, six with highly and four with low attenuating chest wall. CT (Somatom Definition Flash, Siemens Healthineers) was performed at 120kV_p and 80kV_p, each combined with current-time products of 120, 60, 30, and 12mAs, and was reconstructed with filtered back projection (FBP) and IR (Safire, Siemens Healthineers). Mean lung density (LD), air density (AD) and noise were measured by semi-automated region-of interest (ROI) analysis, with 120kV_p/120 mAs serving as the standard of reference.

Results

Using IR, noise in lung parenchyma was reduced by ~ 31% at high attenuating chest wall and by ~ 22% at low attenuating chest wall compared to FBP, respectively (p<0.05). IR induced changes in the order of ±1 HU to mean absolute LD and AD compared to corresponding FBP reconstructions which were statistically significant (p<0.05).

Conclusions

Densitometry is influenced by acquisition parameters and reconstruction algorithms to a degree that may be clinically negligible. However, in longitudinal studies and clinical research identical protocols and potentially other measures for calibration may be required.

Health Care Monitoring and Treatment for Coronary Artery Diseases: Challenges and Issues

In-stent restenosis concerning the coronary artery refers to the blood clotting-caused re-narrowing of the blocked section of the artery, which is opened using a stent. The failure rate for stents is in the range of 10% to 15%, where they do not remain open, thereby leading to about 40% of the patients with stent implantations requiring repeat procedure within one year, despite increased risk factors and the administration of expensive medicines. Hence, today stent restenosis is a significant cause of deaths globally. Monitoring and treatment matter a lot when it comes to early diagnosis and treatment. A review of the present stent monitoring technology as well as the practical treatment for addressing stent restenosis was conducted. The problems and challenges associated with current stent monitoring technology were illustrated, along with its typical applications. Brief suggestions were given and the progress of stent implants was discussed. It was revealed that prime requisites are needed to achieve good quality implanted stent devices in terms of their size, reliability, etc. This review would positively prompt researchers to augment their efforts towards the expansion of healthcare systems. Lastly, the challenges and concerns associated with nurturing a healthcare system were deliberated with meaningful evaluations.

Image Quality and Radiation Dose in CT Venography Using Model-Based Iterative Reconstruction at 80 kVp versus Adaptive Statistical Iterative Reconstruction-V at 70 kVp

Objective

To compare the objective and subjective image quality indicators and radiation doses of computed tomography (CT) venography performed using model-based iterative reconstruction (MBIR) at 80 kVp and adaptive statistical iterative reconstruction (ASIR)-V at 70 kVp.

Materials and Methods

Eighty-three patients who had undergone CT venography of the lower extremities with MBIR at 80 kVp (Group A; 21 men and 20 women; mean age, 55.5 years) or ASIR-V at 70 kVp (Group B; 18 men and 24 women; mean age, 57.3 years) were enrolled. Two radiologists retrospectively evaluated the objective (vascular enhancement, image noise, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR]) and subjective (quantum mottle, delineation of contour, venous enhancement) image quality indicators at the inferior vena cava and femoral and popliteal veins. Clinical information, radiation dose, reconstruction time, and objective and subjective image quality indicators were compared between groups A and B.

Results

Vascular enhancement, SNR, and CNR were significantly greater in Group B than in Group A (p ≤ 0.015). Image noise was significantly lower in Group B (p ≤ 0.021), and all subjective image quality indicators, except for delineation of vein contours, were significantly better in Group B (p ≤ 0.021). Mean reconstruction time was significantly shorter in Group B than in Group A (1 min 43 s vs. 131 min 1 s; p < 0.001). Clinical information and radiation dose were not significantly different between the two groups.

Conclusion

CT venography using ASIR-V at 70 kVp was better than MBIR at 80 kVp in terms of image quality and reconstruction time at similar radiation doses.

Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience

OBJECTIVE. The purpose of this study was to perform quantitative and qualitative evaluation of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic CT of the abdomen. MATERIALS AND METHODS. Retrospective review (April-May 2019) of the cases of adults undergoing oncologic staging with portal venous phase abdominal CT was conducted for evaluation of standard 30% adaptive statistical iterative reconstruction V (30% ASIR-V) reconstruction compared with DLIR at low, medium, and high strengths. Attenuation and noise measurements were performed. Two radiologists, blinded to examination details, scored six categories while comparing reconstructions for overall image quality, lesion diagnostic confidence, artifacts, image noise and texture, lesion conspicuity, and resolution. RESULTS. DLIR had a better contrast-to-noise ratio than 30% ASIR-V did; high-strength DLIR performed the best. High-strength DLIR was associated with 47% reduction in noise, resulting in a 92-94% increase in contrast-to-noise ratio compared with that of 30% ASIR-V. For overall image quality and image noise and texture, DLIR scored significantly higher than 30% ASIR-V with significantly higher scores as DLIR strength increased. A total of 193 lesions were identified. The lesion diagnostic confidence, conspicuity, and artifact scores were significantly higher for all DLIR levels than for 30% ASIR-V. There was no significant difference in perceived resolution between the reconstruction methods. CONCLUSION. Compared with 30% ASIR-V, DLIR improved CT evaluation of the abdomen in the portal venous phase. DLIR strength should be chosen to balance the degree of desired denoising for a clinical task relative to mild blurring, which increases with progressively higher DLIR strengths.

Pediatric head computed tomography with advanced modeled iterative reconstruction: focus on image quality and reduction of radiation dose

BACKGROUND

Iterative reconstruction has become the standard method for reconstructing computed tomography (CT) scans and needs to be verified for adaptation.

OBJECTIVE

To assess the image quality after adapting advanced modeled iterative reconstruction (ADMIRE) for pediatric head CT.

MATERIALS AND METHODS

We included image sets with filtered back projection reconstruction (the cFBP group, n=105) and both filtered back projection and ADMIRE reconstruction (the lower-dose group, n=109) after dose reduction. All five strength levels of ADMIRE and filtered back projection were adapted for the lower-dose group and compared with the cFBP group. Quantitative parameters including noise, signal-to-noise ratio and contrast-to-noise ratio and qualitative parameters including noise, white matter and gray matter differentiation of the supra- and infratentorial levels, sharpness, artifact, and diagnostic accuracy were also evaluated and compared with interobserver agreement.

RESULTS

There was a mean dose reduction of 30.6% in CT dose index volume, 32.1% in dose length product, and 32.1% in effective dose after tube current reduction. There was gradual reduction of noise in air, cerebrospinal fluid and white matter with strength levels of ADMIRE from 1 to 5 (P<0.001). Signal-to-noise ratio and contrast-to-noise ratio in all age groups increased among strength levels of ADMIRE, in sequence from 1 to 5, with statistical significance (P<0.001). Gradual reduction of qualitative parameters was noted among strength levels of ADMIRE in sequence from 1 to 5 (P<0.001).

CONCLUSION

Use of ADMIRE for pediatric head CT can reduce radiation dose without degrading image quality.

Impact of Overlying Personal Items on CT Dose with Use of Automated Tube Current Modulation-Pilot Investigation

PURPOSE

To determine the incidence and impact of overlying radiopaque personal items (e.g., cellular phones, zippers) on CT dose and image quality with use of automated tube current modulation.

METHODS

Topogram images from 100 consecutive adult outpatient CT abdomen pelvis studies were retrospectively reviewed, and the number and type of overlying radiopaque personal items were recorded. Additionally, an anthropomorphic phantom was imaged with overlying personal items 1) present in topogram and axial images; 2) present in topogram but removed prior to axial acquisition; and 3) present in topogram positioned outside the field of view of the axial acquisition. dose length product (DLP) and CT dose index volume (CTDIvol) were compared to acquisitions performed without overlying personal items. Image noise was evaluated by assessing the standard deviation of Hounsfield units at the level of the overlying personal item.

RESULTS

Overlying personal items were visible in topogram images for 55% of CT exams and included underwires (38% of exams), zippers (7%), and cellular phones (1%). DLP increased when a cellular phone was present in the topogram whether or not it was removed before axial image acquisition (3.7% p = 0.002, combined AutomA and SmartmA), and image noise increased (144%, p = 0.002; AutomA). No increase in dose or image noise was observed with overlying zippers or underwires or when any object was visible in the topogram outside the field of view of the axial images.

CONCLUSIONS

Overlying personal items were observed in the majority of abdominopelvic CT scans. Large overlying radiopaque personal items resulted in increased dose and increased image noise. Removal of all overlying personal items will result in optimized dose and image quality.

Usefulness of dictionary learning-based processing for improving image quality of sub-millisievert low-dose chest CT: initial experience

PURPOSE

To develop a dictionary learning (DL)-based processing technique for improving the image quality of sub-millisievert chest computed tomography (CT).

MATERIALS AND METHODS

Standard-dose and sub-millisievert chest CT were acquired in 12 patients. Dictionaries including standard- and low-dose image patches were generated from the CT datasets. For each patient, DL-based processing was performed for low-dose CT using the dictionaries generated from the remaining 11 patients. This procedure was repeated for all 12 patients. Image quality of normal thoracic structures on the processed sub-millisievert CT images was assessed with a 5-point scale (5 = excellent, 1 = very poor). Lung lesion conspicuity was also assessed on a 5-point scale.

RESULTS

Image noise on sub-millisievert CT was significantly decreased with DL-based image processing (48.5 ± 13.7 HU vs 20.4 ± 7.9 HU, p = 0.0005). Image quality of lung structures was significantly improved with DL-based method (middle level of lung, 2.25 ± 0.75 vs 2.92 ± 0.79, p = 0.0078). Lung lesion conspicuity was also significantly improved with DL-based technique (solid nodules, 3.4 ± 0.6 vs 2.7 ± 0.6, p = 0.0273).

CONCLUSION

Image quality and lesion conspicuity on sub-millisievert chest CT images may be improved by DL-based post-processing.

Feasibility of thin-slice abdominal CT in overweight patients using a vendor neutral image-based denoising algorithm: Assessment of image noise, contrast, and quality

The purpose of this study was to investigate whether the novel image-based noise reduction software (NRS) improves image quality, and to assess the feasibility of using this software in combination with hybrid iterative reconstruction (IR) in image quality on thin-slice abdominal CT. In this retrospective study, 54 patients who underwent dynamic liver CT between April and July 2017 and had a body mass index higher than 25 kg/m² were included. Three image sets of each patient were reconstructed as follows: hybrid IR images with 1-mm slice thickness (group A), hybrid IR images with 5-mm slice thickness (group B), and hybrid IR images with 1-mm slice thickness denoised using NRS (group C). The mean image noise and contrast-to-noise ratio relative to the muscle of the aorta and liver were assessed. Subjective image quality was evaluated by two radiologists for sharpness, noise, contrast, and overall quality using 5-point scales. The mean image noise was significantly lower in group C than in group A (p < 0.01), but no significant difference was observed between groups B and C. The contrast-to-noise ratio was significantly higher in group C than in group A (p < 0.01 and p = 0.01, respectively). Subjective image quality was also significantly higher in group C than in group A (p < 0.01), in terms of noise and overall quality, but not in terms of sharpness and contrast (p = 0.65 and 0.07, respectively). The contrast of images in group C was greater than that in group A, but this difference was not significant. Compared with hybrid IR alone, the novel NRS combined with a hybrid IR could result in significant noise reduction without sacrificing image quality on CT. This combined approach will likely be particularly useful for thin-slice abdominal CT examinations of overweight patients.

What a difference a delay makes! CT urogram: a pictorial essay

PURPOSE

The aim of this pictorial essay is to demonstrate several cases where the diagnosis would have been difficult or impossible without the excretory phase image of CT urography.

METHODS

A brief discussion of CT urography technique and dose reduction is followed by several cases illustrating the utility of CT urography.

RESULTS

CT urography has become the primary imaging modality for evaluation of hematuria, as well as in the staging and surveillance of urinary tract malignancies. CT urography includes a non-contrast phase and contrast-enhanced nephrographic and excretory (delayed) phases. While the three phases add to the diagnostic ability of CT urography, it also adds potential patient radiation dose. Several techniques including automatic exposure control, iterative reconstruction algorithms, higher noise tolerance, and split-bolus have been successfully used to mitigate dose. The excretory phase is timed such that the excreted contrast opacifies the urinary collecting system and allows for greater detection of filling defects or other abnormalities. Sixteen cases illustrating the utility of excretory phase imaging are reviewed.

CONCLUSIONS

Excretory phase imaging of CT urography can be an essential tool for detecting and appropriately characterizing urinary tract malignancies, renal papillary and medullary abnormalities, CT radiolucent stones, congenital abnormalities, certain chronic inflammatory conditions, and perinephric collections.

CT urography: how to optimize the technique

PURPOSE

Computed tomography urography (CTU) has emerged as the modality of choice for imaging the urinary tract within the past few decades. It is a powerful tool that enables detailed anatomic evaluation of the urinary tract in order to identify primary urothelial malignancies, benign urinary tract conditions, and associated abdominopelvic pathologies. As such, there have been extensive efforts to optimize CTU protocol.

METHODS

This article reviews the published literature on CTU protocol optimization, including contrast bolus timing, dose reduction, reconstruction algorithms, and ancillary practices.

CONCLUSION

There have been many advances in CTU techniques, which allow for imaging diagnosis of a wide spectrum of diseases while minimizing radiation dose and maximizing urinary tract distension and opacification.

Whole-Body Computed Tomography Using Low-Dose Biphasic Injection Protocol With Adaptive Statistical Iterative Reconstruction V: Assessment of Dose Reduction and Image Quality in Trauma Patients

AIM

This study aimed to evaluate potential dose savings on a revised protocol for whole-body computed tomography and image quality after implementing Adaptive Statistical Iterative Reconstruction V (ASiR-V) algorism for trauma patients and compare it with routine protocol.

MATERIALS AND METHODS

One hundred trauma patients were classified into 2 groups using 2 different scanning protocols. Group A (n = 50; age, 32.48 ± 8.09 years) underwent routine 3-phase protocol. Group B (n = 50; age, 35.94 ± 13.57 years) underwent biphasic injection protocol including unenhanced scan for the brain and cervical spines, followed by a 1-step acquisition of the thorax, abdomen, and pelvis. The ASiR-V level was kept at 50% for all examinations, and then studies were reconstructed at 0% ASiR-V level. Radiation dose, total acquisition time, and image count were compared between groups (A and B). Two radiologists independently graded image quality and artifacts between both groups and 2 ASiR-V levels (0 and 50%).

RESULTS

The mean (±SD) dose-length product value for postcontrast scans in group A was 1602.3 ± 271.8 mGy · cm and higher when compared with group B (P < 0.001), which was 951.1 ± 359.6 mGy · cm. Biphasic injection protocol gave a dose reduction of 40.4% and reduced the total acquisition time by 11.4% and image count by 37.6%. There was no statistically significant difference between the image quality scores for both groups; however, group A scored higher grades (4.62 ± 0.56 and 4.56 ± 0.67). Similarly, the image quality scores for both ASiR-V levels in both groups were not significantly different.

CONCLUSIONS

Biphasic computed tomography protocol reduced radiation dose with maintenance of diagnostic accuracy and image quality after implementing ASiR-V algorism.

Deep Learning-based CT Image Reconstruction: Initial Evaluation Targeting Hypovascular Hepatic Metastases

Purpose

To evaluate the effect of a deep learning-based reconstruction (DLR) method on the conspicuity of hypovascular hepatic metastases on abdominal CT images.

Materials and Methods

This retrospective study with institutional review board approval included 58 patients with hypovascular hepatic metastases. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and the contrast-to-noise ratio (CNR). CNR was calculated as region of interest ([ROI]_L - ROI_T)/N, where ROI_L is the mean liver parenchyma attenuation, ROI_T, the mean tumor attenuation, and N, the noise. Two other radiologists graded the conspicuity of the liver lesion on a five-point scale where 1 is unidentifiable and 5 is detected without diagnostic compromise. Only the smallest liver lesion in each patient, classified as smaller or larger than 10 mm, was evaluated. The difference between hybrid iterative reconstruction (IR) and DLR images was determined by using a two-sided Wilcoxon signed-rank test.

Results

The image noise was significantly lower, and the CNR was significantly higher on DLR images than hybrid IR images (median image noise: 19.2 vs 12.8 HU, P < .001; median CNR: tumors < 10 mm: 1.9 vs 2.5; tumors > 10 mm: 1.7 vs 2.2, both P < .001). The scores for liver lesions were significantly higher for DLR images than hybrid IR images (P < .01 for both in tumors smaller or larger than 10 mm).

Conclusion

DLR improved the quality of abdominal CT images for the evaluation of hypovascular hepatic metastases.© RSNA, 2019Supplemental material is available for this article.

The Application of a New Model-Based Iterative Reconstruction in Low-Dose Upper Abdominal CT

RATIONALE AND OBJECTIVES

To compare upper abdominal computed tomography (CT) image quality of new model-based iterative reconstruction (MBIR) with low-contrast resolution preference (MBIR_NR40), conventional MBIR (MBIR_c), and adaptive statistical iterative reconstruction (ASIR) at low dose with ASIR at routine-dose.

MATERIALS AND METHODS

Study included phantom and 60 patients who had initial and follow-up CT scans. For patients, the delay phase was acquired at routine-dose (noise index = 10 HU) for the initial scan and low dose (noise index = 20 HU) for the follow-up. The low-dose CT was reconstructed with 40% and 60% ASIR, MBIR_c, and MBIR_NR40, while routine-dose CT was reconstructed with 40% ASIR. CT value and noise measurements of the subcutaneous fat, back muscle, liver, and spleen parenchyma were compared using one-way ANOVA. Two radiologists used semiquantitative 7-scale (-3 to +3) to rate image quality and artifacts.

RESULTS

The phantom study revealed superior low-contrast resolution with MBIR_NR40. For patient scans, the CT dose index for the low-dose CT was 3.00 ± 1.32 mGy, 75% lower than the 11.90 ± 4.75 mGy for the routine-dose CT. Image noise for the low-dose MBIR_NR40 images was significantly lower than the low-dose MBIR_c and ASIR images, and routine-dose ASIR images (p < 0.05). Subjective ratings showed higher image quality for low-dose MBIR_NR40, with lower noise, better low-contrast resolution for abdominal structures, and finer lesion contours than those of low-dose MBIR_c and ASIR images, and routine-dose ASIR images (p < 0.05).

CONCLUSION

MBIR_NR40 with low-contrast resolution preference provides significantly lower noise and better image quality than MBIR_c and ASIR in low-dose abdominal CT; significantly better objective and subjective image quality than the routine-dose ASIR with 75% dose reduction.

Impact of CT reconstruction algorithm on auto-segmentation performance

Model‐based iterative reconstruction (MBIR) reduces CT imaging dose while maintaining image quality. However, MBIR reduces noise while preserving edges which may impact intensity‐based tasks such as auto‐segmentation. This work evaluates the sensitivity of an auto‐contouring prostate atlas across multiple MBIR reconstruction protocols and benchmarks the results against filtered back projection (FBP). Images were created from raw projection data for 11 prostate cancer cases using FBP and nine different MBIR reconstructions (3 protocols/3 noise reduction levels) yielding 10 reconstructions/patient. Five bony structures, bladder, rectum, prostate, and seminal vesicles (SVs) were segmented using an auto‐segmentation pipeline that renders 3D binary masks for analysis. Performance was evaluated for volume percent difference (VPD) and Dice similarity coefficient (DSC), using FBP as the gold standard. Nonparametric Friedman tests plus post hoc all pairwise comparisons were employed to test for significant differences (P < 0.05) for soft tissue organs and protocol/level combinations. A physician performed qualitative grading of 396 MBIR contours across the prostate, bladder, SVs, and rectum in comparison to FBP using a six‐point scale. MBIR contours agreed with FBP for bony anatomy (DSC ≥ 0.98), bladder (DSC ≥ 0.94, VPD < 8.5%), and prostate (DSC = 0.94 ± 0.03, VPD = 4.50 ± 4.77% (range: 0.07–26.39%). Increased variability was observed for rectum (VPD = 7.50 ± 7.56% and DSC = 0.90 ± 0.08) and SVs (VPD and DSC of 8.23 ± 9.86% range (0.00–35.80%) and 0.87 ± 0.11, respectively). Over the all protocol/level comparisons, a significant difference was observed for the prostate VPD between BSPL1 and BSTL2 (adjusted P‐value = 0.039). Nevertheless, 300 of 396 (75.8%) of the four soft tissue structures using MBIR were graded as equivalent or better than FBP, suggesting that MBIR offered potential improvements in auto‐segmentation performance when compared to FBP. Future work may involve tuning organ‐specific MBIR parameters to further improve auto‐segmentation performance. Running title: Impact of CT Reconstruction Algorithm on Auto‐segmentation Performance.

Anisotropic crack propagation and deformation in dentin observed by four-dimensional X-ray nano-computed tomography

Understanding the cracking behaviour of biological composite materials is of practical importance. This paper presents the first study to track the interplay between crack initiation, microfracture and plastic deformation in three dimensions (3D) as a function of tubule and collagen fibril arrangement in elephant dentin using in situ X-ray nano-computed tomography (nano-CT). A nano-indenter with a conical tip has been used to incrementally indent three test-pieces oriented at 0°, 45° and 70° to the long axis of the tubules (i.e. radial to the tusk). For the 0° sample two significant cracks formed, one of which linked up with microcracks in the axial-radial plane of the tusk originating from the tubules and the other one occurred as a consequence of shear deformation at the tubules. The 70° test-piece was able to bear the greatest loads despite many small cracks forming around the indenter. These were diverted by the microstructure and did not propagate significantly. The 45° test-piece showed intermediate behaviour. In all cases strains obtained by digital volume correlation were well in excess of the yield strain (0.9%), indeed some plastic deformation could even be seen through bending of the tubules. The hoop strains around the conical indenter were anisotropic with the smallest strains correlating with the primary collagen orientation (axial to the tusk) and the largest strains aligned with the hoop direction of the tusk. STATEMENT OF SIGNIFICANCE: This paper presents the first comprehensive study of the anisotropic nature of microfracture, crack propagation and deformation in elephant dentin using time-lapse X-ray nano-computed tomography. To unravel the interplay of collagen fibrils and local deformation, digital volume correlation (DVC) has been applied to map the local strain field while the crack initiation and propagation is tracked in real time. Our results highlight the intrinsic and extrinsic shielding mechanisms and correlate the crack growth behavior in nature to the service requirement of dentin to resist catastrophic fracture. This is of wide interest not just in terms of understanding dentin fracture but also can extend beyond dentin to other anisotropic structural composite biomaterials such as bone, antler and chitin.

Role of thoracic imaging in the management of lymphangioleiomyomatosis

Lymphangioleiomyomatosis (LAM) is a rare diffuse lung cystic disease (DLCD) that occurs sporadically or in association with Tuberous Sclerosis Complex (TSC). The diagnostic pathway is tracked on the identification of the disease hallmarks on chest High-Resolution Computed Tomography (HRCT). Aim of this review is to discuss the thoracic HRCT pathognomonic features, essential to rule out other DLCD. It also examines the new evidences emerging from Computed Tomography (CT) quantitative studies that, by demonstrating a specific cysts distribution and a pathological aspect of the parenchyma near the cysts, could improve our understanding of this rare disorder and supply pulmonologists with a new tool for a more appropriate long-term management. Finally, the contribution of other image techniques as low dose chest CT, Magnetic Resonance Imaging (MRI) and Ultrasound (US) is discussed.

Iodine load reduction in dual-energy spectral CT portal venography with low energy images combined with adaptive statistical iterative reconstruction

OBJECTIVE

To study the application of using low energy images combined with adaptive statistical iterative reconstruction (ASiR) in dual-energy spectral CT portal venography (CTPV) to reduce iodine load.

METHODS

41 patients for CTPV were prospectively and randomly divided into two groups. Group A ( n = 21) used conventional 120 kVp scanning protocol with contrast dose at 0.6 gI/kg while group B ( n = 20) used dual-energy spectral imaging with reduced contrast dose at 0.3 gI/kg. The 120 kVp images in Group A and 50 keV images in Group B were reconstructed with 40% ASiR. The contrast-to-noise ratio of portal vein was calculated. The image quality and the numbers of intrahepatic portal vein branches were evaluated by two experienced radiologists using a 5-point scoring system.

RESULTS

Group B reduced iodine load by 52% compared to Group A (17.21 ± 3.30 gI vs 35.80 ± 6.18 gI, p < 0.001). All images in both groups were acceptable for diagnosis. CT values and standard deviations in portal veins of Group B were higher than Group A (all p < 0.05); There were no statistical differences in contrast-to-noise ratio, image quality score and the number of observed portal vein branches between the two groups (all p > 0.05), and the two observers had excellent agreement in image quality assessment (all κ > 0.75).

CONCLUSION

The use of 50 keV images in dual-energy spectral CTPV with ASiR reduces total iodine load by 52% while maintaining good image quality.

ADVANCES IN KNOWLEDGE

Spectral CT images combined with ASiR can be used in low contrast dose CTPV portal venography to maintain image quality and reduce contrast dose.

Iterative CT reconstruction in abdominal low-dose CT used for hybrid SPECT-CT applications: effect on image quality, image noise, detectability, and reader's confidence

Background

Iterative computed tomography (CT) image reconstruction shows high potential for the preservation of image quality in diagnostic CT while reducing patients’ exposure; it has become available for low-dose CT (LD-CT) in high-end hybrid imaging systems (e.g. single-photon emission computed tomography [SPECT]-CT).

Purpose

To examine the effect of an iterative CT reconstruction algorithm on image quality, image noise, detectability, and the reader’s confidence for LD-CT data by a subjective assessment.

Material and Methods

The LD-CT data were validated for 40 patients examined by an abdominal hybrid SPECT-CT (U = 120 kV, I = 40 mA, pitch = 1.375). LD-CT was reconstructed using either filtered back projection (FBP) or an iterative image reconstruction algorithm (Adaptive Statistical Iterative Reconstruction [ASIR]®) with different parameters (ASIR levels 50% and 100%). The data were validated by two independent blinded readers using a scoring system for image quality, image noise, detectability, and reader confidence, for a predefined set of 16 anatomic substructures.

Results

The image quality was significantly improved by iterative reconstruction of the LD-CT data compared with FBP (P ≤ 0.0001). While detectability increased in only 2/16 structures (P ≤ 0.03), the reader’s confidence increased significantly due to iterative reconstruction (P ≤ 0.002). Meanwhile, at the ASIR level of 100%, the detectability in bone structure was highly reduced (P = 0.003).

Conclusion

An ASIR level of 50% represents a good compromise in abdominal LD-CT image reconstruction. The specific ASIR level improved image quality (reduced image noise) and reader confidence, while preserving detectability of bone structure.

Effect of a New Model-Based Reconstruction Algorithm for Evaluating Early Peripheral Lung Cancer With Submillisievert Chest Computed Tomography

OBJECTIVE

The aim of this study was to compare a new model-based iterative reconstruction algorithm with either spatial and density resolution balance (MBIRSTND) or spatial resolution preference (MBIRRP20) with the adaptive statistical iterative reconstruction (ASIR) in evaluating early small peripheral lung cancer (SPLC) with submillisievert chest computed tomography (CT).

METHODS

Low-contrast and spatial resolutions were assessed in a phantom and with 30 pathologically confirmed SPLC patients. Images were reconstructed using 40% ASIR, MBIRSTND, and MBIRRP20. Computed tomography value and image noise were measured by placing the regions of interest on back muscle and subcutaneous fat at 3 levels. Two radiologists used a 4-point scale (1, worst, and 4, best) to rate subjective image quality in 3 aspects: image noise, nodule imaging signs, and nodule internal clarity.

RESULTS

The phantom study revealed an improved detectability of low-contrast targets and small objects for MBIRSTND and MBIRRP20 compared with ASIR. The effective dose for patient scans was 0.88 ± 0.83 mSv. There was no significant difference in CT value between the 3 reconstructions (P > 0.05), but MBIRSTND and MBIRRP20 significantly reduced image noise compared with ASIR (P < 0.05): 15.69 ± 1.83 HU and 29.97 ± 3.84 HU versus 51.06 ± 11.02 HU in the back muscle, and 15.96 ± 3.07 HU and 27.37 ± 3.88 HU versus 38.04 ± 8.87 HU in subcutaneous fat, respectively. Among the 3 reconstructions, MBIRSTND was the best in reducing image noise and identifying the internal compositions of cancer nodules, and MBIRRP20 was the best in analyzing the internal and external signs of pulmonary nodules.

CONCLUSIONS

Submillisievert chest CT reconstructed with MBIRSTND and MBIRRP20 provides superior images for the detailed analyses of SPLC compared with ASIR.

Effects of Radiation Dose Reduction on Diagnostic Accuracy of Abdominal CT in Young Adults with Suspected Acute Diverticulitis: A Retrospective Intraindividual Analysis

RATIONALE AND OBJECTIVES

To assess the effects of radiation dose reduction on image quality and diagnostic accuracy of abdominal computed tomography (CT) in young adults with suspected acute diverticulitis.

MATERIALS AND METHODS

Fifty-four patients ≤40 years who received contrast-enhanced abdominal CT for suspected acute diverticulitis were included. Low-dose CT (LDCT) datasets (25%, 50%, and 75% of the original dose) were generated using sinogram synthesis and quantum noise modeling. A five-point scale was used to assess images qualitatively (overall image quality, noise, artefacts, and sharpness) and for diagnostic confidence (5 being the best possible outcome). Furthermore, the diagnostic accuracy was determined for the presence of acute diverticulitis.

RESULTS

Among 54 patients (mean age: 35.2 ± 5.3 years, 77.8% male), the prevalence of acute diverticulitis was high (57.4%). Subjective image quality was highest for original datasets and lowest for LDCT datasets with 25% of the original dose (median [interquartile range]: 5 [5] vs. 3 [2-3], p < 0.001). Diagnostic confidence was high for all datasets down to 50% of the original dose, while 25% LDCT datasets were associated with a significantly decreased diagnostic confidence (p < 0.001). Diagnostic accuracy was high for all LDCT and original datasets (sensitivity: 100%, negative predictive value [NPV]: 100% for 75% and 100% dose levels; sensitivity: 96.8%, NPV: 95.8% for 50% dose level; sensitivity: 93.6%, NPV: 91.7% for 25% dose level, respectively). Inter-rater agreement regarding the detection of diverticulitis was almost perfect at doses ≥50% (kappa: >0.81), while lower for datasets of 25% of the original radiation dose agreement (kappa: 0.67-0.78).

CONCLUSION

Radiation dose reduction down to 50% of the original radiation exposure permits high image quality, diagnostic confidence, and accuracy for the assessment of acute diverticulitis in abdominal CT in young adults without the use of iterative reconstruction algorithms.