Dataset Information

Influence of adaptive statistical iterative reconstruction algorithm on image quality in coronary computed tomography angiography.

ABSTRACT:

Background

Coronary computed tomography angiography (CCTA) requires high spatial and temporal resolution, increased low contrast resolution for the assessment of coronary artery stenosis, plaque detection, and/or non-coronary pathology. Therefore, new reconstruction algorithms, particularly iterative reconstruction (IR) techniques, have been developed in an attempt to improve image quality with no cost in radiation exposure.

Purpose

To evaluate whether adaptive statistical iterative reconstruction (ASIR) enhances perceived image quality in CCTA compared to filtered back projection (FBP).

Material and methods

Thirty patients underwent CCTA due to suspected coronary artery disease. Images were reconstructed using FBP, 30% ASIR, and 60% ASIR. Ninety image sets were evaluated by five observers using the subjective visual grading analysis (VGA) and assessed by proportional odds modeling. Objective quality assessment (contrast, noise, and the contrast-to-noise ratio [CNR]) was analyzed with linear mixed effects modeling on log-transformed data. The need for ethical approval was waived by the local ethics committee as the study only involved anonymously collected clinical data.

Results

VGA showed significant improvements in sharpness by comparing FBP with ASIR, resulting in odds ratios of 1.54 for 30% ASIR and 1.89 for 60% ASIR (P?=?0.004). The objective measures showed significant differences between FBP and 60% ASIR (P?ConclusionASIR improved the subjective image quality of parameter sharpness and, objectively, reduced noise and increased CNR.

SUBMITTER: Precht H

PROVIDER: S-EPMC5384491 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Publications

Influence of adaptive statistical iterative reconstruction algorithm on image quality in coronary computed tomography angiography.

Precht Helle H Thygesen Jesper J Gerke Oke O Egstrup Kenneth K Waaler Dag D Lambrechtsen Jess J

Acta radiologica open 20161201 12

<h4>Background</h4>Coronary computed tomography angiography (CCTA) requires high spatial and temporal resolution, increased low contrast resolution for the assessment of coronary artery stenosis, plaque detection, and/or non-coronary pathology. Therefore, new reconstruction algorithms, particularly iterative reconstruction (IR) techniques, have been developed in an attempt to improve image quality with no cost in radiation exposure.<h4>Purpose</h4>To evaluate whether adaptive statistical iterati ...[more]

PMID: 28405477

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Project description:PurposeThe purpose of this study was to evaluate the image quality and radiation dose in computed tomography urography (CTU) images acquired with a low kilovoltage peak (kVp) in combination with an adaptive statistical iterative reconstruction (ASiR) algorithm.MethodsA total of 45 subjects (18 women, 27 men) who underwent CTU with kV assist software for automatic selection of the optimal kVp were included and divided into two groups (A and B) based on the kVp and image reconstruction algorithm: group A consisted of patients who underwent CTU with a 80 or 100 kVp and whose images were reconstructed with the 50% ASiR algorithm (n=32); group B consisted of patients who underwent CTU with a 120 kVp and whose images were reconstructed with the filtered back projection (FBP) algorithm (n=13). The images were separately reconstructed with volume rendering (VR) and maximum intensity projection (MIP). Finally, the image quality was evaluated using an image score, CT attenuation, image noise, the contrast-to-noise ratio (CNR) of the renal pelvis-to-abdominal visceral fat and the signal-to-noise ratio (SNR) of the renal pelvis. The radiation dose was assessed using volume CT dose index (CTDIvol), dose-length product (DLP) and effective dose (ED).ResultsFor groups A and B, the subjective image scores for the VR reconstruction images were 3.9±0.4 and 3.8±0.4, respectively, while those for the MIP reconstruction images were 3.8±0.4 and 3.6±0.6, respectively. No significant difference was found (p>0.05) between the two groups' image scores for either the VR or MIP reconstruction images. Additionally, the inter-reviewer image scores did not significantly differ (p>0.05). The mean attenuation of the bilateral renal pelvis in group A was significantly higher than that in group B (271.4±57.6 vs. 221.8±35.3 HU, p<0.05), whereas the image noise in group A was significantly lower than that in group B (7.9±2.1 vs. 10.5±2.3 HU, p<0.05). The CNR and SNR in group A were both significantly higher than those in group B (53.61±24.74 vs. 32.30±6.52 for CNR; 38.13±19.86 vs. 21.76±4.85 for SNR; all p<0.05). The CTDIvol, DLP and ED in group A were significantly lower than those in group B (9.26±2.77 vs. 16.19±5.60 mGy for CTDIvol; 368.86±119.38 vs. 674.38±239.37 mGy×cm-1 for DLP; 5.53±1.79 vs. 10.12±3.59 mSv for ED; all p<0.05).ConclusionsThe low kVp CTU images with 50% ASiR reconstruction exhibit sufficient image quality and facilitate up to a 44% radiation dose reduction.

Project description:ObjectivesDeep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification.MethodsThis prospective study includes 50 patients who underwent two sequential CCTA scans at normal-dose (ND) and lower-dose (LD). ND scans were reconstructed with Adaptive Statistical Iterative Reconstruction-Veo (ASiR-V) 100%, and LD scans with DLIR. Image noise (in Hounsfield units, HU) and quantitative plaque volumes (in mm3) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis (< 20%, 20-50%, 51-70%, 71-90%, 91-99%), and occlusion (100%). Plaque composition was classified as calcified, non-calcified, or mixed.ResultsReduction of radiation dose from ND scans with ASiR-V 100% to LD scans with DLIR at the highest level (DLIR-H; 1.4 mSv vs. 0.8 mSv, p < 0.001) had no impact on image noise (28 vs. 27 HU, p = 0.598). Reliability of stenosis severity and plaque composition was excellent between ND scans with ASiR-V 100% and LD scans with DLIR-H (intraclass correlation coefficients of 0.995 and 0.974, respectively). Comparison of plaque volumes using Bland-Altman analysis revealed a mean difference of - 0.8 mm3 (± 2.5 mm3) and limits of agreement between - 5.8 and + 4.1 mm3.ConclusionDLIR enables a reduction in radiation dose from CCTA by 43% without significant impact on image noise, stenosis severity, plaque composition, and quantitative plaque volume.Key points•Deep-learning image reconstruction (DLIR) enables radiation dose reduction by over 40% for coronary computed tomography angiography (CCTA). •Image noise remains unchanged between a normal-dose CCTA reconstructed by ASiR-V and a lower-dose CCTA reconstructed by DLIR. •There is no impact on the assessment of stenosis severity, plaque composition, and quantitative plaque volume between the two scans.

Project description:BackgroundHead and neck computed tomography angiography (CTA) technology has become the noninvasive imaging method of choice for the diagnosis and long-term follow-up of vascular lesions of the head and neck. However, issues of radiation safety and contrast nephropathy associated with CTA examinations remain concerns. In recent years, deep learning image reconstruction (DLIR) algorithms have been increasingly used in clinical studies, demonstrating their potential for dose optimization. This study aimed to investigate the value of using a DLIR algorithm to reduce radiation and contrast doses in head and neck CTA.MethodsA total of 100 patients were prospectively enrolled and randomly divided into two groups. Group A (50 patients) consisted of those who underwent 70-kVp CTA with a low contrast volume and injection rate and who were classified according to the reconstruction algorithm into subgroups A1 [DLIR at high weighting (DLIR-H)], A2 [DLIR at low weighting (DLIR-L)], and A3 [volume-based adaptive statistical iterative reconstruction with 50% weighting (ASIR-V50%)]. Meanwhile, group B (50 patients) consisted of those who underwent standard radiation and contrast doses at 100 kVp with ASIR-V50% reconstruction. The computed tomography (CT) attenuation, background noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective image quality score (SIQS) were statistically compared for several vessels among the four groups.ResultsGroup A showed significant reductions in contrast dosage, injection rate, and radiation dose of 36.09%, 20.88%, and 47.80%, respectively, compared to group B (all P<0.001). The four groups differed significantly in terms of background noise (all P<0.05) with group A1 having the lowest value. Group A1 also had significantly higher SNR and CNR values compared to group B in all vessels (all P<0.05) except the M1 of the middle cerebral artery for the SNR. Group A1 also had the highest SIQS, followed by the A2, B, and A3 groups. The SIQS showed good agreement between the two reviewers in all groups, with κ values between 0.88 and 1.ConclusionsCompared to the standard-dose protocol using 100 kVp and ASIR-V50%, a protocol of 70 kVp combined with DLIR-H significantly reduces the radiation dose, contrast dose, and injection rate in head and neck CTA while still significantly improving image quality for patients with a standard body size.

Project description:The recent advent of tensor tomography techniques has enabled tomographic investigations of the 3D nanostructure organization of biological and material science samples. These techniques extended the concept of conventional X-ray tomography by reconstructing not only a scalar value such as the attenuation coefficient per voxel, but also a set of parameters that capture the local anisotropy of nanostructures within every voxel of the sample. Tensor tomography data sets are intrinsically large as each pixel of a conventional X-ray projection is substituted by a scattering pattern, and projections have to be recorded at different sample angular orientations with several tilts of the rotation axis with respect to the X-ray propagation direction. Currently available reconstruction approaches for such large data sets are computationally expensive. Here, a novel, fast reconstruction algorithm, named iterative reconstruction tensor tomography (IRTT), is presented to simplify and accelerate tensor tomography reconstructions. IRTT is based on a second-rank tensor model to describe the anisotropy of the nanostructure in every voxel and on an iterative error backpropagation reconstruction algorithm to achieve high convergence speed. The feasibility and accuracy of IRTT are demonstrated by reconstructing the nanostructure anisotropy of three samples: a carbon fiber knot, a human bone trabecula specimen and a fixed mouse brain. Results and reconstruction speed were compared with those obtained by the small-angle scattering tensor tomography (SASTT) reconstruction method introduced by Liebi et al. [Nature (2015), 527, 349-352]. The principal orientation of the nanostructure within each voxel revealed a high level of agreement between the two methods. Yet, for identical data sets and computer hardware used, IRTT was shown to be more than an order of magnitude faster. IRTT was found to yield robust results, it does not require prior knowledge of the sample for initializing parameters, and can be used in cases where simple anisotropy metrics are sufficient, i.e. the tensor approximation adequately captures the level of anisotropy and the dominant orientation within a voxel. In addition, by greatly accelerating the reconstruction, IRTT is particularly suitable for handling large tomographic data sets of samples with internal structure or as a real-time analysis tool during the experiment for online feedback during data acquisition. Alternatively, the IRTT results might be used as an initial guess for models capturing a higher complexity of structural anisotropy such as spherical harmonics based SASTT in Liebi et al. (2015), improving both overall convergence speed and robustness of the reconstruction.

Dataset Information

Influence of adaptive statistical iterative reconstruction algorithm on image quality in coronary computed tomography angiography.

Background

Purpose

Material and methods

Results

Publications

Influence of adaptive statistical iterative reconstruction algorithm on image quality in coronary computed tomography angiography.

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