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: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.

Similar Datasets

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