Optimization and image quality assessment of the alpha-image reconstruction algorithm: iterative reconstruction with well-defined image quality metrics

Many objective quality metrics for assessing the visual quality of images have been developed during the last decade. A simple way to fine tune the efficiency of assessment is through permutation and combination of these metrics. The goal of this fusion approach is to take advantage of the metrics utilized and minimize the influence of their drawbacks. In this paper, a symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for predicting subject scores of images in datasets using a combination of objective scores of a set of image quality metrics (IQM). By learning from image datasets, the MGGP algorithm can determine appropriate image quality metrics, from 21 metrics utilized, whose objective scores employed as predictors in the symbolic regression model, by optimizing simultaneously two competing objectives of model ‘goodness of fit’ to data and model ‘complexity’. Six large image databases (namely LIVE, CSIQ, TID2008, TID2013, IVC and MDID) that are available in public domain are used for learning and testing the predictive models, according the k-fold-cross-validation and the cross dataset strategies. The proposed approach is compared against state-of-the-art objective image quality assessment approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.

Download Full-text

Image quality assessment of an iterative reconstruction algorithm applied to abdominal CT imaging

Physica Medica ◽

10.1016/j.ejmp.2014.02.005 ◽

2014 ◽

Vol 30 (4) ◽

pp. 527-534 ◽

Cited By ~ 21

Author(s):

Yoshinori Funama ◽

Katsuyuki Taguchi ◽

Daisuke Utsunomiya ◽

Seitaro Oda ◽

Kazuhiro Katahira ◽

...

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Iterative Reconstruction ◽

Image Quality Assessment ◽

Reconstruction Algorithm ◽

Ct Imaging ◽

Abdominal Ct ◽

Iterative Reconstruction Algorithm

Download Full-text

Spatially adaptive image quality metrics for perceptual image quality assessment

10.1117/12.825904 ◽

2009 ◽

Author(s):

Karel Fliegel

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Quality Metrics ◽

Image Quality Metrics ◽

Spatially Adaptive ◽

Perceptual Image Quality

Download Full-text

Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration

European Radiology ◽

10.1007/s00330-021-08121-3 ◽

2021 ◽

Author(s):

Yoshifumi Noda ◽

Nobuyuki Kawai ◽

Shoma Nagata ◽

Fumihiko Nakamura ◽

Takayuki Mori ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Image Reconstruction ◽

Image Quality ◽

Reconstruction Algorithm ◽

Iodine Concentration ◽

Dual Energy ◽

Image Reconstruction Algorithm ◽

Tomography Image ◽

Computed Tomography Image

Download Full-text

Comparison of low-contrast detectability between uniform and anatomically realistic phantoms—influences on CT image quality assessment

European Radiology ◽

10.1007/s00330-021-08248-3 ◽

2021 ◽

Author(s):

Juliane Conzelmann ◽

Ulrich Genske ◽

Arthur Emig ◽

Michael Scheel ◽

Bernd Hamm ◽

...

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Dose Reduction ◽

Iterative Reconstruction ◽

Image Quality Assessment ◽

Lesion Detection ◽

Detection Accuracy ◽

Test Environment ◽

Low Contrast ◽

Dose Effects

Abstract Objectives To evaluate the effects of anatomical phantom structure on task-based image quality assessment compared with a uniform phantom background. Methods Two neck phantom types of identical shape were investigated: a uniform type containing 10-mm lesions with 4, 9, 18, 30, and 38 HU contrast to the surrounding area and an anatomically realistic type containing lesions of the same size and location with 10, 18, 30, and 38 HU contrast. Phantom images were acquired at two dose levels (CTDIvol of 1.4 and 5.6 mGy) and reconstructed using filtered back projection (FBP) and adaptive iterative dose reduction 3D (AIDR 3D). Detection accuracy was evaluated by seven radiologists in a 4-alternative forced choice experiment. Results Anatomical phantom structure impaired lesion detection at all lesion contrasts (p < 0.01). Detectability in the anatomical phantom at 30 HU contrast was similar to 9 HU contrast in uniform images (91.1% vs. 89.5%). Detection accuracy decreased from 83.6% at 5.6 mGy to 55.4% at 1.4 mGy in uniform FBP images (p < 0.001), whereas AIDR 3D preserved detectability at 1.4 mGy (80.7% vs. 85% at 5.6 mGy, p = 0.375) and was superior to FBP (p < 0.001). In the assessment of anatomical images, superiority of AIDR 3D was not confirmed and dose reduction moderately affected detectability (74.6% vs. 68.2%, p = 0.027 for FBP and 81.1% vs. 73%, p = 0.018 for AIDR 3D). Conclusions A lesion contrast increase from 9 to 30 HU is necessary for similar detectability in anatomical and uniform neck phantom images. Anatomical phantom structure influences task-based assessment of iterative reconstruction and dose effects. Key Points • A lesion contrast increase from 9 to 30 HU is necessary for similar low-contrast detectability in anatomical and uniform neck phantom images. • Phantom background structure influences task-based assessment of iterative reconstruction and dose effects. • Transferability of CT assessment to clinical imaging can be expected to improve as the realism of the test environment increases.

Download Full-text