Impact of image denoising on image quality, quantitative parameters and sensitivity of ultra-low-dose volume perfusion CT imaging

Othman, Ahmed E.; Brockmann, Carolin; Yang, Zhou; Kim, Changwon; Afat, Saif; Pjontek, Rastislav; Nikoubashman, Omid; Brockmann, Marc A.; Nikolaou, Konstantin; Wiesmann, Martin; Kim, Jong Hyo

doi:10.1007/s00330-015-3853-6

Cited by 17 publications

(7 citation statements)

References 16 publications

(20 reference statements)

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“…Perfusion abnormalities in patients with vasospasm are often more subtle compared with patients with acute stroke because of vessel occlusion (17,25,30). Therefore, results of low-dose VP CT in stroke patients cannot be transferred to this patient group (13)(14)(15)(31)(32)(33), and the effects of radiation dose reduction in these patients have to be evaluated carefully before prospective application. Given the lack of data on this topic, we chose a retrospective low-dose simulation study design as an initial step toward preparation and application of a low-dose VP CT protocol for patients…”

Section: Quantitative Analyses Of Perfusion Mapsmentioning

confidence: 99%

Diagnostic Accuracy of Simulated Low-Dose Perfusion CT to Detect Cerebral Perfusion Impairment after Aneurysmal Subarachnoid Hemorrhage: A Retrospective Analysis

Afat¹,

Brockmann²,

Nikoubashman³

et al. 2018

Radiology

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Purpose To evaluate diagnostic accuracy of low-dose volume perfusion (VP) computed tomography (CT) compared with original VP CT regarding the detection of cerebral perfusion impairment after aneurysmal subarachnoid hemorrhage. Materials and Methods In this retrospective study, 85 patients (mean age, 59.6 years; 62 women) with aneurysmal subarachnoid hemorrhage and who were suspected of having cerebral vasospasm at unenhanced CT and VP CT (tube voltage, 80 kVp; tube current-time product, 180 mAs) were included, 37 of whom underwent digital subtraction angiography (DSA) within 6 hours. Low-dose VP CT data sets at tube current-time product of 72 mAs were retrospectively generated by validated realistic simulation. Perfusion maps were generated from both data sets and reviewed by two neuroradiologists for overall image quality, diagnostic confidence and presence and/or severity of perfusion impairment indicating vasospasm. An interventional neuroradiologist evaluated 16 vascular segments at DSA. Diagnostic accuracy of low-dose VP CT was calculated with original VP CT as reference standard. Agreement between findings of both data sets was assessed by using weighted Cohen κ and findings were correlated with DSA by using Spearman correlation. After quantitative volumetric analysis, lesion volumes were compared on both VP CT data sets. Results Low-dose VP CT yielded good ratings of image quality and diagnostic confidence and classified all patients correctly with high diagnostic accuracy (sensitivity, 99.0%; specificity, 99.5%) without significant differences regarding presence and/or severity of perfusion impairment between original and low-dose data sets (Z = -0.447; P = .655). Findings of both data sets correlated significantly with DSA (original, r = 0.671; low dose, r = 0.667). Lesion volume was comparable for both data sets (relative difference, 5.9% ± 5.1 [range, 0.2%-25.0%; median, 4.0%]) with strong correlation (r = 0.955). Conclusion The results suggest that radiation dose reduction to 40% of original dose levels (tube current-time product, 72 mAs) may be performed in VP CT imaging of patients with aneurysmal subarachnoid hemorrhage without compromising the diagnostic accuracy regarding detection of cerebral perfusion impairment indicating vasospasm. RSNA, 2018 Online supplemental material is available for this article.

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Section: Quantitative Analyses Of Perfusion Mapsmentioning

confidence: 99%

Diagnostic Accuracy of Simulated Low-Dose Perfusion CT to Detect Cerebral Perfusion Impairment after Aneurysmal Subarachnoid Hemorrhage: A Retrospective Analysis

Afat¹,

Brockmann²,

Nikoubashman³

et al. 2018

Radiology

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“…In this case low-dose simu-lation can be useful since it allows retrospectively dose-reduced CT data by simulating CT examinations with reduced tube current by adding realistic image noise to existing original data [62]. This is being increasingly employed, for example in developing a cranial low-dose CT perfusion protocol [22,23,63,64]. It was demonstrated that a dose reduction of up to 60 % of the original standard dose does not result in image quality deterioration or diagnostic accuracy using this method.…”

Section: Low-dose Simulationmentioning

confidence: 99%

Dose Reduction and Dose Management in Computed Tomography – State of the Art

Zinsser¹,

Marcus²,

Othman³

et al. 2018

Fortschr Röntgenstr

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“…The scan protocol optimization requires balancing the radiation dose with sufficient image quality such that the necessary diagnostic information is not compromised [4]. Last decade has seen the developments of numerous techniques for radiation dose reduction including automatic exposure control [5][6][7], dual-energy imaging [8,9], iterative reconstruction (IR) [7,[10][11][12], and deep learning-based reconstruction (DLR) [13]. IR techniques have been highlighted over the last decade with their significant dose reduction performance along with image quality improvements [14][15][16][17], and are widely accepted in routine imaging practices [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study

et al. 2022

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While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of image quality in patient scans has not yet been established. In this study, we present a patient-specific CT image quality evaluation method that includes fully automated measurements of noise level, structure sharpness, and alteration of structure. This study used the CT images of 120 patients from four different CT scanners reconstructed with three types of algorithm: filtered back projection (FBP), vendor-specific iterative reconstruction (IR), and a vendor-agnostic deep learning model (DLM, ClariCT.AI, ClariPi Inc.). The structure coherence feature (SCF) was used to divide an image into the homogeneous (RH) and structure edge (RS) regions, which in turn were used to localize the regions of interests (ROIs) for subsequent analysis of image quality indices. The noise level was calculated by averaging the standard deviations from five randomly selected ROIs on RH, and the mean SCFs on RS was used to estimate the structure sharpness. The structure alteration was defined by the standard deviation ratio between RS and RH on the subtraction image between FBP and IR or DLM, in which lower structure alterations indicate successful noise reduction without degradation of structure details. The estimated structure sharpness showed a high correlation of 0.793 with manually measured edge slopes. Compared to FBP, IR and DLM showed 34.38% and 51.30% noise reduction, 2.87% and 0.59% lower structure sharpness, and 2.20% and -12.03% structure alteration, respectively, on an average. DLM showed statistically superior performance to IR in all three image quality metrics. This study is expected to contribute to enhance the CT protocol optimization process by allowing a high throughput and quantitative image quality evaluation during the introduction or adjustment of lower-dose CT protocol into routine practice.

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Impact of image denoising on image quality, quantitative parameters and sensitivity of ultra-low-dose volume perfusion CT imaging

Cited by 17 publications

References 16 publications

Diagnostic Accuracy of Simulated Low-Dose Perfusion CT to Detect Cerebral Perfusion Impairment after Aneurysmal Subarachnoid Hemorrhage: A Retrospective Analysis

Diagnostic Accuracy of Simulated Low-Dose Perfusion CT to Detect Cerebral Perfusion Impairment after Aneurysmal Subarachnoid Hemorrhage: A Retrospective Analysis

Dose Reduction and Dose Management in Computed Tomography – State of the Art

Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study

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