Impact of noise reduction on radiation dose reduction potential of virtual monochromatic spectral images: Comparison of phantom images with conventional 120 kVp images using deep learning image reconstruction and hybrid iterative reconstruction

Masuda, Shota; Yamada, Yoshitake; Minamishima, Kazuya; Owaki, Yoshiki; Yamazaki, Akihisa; Jinzaki, Masahiro

doi:10.1016/j.ejrad.2022.110198

Cited by 9 publications

(1 citation statement)

References 37 publications

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“…These applications permit to enhance vascular contrast, to reduce image artifacts, and to evaluate composition of different tissues or lesions using iodine map and Z- effective images [ 18 , 19 ]. In addition, these applications allow both a possible reduction in radiation dose and amount of contrast agent to be administered [ 19 , 20 ]. Z- effective images are colour-coded images based on the effective atomic number of the tissue; this information is obtained from the spectral decomposition process into photoelectric and Compton scattering data [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Dual-layer spectral CT fusion imaging for lung biopsies: more accurate targets, diagnostic samplings, and biomarker information?

et al. 2022

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The increasingly widespread use of computed tomography (CT) has increased the number of detected lung lesions, which are then subjected to needle biopsy to obtain histopathological diagnosis. Obtaining high-quality biopsy specimens is fundamental for diagnosis and biomolecular characterisation that guide therapy decision-making. In order to obtain samples with high diagnostic potential, fusion imaging techniques, such as fusion between positron emission tomography and CT, have been introduced to target the biopsy where there more viable neoplastic cells can be sampled. Nowadays, dual-layer spectral CT represents a novel technology enabling an increased tissue characterisation. In particular, Z-effective images, i.e., colour-coded images based on the effective atomic number of tissue components, provide a higher level of discrimination than usual imaged based on x-ray attenuation in Hounsfield units and offer the potential of a better tissue characterisation. Our hypothesis is based on the future use of data provided by spectral CT, in particular by Z-effective images, as a guide for appropriate biopsy sampling for histopathological and biomolecular characterisation in the era of patient tailored-therapy.

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Section: Introductionmentioning

confidence: 99%

Dual-layer spectral CT fusion imaging for lung biopsies: more accurate targets, diagnostic samplings, and biomarker information?

et al. 2022

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Image quality evaluation in deep‐learning‐based CT noise reduction using virtual imaging trial methods: Contrast‐dependent spatial resolution

Zhou,

Gong,

Hsieh

et al. 2024

Medical Physics

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BackgroundDeep‐learning‐based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images.PurposeIn this work, we aim to develop a patient‐data‐based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method.MethodsThe patient‐data‐based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR‐processed images from projection or image data; (4) creation of ensembles of DLIR‐processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (−500, −100, −50, −20, −10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered‐backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN‐weak, DCNN‐medium, and DCNN‐strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion‐present images from the lesion absent images. Modulation transfer functions (MTFs) both in‐plane and along the z‐axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t‐test was used to compare the z‐axis and in‐plane spatial resolution of five algorithms for five different contrasts and three dose levels.ResultsThe in‐plane and z‐axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in‐plane and z‐axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (−10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in‐plane and z‐axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of −100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in‐plane and cross‐plane spatial resolution: FBP > DCNN‐Weak > IR > DCNN‐Medium > DCNN‐Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels.ConclusionsA patient‐data‐based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non‐linear image reconstruction and post‐processing techniques, the evaluated DCNN method degraded the in‐plane and z‐axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.

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Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis

et al. 2023

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Impact of noise reduction on radiation dose reduction potential of virtual monochromatic spectral images: Comparison of phantom images with conventional 120 kVp images using deep learning image reconstruction and hybrid iterative reconstruction

Cited by 9 publications

References 37 publications

Dual-layer spectral CT fusion imaging for lung biopsies: more accurate targets, diagnostic samplings, and biomarker information?

Dual-layer spectral CT fusion imaging for lung biopsies: more accurate targets, diagnostic samplings, and biomarker information?

Image quality evaluation in deep‐learning‐based CT noise reduction using virtual imaging trial methods: Contrast‐dependent spatial resolution

Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis

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