Deep Learning–based CT Image Reconstruction: Initial                     Evaluation Targeting Hypovascular Hepatic Metastases

Nakamura, Yuko; Higaki, Toru; Tatsugami, Fuminari; Zhou, Jian; Zhou, Yu; Akino, Naruomi; Ito, Yuya; Iida, Michihisa; Awai, Kazuo

doi:10.1148/ryai.2019180011

Cited by 60 publications

(40 citation statements)

References 38 publications

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“…Another study in 2019 by Nakamura et al evaluated the detectability of hypovascular hepatic metastasis in images reconstructed with AiCE in addition to measuring noise and CNR. They demonstrated less image noise and superior conspicuity for DLR compared to the iterative algorithm [35]. A phantom study by Higaki et al from 2020 additionally examined spatial resolution with a task-based modulation transfer function at 10 % [36].…”

Section: Deep Learning Reconstruction Algorithms In the Clinical Routinementioning

confidence: 99%

Deep Learning CT Image Reconstruction in Clinical Practice

Arndt

Güttler

Heinrich

et al. 2020

Rofo

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Background Computed tomography (CT) is a central modality in modern radiology contributing to diagnostic medicine in almost every medical subspecialty, but particularly in emergency services. To solve the inverse problem of reconstructing anatomical slice images from the raw output the scanner measures, several methods have been developed, with filtered back projection (FBP) and iterative reconstruction (IR) subsequently providing criterion standards. Currently there are new approaches to reconstruction in the field of artificial intelligence utilizing the upcoming possibilities of machine learning (ML), or more specifically, deep learning (DL). Method This review covers the principles of present CT image reconstruction as well as the basic concepts of DL and its implementation in reconstruction. Subsequently commercially available algorithms and current limitations are being discussed. Results and Conclusion DL is an ML method that utilizes a trained artificial neural network to solve specific problems. Currently two vendors are providing DL image reconstruction algorithms for the clinical routine. For these algorithms, a decrease in image noise and an increase in overall image quality that could potentially facilitate the diagnostic confidence in lesion conspicuity or may translate to dose reduction for given clinical tasks have been shown. One study showed equal diagnostic accuracy in the detection of coronary artery stenosis for DL reconstructed images compared to IR at higher image quality levels. Consequently, a lot more research is necessary and should aim at diagnostic superiority in the clinical context covering a broadness of pathologies to demonstrate the reliability of such DL approaches. Key Points: Citation Format

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Section: Deep Learning Reconstruction Algorithms In the Clinical Routinementioning

confidence: 99%

Deep Learning CT Image Reconstruction in Clinical Practice

Arndt

Güttler

Heinrich

et al. 2020

Rofo

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show abstract

“…A few studies on deep learning-based reconstruction have shown that it improved image quality and reduced noise and artifacts better than hybrid IR and MBIR [8,16,28]. Nakamura et al reported that deep learning reconstruction could reduce noise and artifacts more than hybrid IR could and that it may improve the detection of low-contrast lesions when evaluating hypovascular hepatic metastases [16]. While their study did not evaluate low-dose CT, the deep learning model is also considered an effective method with the potential to use lower-dose CT techniques such as sparse sampling with clinically acceptable results [5].…”

Section: Discussionmentioning

confidence: 99%

“…However, most of them focused on quantitative measures such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). To the best of our knowledge, there are few studies that radiologists visually evaluate abnormal lesions such as metastasis on CT images processed with deep learning [16]. Furthermore, the quantitative measure, such as PSNR and SSIM, and human perceived quality were not always consistent in agreement [17].…”

Section: Introductionmentioning

confidence: 93%

“…To improve perceptual image quality, generative adversarial network (GAN) was introduced for CT noise reduction [12,13]. Following the advancement in noise reduction using deep learning, Nakamura et al evaluated noise reduction using deep learning on a real CT scanner [16]. However, most of them focused on quantitative measures such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).…”

Section: Introductionmentioning

confidence: 99%

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Simulation Study of Low-Dose Sparse-Sampling CT with Deep Learning-Based Reconstruction: Usefulness for Evaluation of Ovarian Cancer Metastasis

et al. 2020

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The usefulness of sparse-sampling CT with deep learning-based reconstruction for detection of metastasis of malignant ovarian tumors was evaluated. We obtained contrast-enhanced CT images (n = 141) of ovarian cancers from a public database, whose images were randomly divided into 71 training, 20 validation, and 50 test cases. Sparse-sampling CT images were calculated slice-by-slice by software simulation. Two deep-learning models for deep learning-based reconstruction were evaluated: Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) and deeper U-net. For 50 test cases, we evaluated the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as quantitative measures. Two radiologists independently performed a qualitative evaluation for the following points: entire CT image quality; visibility of the iliac artery; and visibility of peritoneal dissemination, liver metastasis, and lymph node metastasis. Wilcoxon signed-rank test and McNemar test were used to compare image quality and metastasis detectability between the two models, respectively. The mean PSNR and SSIM performed better with deeper U-net over RED-CNN. For all items of the visual evaluation, deeper U-net scored significantly better than RED-CNN. The metastasis detectability with deeper U-net was more than 95%. Sparse-sampling CT with deep learning-based reconstruction proved useful in detecting metastasis of malignant ovarian tumors and might contribute to reducing overall CT-radiation exposure.

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“…In addition, the TrueFidelity DNN was trained with both patient and phantom data whereas it was trained only with patient data for AiCE. CT images obtained with these algorithms using denoising techniques showed suppressed noise with no change of noise texture or distortion of anatomical and pathological structures (19,(22)(23)(24)(25)(26)(27)(28)(29)(30).…”

Section: Introductionmentioning

confidence: 99%

Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

Greffier¹,

Dabli²,

Hamard³

et al. 2022

Quant Imaging Med Surg

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Background: New reconstruction algorithms based on deep learning have been developed to correct the image texture changes related to the use of iterative reconstruction algorithms. The purpose of this study was to evaluate the impact of a new deep learning image reconstruction [Advanced intelligent Clear-IQ Engine (AiCE)] algorithm on image-quality and dose reduction compared to a hybrid iterative reconstruction (AIDR 3D) algorithm and a model-based iterative reconstruction (FIRST) algorithm.Methods: Acquisitions were carried out using the ACR 464 phantom (and its body ring) at six dose levels (volume computed tomography dose index 15/10/7.5/5/2.5/1 mGy). Raw data were reconstructed using three levels (Mild/Standard/Strong) of AIDR 3D, of FIRST and AiCE. Noise-power-spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index was computed to model the detection of a small calcification (1.5-mm diameter and 500 HU) and a large mass in the liver (25-mm diameter and 120 HU).Results: NPS peaks were lower with AiCE than with AIDR 3D (−41%±6% for all levels) or FIRST (−15%±6% for Strong level and −41%±11% for both other levels). The average NPS spatial frequency was lower with AICE than AIDR 3D (−9%±2% using Mild and −3%±2% using Strong) but higher than FIRST for Standard (6%±3%) and Strong (25%±3%) levels. For acrylic insert, values of TTF at 50 percent were higher with AICE than AIDR 3D and FIRST, except for Mild level (−6%±6% and −13%±3%, respectively).For bone insert, values of TTF at 50 percent were higher with AICE than AIDR 3D but lower than FIRST (−19%±14%). For both simulated lesions, detectability index values were higher with AICE than AIDR 3D and FIRST (except for Strong level and for the small feature; −21%±14%). Using the Standard level, dose could be reduced by −79% for the small calcification and −57% for the large mass using AICE compared to AIDR 3D. Conclusions:The new deep learning image reconstruction algorithm AiCE generates an image-quality with less noise and/or less smudged/smooth images and a higher detectability than the AIDR 3D or FIRST algorithms. The outcomes of our phantom study suggest a good potential of dose reduction using AiCE but it should be confirmed clinically in patients.

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Deep Learning–based CT Image Reconstruction: Initial Evaluation Targeting Hypovascular Hepatic Metastases

Cited by 60 publications

References 38 publications

Deep Learning CT Image Reconstruction in Clinical Practice

Deep Learning CT Image Reconstruction in Clinical Practice

Simulation Study of Low-Dose Sparse-Sampling CT with Deep Learning-Based Reconstruction: Usefulness for Evaluation of Ovarian Cancer Metastasis

Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

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