Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study

Greffier, Joël; Dabli, Djamel; Frandon, Julien; Hamard, Aymeric; Belaouni, Asmaa; Akessoul, Philippe; Fuamba, Yannick; Roy, Julien Le; Guiu, Boris; Beregi, Jean‐Paul

doi:10.1002/mp.15180

Cited by 37 publications

(45 citation statements)

References 39 publications

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“…The task-based MTF for DLR was also observed to be robust across a range of doses, with spatial resolution preserved as dose was decreased [48], but with a tendency for a drop off at very low doses and with lower contrast tissues [49,50]. This behavior is reported to be less of an issue in newer versions of DLR [46]. Vendor neutral, image denoising-based methods have also demonstrated superiority over IR and MBR methods with respect to noise texture performance.…”

Section: Technical Review Of Deep Learning Performancementioning

confidence: 90%

“…As with previous generations of nonlinear reconstruction algorithms, the performance of DLR is not adequately quantified using classical image quality metrics such as noise, contrast-to-noise ratio, and signal-to-noise ratio. More advanced metrics such as the noise power spectrum (NPS), the task-based modulation transfer function, and model observer metrics are required [46]. Further, subjective image quality has been essential for characterizing the preferences of radiologists.…”

Section: Technical Review Of Deep Learning Performancementioning

confidence: 99%

“…There is evidence that the strength (or weight) of DLR processing impacts noise texture, with a shift to lower frequencies at high DLR strengths for both on-scanner algorithms currently available [52], albeit that work of Hasegawa et al that demonstrated this did so at phantom sizes and dose levels not representative of clinical reality. However, there is evidence that this shift is alleviated with newer versions of DLR [46]. In clinical use, DLR images are described as producing a more ''natural'' image appearance [45,46,53].…”

Section: Technical Review Of Deep Learning Performancementioning

confidence: 99%

“…However, there is evidence that this shift is alleviated with newer versions of DLR [46]. In clinical use, DLR images are described as producing a more ''natural'' image appearance [45,46,53]. In summary, DLIR does not suffer from the ''plastic'' noise texture shown in Fig.…”

Section: Technical Review Of Deep Learning Performancementioning

confidence: 99%

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A Review of Deep Learning CT Reconstruction: Concepts, Limitations, and Promise in Clinical Practice

et al. 2022

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Purpose of Review Deep Learning reconstruction (DLR) is the current state-of-the-art method for CT image formation. Comparisons to existing filter back-projection, iterative, and model-based reconstructions are now available in the literature. This review summarizes the prior reconstruction methods, introduces DLR, and then reviews recent findings from DLR from a physics and clinical perspective. Recent Findings DLR has been shown to allow for noise magnitude reductions relative to filtered back-projection without suffering from “plastic” or “blotchy” noise texture that was found objectionable with most iterative and model-based solutions. Clinically, early reader studies have reported increases in subjective quality scores and studies have successfully implemented DLR-enabled dose reductions. Summary The future of CT image reconstruction is bright; deep learning methods have only started to tackle problems in this space via addressing noise reduction. Artifact mitigation and spectral applications likely be future candidates for DLR applications.

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Section: Technical Review Of Deep Learning Performancementioning

confidence: 90%

Section: Technical Review Of Deep Learning Performancementioning

confidence: 99%

Section: Technical Review Of Deep Learning Performancementioning

confidence: 99%

Section: Technical Review Of Deep Learning Performancementioning

confidence: 99%

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A Review of Deep Learning CT Reconstruction: Concepts, Limitations, and Promise in Clinical Practice

et al. 2022

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“…No articles or reports have yet been published to describe the iQMetrix-CT software; however, this software has been used in various studies. 11,12,30…”

Section: Task-based Image Quality Assessment-acr Geometric Phantommentioning

confidence: 99%

Impact of an artificial intelligence deep‐learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study

et al. 2022

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Background Recently, computed tomography (CT) manufacturers have developed deep‐learning‐based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolution's dependence on contrast and dose levels. Purpose To assess the impact of an artificial intelligence deep‐learning reconstruction (AI‐DLR) algorithm on image quality and dose reduction compared with a hybrid IR algorithm in chest CT for different clinical indications. Methods Acquisitions on the CT American College of Radiology (ACR) 464 and CT Torso CTU‐41 phantoms were performed at five dose levels (CTDIvol: 9.5/7.5/6/2.5/0.4 mGy) used for chest CT conditions. Raw data were reconstructed using filtered backprojection, two levels of IR (iDose4 levels 4 (i4) and 7 (i7)), and five levels of AI‐DLR (Precise Image; Smoother, Smooth, Standard, Sharp, Sharper). Noise power spectrum (NPS), task‐based transfer function, and detectability index (d′) were computed: d′‐modeled detection of a soft tissue mediastinal nodule (low‐contrast soft tissue chest nodule within the mediastinum [LCN]), ground‐glass opacity (GGO), or high‐contrast pulmonary (HCP) lesion. The subjective image quality of chest anthropomorphic phantom images was independently evaluated by two radiologists. They assessed image noise, image smoothing, contrast between vessels and fat in the mediastinum for mediastinal images, visual border detection between bronchus and lung parenchyma for parenchymal images, and overall image quality using a commonly used four‐ or five‐point scale. Results From Standard to Smoother levels, on average, the noise magnitude decreased (for all dose levels: −66.3% ± 0.5% for mediastinal images and −63.1% ± 0.1% for parenchymal images), the average NPS spatial frequency decreased (for all dose levels: −35.3% ± 2.2% for mediastinal images and −13.3% ± 2.2% for parenchymal images), and the detectability (d′) of the three lesions increased. The opposite pattern was found from Standard to Sharper levels. From Smoother to Sharper levels, the spatial resolution increased for the low‐contrast polyethylene insert and the opposite for the high‐contrast air insert. Compared to the i4 used in clinical practice, d′ values were higher using Smoother (mean for all dose levels: 338.7% ± 29.4%), Smooth (103.4% ± 11.2%), and Standard (34.1% ± 6.6%) levels for the LCN on mediastinal images and Smoother (169.5% ± 53.2% for GGO and 136.9% ± 1.6% for HCP) and Smooth (36.4% ± 22.1% and 24.1% ± 0.9%, respectively) levels for parenchymal images. Radiologists considered the images satisfactory for clinical use at these levels, but adaptation to the dose level of the protocol is required. Conclusion With AI‐DLR, the smoothest levels reduced the noise and improved the detectability of chest lesions but increased the image smoothing. The opposite was found with the sharpest levels. The choice of level depends on the dose level and type of image: mediastinal or parenchymal.

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Comparative assessment of noise properties for two deep learning CT image reconstruction techniques and filtered back projection

et al. 2022

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Background: Two deep learning image reconstruction (DLIR) techniques from two different computed tomography (CT) vendors have recently been introduced into clinical practice.Purpose: To characterize the noise properties of two DLIR techniques with different training methods, using a phantom containing a simple uniform and a complex non-uniform region. Methods: A water-bath phantom with a diameter of 300 mm was used as a base phantom. A textured phantom with a diameter of 128 mm, which was made of two materials, one equivalent to water and the other being 12 mg/ml diluted iodine, irregularly mixed to create a complex texture (non-uniform region), was placed in the base phantom. Thirty repeated phantom scans were performed using two CT scanners (Revolution CT with Apex Edition, GE Healthcare; Aquilion One PRISM Edition, Canon Medical Systems) at two dose levels (CT dose index: 5 and 15 mGy). Images were reconstructed with each CT system's filtered back projection (FBP) and DLIR [TrueFidelity (TF), GE Healthcare; Advanced intelligent Clear-IQ Engine Body Sharp (AC), Canon Medical Systems] for three process strengths. For basic characteristics of noise, the standard deviation (SD) and noise power spectrum (NPS) were measured for the uniform (water) region.A noise magnitude map was generated by calculating the inter-image SD at each pixel position across the 30 images. Then, a noise reduction map (NRM), which visualizes the relative differences in noise magnitude between FBP and DLIR, was calculated. The NRM values ranged from 0.0 to 1.0. A low NRM value represents a less aggressive noise reduction. The histograms of the NRM value were analyzed for the uniform and non-uniform regions. Results:The reduction in noise magnitude compared with FBP tended to be greater with AC (45%-85%) than with TF (32%-65%). The average NPS frequencies of TF and AC were almost comparable to those of FBP, except for the low-dose condition and the high noise reduction strength for AC. The NRM values of TF and AC were higher in the uniform region than in the non-uniform region. In the non-uniform region, TF's average NRM values (0.21-0.48) tended to be lower than AC's (0.39-0.78). The histograms for TF showed a small overlap between the uniform and the non-uniform regions; in contrast, those for AC showed a greater overlap. This difference seems to indicate that TF processes the uniform and non-uniform regions more differently than AC does. Conclusion:This study has revealed a distinct difference in characteristics between the two DLIR techniques:TF tends to offer less aggressive noise reduction in non-uniform regions and preserve the original signals, whereas AC tends

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Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study

Cited by 37 publications

References 39 publications

A Review of Deep Learning CT Reconstruction: Concepts, Limitations, and Promise in Clinical Practice

A Review of Deep Learning CT Reconstruction: Concepts, Limitations, and Promise in Clinical Practice

Impact of an artificial intelligence deep‐learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study

Comparative assessment of noise properties for two deep learning CT image reconstruction techniques and filtered back projection

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