State of the Art in Abdominal CT: The Limits of Iterative                    Reconstruction Algorithms

Mileto, Achille; Guimarães, Luís; McCollough, Cynthia H.; Fletcher, Joel G.; Yu, Lifeng

doi:10.1148/radiol.2019191422

Cited by 164 publications

(149 citation statements)

References 75 publications

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“…As a result, many new image reconstruction techniques have become available in recent years on clinical CT systems. [1][2][3][4] As new algorithms are commercially deployed, it is important that their impact on image quality be independently characterized so that their benefits and weaknesses can be properly understood. This is especially true for the ever more advanced reconstruction techniques that are being released every year by manufacturers.…”

Section: Introductionmentioning

confidence: 99%

“…This is partly driven by an increasing pressure to perform CT examinations at lower radiation doses while maintaining acceptable image quality. As a result, many new image reconstruction techniques have become available in recent years on clinical CT systems 1–4 . As new algorithms are commercially deployed, it is important that their impact on image quality be independently characterized so that their benefits and weaknesses can be properly understood.…”

Section: Introductionmentioning

confidence: 99%

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Noise and spatial resolution properties of a commercially available deep learning‐based CT reconstruction algorithm

et al. 2020

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To characterize the noise and spatial resolution properties of a commercially available deep learning-based computed tomography (CT) reconstruction algorithm. Methods: Two phantom experiments were performed. The first used a multisized image quality phantom (Mercury v3.0, Duke University) imaged at five radiation dose levels (CTDI vol : 0.9, 1.2, 3.6, 7.0, and 22.3 mGy) with a fixed tube current technique on a commercial CT scanner (GE Revolution CT). Images were reconstructed with conventional (FBP), iterative (GE ASiR-V), and deep learning-based (GE True Fidelity) reconstruction algorithms. Noise power spectrum (NPS), highcontrast (air-polyethylene interface), and intermediate-contrast (water-polyethylene interface) task transfer functions (TTF) were measured for each dose level and phantom size and summarized in terms of average noise frequency (f av) and frequency at which the TTF was reduced to 50% (f 50%), respectively. The second experiment used a custom phantom with low-contrast rods and lung texture sections for the assessment of low-contrast TTF and noise spatial distribution. The phantom was imaged at five dose levels (CTDI vol : 1.0, 2.1, 3.0, 6.0, and 10.0 mGy) with 20 repeated scans at each dose, and images reconstructed with the same reconstruction algorithms. The local noise stationarity was assessed by generating spatial noise maps from the ensemble of repeated images and computing a noise inhomogeneity index, η , following AAPM TG233 methods. All measurements were compared among the algorithms. Results: Compared to FBP, noise magnitude was reduced on average (AE one standard deviation) by 74 AE 6% and 68 AE 4% for ASiR-V (at "100%" setting) and True Fidelity (at "High" setting), respectively. The noise texture from ASiR-V had substantially lower noise frequency content with 55 AE 4% lower NPS f av compared to FBP while True Fidelity had only marginally different noise frequency content with 9 AE 5% lower NPS f av compared to FBP. Both ASiR-V and True Fidelity demonstrated locally nonstationary noise in a lung texture background at all radiation dose levels, with higher noise near high-contrast edges of vessels and lower noise in uniform regions. At the 1.0 mGy dose level η values were 314% and 271% higher in ASiR-V and True Fidelity compared to FBP, respectively. High-contrast spatial resolution was similar between all algorithms for all dose levels and phantom sizes (<3% difference in TTF f 50%). Compared to FBP, low-contrast spatial resolution was lower for ASiR-V and True Fidelity with a reduction of TTF f 50% of up to 42% and 36%, respectively. Conclusions: The deep learning-based CT reconstruction demonstrated a strong noise magnitude reduction compared to FBP while maintaining similar noise texture and high-contrast spatial resolution. However, the algorithm resulted in images with a locally nonstationary noise in lung textured backgrounds and had somewhat degraded low-contrast spatial resolution similar to what has been observed in currently available iterative reconstruction techniques.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noise and spatial resolution properties of a commercially available deep learning‐based CT reconstruction algorithm

et al. 2020

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“…Several single-and multi-vendor studies have reported advantages of iterative reconstruction techniques over FBP with improved image quality at 23-76% lower radiation dose burden [51]. Conversely, some studies have reported that CT images generated from iterative reconstruction methods are too smooth (less noisy) and, therefore, not superior to FBP [52,57,58].…”

Section: Iterative Reconstructionmentioning

confidence: 99%

“…Compared to HI-RT, MI-RT have greater noise reduction and artifact reduction potential, and therefore, can enable a greater reduction in radiation dose associated with CT scanning. Several studies have reported that MI-RT can reduce radiation dose further than HI-RT and FBP while maintaining image quality for several routine and non-routine clinical indications and body regions in both adult and pediatric patients [57][58][59]. Although most studies have found better image quality at lower radiation dose with iterative reconstruction techniques compared to FBP, some investigators have described a loss of image resolution and low-contrast resolution at low radiation dose [60,61].…”

Section: Iterative Reconstructionmentioning

confidence: 99%

“…Altered image texture and visual appearance with several iterative reconstruction techniques have been variably reported as blotchy, artificial, waxy, or plastic appearance which worsens with increasing strength of iterative reconstruction [58,63]. Loss of low-contrast resolution on reduced-dose iterative reconstruction can affect the evaluation of lesions in solid abdominal organs.…”

Section: Deep Learning-based Reconstructionmentioning

confidence: 99%

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Artificial intelligence in image reconstruction: The change is here

Singh

Wang

et al. 2020

Physica Medica

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Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and faster gantry rotation speeds. To tackle concerns over rising radiation doses from its increasing use and to improve image quality, CT reconstruction techniques evolved from filtered back projection to commercial release of iterative reconstruction techniques, and recently, of deep learning (DL)based image reconstruction. These newer reconstruction techniques enable improved or retained image quality versus filtered back projection at lower radiation doses. DL can aid in image reconstruction with training data without total reliance on the physical model of the imaging process, unique artifacts of PCD-CT due to charge sharing, K-escape, fluorescence x-ray emission, and pulse pileups can be handled in the data-driven fashion. With sufficiently reconstructed images, a well-designed network can be trained to upgrade image quality over a practical/clinical threshold or define new/killer applications. Besides, the much smaller detector pixel for PCD-CT can lead to huge computational costs with traditional model-based iterative reconstruction methods whereas deep networks can be much faster with training and validation. In this review, we present techniques, applications, uses, and limitations of deep learning-based image reconstruction methods in CT.

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An adult and pediatric size‐based contrast administration reduction phantom study for single and dual‐energy CT through preservation of contrast‐to‐noise ratio

Wang,

Duan,

Mahmood

et al. 2024

J Applied Clin Med Phys

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BackgroundGlobal shortages of iodinated contrast media (ICM) during COVID‐19 pandemic forced the imaging community to use ICM more strategically in CT exams.PurposeThe purpose of this work is to provide a quantitative framework for preserving iodine CNR while reducing ICM dosage by either lowering kV in single‐energy CT (SECT) or using lower energy virtual monochromatic images (VMI) from dual‐energy CT (DECT) in a phantom study.Materials and MethodsIn SECT study, phantoms with effective diameters of 9.7, 15.9, 21.1, and 28.5 cm were scanned on SECT scanners of two different manufacturers at a range of tube voltages. Statistical based iterative reconstruction and deep learning reconstruction were used. In DECT study, phantoms with effective diameters of 20, 29.5, 34.6, and 39.7 cm were scanned on DECT scanners from three different manufacturers. VMIs were created from 40 to 140 keV. ICM reduction by lowering kV levels for SECT or switching from SECT to DECT was calculated based on the linear relationship between iodine CNR and its concentration under different scanning conditions.ResultsOn SECT scanner A, while matching CNR at 120 kV, ICM reductions of 21%, 58%, and 72% were achieved at 100, 80, and 70 kV, respectively. On SECT scanner B, 27% and 80% ICM reduction was obtained at 80 and 100 kV. On the Fast‐kV switch DECT, with CNR matched at 120 kV, ICM reductions were 35%, 30%, 23%, and 15% with VMIs at 40, 50, 60, and 68 keV, respectively. On the dual‐source DECT, ICM reductions were 52%, 48%, 42%, 33%, and 22% with VMIs at 40, 50, 60, 70, and 80 keV. On the dual‐layer DECT, ICM reductions were 74%, 62%, 45%, and 22% with VMIs at 40, 50, 60, and 70 keV.ConclusionsOur work provided a quantitative baseline for other institutions to further optimize their scanning protocols to reduce the use of ICM.

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State of the Art in Abdominal CT: The Limits of Iterative Reconstruction Algorithms

Cited by 164 publications

References 75 publications

Noise and spatial resolution properties of a commercially available deep learning‐based CT reconstruction algorithm

Noise and spatial resolution properties of a commercially available deep learning‐based CT reconstruction algorithm

Artificial intelligence in image reconstruction: The change is here

An adult and pediatric size‐based contrast administration reduction phantom study for single and dual‐energy CT through preservation of contrast‐to‐noise ratio

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