Forward-Projected Model-Based Iterative Reconstruction in Screening Low-Dose Chest CT: Comparison With Adaptive Iterative Dose Reduction 3D

Hassani, Cameron; Ronco, Anthony; Prosper, Ashley; Dissanayake, Sumudu N; Cen, Steven; Lee, Christopher

doi:10.2214/ajr.17.19245

Cited by 23 publications

(22 citation statements)

References 28 publications

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“…Our radiation doses (2.2 ± 0.8 mGy) were substantially lower than the prior abdominal CT angiography study with FIRST (7 ± 2.8 mGy) [12]. Despite the differences in radiation doses between our study and that of Wu et al [12], we noticed an improved lesion detection with the LD-FIRST images as compared to the LD-FBP images. Both radiologists detected more lesions on LD-FIRST images than on LD-FBP images.…”

Section: Discussioncontrasting

confidence: 71%

“…FIRST algorithm has been evaluated for radiation dose reduction in patients with lung nodules [14,17] and those undergoing CT angiography of the abdomen [12] or coronary arteries [13,[18], [19], [20]]. Our radiation doses (2.2 ± 0.8 mGy) were substantially lower than the prior abdominal CT angiography study with FIRST (7 ± 2.8 mGy) [12]. Despite the differences in radiation doses between our study and that of Wu et al [12], we noticed an improved lesion detection with the LD-FIRST images as compared to the LD-FBP images.…”

Section: Discussionmentioning

confidence: 99%

“…The recently introduced Forward projected model-based Iterative Reconstruction SoluTion (FIRST) deploys a three-dimensional (3D) image reconstruction approach based on a forward and statistical model in the projection data to enhance the spatial resolution and reduce the image noise [12]. Compared to AIDR-3D, the FIRST optimizes image quality jointly in sinogram and image spaces [13].…”

Section: Introductionmentioning

confidence: 99%

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Can fully iterative reconstruction technique enable routine abdominal CT at less than 1 mSv?

Tabari

Ramandeep

Khera

et al. 2019

European Journal of Radiology Open

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Objective We assessed the effect of the forward projected model-based reconstruction technique (FIRST) on lesion detection of routine abdomen CT at <1 mSv. Materials and methods Thirty-seven adult patients gave written informed consent for acquisition of low-dose CT (LDCT) immediately after their clinically-indicated, standard of care dose (SDCT), routine abdomen CT on a 640-slice MDCT (Aquillion One, Canon Medical System). The LDCT series were reconstructed with FIRST (at STD (Standard) and STR (Strong) levels), and SDCT series with filtered back projection (FBP). Two radiologists assessed lesions in LD-FBP and FIRST images followed by SDCT images. Then, SDCT and LDCT were compared for presence of artifacts in a randomized and blinded fashion. Patient demographics, size and radiation dose descriptors (CTDIvol, DLP) were recorded. Descriptive statistics and inter-observer variability were calculated for data analysis. Results Mean CTDIvol for SDCT and LDCT were 13 ± 4.7 mGy and 2.2 ± 0.8 mGy, respectively. There were 46 true positive lesions detected on SDCT. Radiologists detected 38/46 lesions on LD-FIRST-STD compared to 26/46 lesions on LD-FIRST-STR. The eight lesions (liver and kidney cysts, pancreatic lesions, sub-cm peritoneal lymph node) missed on LD-FIRST-STD were seen in patients with BMI > 25.8 kg/m 2 . Diagnostic confidence for lesion assessment was optimal in LD-FIRST-STD setting in most patients regardless of their size. The inter-observer agreement (kappa-value) for overall image quality were 0.98 and 0.84 for LD-FIRST-STD and STR levels, respectively. Conclusion FIRST enabled optimal lesion detection in routine abdomen CT at less than 1 mSv radiation dose in patients with body mass less than ≤25.8 kg/m 2 .

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Section: Discussioncontrasting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Can fully iterative reconstruction technique enable routine abdominal CT at less than 1 mSv?

Tabari

Ramandeep

Khera

et al. 2019

European Journal of Radiology Open

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“…Under prudent assumptions of CTDI vol = 1.5 mGy per CT screening, a dose and dose-rate effectiveness factor (DDREF) of 1.0 for all organ-tumor entities, and conservative weightings of absolute (additive) vs. relative (multiplicative) risk increases, the German Federal Office for Radiation Protection (Bundesamt für Strahlenschutz, BfS) estimated that, in the German population, annual screening between the ages of 50 and 54, with an effective radiation dose of 1.5 mSv per CT scan, may cause a lifelong risk of death from radiation-induced cancer (lung, breast, and other major cancers) of 0.07 % among women and about 0.03 % among men [20]. With improving technology, however, the effective LDCT dose per CT scan is likely to decrease to 1.0 mSv or even lower in the near future [21,22].…”

Section: Radiation Risksmentioning

confidence: 99%

Lung Cancer Screening by Low-Dose Computed Tomography – Part 1: Expected Benefits, Possible Harms, and Criteria for Eligibility and Population Targeting

Kaaks

Delorme

2020

Rofo

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Background Trials in the USA and Europe have convincingly demonstrated the efficacy of screening by low-dose computed tomography (LDCT) as a means to lower lung cancer mortality, but also document potential harms related to radiation, psychosocial stress, and invasive examinations triggered by false-positive screening tests and overdiagnosis. To ensure that benefits (lung cancer deaths averted; life years gained) outweigh the risk of harm, lung cancer screening should be targeted exclusively to individuals who have an elevated risk of lung cancer, plus sufficient residual life expectancy. Methods and Conclusions Overall, randomized screening trials show an approximate 20 % reduction in lung cancer mortality by LDCT screening. In view of declining residual life expectancy, especially among continuing long-term smokers, risk of being over-diagnosed is likely to increase rapidly above the age of 75. In contrast, before age 50, the incidence of LC may be generally too low for screening to provide a positive balance of benefits to harms and financial costs. Concise criteria as used in the NLST or NELSON trials may provide a basic guideline for screening eligibility. An alternative would be the use of risk prediction models based on smoking history, sex, and age as a continuous risk factor. Compared to concise criteria, such models have been found to identify a 10 % to 20 % larger number of LC patients for an equivalent number of individuals to be screened, and additionally may help provide security that screening participants will all have a high-enough LC risk to balance out harm potentially caused by radiation or false-positive screening tests. Key Points: Citation Format

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“…Its potential for diagnostic CT imaging has been shown in a number of studies. [1][2][3][4][5][6][7][8][9] A popular MBIR formulation is penalized weighted least squares (PWLS) reconstruction, 10 which includes two key components: (a) the data fidelity term, which is characterized by the choice of a forward projection model and the option of a statistical weighting of the projections; and (b) the penalty term, which defines a regularization process often involving a potential function applied to voxel differences, together with additional incorporation of a priori knowledge, such as the non-negativity constraint.…”

Section: Introductionmentioning

confidence: 99%

Impact of the non‐negativity constraint in model‐based iterative reconstruction from CT data

et al. 2019

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Purpose Model‐based iterative reconstruction is a promising approach to achieve dose reduction without affecting image quality in diagnostic x‐ray computed tomography (CT). In the problem formulation, it is common to enforce non‐negative values to accommodate the physical non‐negativity of x‐ray attenuation. Using this a priori information is believed to be beneficial in terms of image quality and convergence speed. However, enforcing non‐negativity imposes limitations on the problem formulation and the choice of optimization algorithm. For these reasons, it is critical to understand the value of the non‐negativity constraint. In this work, we present an investigation that sheds light on the impact of this constraint. Methods We primarily focus our investigation on the examination of properties of the converged solution. To avoid any possibly confounding bias, the reconstructions are all performed using a provably converging algorithm started from a zero volume. To keep the computational cost manageable, an axial CT scanning geometry with narrow collimation is employed. The investigation is divided into five experimental studies that challenge the non‐negativity constraint in various ways, including noise, beam hardening, parametric choices, truncation, and photon starvation. These studies are complemented by a sixth one that examines the effect of using ordered subsets to obtain a satisfactory approximate result within 50 iterations. All studies are based on real data, which come from three phantom scans and one clinical patient scan. The reconstructions with and without the non‐negativity constraint are compared in terms of image similarity and convergence speed. In select cases, the image similarity evaluation is augmented with quantitative image quality metrics such as the noise power spectrum and closeness to a known ground truth. Results For cases with moderate inconsistencies in the data, associated with noise and bone‐induced beam hardening, our results show that the non‐negativity constraint offers little benefit. By varying the regularization parameters in one of the studies, we observed that sufficient edge‐preserving regularization tends to dilute the value of the constraint. For cases with strong data inconsistencies, the results are mixed: the constraint can be both beneficial and deleterious; in either case, however, the difference between using the constraint or not is small relative to the overall level of error in the image. The results with ordered subsets are encouraging in that they show similar observations. In terms of convergence speed, we only observed one major effect, in the study with data truncation; this effect favored the use of the constraint, but had no impact on our ability to obtain the converged solution without constraint. Conclusions Our results did not highlight the non‐negativity constraint as being strongly beneficial for diagnostic CT imaging. Altogether, we thus conclude that in some imaging scenarios, the non‐negativity constraint could be disregarded to simplify the op...

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Forward-Projected Model-Based Iterative Reconstruction in Screening Low-Dose Chest CT: Comparison With Adaptive Iterative Dose Reduction 3D

Abstract: Images reconstructed with FIRST are superior to those reconstructed AIDR 3D with regard to image noise and are equivalent with regard to subjective image quality, pulmonary nodule count, and nodule characterization.

Cited by 23 publications

References 28 publications

Can fully iterative reconstruction technique enable routine abdominal CT at less than 1 mSv?

Can fully iterative reconstruction technique enable routine abdominal CT at less than 1 mSv?

Lung Cancer Screening by Low-Dose Computed Tomography – Part 1: Expected Benefits, Possible Harms, and Criteria for Eligibility and Population Targeting

Impact of the non‐negativity constraint in model‐based iterative reconstruction from CT data

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