A Novel Low-Dose Dual-Energy Imaging Method for a Fast-Rotating Gantry-Type CT Scanner

Cho, Sanghoon; Lee, Seo-Young; Lee, Jong-Ha; Lee, Donghyeon; Kim, Hyoyi; Ryu, Jong-Hyun; Jeong, Kilhwan; Kim, Kyu-Gyum; Yoon, Kwon‐Ha; Cho, Seungryong

doi:10.1109/tmi.2020.3044357

Cited by 9 publications

(6 citation statements)

References 54 publications

(48 reference statements)

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“…The innovation of the proposed method compared with previously reported beam-modulated single-scan DECT methods (Lee et al, 2017;Cho et al, 2020;Petrongolo and Zhu, 2018) is mainly three-fold: small primary loss, deep learning-based data restoration scheme and easy manipulation. First of all, the proposed method employs the detector modulation scheme to reduce the dimension of the penumbra area compared with the source-side modulation methods by up to 80%.…”

Section: Discussionmentioning

confidence: 99%

“…Strips with equal width and spacing are employed in this work to realize uniform modulation frequency across the whole object. In this work, 5-mm strip width is empirically determined to construct the beam modulator, which has a higher modulation frequency and reduced penumbra areas compared to previous beam-modulation works (Petrongolo and Zhu, 2018;Lee et al, 2017;Cho et al, 2020).…”

Section: Implementation Detailsmentioning

confidence: 99%

“…where 𝜌 B ABC is the reference value of electron density for each ROI provided in the phantom's user manual. Based on the modulator selection in previous works (Lee et al, 2017;Petrongolo and Zhu, 2018;Cho et al, 2020), six metal sheets including tin and molybdenum from 0.1 to 0.3 mm act as candidates in the strip thickness optimization study. To perform material decomposition and subsequent electron density calculation, the electron density phantom is scanned twice -with and without the metal sheet covering the detector surface.…”

Section: Image Quality Metricsmentioning

confidence: 99%

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Single-scan half-fan DECT using static detector modulation: a preliminary study

Peng

2022

Medical Imaging 2022: Physics of Medical Imaging

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Cone-beam CT (CBCT) is installed in the treatment room to facilitate online clinical applications, including image guidance in radiation and surgery. Half-fan and short-can are the commonly used modes in clinical applications to expand the imaging volume and reduce radiation exposure. Anatomical structures are faithfully reconstructed using CBCT while deep information including material composition is not feasible in its current form. Dual-energy computed tomography (DECT) is an advanced medical imaging technology with superior capability of material differentiation by scanning the patient using two different energy spectra. Conventional DECT systems require expensive and sophisticated hardware components, significantly limiting DECT applications to cone-beam CT (CBCT) machines. In this work, we propose a dual-energy cone-beam CT (DECBCT) system by placing a dedicatedly designed beam modulator in front of the detector surface to acquire dual-energy projection data in a single scan. A deep learning-based data restoration model is introduced to generate complete dual-energy data from the detector-modulated data for DECT image reconstruction and material decomposition. The performance of the proposed method is evaluated using an electron density phantom and clinical CT. The mean error for electron density calculation is lower than 1.16% in the electron density phantom study. The mean errors for reconstructed linear attenuation coefficient (LAC) are lower than 1.41% and 0.89% in the head and pelvis studies, respectively. The results indicate that the proposed method is a promising solution for single-scan DECBCT in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Section: Implementation Detailsmentioning

confidence: 99%

Section: Image Quality Metricsmentioning

confidence: 99%

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Single-scan half-fan DECT using static detector modulation: a preliminary study

Peng

2022

Medical Imaging 2022: Physics of Medical Imaging

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“…Lee et al, designed a multi‐slit filter and acquired DE‐CBCT data via linear reciprocation motion of the filter 31 . This method was then adopted by Cho et al, for fast‐rotating gantry‐type CT scanner 37 . Petrongolo and Zhu proposed to achieve single‐scan DECT/DE‐CBCT via inserting a stationary primary modulator, 36 which is also promising for simultaneous cone‐beam scatter removal and DE‐CBCT imaging.…”

Section: Introductionmentioning

confidence: 99%

“…31 This method was then adopted by Cho et al, for fast-rotating gantry-type CT scanner. 37 Petrongolo and Zhu proposed to achieve single-scan DECT/DE-CBCT via inserting a stationary primary modulator, 36 which is also promising for simultaneous cone-beam scatter removal and DE-CBCT imaging. All of these methods enabled single-scan DE-CBCT, and achieved satisfied performance in dual-energy imaging and material decomposition.…”

Section: Introductionmentioning

confidence: 99%

Fast and effective single‐scan dual‐energy cone‐beam CT reconstruction and decomposition denoising based on dual‐energy vectorization

Jiang

Fang

et al. 2021

Medical Physics

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Purpose Flat‐panel detector (FPD) based dual‐energy cone‐beam computed tomography (DE‐CBCT) is a promising imaging technique for dedicated clinical applications. In this paper, we proposed a fully analytical method for fast and effective single‐scan DE‐CBCT image reconstruction and decomposition. Methods A rotatable Mo filter was inserted between an x‐ray source and imaged object to alternately produce low and high‐energy x‐ray spectra. First, filtered‐backprojection (FBP) method was applied on down‐sampled projections to reconstruct low and high‐energy images. Then, the two images were converted into a vectorized form represented with an amplitude and an argument image. Using amplitude image as a guide, a joint bilateral filter was applied to denoise the argument image. Then, high‐quality dual‐energy images were recovered from the amplitude image and the denoised argument image. Finally, the recovered dual‐energy images were further used for low‐noise material decomposition and electron density synthesis. Imaging was conducted on a Catphan®600 phantom and an anthropomorphic head phantom. The proposed method was evaluated via comparison with the traditional two‐scan method and a commonly used filtering method (HYPR‐LR). Results On the Catphan®600 phantom, the proposed method successfully reduced streaking artifacts and preserved spatial resolution and noise‐power‐spectrum (NPS) pattern. In the electron density image, the proposed method increased contrast‐to‐noise ratio (CNR) by more than 2.5 times and achieved <1.2% error for electron density values. On the anthropomorphic head phantom, the proposed method greatly improved the soft‐tissue contrast and the fine detail differentiation ability. In the selected ROIs on different human tissues, the differences between the CT number obtained by the proposed method and that by the two‐scan method were less than 4 HU. In the material images, the proposed method suppressed noise by over 75.5% compared with two‐scan results, and by over 40.4% compared with HYPR‐LR results. Implementation of the whole algorithm took 44.5 s for volumetric imaging, including projection preprocessing, FBP reconstruction, joint bilateral filtering, and material decomposition. Conclusions Using down‐sampled projections in single‐scan DE‐CBCT, the proposed method could effectively and efficiently produce high‐quality DE‐CBCT images and low‐noise material decomposition images. This method demonstrated superior performance on spatial resolution enhancement, NPS preservation, noise reduction, and electron density accuracy, indicating better prospect in material differentiation and dose calculation.

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A feasibility study on deep‐neural‐network‐based dose‐neutral dual‐energy digital breast tomosynthesis

Kim

Lee

et al. 2022

Medical Physics

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Background: Diagnostic performance based on x-ray breast imaging is subject to breast density. Although digital breast tomosynthesis (DBT) is reported to outperform conventional mammography in denser breasts, mass detection and malignancy characterization are often considered challenging yet. Purpose: As an improved diagnostic solution to the dense breast cases, we propose a dual-energy DBT imaging technique that enables breast compositional imaging at comparable scanning time and patient dose compared to the conventional single-energy DBT. Methods: The proposed dual-energy DBT acquires projection data by alternating two different energy spectra. Then, we synthesize unmeasured projection data using a deep neural network that exploits the measured projection data and adjacent projection data obtained under the other x-ray energy spectrum. For material decomposition, we estimate partial path lengths of an x-ray through water, lipid, and protein from the measured and the synthesized projection data with the object thickness information. After material decomposition in the projection domain, we reconstruct material-selective DBT images. The deep neural network is trained with the numerical breast phantoms. A pork meat phantom is scanned with a prototype dual-energy DBT system to demonstrate the feasibility of the proposed imaging method. Results: The developed deep neural network successfully synthesized missing projections. Material-selective images reconstructed from the synthesized data present comparable compositional contrast of the cancerous masses compared with those from the fully measured data. Conclusions: The proposed dual-energy DBT scheme is expected to substantially contribute to enhancing mass malignancy detection accuracy particularly in dense breasts. K E Y W O R D Sdeep learning, digital breast tomosynthesis, dual-energy x-ray imaging, image reconstruction, material decomposition Hyeongseok Kim and Hoyeon Lee contributed equally to this work.

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A Novel Low-Dose Dual-Energy Imaging Method for a Fast-Rotating Gantry-Type CT Scanner

Cited by 9 publications

References 54 publications

Single-scan half-fan DECT using static detector modulation: a preliminary study

Single-scan half-fan DECT using static detector modulation: a preliminary study

Fast and effective single‐scan dual‐energy cone‐beam CT reconstruction and decomposition denoising based on dual‐energy vectorization

A feasibility study on deep‐neural‐network‐based dose‐neutral dual‐energy digital breast tomosynthesis

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