Noise-optimized advanced image-based virtual monoenergetic imaging for improved visualization of lung cancer: Comparison with traditional virtual monoenergetic imaging

Frellesen, Claudia; Kaup, Moritz; Wichmann, Julian L.; Hüsers, Kristina; Scholtz, Jan-Erik; Albrecht, Moritz H.; Metzger, Sarah C.; Bauer, Ralf W.; Kerl, J. Matthias; Lehnert, Thomas; Vogl, Thomas J.; Bodelle, Boris

doi:10.1016/j.ejrad.2015.12.022

Cited by 33 publications

(14 citation statements)

References 29 publications

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“…The high CNR of low‐keV (i.e., 40–60 keV) VMI was enabled by a commercially available, frequency‐split algorithm (syngo.CT DE Monoenergetic Plus, Siemens Healthcare) designed to preserve low‐frequency components of low‐keV (high contrast) and high‐frequency components (low noise) provided by 70 keV VMI . This algorithm allows clinicians to take advantage of the increased iodine contrast, and low‐keV VMIs are being rapidly adopted in multiple applications . However, the perceived noise in low‐keV images is still high, limiting their clinical acceptance.…”

Section: Discussionmentioning

confidence: 99%

“…6 This algorithm allows clinicians to take advantage of the increased iodine contrast, and low-keV VMIs are being rapidly adopted in multiple applications. 7,10,[12][13][14][15][16] However, the perceived noise in low-keV images is still high, limiting their clinical acceptance. Adaptation of the window settings can be used to overcome this limitation.…”

Section: Discussionmentioning

confidence: 99%

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Optimizing window settings for improved presentation of virtual monoenergetic images in dual‐energy computed tomography

Marin

Ramírez-Giraldo

et al. 2017

Medical Physics

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Optimizing window settings for improved presentation of virtual monoenergetic images in dual‐energy computed tomography

Marin

Ramírez-Giraldo

et al. 2017

Medical Physics

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“…For thoracic oncologic CT applications, studies suggest that subjective image quality for visualization of lung carcinoma is improved at 55–70 keV [ 9 10 ] compared with conventional CT scans. In thoracic oncology, DECT can be used to differentiate benign and malignant pulmonary nodules and masses [ 11 ].…”

Section: Techniquesmentioning

confidence: 99%

Clinical Applications of Dual-Energy CT

Hamid

Nasir

et al. 2021

Korean J Radiol

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Dual-energy CT (DECT) provides insights into the material properties of tissues and can differentiate between tissues with similar attenuation on conventional single-energy imaging. In the conventional CT scanner, differences in the X-ray attenuation between adjacent structures are dependent on the atomic number of the materials involved, whereas in DECT, the difference in the attenuation is dependent on both the atomic number and electron density. The basic principle of DECT is to obtain two datasets with different X-ray energy levels from the same anatomic region and material decomposition based on attenuation differences at different energy levels. In this article, we discuss the clinical applications of DECT and its potential robust improvements in performance and postprocessing capabilities.

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“…At lower x-ray energy levels, the noise in VMIs is substantially increased, which may compromise the benefit from increased image contrast, and consequently result in a decreased iodine contrast to noise ratio (CNR). Various methods have been developed to reduce image noise in VMI, such as that based on a spatial frequency-split technique (Grant et al 2014, Frellesen et al 2016), anti-correlated noise reduction (Kalisz et al 2018), edge-preserving filter-based denoising (Li et al 2014), as well as techniques based on learned dictionary (Mechlem et al 2017, Xie et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Improving iodine contrast to noise ratio using virtual monoenergetic imaging and prior-knowledge-aware iterative denoising (mono-PKAID)

et al. 2019

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Multi-energy CT acquires simultaneous multiple x-ray attenuation measurements from different energy spectra which facilitates the computation of virtual monoenergetic images (VMI) at a specific photon energy (keV). Since the contrast between iodine attenuation and the attenuation of surrounding soft tissues increases at lower x-ray energies, VMIs in the range of 40–70 keV can be used to improve iodine visualization. However, at lower energy levels, image noise in VMIs is substantially increased, which counteracts the benefits from the increased iodine contrast, resulting in a decreased iodine contrast-to-noise ratio (CNR). There exists considerable data redundancy between multi-energy CT images created from the same acquisition. Similarly, a substantial spatio-spectral data redundancy exists between multi-energy CT images and the corresponding VMIs. In this work, we develop a denoising framework that exploits this data redundancy to improve iodine CNR in the VMIs. We accomplish this by applying prior-knowledge-aware iterative denoising to low-energy VMIs; we refer to the denoised images as mono-PKAID images. The proposed framework was evaluated using phantom and in vivo data acquired on a research whole-body photon-counting-detector CT, as well as using data from a commercial dual-source dual-energy CT system. The results of phantom experiments show that the proposed framework can preserve image resolution and noise texture compared to the original VMIs, while reducing noise to improve iodine CNR. Quantitative measurements show that the iodine CNR of 50 keV VMI is improved by 1.8-fold using the proposed method, relative to the VMI produced using commercial software (Mono+). With mono-PKAID, VMIs at lower keV take full advantage of higher iodine contrast without substantially increasing image noise. These observations were confirmed using patient data sets, which demonstrated that mono-PKAID reduced image noise, improved CNR in anatomical regions with iodine perfusion by 1.8-fold, and potentially enhanced the visibility of focal liver lesions.

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Noise-optimized advanced image-based virtual monoenergetic imaging for improved visualization of lung cancer: Comparison with traditional virtual monoenergetic imaging

Cited by 33 publications

References 29 publications

Optimizing window settings for improved presentation of virtual monoenergetic images in dual‐energy computed tomography

Optimizing window settings for improved presentation of virtual monoenergetic images in dual‐energy computed tomography

Clinical Applications of Dual-Energy CT

Improving iodine contrast to noise ratio using virtual monoenergetic imaging and prior-knowledge-aware iterative denoising (mono-PKAID)

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