Noise reduction in low-dose ct using a 3D multiscale sparse denoising autoencoder

Mentl, Katrin; Mailhé, Boris; Ghesu, Florin C.; Schebesch, Frank; Haderlein, Tino; Maier, Andreas; Nadar, Mariappan S.

doi:10.1109/mlsp.2017.8168176

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…The VM images provide data at a particular energy value, and may provide improved soft tissue contrast (Noid et al 2018), particularly at low energies. However, the increased contrast often comes at the expense of increased image noise (Gondara 2016, Hatton et al 2009, Mentl et al 2017. To evaluate this effect, the contrast-to-noise ratio (CNR) was assessed and defined as:…”

Section: Contrast-to-noise Ratiomentioning

confidence: 99%

Fast-switching dual energy cone beam computed tomography using the on-board imager of a commercial linear accelerator

et al. 2020

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Purpose: To evaluate fast-kV switching (FS) dual energy (DE) cone beam computed tomography (CBCT) using the on-board imager (OBI) of a commercial linear accelerator to produce virtual monoenergetic (VM) and relative electron density (RED) images. Methods:Using an polynomial attenuation mapping model, CBCT phantom projections obtained at 80 and 140 kVp with FS imaging, were decomposed into equivalent thicknesses of aluminum (Al) and polymethyl methacrylate (PMMA). All projections were obtained with the titanium foil and bowtie filter in place. Basis material projections were then recombined to create VM images by using the linear attenuation coefficients at the specified energy for each material. Similarly, RED images were produced by replacing the linear attenuation values of Al and PMMA by their respective relative electron density (RED) values in the projection space. VM and RED images were reconstructed using Feldkamp-Davis-Kress (FDK) and an iterative algorithm (iCBCT, Varian Medical Systems). Hounsfield units (HU), contrast-to-noise ratio (CNR) and RED values were compared against known values. Results:The results after VM-CBCT production showed good material decomposition and consistent HU VM values, with measured root mean square errors (RMSE) from theoretical values, after FDK reconstruction, of 20.5, 5.7, 12.8 and 21.7 HU for 50, 80, 100 and 150 keV, respectively. The largest CNR improvements, when compared to polychromatic images, were observed for the 50 keV VM images. Image noise was reduced up to 28% in the VM-CBCT images after iterative image reconstruction. Relative electron density values measured for our method resulted in a mean percentage error of 0.0 ± 1.8%. Conclusions:This study describes a method to generate VM-CBCT and RED images using FS-DE scans obtained using the OBI of a linac, including the effects of the bowtie filter. The creation of VM and RED images increases the dynamic range of CBCT images, and provides additional

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