A photon recycling approach to the denoising of ultra-low dose X-ray sequences

Hariharan, S; Strobel, Norbert; Kaethner, Christian; Kowarschik, Markus; Demirci, Stefanie; Albarqouni, Shadi; Fahrig, Rebecca; Navab, Nassir

doi:10.1007/s11548-018-1746-2

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…However, simple averaging compromises image sharpness, smears edges and produces motion blur. Several authors propose methods which couple filtering processes with edge detection techniques [19, 39, 40]. Although these approaches permit relative good results in terms of noise reduction and edge preservation, the implementation algorithms are still too complex and could compromise the real-time computation for on-line implementation during fluoroscopic exams.…”

Section: Discussionmentioning

confidence: 99%

“…The algorithm proved capable to reduce noise (CNR resulted increased) and, at the same time, not to smooth out image edges. As in recent studies on low-dose X-ray-based imaging denoising [32, 40, 41], the NVCA performances were tested against the current state-of-the-art block-matching four-dimensional VBM4D video denoising. The noise variance of the fluoroscopic sequences considered for the comparison was stabilized via the Anscombe transformation to successfully apply the VBM4D.…”

Section: Discussionmentioning

confidence: 99%

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Real-time algorithm for Poissonian noise reduction in low-dose fluoroscopy: performance evaluation

Sarno

Andreozzi

et al. 2019

BioMed Eng OnLine

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Background Quantum noise intrinsically limits the quality of fluoroscopic images. The lower is the X-ray dose the higher is the noise. Fluoroscopy video processing can enhance image quality and allows further patient’s dose lowering. This study aims to assess the performances achieved by a Noise Variance Conditioned Average (NVCA) spatio-temporal filter for real-time denoising of fluoroscopic sequences. The filter is specifically designed for quantum noise suppression and edge preservation. It is an average filter that excludes neighborhood pixel values exceeding noise statistic limits, by means of a threshold which depends on the local noise standard deviation, to preserve the image spatial resolution. The performances were evaluated in terms of contrast-to-noise-ratio (CNR) increment, image blurring (full width of the half maximum of the line spread function) and computational time. The NVCA filter performances were compared to those achieved by simple moving average filters and the state-of-the-art video denoising block matching-4D (VBM4D) algorithm. The influence of the NVCA filter size and threshold on the final image quality was evaluated too. Results For NVCA filter mask size of 5 × 5 × 5 pixels (the third dimension represents the temporal extent of the filter) and a threshold level equal to 2 times the local noise standard deviation, the NVCA filter achieved a 10% increase of the CNR with respect to the unfiltered sequence, while the VBM4D achieved a 14% increase. In the case of NVCA, the edge blurring did not depend on the speed of the moving objects; on the other hand, the spatial resolution worsened of about 2.2 times by doubling the objects speed with VBM4D. The NVCA mask size and the local noise-threshold level are critical for final image quality. The computational time of the NVCA filter was found to be just few percentages of that required for the VBM4D filter. Conclusions The NVCA filter obtained a better image quality compared to simple moving average filters, and a lower but comparable quality when compared with the VBM4D filter. The NVCA filter showed to preserve edge sharpness, in particular in the case of moving objects (performing even better than VBM4D). The simplicity of the NVCA filter and its low computational burden make this filter suitable for real-time video processing and its hardware implementation is ready to be included in future fluoroscopy devices, offering further lowering of patient’s X-ray dose.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Real-time algorithm for Poissonian noise reduction in low-dose fluoroscopy: performance evaluation

Sarno

Andreozzi

et al. 2019

BioMed Eng OnLine

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“…, as suggested in [14,29]: , can be assumed to have signal-independent noise with unit variance. Figure 3 shows the power spectral densities (PSDs) and the noise power spectra (NPSs) derived in the GAT ¢ y l i 2 , respectively.…”

Section: X-ray Imaging Modelmentioning

confidence: 99%

“…Prominent conventional denoising approaches can be broadly classified as: (i) transform-based techniques that involve sophisticated thresholding of coefficients in a transform domain [2][3][4], (ii) methods that take advantage of self-similar structures in images [5][6][7][8][9][10][11][12][13][14][15], and (iii) variational approaches that are based on partial differential equations [16,17]. For example, block matching 3D (BM3D) [7] (still considered one of the most effective methods [18][19][20]) and denoising based on a weighted nuclear norm minimization (WNNM) [8] are well-known patch-based approaches that combine non-local self similarity with transformbased processing.…”

Section: Introductionmentioning

confidence: 99%

Robust learning-based x-ray image denoising—potential pitfalls, their analysis and solutions

Hariharan¹,

Kaethner²,

Strobel³

et al. 2022

Biomed. Phys. Eng. Express

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Purpose: Since guidance based on X-ray imaging is an integral part of interventional procedures, continuous efforts are taken towards reducing the exposure of patients and clinical staff to ionizing radiation. Even though a reduction in the X-ray dose may lower associated radiation risks, it is likely to impair the quality of the acquired images, potentially making it more difficult for physicians to carry out their procedures. Method: We present a robust learning-based denoising strategy involving model- based simulations of low-dose X-ray images during the training phase. The method also utilizes a data-driven normalization step - based on an X-ray imaging model - to stabilize the mixed signal-dependent noise associated with X-ray images. We thoroughly analyze the method's sensitivity to a mismatch in dose levels used for training and application. We also study the impact of differing noise models used when training for low and very low-dose X-ray images on the denoising results. Results: A quantitative and qualitative analysis based on acquired phantom and clinical data has shown that the proposed learning-based strategy is stable across different dose levels and yields excellent denoising results, if an accurate noise model is applied. We also found that there can be severe artifacts when the noise characteristics of the training images are significantly different from those in the actual images to be processed. This problem can be especially acute at very low dose levels. During a thorough analysis of our experimental results, we further discovered that viewing the results from the perspective of denoising via thresholding of sub-band co efficients can be very beneficial to get a better understanding of the proposed learning-based denoising strategy. Conclusion: The proposed learning-based denoising strategy provides scope for significant X-ray dose reduction without the loss of important image information if the characteristics of noise is accurately accounted for during the training ph

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