Temporal and volumetric denoising via quantile sparse image prior

Abstract

This paper introduces an universal and structure-preserving regularization term, called quantile sparse image (QuaSI) prior. The prior is suitable for denoising images from various medical imaging modalities. We demonstrate its effectiveness on volumetric optical coherence tomography (OCT) and computed tomography (CT) data, which show different noise and image characteristics. OCT offers high-resolution scans of the human retina but is inherently impaired by speckle noise. CT on the other hand has a lower resolution and shows high-frequency noise. For the purpose of denoising, we propose a variational framework based on the QuaSI prior and a Huber data fidelity model that can handle 3-D and 3-D+t data. Efficient optimization is facilitated through the use of an alternating direction method of multipliers (ADMM) scheme and the linearization of the quantile filter. Experiments on multiple datasets emphasize the excellent performance of the proposed method.

Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

“…Medical imaging uses many different methods such as magnetic resonance (MR) imaging [1][2][3][4][5][6][7][8], radiography [4,[9][10][11], radionuclide [8,12], optical [11,13,14], ultrasound [1,15] and medical robotics [16,17]. The typical medical imaging system consists of three components (Figure 1): data acquisition, data consolidation and data processing.…”

“…Rheological methods of increasing MR elastography resolution determine viscoelastic properties through wave inversion, which is highly ill posed and sensitive to noise [1]. In radiology using computed tomography (CT) or related morphological imaging modalities, noise affects the analysis of anatomical structures and thus impedes diagnostic applications [11]. Low dose radiation exposure for patient safety leads to noisy and low-contrast fluoroscopic sequences [11].…”

“…In radiology using computed tomography (CT) or related morphological imaging modalities, noise affects the analysis of anatomical structures and thus impedes diagnostic applications [11]. Low dose radiation exposure for patient safety leads to noisy and low-contrast fluoroscopic sequences [11]. The reconstruction process of the positron emission tomography images includes inherent multiplicative noise, which prevents the analysis of visual data [12].…”

“…The reconstruction process of the positron emission tomography images includes inherent multiplicative noise, which prevents the analysis of visual data [12]. In optical CT for retinal imaging as another example use case, noise limits the measurement of structural features in the human eye, e.g., retinal layer properties [11]. Denoising facilitates visual data interpretation from echocardiography [15].…”

“…Various transforms are used to solve problems of 2D and 3D medical images denoising and compression in practice. The most common of them are discrete Fourier transform (DFT) [3,7,14,22] and discrete wavelet transform (DWT) [1,9,11,14]. DFT is widely used in the frequency domain but the domain characteristics disappeared after it.…”