Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT

Abstract

Purpose: To investigate a novel locally adaptive projection space denoising algorithm for low-dose CT data. Methods: The denoising algorithm is based on bilateral filtering, which smooths values using a weighted average in a local neighborhood, with weights determined according to both spatial proximity and intensity similarity between the center pixel and the neighboring pixels. This filtering is locally adaptive and can preserve important edge information in the sinogram, thus maintaining high spatial resolution. A CT noise model that takes into account the bowtie filter and patientspecific automatic exposure control effects is also incorporated into the denoising process. The authors evaluated the noise-resolution properties of bilateral filtering incorporating such a CT noise model in phantom studies and preliminary patient studies with contrast-enhanced abdominal CT exams. Results: On a thin wire phantom, the noise-resolution properties were significantly improved with the denoising algorithm compared to commercial reconstruction kernels. The noise-resolution properties on low-dose ͑40 mA s͒ data after denoising approximated those of conventional reconstructions at twice the dose level. A separate contrast plate phantom showed improved depiction of low-contrast plates with the denoising algorithm over conventional reconstructions when noise levels were matched. Similar improvement in noise-resolution properties was found on CT colonography data and on five abdominal low-energy ͑80 kV͒ CT exams. In each abdominal case, a board-certified subspecialized radiologist rated the denoised 80 kV images markedly superior in image quality compared to the commercially available reconstructions, and denoising improved the image quality to the point where the 80 kV images alone were considered to be of diagnostic quality. Conclusions:The results demonstrate that bilateral filtering incorporating a CT noise model can achieve a significantly better noise-resolution trade-off than a series of commercial reconstruction kernels. This improvement in noise-resolution properties can be used for improving image quality in CT and can be translated into substantial dose reduction.

Achieving Routine Submillisievert CT Scanning: Report from the Summit on Management of Radiation Dose in CT

“…Some of these techniques are already commercially available, including automatic exposure control (10)(11)(12), automatic tube potential selection (13), beam-shaping filters, and dynamic z-axis collimators (14,15). Others, such as iterative reconstruction algorithms and noise reduction methods, are just becoming widely available (16)(17)(18)(19)(20)(21)(22)(23). Together, these techniques can reduce dose by a factor of two to four (Table).…”

“…In contrast to iterative reconstruction methods, which form an image from the projection data, noise reduction techniques only reduce noise levels, either in the projection data (17)(18)(19)23,(51)(52)(53) or on the reconstructed image (22,54). If the noise reduction is performed on projection data, image reconstruction must still be able to be produced from measurements made by using low photon intensities.…”

Automated Detection of Z-Axis Coverage with Abdomen-Pelvis Computed Tomography Examinations

“…A number of advanced technologies and practice changes are now used to lower radiation dose with CT, such as automatic tube current modulation (ATCM) [2], a variety of de-noising techniques enabling reduced patient exposure [3], reducing kVp for smaller patient size [4], and reducing the number of contrast phase acquisitions [5]. One additional source of potential unnecessary exposure to a patient can occur due to excessive Z-axis coverage.…”

Monitoring of train-induced vibrations on rock slopes

“…This signal preprocessing includes the following three steps: removal of the direct current (DC) signal component, denoising and filtering. 19,20 DC removal. The DC signal component, which should be removed from the original signal, was estimated using the mathematical expectation value calculated from the recorded signals.…”