Abstract
The use of image denoising techniques is an important part of many medical imaging applications. One common application is to improve the image quality of low-dose (noisy) computed tomography (CT) data. While 3D image denoising previously has been applied to several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considers several volumes at the same time. The problem with 4D image denoising, compared to 2D and 3D denoising, is that the computational complexity increases exponentially. In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implement it on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512 × 512 × 445 × 20. The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFT-based filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutes for FFT-based filtering. The short processing time increases the clinical value of true 4D image denoising significantly.
Highlights
Image denoising is commonly used in medical imaging in order to help medical doctors to see abnormalities in the images
In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implement it on the graphics processing unit (GPU)
The used GPU was a Nvidia GTX 580, equipped with 512 processor cores and 3 GB of memory
Summary
Image denoising is commonly used in medical imaging in order to help medical doctors to see abnormalities in the images. A number of medical imaging modalities (e.g., computed tomography (CT), ultrasound (US) and magnetic resonance imaging (MRI)) provide the possibility to collect 4D data, that is, time-resolved volume data. This makes it possible to, for example, examine what parts of the brain that are active during a certain task (functional magnetic resonance imaging (fMRI)). While 4D CT data makes it possible to see the heart beat in 3D, the drawback is that a lower amount of X-ray exposure has to be used for 4D CT data collection, compared to 3D CT data collection, in order to not harm the patient. When the amount of exposure is decreased, the amount of noise in the data increases significantly
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