Remote visualization of volumetric data has gained importance over the past few years in order to realize the full potential of tele-radiology. Volume rendering is a computationally intensive process, often requiring hardware acceleration to achieve real time visualization. Hence a remote visualization model that is well-suited for high speed networks would be to transmit rendered images from the server (with dedicated hardware) based on view point requests from clients. In this regard, a compression scheme for the rendered images is vital for efficient utilization of the server-client bandwidth. Also, the complexity of the decompressor should be considered so that
a low end client workstation can decode images at the desired frame rate. We present a scalable low complexity image coder that has good compression efficiency and high throughput.
Proc. SPIE. 7073, Applications of Digital Image Processing XXXI
KEYWORDS: Magnetic resonance imaging, Image restoration, Fourier transforms, Receivers, Data acquisition, Photonic integrated circuits, In vivo imaging, Detection theory, Alternate lighting of surfaces, Compressed sensing
Although magnetic resonance imaging (MRI) is routinely used in clinical practice, long acquisition times limit its practical utility in many applications. To increase the data acquisition speed of MRI, parallel MRI (pMRI) techniques have recently been proposed. These techniques utilize multi-channel receiver arrays and are based on simultaneous acquisition of data from multiple receiver coils. Recently, a novel framework called Compressed Sensing (CS) was introduced. Since this new framework illustrates how signals can be
reconstructed from much fewer samples than suggested by the Nyquist theory, it has the potential to significantly accelerate data acquisition in MRI. This paper illustrates that CS and pMRI techniques can be combined and such joint processing yields results that are superior to those obtained from independent utilization of each technique.
Many medical imaging techniques available today generate 4D data sets. One such technique is functional magnetic resonance imaging (fMRI) which aims to determine regions of the brain that are activated due to various cognitive and/or motor functions or sensory stimuli. These data sets often require substantial resources for storage and transmission and hence call for efficient compression algorithms. fMRI data can be seen as a time-series of 3D images of the brain. Many different strategies can be employed for compressing such data. One possibility is to treat each 2D slice independently. Alternatively, it is also possible to compress each 3D image independently. Such methods do not fully exploit the redundancy present in 4D data. In this work, methods using 4D wavelet transforms are proposed. They are compared to different 2D and 3D methods. The proposed schemes are based on JPEG2000, which is included in the DICOM standard as a transfer syntax. Methodologies to test the effects of lossy compression on the end result of fMRI analysis are introduced and used to compare different compression algorithms.