KEYWORDS: Digital signal processing, Digital filtering, Filtering (signal processing), 3D image processing, Signal processing, Image filtering, Medical imaging, Image processing, Imaging devices, Ultrasonography
Portable medical imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. Despite their constraints on power, size and cost, portable imaging devices must still deliver high quality images.
3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often cannot be run with sufficient performance on a portable platform.
In recent years, advanced multicore digital signal processors (DSP) have been developed that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms on a portable platform.
In this study, the performance of a 3D adaptive filtering algorithm on a DSP is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec with an Ultrasound 3D probe. Relative performance and power is addressed between a reference PC (Quad Core CPU) and a TMS320C6678 DSP from Texas Instruments.
KEYWORDS: Digital signal processing, Filtering (signal processing), Digital filtering, Image filtering, 3D image processing, Signal processing, Image processing, Convolution, Ultrasonography, Imaging devices
Portable imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often not able to run with sufficient performance on a portable platform. In recent years, advanced multicore DSPs have been introduced that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms like 3D adaptive filtering, improving the image quality of portable medical imaging devices. In this study, the performance of a 3D adaptive filtering algorithm on a digital signal processor (DSP) is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec.
The transition of optical coherence tomography (OCT) technology from the lab environment towards the more challenging clinical and point-of-care settings is continuing at a rapid pace. On one hand this translation opens new opportunities and avenues for growth, while on the other hand it also presents a new set of challenges and constraints under which OCT systems have to operate. OCT systems in the clinical environment are not only required to be user friendly and easy to operate, but should also be portable, have a smaller form factor coupled with low cost and reduced power consumption. Digital signal processors (DSP) are in a unique position to satisfy the computational requirements for OCT at a much lower cost and power consumption compared to the existing platforms such as CPU and graphics processing units (GPUs). In this work, we describe the implementation of optical coherence tomography (OCT) and interferometric synthetic aperture microscopy (ISAM) processing on a floating point multi-core DSP (C6678, Texas Instruments). ISAM is a computationally intensive data processing technique that is based on the re-sampling of the Fourier space of the data to yield spatially invariant transverse resolution in OCT. Preliminary results indicate that 2DISAM processing at 70,000 A-lines/sec and OCT at 180,000 A-lines/sec can be achieved with the current implementation using available DSP hardware.
Low dose X-ray image sequences, as obtained in fluoroscopy, exhibit high levels of noise that must be
suppressed in real-time, while preserving diagnostic structures. Multi-step adaptive filtering approaches, often
involving spatio-temporal filters, are typically used to achieve this goal. In this work typical fluoroscopic image
sequences, corrupted with Poisson noise, were processed using various filtering schemes. The noise
suppression of the schemes was evaluated using objective image quality measures. Two adaptive spatio-temporal
schemes, the first one using object detection and the second one using unsharp masking, were
chosen as representative approaches for different fluoroscopy procedures and mapped on to Texas
Instrument's (TI) high performance digital signal processors (DSP). The paper explains the fixed point design
of these algorithms and evaluates its impact on overall system performance. The fixed point versions of these
algorithms are mapped onto the C64x+TM core using instruction-level parallelism to effectively use its VLIW
architecture. The overall data flow was carefully planned to reduce cache and data movement overhead,
while working with large medical data sets. Apart from mapping these algorithms on to TI's single core DSP
architecture, this work also distributes the operations to leverage multi-core DSP architectures. The data
arrangement and flow were optimized to minimize inter-processor messaging and data movement overhead.
KEYWORDS: Digital signal processing, Signal processing, Optical coherence tomography, Imaging systems, Image restoration, Optical signal processing, Coherence imaging, Image processing, Coherence (optics), Surgery
Optical Coherence Tomography (OCT) imaging is a high-resolution, sub-surface non-invasive imaging technique,
using the principle of low coherence interferometry, that has become increasingly popular for various applications for
structural and quantitative imaging [1]. Applications for OCT technology have been demonstrated in ophthalmology,
dentistry, cardiology/intravascular imaging, endoscopy and intra-operative surgery, and many new applications are being
researched.
Due to higher sensitivity and faster rate of image acquisition, frequency domain OCT systems are now replacing the
first generation time domain systems. These include spectral domain systems, which use a broadband low coherent
source with spectrometer and a line scan camera based receive system, and swept source systems, that use wavelength
sweeping source with a photo-detector based receive system. Both of these systems require very similar signal
processing to recover the desired image from the captured digitized interference or fringe data.
DSP chips are gaining importance in ultrasound applications as the need for portability and low power grows. One of the
more computationally demanding applications for ultrasound involves estimating blood flow characteristics using
Doppler techniques. This ultrasound mode, called color Doppler ultrasound, is used to diagnose many conditions like
blood clots, valve defects and blocked arteries. This work looks at mapping some typical color Doppler algorithms onto
Texas Instruments' (TI's) high performance C64x+(TM) core. The algorithms include RF demodulation, wall filtering and
flow power, velocity and turbulence estimation. This paper starts with a general technique for analyzing algorithm
complexity in terms of CPU instruction cycles on VLIW architectures like the C64x+(TM). It then applies this technique to
Doppler processing algorithms, explains their mapping to the C64x+(TM) architecture and derives lower bounds for the
computational complexity for these algorithm kernels. For each of these algorithms, these estimates are finally compared
to actual implementations, and various implementation tradeoffs will be illustrated. Based on these implementations, it
will be shown that these algorithms can run on TI's C64x+(TM) based DSPs using a fraction of the available processing
power.
KEYWORDS: Wavelets, Wavelet transforms, Computer programming, Linear filtering, Compact discs, Digital signal processing, Discrete wavelet transforms, Signal analyzers, Signal processing, Error analysis
This paper describes real-time implementation of a novel wavelet- based audio compression method. This method is based on the discrete wavelet (DWT) representation of signals. A bit allocation procedure is used to allocate bits to the transform coefficients in an adaptive fashion. The bit allocation procedure has been designed to take advantage of the masking effect in human hearing. The procedure minimizes the number of bits required to represent each frame of audio signals at a fixed distortion level. The real-time implementation provides almost transparent compression of monophonic CD quality audio signals (samples at 44.1 KHz and quantized using 16 bits/sample) at bit rates of 64-78 Kbits/sec. Our implementation uses two ASPI Elf boards, each of which is built around a TI TMS230C31 DSP chip. The time required for encoding of a mono CD signal is about 92 percent of real time and that for decoding about 61 percent.
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