Development trend and implementation method of medical image algorithm
Medical imaging technology is playing an increasingly important role in the healthcare industry. The trend in this industry is to achieve early disease prediction and treatment through non-invasive means, reducing patient expenses. The convergence of multiple diagnostic imaging methods and advances in algorithm development are the main driving forces behind the design of new devices to meet patient needs.
To achieve the functionality required for these industry goals, device developers are beginning to adopt data-acquisition and co-processing with an FPGA-supported, up-to-date, off-the-shelf (COTS) CPU platform. There are several factors to consider when developing a scalable medical imaging device flexibly and efficiently, including the development of imaging algorithms, the integration of multiple diagnostic methods, and an updatable platform.
Developing image algorithms requires the use of intuitive, advanced modeling tools to continually improve digital signal processing (DSP) capabilities. Advanced algorithms require an updatable system platform that greatly improves image processing performance, and that implements devices that are smaller, easier to use, and easier to carry.
The performance requirements of real-time analytics require that the system platform be tuned with software (CPU) and hardware (configurable logic). These processing platforms must be able to meet a variety of performance price requirements and support the integration of multiple imaging methods. FPGAs are easy to integrate into multi-core CPU platforms, providing DSP functionality for the most flexible high-performance systems.
System planners and design engineers use advanced development tools and intellectual property (IP) libraries to quickly segment and debug algorithms on these platforms, speeding design implementation and increasing profitability. Transfer from electrical automation technology network
This article describes some of the trends in medical imaging algorithms, the convergence of multiple diagnostic approaches, and an updatable platform to implement these algorithms.
Algorithm development for medical imaging
First, let's take a look at the trends in imaging algorithms for each type of treatment and how to use FPGAs and intellectual property.
1.MRI
Magnetic resonance imaging (MRI) reconstruction technology creates a cross-sectional image of the human body. With the help of FPGA, three functions are used to reconstruct 3D human body images. From the frequency domain data, the 2D reconstructed slice produces a gray level slice by a fast Fourier transform (FFT), typically in the form of a matrix. 3D Human Body Image Reconstruction The slice spacing is close to the pixel spacing by slice interpolation so that the image can be viewed from any 2D plane. Iterative resolution sharpening uses a spatial deblurring technique based on an iterative inverse filtering process to reconstruct the image while reducing noise. In this way, the visual diagnostic resolution of the cross section is greatly improved.
2. Ultrasound
Small particles appearing in the ultrasound image are called spots. Various extraneous scatterer interactions produce ultrasound spots (similar to multipath RF reflections in the wireless domain), which is essentially a multiplicative noise. Spotless ultrasound images can be achieved using lossy compression techniques. The image is first processed logarithmically, and the speckle noise becomes additive noise with respect to the useful signal. Loss of wavelet noise can be reduced by using JPEG2000 encoder for lossy wavelet compression.
3. X-ray image
Coronal X-ray image motion correction techniques are used to reduce the effects of breathing and heart beat during imaging (heartbeat breathing cycle). The movement of the "3D plus time" coronal model is projected onto a 2D image for calculating the correction function (conversion and amplification), correcting the movement to obtain a clear image.
4. Molecular imaging
Molecular imaging is the characterization and measurement of biomedical processes at the cellular and molecular levels. Its purpose is to detect, collect, and monitor abnormal conditions that cause disease. For example, X-ray, positron emission tomography (PET), and SPECT techniques combine to map low-resolution functional/cell/molecular images to corresponding high-resolution anatomical images up to 0.5 mm. Miniaturization and algorithm development have driven the use of FPGAs on these compact system platforms, further improving performance on a multi-core CPU basis.
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