Medical imaging technology plays an increasingly important role in the healthcare industry. The development trend of this industry is to realize early disease prediction and treatment through non-implantation methods and reduce patient expenses. The fusion of multiple diagnostic imaging methods and the progress of algorithm development are the main driving forces for designing new equipment to meet the needs of patients.
In order to achieve the functions required by these industry goals, device developers began to use FPGA-supported, up-to-date commercial off-the-shelf (COTS) CPU platforms for data collection and co-processing. When flexibly and efficiently developing updatable medical imaging equipment, several factors need to be considered, including the development of imaging algorithms, the fusion of multiple diagnostic methods, and updatable platforms.
The development of imaging algorithms requires the use of intuitive advanced modeling tools to continuously improve digital signal processing (DSP) functions. Advanced algorithms require an updatable system platform, which greatly improves image processing performance, and the realized device is smaller in size, more convenient to use, and easier to carry.
The performance requirements of real-time analysis require that the system platform can be adjusted with software (CPU) and hardware (configurable logic). These processing platforms must be able to meet various performance and price requirements and support the integration of multiple imaging diagnosis and treatment methods. FPGAs are easily integrated into multi-core CPU platforms, providing DSP functions for the most flexible, high-performance systems.
System planners and design engineers use advanced development tools and intellectual property (IP) libraries to quickly divide and debug algorithms on these platforms to accelerate design implementation and increase profits.
This article introduces some development trends of medical imaging algorithms, the fusion of multiple diagnostic methods and an updateable platform to implement these algorithms.
Algorithm development for medical imaging
First of all, let us understand the development trend of imaging algorithm for each diagnosis and treatment method, and how to use FPGA 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 images. From frequency-domain data, 2D reconstructed slices generate gray-scale slices through fast Fourier transform (FFT), generally in the form of a matrix. 3D human image reconstruction uses slice interpolation to make the slice pitch close to the pixel pitch, so that the image can be viewed from any 2D plane. Iterative resolution sharpening uses a spatial defuzzification 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
The small particles that appear in the ultrasound image are called spots. The interaction of various irrelevant scatterers produces an ultrasonic spot (similar to multipath RF reflections in the wireless field), which is essentially a multiplicative noise. The use of lossy compression technology can achieve spotless ultrasound images. First perform logarithmic processing on the image, the speckle noise becomes additive noise relative to the useful signal. Using JPEG2000 encoder for lossy wavelet compression can reduce speckle noise.
3. X-ray image
Coronal X-ray image movement correction technology is 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 the 2D image, which is used to calculate the correction function (transformation and enlargement) to correct the movement and 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 the abnormal state that causes the disease. For example, the combination of X-ray, positron emission tomography (PET) and SPECT technology maps low-resolution functional / cell / molecular images to corresponding high-resolution anatomical images, which can be as small as 0.5 mm. Miniaturization and algorithm development have driven the use of FPGAs on these compact system platforms, further improving performance on the basis of multi-core CPUs.
5. Fusion of diagnostic methods
Early prediction and non-implantation therapy have promoted the fusion of PET / computer-aided tomography (CT) and X-ray diagnosis / CT equipment. To achieve higher image resolution, it is required to use fine geometric microarray detectors, combined with FPGA, to preprocess the photoelectric signals. After the preprocessing is completed, the CPU and FPGA co-processor process the collected signals together to reconstruct the human body image.
Non-real-time (NRT) image fusion (coincidence) technology is generally used to analyze functional and anatomical images obtained at different times. However, due to the patient's position, the shape of the scanning bed, and the natural movement of internal organs, it is difficult to perform the NRT image registration process. Using FPGA processing technology to fuse PET and CT in real time can simultaneously obtain functional and anatomical images in one imaging process, rather than synthesizing images afterwards. In surgical treatment, the fused image has higher definition and more accurate position.
Surgical guided surgery image processing uses pre-operative (CT or MR) images and real-time 3D (ultrasound and X-ray) image registration (related) technology to perform surgical treatment of diseases through non-implantation methods (ultrasound, MR intervention and X-ray therapy) . Various algorithms have been developed to achieve the best image coincidence results for the fusion of diagnosis and treatment methods and treatment types.
In this type of fusion system, FPGAs that support high-speed serial interconnection can reduce the interconnection of some data acquisition functions in the system post-processing, greatly reducing the total system cost associated with circuit boards and cables.
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