Research On Medical Image Measurement And Reconstruction Based On Compressed Sensing

In the field of modern signal processing, the rapid development of informationtechnology makes the data volume rapid growth, the sampling method guided by theNyquist sampling theoremmakes the increasing amount of data. So Hardware systemis hard to be realized to meet the actual needs. In the process of medical imaging, weget the required sampling data by scanning the lesion site of the patientwhich are usedto reconstruct medical image.Because a long time scanning can bring higher radiationor discomfort to the patient, seeking a way that needs fewer sampling data toreconstruct medical image accurately is a research hotspot in the field ofmedicalimage processing.Candes et al. proposed a compressed sensing theory(CS), which breaks theconstraint of the traditional Nyquist sampling theorem. A measurement matrix isdesigned to conduct sparse measurement for signals, and then the image isreconstructed according to the reconstruction algorithm. CS theorysamples andcompresses data at the same time, so the data reconstruction with low sampling rate isachieved.. the following is the main work and innovationof this paper:1. This paperhas analyzed the feasibility and rationality of the application of CStheory in medical image reconstruction in-depth. On the basis of analyzing theessence of CS theory’s successful application in medical image, the two keytechnologies, measurement matrix and reconstruction algorithm, are deeplyresearched in this paper.Several improvements have been conducted for them and Theimproved algorithm owns higher reconstruction accuracy and shorter running time.2. This paper introduces the structure style and basic structure of measurementmatrix.And with the same reconstruction algorithm, several compressed sensingreconstruction experiments are conducted for some medical images and the reconstruction performances are evaluated.According to the characteristics ofmeasurement matrix, this paperproposesthe measurement matrix optimization methodbased on singular value decomposition. Then medical images are reconstructed withthe optimized measurement matrix. The experimental results show that the optimizedmeasurement matrix owns higher reconstruction accuracy than the original matrix,and can increase PSNR1~2dB with the same running time.3. This paper introduces the main reconstruction algorithms of compressedsensing andresearchesbasic idea and implementation steps of the reconstructionalgorithm, and then validates, evaluates and analyses the advantages anddisadvantages of each algorithm.This paper proposes a new reconstruction algorithmof compressed sensing based on TVnormminimization, named SP_TV algorithm,which combines the SP’s faster running and the TV’s higher reconstruction accuracy.The experimental results show that the improved reconstruction algorithm ownshigher reconstruction accuracy and shorter running time.