| Medical Imaging is the science that assists diagnosis and treatment of diseases by displayed the human body structure and function information within image, mainly including X ray, ultrasound, radionuclide, CT and magnetic resonance imaging。The development of medical imaging through three steps:X-ray (Roentgenology),Radiology and Imaging. X ray is the imaging source of the diagnosis and radiographic techniques. And multiple image sources appeared such as ultrasound, radionuclide, and magnetic resonance and so on.The clinical application of MRI and CT creates a new era in diagnostic imaging. Magnetic resonance imaging is one of the most important means of imaging technologies. Compared with other modalities of medical imaging, MRI has many advantages, such as excellent soft-tissue contrast,imaging in arbitrary plane,excellent spatial resolution and with no ionizing radiation. Since MRI was applied in clinical from the early 1980s, magnetic resonance angiography (MR angiography, MRA), magnetic resonance spectroscopy (MR spectroscopy, MRS), the parallel magnetic resonance imaging (parallel MR imaging, PMRI) and other technologies becoming more and more mature. All those advantages make MRI a very important tool for both clinical applications and scientific research. Computed tomography (computed tomography, CT) was invented by British engineer Godfrey Hounsfield。In recent years, with the emergence of multi-slice CT and dual-source CT, some difficult technique, such as high-quality imaging of the coronary arteries and major organ perfusion, have been applied in clinical.As an important clinical imaging modalities, the main disadvantages of MRI is the long data acquisition time and thus results in the long imaging time. Since 1990, Researchers committed to improving the field strength of magnetic field,developing the new fast imaging sequence and quickly switched gradient magnetic field to increase the imaging speed. However, rapidly switched field gradient would produce neuromuscular stimulation and lead to body unease. At the same time, owing to the limitation that caused by the status of hardware development, it almost reaches the limit that rely on gradient switching speed increase to improve imaging speed.On the other hand, as the combining of the rapid developmental modern computer technology and X-ray examination technology, CT provides high-quality information for clinical imaging. But when the X ray is absorbed through the body, it can produce inhibition of tissue damage or necrosis, referred to as X-biological effects. X ray damage to the body is proportional to the absorption of X-ray dose. CT can get better quality images at higher X-ray doses, but it also increased the overall radiation level affected by people. In particular, as one of the main functions imaging modalities, CT perfusion imaging needs a long period of continuous exposure, and oit will lead to increased radiation dose exposure. With the principle of acquire the best diagnostic results with minimal damage to the human, it is necessary to research on the low-dose CT.To sum up, currently, the problems need to be solved of the two technologies are, MRI need to shorten the acquisition time, CT need to reduce the X-ray dose, for these two issues, all can be solved by insufficient data collecting. the main time-consuming factors of MRI imaging is the long data sampling time, insufficient data collection can reduce the acquisition time; the X-ray radiation dose of CT imaging increases with the increase in examination time, the use of insufficient data collection methods can shorten the exposure time, which can reduce the radiation dose exposure to examiners.Currently, Parallel magnetic resonance imaging technology that based on the multi-channel acquisition had a profound impact on magnetic resonance imaging, which uses phased array coil array to collect part of the data sets simultaneously. CT can reduce the exposure each time with a limited angle projection data collection, in the perfusion image scanning, the radiation dose reduction is more evident.PMI makes use of the spatial information contained in phased array to replace the usual time consuming gradient encoding steps to speed up the imaging speed. However, as a new technology, there are deficiencies in the imaging algorithm There are mainly three kinds of PMRI algorithms, which are methods based on image domain, methods based on k space domain, and methods based on both image and k space domain. We focused on the SENSE (sensitivity encoding) method which is based on image domain. When a large acceleration factor is used the SNR of SENSE is dramatically deteriorated, and it will become much worse when the non-Cartesian sampling trajectories were adopted. Regularization methods have been shown to alleviate the problem, regularization based on Tikhonov and TV is the most widely used methods. In this paper, we propose an adaptive constraint model for SENSE, which makes use of the gradient feature of the prior image to decide the penalty function to deal with non-Cartesian data from multiple coils, in regions of higher gradient, TV based constraint which is an anisotropically smoothing method is adopted, which can protect edge information better. In more ambiguous regions with lower gradient, Tikhonov regularization method is adopted which can smooth noise better.Currently, numbers of sparse angle image reconstruction algorithm have been proposed, there are basically divided into two categories:the iterative transform-analysis reconstruction algorithm base on the transform domain; another, iterative-algebra/statistics reconstruction algorithms based on series expansion. Iterative reconstruction algorithm is an efficient image reconstruction algorithm for noisy data or incomplete projections in the uneven distribution of projection between 180 or 360. To improve the imaging accuracy and resolution, we often take measures of use regularization method or increase the number of iterations. In 2004, Cands, et al proposed the compressed sensing (CS, Compressed Sensing) theory. According to the applications in image reconstruction of CS, if the to be reconstructed image is sparse in a transform space, and then we can use the partially sampling data to acquire a high quality image use CS method. For the sparse angular CT perfusion imaging, compared with the previous scanned image, it can be considered that the perfusion information of the perfusion image series is sparse in the image domain in every moment. We proposed the sparse angular perfusion CT image reconstruction based on the subtraction in projection domain, and acquired the perfusion information images with iterative regularization constrained CS reconstruction methods.In conclusion, this study makes use of insufficient data collection methods to reduce the time of MRI and CT scan imaging radiation dose. We analyzed on the problem of SENSE constraint reconstruction algorithm, and proposed a new algorithm for non-Cartesian parallel MRI data reconstruction with adaptive constraint based on SENSE。Experiment data were simulated using collected projection X-ray of an arterial bolus injection in a patient with an AVM using an 8-channel head coil, and the under-sampling factor is 2.6 in our experiments. Simulation Experiments show that our method can better remove the noise and artifact caused by under-sampled data, and comes up with a better image quality with well protected image edge information especially some small details than the conventional SOS,SENSE and TV constraint SENSE reconstruction methods. In addition, we researched on the CT perfusion imaging, we can know that the change of CT perfusion information in time series is relatively smaller compared with the non-perfusion image, and the perfusion information is sparse. So we made use of compressed sensing theory to acquire the perfusion information image, and comes up with a better image quality compared with the traditional methods. Shepp-Logan Phantom experiments and human brain perfusion imaging proved the effectiveness of the proposed algorithm, the background area and Target areas of the image which was acquired with only 18 projections in [0,p] are highly consistent with the original image. Human brain perfusion experiments show that our algorithm can effectively restore detail information of the perfusion image, and we can still recover the acceptable perfusion images with a 96.3% reduction in dose (36 projection). |
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