scholarly journals Magnetic Resonance Imaging Artifact Elimination in the Diagnosis of Female Pelvic Abscess under Phase Correction Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ying Xia ◽  
Shaozheng Chen
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Pelvic Abscess ◽  
Phase Correction ◽  
Motion Artifacts ◽  
Correction Algorithm ◽  
Resonance Imaging ◽  
Multislice Spiral Ct ◽  
Ct Examination ◽  
The Difference

In order to explore the effect of magnetic resonance imaging (MRI) based on phase correction algorithm in diagnosing female pelvic abscess, firstly, the effect of phase correction algorithm on eliminating MRI image motion artifacts was studied, then it was applied to 71 female pelvic cases admitted to our hospital in the diagnosis of abscess patients with magnetic resonance imaging technology, and the results were compared with the results of multislice spiral CT and laparoscopy to explore the accuracy of MRI and CT. It was found that the results of MRI examination were close to those of laparoscopy, and the difference was not statistically significant ( P > 0.05 ); the results of CT examination and laparoscopy were significantly different, and the difference was statistically significant ( P < 0.05 ); in addition, the results of CT examination, the number of bacterial cysts (43 cases) and tuberculous cysts (12 cases), were significantly lower than the results of MRI (50 cases, 18 cases), and the difference was statistically significant ( P < 0.05 ). The size of the mass shown by the MRI examination (4.1  cm × 4.2  cm × 3.9 cm～13.9  cm × 9.5  cm × 8.7 cm) was larger than the size of the mass revealed by the CT examination (5.2  cm × 4.3  cm × 4.1 cm～15.5  cm × 10.1  cm × 9.6 cm), the difference between the two was statistically significant ( P < 0.05 ), and it was closer to the results of laparoscopic pathology (4.1  cm × 4.3  cm × 3.9 cm～14.1  cm × 9.3  cm P < 0.05 8.7 cm). In short, the phase correction algorithm could eliminate the motion artifacts of MRI images. In the imaging diagnosis of female pelvic abscess, the MRI diagnosis based on the phase correction algorithm is more ideal than the diagnosis of multislice spiral CT. It can be used as a reference basis for clinical disease treatment.

Systematic Analysis and Review of Magnetic Resonance Imaging (MRI) Reconstruction Techniques

2021 ◽  
Vol 16 ◽  
Author(s):  
Penta Anil Kumar ◽  
R. Gunasundari ◽  
R. Aarthi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Optimization Technique ◽  
Motion Artifacts ◽  
Reconstruction Technique ◽  
Resonance Imaging ◽  
Research Gaps ◽  
Conventional Mri ◽  
Mri Reconstruction ◽  
Magnetic Resonance Imaging Mri

Background: Magnetic Resonance Imaging (MRI) plays an important role in the field of medical diagnostic imaging as it poses non-invasive acquisition and high soft-tissue contrast. However, the huge time is needed for the MRI scanning process that results in motion artifacts, degrades image quality, misinterpretation of data, and may cause uncomfortable to the patient. Thus, the main goal of MRI research is to accelerate data acquisition processing without affecting the quality of the image. Introduction: This paper presents a survey based on distinct conventional MRI reconstruction methodologies. In addition, a novel MRI reconstruction strategy is proposed based on weighted Compressive Sensing (CS), Penalty-aided minimization function, and Meta-heuristic optimization technique. Methods: An illustrative analysis is done concerning adapted methods, datasets used, execution tools, performance measures, and values of evaluation metrics. Moreover, the issues of existing methods and the research gaps considering conventional MRI reconstruction schemes are elaborated to obtain improved contribution for devising significant MRI reconstruction techniques. Results: The proposed method will reduce conventional aliasing artifacts problems, may attain lower Mean Square Error (MSE), higher Peak Signal-to-Noise Ratio (PSNR), and Structural SIMilarity (SSIM) index. Conclusion: The issues of existing methods and the research gaps considering conventional MRI reconstruction schemes are elaborated to devising an improved significant MRI reconstruction technique.

scholarly journals Improved motion correction of submillimetre 7T fMRI time series with boundary-based registration (BBR)

2019 ◽  
Author(s):  
Pei Huang ◽  
Johan D. Carlin ◽  
Richard N. Henson ◽  
Marta M. Correia
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Functional Data ◽  
Structural Data ◽  
Target Volume ◽  
Head Motion ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
The Difference

AbstractUltra-high field functional magnetic resonance imaging (fMRI) has allowed us to acquire images with submillimetre voxels. However, in order to interpret the data clearly, we need to accurately correct head motion and the resultant distortions. Here, we present a novel application of Boundary Based Registration (BBR) to realign functional Magnetic Resonance Imaging (fMRI) data and evaluate its effectiveness on a set of 7T submillimetre data, as well as millimetre 3T data for comparison. BBR utilizes the boundary information from high contrast present in structural data to drive registration of functional data to the structural data. In our application, we realign each functional volume individually to the structural data, effectively realigning them to each other. In addition, this realignment method removes the need for a secondary aligning of functional data to structural data for purposes such as laminar segmentation or registration to data from other scanners. We demonstrate that BBR realignment outperforms standard realignment methods across a variety of data analysis methods. Further analysis shows that this benefit is an inherent property of the BBR cost function and not due to the difference in target volume. Our results show that BBR realignment is able to accurately correct head motion in 7T data and can be utilized in preprocessing pipelines to improve the quality of 7T data.

scholarly journals Diffusion-Weighted Imaging-Magnetic Resonance Imaging Information under Class-Structured Deep Convolutional Neural Network Algorithm in the Prognostic Chemotherapy of Osteosarcoma

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yong Hu ◽  
Jie Tang ◽  
Shenghao Zhao ◽  
Ye Li
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Neoadjuvant Chemotherapy ◽  
Convolutional Neural Network ◽  
Diffusion Weighted Imaging ◽  
Resonance Imaging ◽  
Imaging Diagnosis ◽  
The Difference ◽  
Mri Image

In order to improve the efficiency of early imaging diagnosis of patients with osteosarcoma and the effect of neoadjuvant chemotherapy based on the results of imaging examinations, 48 patients with suspected osteosarcoma were selected as the research objects and their diffusion-weighted imaging (DWI)-magnetic resonance imaging (MRI) images were regularized in this study. Then, a DWI-MRI image discrimination model was established based on the class-structured deep convolutional neural network (CSDCNN) algorithm. The peak signal-to-noise ratio (PSNR), mean square error (MSE), and edge preserve index (EPI) were applied to evaluate the image quality after processing by the CSDCNN algorithm; the accuracy, recall rate, precise rate, and F1 score were employed to evaluate the diagnostic efficiency of CSDCNN algorithm; the apparent diffusion coefficient (ADC) was adopted to evaluate the therapeutic effect of neoadjuvant chemotherapy based on the CSDCNN algorithm, and SegNet, LeNet, and AlexNet algorithms were introduced for comparison. The results showed that the PSNR, MSE, and EPI values of DWI-MRI images of patients with osteosarcoma were 29.1941, 0.0016, and 0.9688, respectively, after using the CSDCNN algorithm to process the DWI-MRI images. The three indicators were significantly better than other algorithms, and the difference was statistically significant ( P < 0.05 ). According to the results of imaging diagnosis of patients with osteosarcoma, there was no significant difference between the assisted diagnosis effect of the CSDCNN algorithm and the pathological examination results ( P > 0.05 ). The results of adjuvant chemotherapy based on the CSDCNN algorithm found that the ADCmean value of the patients after chemotherapy was 1.66 ± 0.17 and the ADCmin value was 1.33 ± 0.15; the two indicators were significantly higher than other algorithms, and the difference was statistically significant ( P < 0.05 ). In conclusion, the CSDCNN algorithm had a good effect on DWI-MRI image processing of patients with osteosarcoma, which could improve the diagnostic accuracy of patients with osteosarcoma. Moreover, the diagnosis results based on this algorithm could achieve better neoadjuvant chemotherapy effects and assist clinicians in imaging diagnosis and clinical treatment of patients with osteosarcoma.

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