3 research outputs found

    Medical image registration using unsupervised deep neural network: A scoping literature review

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    In medicine, image registration is vital in image-guided interventions and other clinical applications. However, it is a difficult subject to be addressed which by the advent of machine learning, there have been considerable progress in algorithmic performance has recently been achieved for medical image registration in this area. The implementation of deep neural networks provides an opportunity for some medical applications such as conducting image registration in less time with high accuracy, playing a key role in countering tumors during the operation. The current study presents a comprehensive scoping review on the state-of-the-art literature of medical image registration studies based on unsupervised deep neural networks is conducted, encompassing all the related studies published in this field to this date. Here, we have tried to summarize the latest developments and applications of unsupervised deep learning-based registration methods in the medical field. Fundamental and main concepts, techniques, statistical analysis from different viewpoints, novelties, and future directions are elaborately discussed and conveyed in the current comprehensive scoping review. Besides, this review hopes to help those active readers, who are riveted by this field, achieve deep insight into this exciting field

    Model-driven registration for multi-parametric renal MRI

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    The use of MR imaging biomarkers is a promising technique that may assist towards faster prognosis and more accurate diagnosis of diseases like diabetic kidney disease (DKD). The quantification of MR Imaging renal biomarkers from multiparametric MRI is a process that requires a physiological model to be fitted on the data. This process can provide accurate estimates only under the assumption that there is pixelto-pixel correspondence between images acquired over different time points. However, this is rarely the case due to motion artifacts (breathing, involuntary muscle relaxation) introduced during the acquisition. Hence, it is of vital importance for a biomarkers quantification pipeline to include a motion correctionstep in order to properly align the images and enable a more accurate parameter estimation. This study aims in testing whether a Model Driven Registration (MDR), which integrates physiological models in the registration process itself, can serve as a universal solution for the registration of multiparametric renal MRI. MDR is compared with a state-of-the-art model-free motion correction approach for multiparametric MRI, that minimizes a Principal Components Analysis based metric, performing a groupwise registration. The results of the two methods are compared on T1, DTI and DCE-MRI data for a small cohort of 10 DKD patients, obtained from BEAt-DKD project’s digital database. The majority of the evaluation metrics used to compare the two methods indicated that MDR achieved better registration results, while requiring significantly lower computational times. In conclusion, MDR could be considered as the method of choice for motion correction of multiparametric quantitative renal MRI
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