3 research outputs found
Medical image registration using unsupervised deep neural network: A scoping literature review
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
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