Patent AU-2020102977-A4 (AU)
Title: A Deep learning technique to recognise brain activity by fMRI and DTI image fusion
Inventor: CHANDANA BOLEM SAI
IPC: A61B5/055
Filed: 2020-10-23
Granted: 2020-12-24
Abstract: A Deep learning technique to recognise brain activity by fMRI and DTI image 
fusion.  
ABSTRACT: 
Neuroimaging has improved significantly in the preceding 20 years, due to clearer visualisation 
recognizing the electromagnetic field and radiofrequency waves, in addition to protons in specific 
compounds inside brain cells. Innovative developments enable for non-invasive spatial 
visualisation and interpretation of brain functions while predefined activities. By series digitized 
segments of the brain, behaviour among neurons can be confirmed and supervised in distinct 
aspects of the brain. More modem technology employing higher-frequency processing will define 
the dissemination of specific contaminants and prescription medications. This invention 
proposes a deep neural network (DNN) for directly decoding multiple brain task states from fMRI 
and DTI signals of the brain. The network visualization study revealed that the DNN dynamically 
recognized characteristics from brain regions relevant to each activity. We adopted guided back
propagation, a commonly utilized network visualization tool, to simulate the characteristics of the 
information observed by CNN for the classification of every data element penetrated. Visualized 
samples with values other than zero had features that were critical for identification. Guided back
propagation, a commonly utilized network visualization tool, is adopted to simulate the 
characteristics of the information observed by CNN for the classification of every data element 
penetrated. Visualized samples with dimensions other than zero had features that were critical for 
identification. This versatile and effective brain-decoding approach can be extended to both large
scale large datasets and fine, small-scale neuroscience information. In addition, its service features, 
precision, and interpretability make it possible to extend a deep paradigm to an innovative 
demographic and a broad range of neuroimaging studies, involving internal mental status 
classification, psychiatric disease detection, and real-time fMRI neurofeedback. The suggested 
deep model has the ability to decode numerous brain phases from the entire brain fMRI time series 
and contribute them to the input handling in real time. The network visualization analysis indicated 
that the DNN immediately recognized functions from brain regions relevant to each activity.  
Without inflicting the responsibility of developing functionality, the proposed deep decoding 
approach can categorise brain activity states very precisely.
A Deep learning technique to recognise brain activity by fMRI and DTI image 
fusion.  
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