31P-SPAWNN, in order to fully analyze phosphorus-31 ( 31 P) magnetic resonance spectra. The flexibility and speed of the technique rival traditional least-square fitting methods, with the performance of the two approaches, are compared in this work.Theory and Methods: Convolutional neural network architectures have been proposed for the analysis and quantification of 31 P-spectroscopy. The generation of training and test data using a fully parameterized model is presented herein.In vivo unlocalized free induction decay and three-dimensional 31 P-magnetic resonance spectroscopy imaging data were acquired from healthy volunteers before being quantified using either 31P-SPAWNN or traditional least-square fitting techniques.
Results:The presented experiment has demonstrated both the reliability and accuracy of 31P-SPAWNN for estimating metabolite concentrations and spectral parameters. Simulated test data showed improved quantification using 31P-SPAWNN compared with LCModel. In vivo data analysis revealed higher accuracy at low signal-to-noise ratio using 31P-SPAWNN, yet with equivalent precision. Processing time using 31P-SPAWNN can be further shortened up to two orders of magnitude.
Conclusion:The accuracy, reliability, and computational speed of the method open new perspectives for integrating these applications in a clinical setting.
In the age of artificial intelligence, there is a need for simple and inexpensive frameworks aimed at performing standardized reproducible motion-controlled experiments allowing the creation of datasets of motion corrupted in vivo MRI scans. Focused on human brain imaging, we propose ChoCo: a choreography controlled protocol for reproducible head motion and an ad-hoc hardware device for synchronization between an MRI scanner and a movie displaying motion to be performed. A proof of concept demonstrated that 6 participants were able to reproduce head choreographies accurately. The resulting motion corrupted brain images show qualitatively similar artifacts, confirming consistent motion reproduction among subjects.
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