Additionally, the 3D-printed phantom mold featured a form-fitting adapter to the µRIGS system, ensuring reproducibility. Experiments The evaluation consisted of a calibration of st-values, phantom punctures in comparison with a compression testing machine (CTM, Xforce HP 50 N, zwickiLine Z0.5...
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Full size image Conclusion Despite the vast amounts of data being collected for individual patients, the promise of personalized medicine still remains to be fully realized. Data driven, machine learning processes provide a way to harness more and more of the incoming data. However, in the case...
Full size image Methods Cold diffusion Diffusion model is a class of latent variable models which uses a Markov chain to convert the noise distribution to the data distribution. It has the form \(p_{\theta } \left( {{\text{x}}_{0} } \right){:}{=} \smallint p_{\theta } \left...
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Large initiatives for automated tumour quantification in whole-body DWI using machine learning have however been described in e.g. myeloma [25]. This work aims to create, evaluate and employ a normal atlas of whole-body DWI and ADC of healthy volunteers scanned at 1.5T and 3T. The atlas ...
To investigate the application value of support vector machine (SVM) model based on diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) and amide proton transfer- weighted (APTW) imaging in predicting isocitrate dehydrogenase 1(IDH-1) mutation and Ki-67 expression in glioma. Methods...
Full size image MRI acquisition MRI examinations were performed on a 1.5 T MRI scanner (Excite HD; GE Healthcare, Milwaukee, WI, USA) equipped with an 8-channel phased-array thyroid coil (Chenguang Medical Technologies, Shanghai, China). The MRI protocols included: (1) coronal fat-suppressed...
Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual
Table 6 Performance of machine learning–based classifications of anti-ARS-antibodies. Full size table Discussion In the present study, we found that ML-based TA of muscle MRI has the potential to distinguish between PM, DM, and ADM. In contrast, ML models distinguishing between non-IIM and ...