Magnetic Resonance Imaging (MRI) is a useful diagnostic method for producing anatomical images of the body. It differentiates tissues thanks to the measure of their proton density (Formula presented.) and relaxation times (Formula presented.) and (Formula presented.). Since pathological tissues often present altered (Formula presented.), (Formula presented.) and (Formula presented.) with respect to the physiological ones, MRI is largely used for the detection of large number of conditions. In addition, MRI scan presents an effective resolution able to detect very small anatomical elements, making the imaging system well suitable in contexts like prevention and early diagnosis. Nevertheless, system resolution imposes pathological volume to be larger than the voxel dimension. Since in some pathologies and conditions it could be very helpful to find tissues even smaller than the voxel dimension, this manuscript proposes an algorithm able to analyze voxel content and detect which one presents more than one tissue in its volume (i.e., which one is heterogeneous). More in detail, a machine learning algorithm is proposed, able to highlight, in the MR image, which pixel corresponds to an heterogeneous voxel. Method shows to be promising in combining good results and near real-time processing in both simulated and real scenario.

Intra voxel analysis in magnetic resonance imaging via deep learning

Autorino M. M.
;
Franceschini S.;Ambrosanio M.;Pascazio V.;Baselice F.
2023-01-01

Abstract

Magnetic Resonance Imaging (MRI) is a useful diagnostic method for producing anatomical images of the body. It differentiates tissues thanks to the measure of their proton density (Formula presented.) and relaxation times (Formula presented.) and (Formula presented.). Since pathological tissues often present altered (Formula presented.), (Formula presented.) and (Formula presented.) with respect to the physiological ones, MRI is largely used for the detection of large number of conditions. In addition, MRI scan presents an effective resolution able to detect very small anatomical elements, making the imaging system well suitable in contexts like prevention and early diagnosis. Nevertheless, system resolution imposes pathological volume to be larger than the voxel dimension. Since in some pathologies and conditions it could be very helpful to find tissues even smaller than the voxel dimension, this manuscript proposes an algorithm able to analyze voxel content and detect which one presents more than one tissue in its volume (i.e., which one is heterogeneous). More in detail, a machine learning algorithm is proposed, able to highlight, in the MR image, which pixel corresponds to an heterogeneous voxel. Method shows to be promising in combining good results and near real-time processing in both simulated and real scenario.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/124836
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