The partial volume effect (PVE) arises in volumetric images when more than one tissue type occurs in a voxel. In such cases, the voxel
intensity depends not only on the imaging sequence and tissue properties, but also on the proportions of each tissue type present in the
voxel. We have demonstrated in previous work that ignoring this effect by establishing binary voxel-based segmentations introduces
significant errors in quantitative measurements, such as estimations of the volumes of brain structures. In this paper, we provide a
statistical estimation framework to quantify PVE and to propagate voxel-based estimates in order to compute global magnitudes, such as
volume, with associated estimates of uncertainty. Validation is performed on ground truth synthetic images and MRI phantoms, and a
clinical study is reported. Results show that the method allows for robust morphometric studies and provides resolution unattainable to
date
The partial volume effect (PVE) arises in volumetric images when more than one tissue type occurs in a voxel. In such cases, the voxelintensity depends not only on the imaging sequence and tissue properties, but also on the proportions of each tissue type present in thevoxel. We have demonstrated in previous work that ignoring this effect by establishing binary voxel-based segmentations introducessignificant errors in quantitative measurements, such as estimations of the volumes of brain structures. In this paper, we provide astatistical estimation framework to quantify PVE and to propagate voxel-based estimates in order to compute global magnitudes, such asvolume, with associated estimates of uncertainty. Validation is performed on ground truth synthetic images and MRI phantoms, and aclinical study is reported. Results show that the method allows for robust morphometric studies and provides resolution unattainable todate
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