Edge-based partial volume averaging estimation for FLAIR MRI with white matter lesions

Generalized method for partial volume estimation and tissue segmentation in cerebral magnetic resonance images

An artifact found in magnetic resonance images (MRI) called partial volume averaging (PVA) has received much attention since accurate segmentation of cerebral anatomy and pathology is impeded by this artifact. Traditional neurological segmentation techniques rely on Gaussian mixture models to handle noise and PVA, or high-dimensional feature sets that exploit redundancy in multispectral datasets. Unfortunately, model-based techniques may not be optimal for images with non-Gaussian noise distributions and/or pathology, and multispectral techniques model probabilities instead of the partial volume (PV) fraction. For robust segmentation, a PV fraction estimation approach is developed for cerebral MRI that does not depend on predetermined intensity distribution models or multispectral scans. Instead, the PV fraction is estimated directly from each image using an adaptively defined global edge map constructed by exploiting a relationship between edge content and PVA. The final PVA map is used to segment anatomy and pathology with subvoxel accuracy. Validation on simulated and real, pathology-free T1 MRI (Gaussian noise), as well as pathological fluid attenuation inversion recovery MRI (non-Gaussian noise), demonstrate that the PV fraction is accurately estimated and the resultant segmentation is robust. Comparison to model-based methods further highlight the benefits of the current approach.

Improved left ventricular mass quantification with partial voxel interpolation: in vivo and necropsy validation of a novel cardiac MRI segmentation algorithm

BACKGROUND

Cardiac magnetic resonance (CMR) typically quantifies LV mass (LVM) by means of manual planimetry (MP), but this approach is time-consuming and does not account for partial voxel components--myocardium admixed with blood in a single voxel. Automated segmentation (AS) can account for partial voxels, but this has not been used for LVM quantification. This study used automated CMR segmentation to test the influence of partial voxels on quantification of LVM.

METHODS AND RESULTS

LVM was quantified by AS and MP in 126 consecutive patients and 10 laboratory animals undergoing CMR. AS yielded both partial voxel (AS(PV)) and full voxel (AS(FV)) measurements. Methods were independently compared with LVM quantified on echocardiography (echo) and an ex vivo standard of LVM at necropsy. AS quantified LVM in all patients, yielding a 12-fold decrease in processing time versus MP (0:21±0:04 versus 4:18±1:02 minutes; P<0.001). AS(FV) mass (136±35 g) was slightly lower than MP (139±35; Δ=3±9 g, P<0.001). Both methods yielded similar proportions of patients with LV remodeling (P=0.73) and hypertrophy (P=1.00). Regarding partial voxel segmentation, AS(PV) yielded higher LVM (159±38 g) than MP (Δ=20±10 g) and AS(FV) (Δ=23±6 g, both P<0.001), corresponding to relative increases of 14% and 17%. In multivariable analysis, magnitude of difference between AS(PV) and AS(FV) correlated with larger voxel size (partial r=0.37, P<0.001) even after controlling for LV chamber volume (r=0.28, P=0.002) and total LVM (r=0.19, P=0.03). Among patients, AS(PV) yielded better agreement with echo (Δ=20±25 g) than did AS(FV) (Δ=43±24 g) or MP (Δ=40±22 g, both P<0.001). Among laboratory animals, AS(PV) and ex vivo results were similar (Δ=1±3 g, P=0.3), whereas AS(FV) (6±3 g, P<0.001) and MP (4±5 g, P=0.02) yielded small but significant differences with LVM at necropsy.

CONCLUSIONS

Automated segmentation of myocardial partial voxels yields a 14-17% increase in LVM versus full voxel segmentation, with increased differences correlated with lower spatial resolution. Partial voxel segmentation yields improved CMR agreement with echo and necropsy-verified LVM.