Limits...
Automatic per-segment analysis of myocardial perfusion MRI

View Article: PubMed Central - HTML

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

We have previously proposed an automatic solution to calculate semi-quantitative measurbes of myocardial perfusion and to present them to the user as parametric maps... Such maps have high spatial resolution which can potentially be an advantage when evaluating small regions of myocardium... On the other hand, per-segment analyses potentially benefit from higher SNR and improved clarity of presentation... To demonstrate feasibility of automatic semi-quantitative per-segment analysis of myocardial perfusion time series according to the AHA 17-segment model... Starting from the most basal slice, the two RV insertion points and the LV center point were automatically determined by a landmark detection algorithm, based on probabilistic boosting trees and marginal space learning... Landmark detection was evaluated by measuring the distance between the detected RV insertion points and manual annotation... Myocardial segmentation was evaluated by comparing the automatic results against manual delineation... The overall effectiveness of the solution was confirmed in all five patients by visual assessment... This defines the position of the segment model accurately... The Dice ratio for myocardial segmentation was 0.93±0.025... Median MBE (minimal distance between segmentation and reference) was 1.13/1.02mm for endo/epi... The typical performance of the proposed workflow is illustrated in Figure 2 and Figure 3... In both cases, the parametric maps and per-segment results correctly characterize the status of the myocardium... Feasibility of automatic semi-quantitative per-segment analysis of myocardial perfusion time series was successfully demonstrated with data from five subjects with images of rather good quality, warranting a larger clinical study of this method.

No MeSH data available.


Related in: MedlinePlus

Automatic Per-Segment Analysis of Myocardial Perfusion MRI (without perfusion deficit). (a) Original time series without perfusion deficit; (b) Processed series after motion correction, B1 surface coil inhonogeneity correction and adaptive noise suppression; (c) PD images; (d) estimated inhomogeneity field; (e-f) Estimated parameters maps (e: up-slope; f: area-under-curve); (g) Segmentation result with detected RV insertion and LV center points; (h-j) Per-segment delineation for basal (h), medial (i), and apical (j) slices; (k) AHA 17-segment model with averaged up-slope values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3106465&req=5

Figure 3: Automatic Per-Segment Analysis of Myocardial Perfusion MRI (without perfusion deficit). (a) Original time series without perfusion deficit; (b) Processed series after motion correction, B1 surface coil inhonogeneity correction and adaptive noise suppression; (c) PD images; (d) estimated inhomogeneity field; (e-f) Estimated parameters maps (e: up-slope; f: area-under-curve); (g) Segmentation result with detected RV insertion and LV center points; (h-j) Per-segment delineation for basal (h), medial (i), and apical (j) slices; (k) AHA 17-segment model with averaged up-slope values.

Mentions: The typical performance of the proposed workflow is illustrated in Figure 2 and Figure 3. In both cases, the parametric maps and per-segment results correctly characterize the status of the myocardium.


Automatic per-segment analysis of myocardial perfusion MRI
Automatic Per-Segment Analysis of Myocardial Perfusion MRI (without perfusion deficit). (a) Original time series without perfusion deficit; (b) Processed series after motion correction, B1 surface coil inhonogeneity correction and adaptive noise suppression; (c) PD images; (d) estimated inhomogeneity field; (e-f) Estimated parameters maps (e: up-slope; f: area-under-curve); (g) Segmentation result with detected RV insertion and LV center points; (h-j) Per-segment delineation for basal (h), medial (i), and apical (j) slices; (k) AHA 17-segment model with averaged up-slope values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3106465&req=5

Figure 3: Automatic Per-Segment Analysis of Myocardial Perfusion MRI (without perfusion deficit). (a) Original time series without perfusion deficit; (b) Processed series after motion correction, B1 surface coil inhonogeneity correction and adaptive noise suppression; (c) PD images; (d) estimated inhomogeneity field; (e-f) Estimated parameters maps (e: up-slope; f: area-under-curve); (g) Segmentation result with detected RV insertion and LV center points; (h-j) Per-segment delineation for basal (h), medial (i), and apical (j) slices; (k) AHA 17-segment model with averaged up-slope values.
Mentions: The typical performance of the proposed workflow is illustrated in Figure 2 and Figure 3. In both cases, the parametric maps and per-segment results correctly characterize the status of the myocardium.

View Article: PubMed Central - HTML

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

We have previously proposed an automatic solution to calculate semi-quantitative measurbes of myocardial perfusion and to present them to the user as parametric maps... Such maps have high spatial resolution which can potentially be an advantage when evaluating small regions of myocardium... On the other hand, per-segment analyses potentially benefit from higher SNR and improved clarity of presentation... To demonstrate feasibility of automatic semi-quantitative per-segment analysis of myocardial perfusion time series according to the AHA 17-segment model... Starting from the most basal slice, the two RV insertion points and the LV center point were automatically determined by a landmark detection algorithm, based on probabilistic boosting trees and marginal space learning... Landmark detection was evaluated by measuring the distance between the detected RV insertion points and manual annotation... Myocardial segmentation was evaluated by comparing the automatic results against manual delineation... The overall effectiveness of the solution was confirmed in all five patients by visual assessment... This defines the position of the segment model accurately... The Dice ratio for myocardial segmentation was 0.93±0.025... Median MBE (minimal distance between segmentation and reference) was 1.13/1.02mm for endo/epi... The typical performance of the proposed workflow is illustrated in Figure 2 and Figure 3... In both cases, the parametric maps and per-segment results correctly characterize the status of the myocardium... Feasibility of automatic semi-quantitative per-segment analysis of myocardial perfusion time series was successfully demonstrated with data from five subjects with images of rather good quality, warranting a larger clinical study of this method.

No MeSH data available.


Related in: MedlinePlus