Volume 12 Supplement 1
Feasibility and validation of estimating Global LV functional indices from limited projections using a Modified Simpson's Algorithm
© Krishnamurthy et al; licensee BioMed Central Ltd. 2010
Published: 21 January 2010
A stack of 10-12 cine SSFP slices covering the LV are typically acquired to estimate global LV function. But, in instances such as dobutamine stress MR, it is difficult to acquire 10-12 contiguous short axis slices, and acquisition is limited to cine imaging at three short-axis (located at basal, mid and apical portions of the LV), and three long axis orientations (2-, 3- and 4-chamber views) . It is unclear if it is feasible obtain an estimate of global LV function, e.g., EDV, ESV, etc. from these limited views.
The purpose of this work is to test the feasibility of developing a modified Simpson's algorithm that can calculate LV volumes from a limited sub-set of cardiac cine MR images (three short-axis views, and one long axis view), and validate the algorithm in human subjects.
Data acquisition: In 20 subjects (14 M, age: 38+9 years) a set of contiguous cardiac cine SSFP images in the short-axis and in the three standard long-axis orientations were acquired at 1.5 T. The acquisition parameters were: TR(ms)/TE(ms)/flip: 3.2/1.6/60°; acquired voxel-size: 2.5 × 2.5 × 8 mm3; temporal resolution: 40-60 ms; breath-hold time: 5-8 s/slice.
Modified Simpson's algorithm
The total LV volume was calculated using the modified Simpson's algorithm, and compared against the LV volume extracted from the expert drawn manual contours on the stack of contiguous short axis slices.
Bland-Altman Analysis of LV function between Modified Simpson"s method and expert observer
Modified Simpson Method
Percent Mean Bias ± SD (%)
155.6 ± 32.9
166.8 ± 35.1
-7.3 ± 6.8
65.0 ± 15.4
66.6 ± 15.3
2.4 ± 15.3
58.1 ± 5.2
59.7 ± 6.6
-1.7 ± 6.1
Total LV volume can be computed from three short-axis slices acquired at the basal, mid, and apical portions of the LV using the modified Simpson's algorithm as described. This approach can pave way for estimating metrics characterizing both regional and global LV function.