Are echo techniques comparable for measurement of left ventricular dysynchrony? Comparison of real time 3D and tissue Doppler echocardiography in patients with ischaemic cardiomyopathy

Tissue Doppler (TD) assessment of dysynchrony (DYS) is established in evaluation for bi-ventricular pacing. Time to regional minimal volume by real-time 3D echo (3D) has been applied to DYS. 3D offers simultaneous assessment of all segments and may limit errors in localization of maximum delay due to off-axis images.We compared TD and 3D for assessment of DYS. 27 patients with ischaemic cardiomyopathy (aged 60±11 years, 85% male) underwent TD with generation of regional velocity curves. The interval between QRS onset and maximal systolic velocity (TTV) was measured in 6 basal and 6 mid-cavity segments. Onthe same day,3Dwas performed and data analysed offline with Q-Lab software (Philips, Andover, MA). Using 12 analogous regional time-volume curves time to minimal volume (T3D)was calculated. The standard deviation (S.D.) between segments in TTV and T3D was calculated as a measure ofDYS. In 7 patients itwas not possible to measureT3D due to poor images. In the remaining 20, LV diastolic volume, systolic volume and EF were 128±35 ml, 68±23 ml and 46±13%, respectively. Mean TTV was less than mean T3D (150±33ms versus 348±54 ms; p < 0.01). The intrapatient range was 20–210ms for TTV and 0–410ms for T3D. Of 9 patients (45%) with significantDYS (S.D. TTV > 32 ms), S.D. T3D was 69±37ms compared to 48±34ms in those without DYS (p = ns). In DYS patients there was concordance of the most delayed segment in 4 (44%) cases.Therefore, different techniques for assessing DYS are not directly comparable. Specific cut-offs for DYS are needed for each technique.

Automatic segmentation of the knee bones using 3D active shape models

This paper presents an automated segmentation approach for MR images of the knee bones. The bones are the first stage of a segmentation system for the knee, primarily aimed at the automated segmentation of the cartilages. The segmentation is performed using 3D active shape models (ASM), which are initialized using an affine registration to an atlas. The 3D ASMs of the bones are created automatically using a point distribution model optimization scheme. The accuracy and robustness of the segmentation approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images.