J Alison Noble — Page 2

PubMed2002

Automated 3-D echocardiography analysis compared with manual delineations and SPECT MUGA.

A major barrier for using 3-D echocardiography for quantitative analysis of heart function in routine clinical practice is the absence of accurate and robust segmentation and tracking methods necessary to make the analysis automatic. In this paper, we present an automated three-dimensional (3-D) echocardiographic acquisition and image-processing methodology for assessment of left ventricular (LV) function. We combine global image information provided by a novel multiscale fuzzy-clustering segmentation algorithm, with local boundaries obtained with phase-based acoustic feature detection. We then use the segmentation results to fit and track the LV endocardial surface using a 3-D continuous transformation. To our knowledge, this is the first report of a completely automated method. The protocol is evaluated in a small clinical case study (nine patients). We compare ejection fractions (EFs) computed with the new approach to those obtained using the standard clinical technique, single-photon emission computed tomography multigated acquisition. Errors on six datasets were found to be within six percentage points. A further two, with poor image quality, improved upon EFs from manually delineated contours, and the last failed due to artifacts in the data. Volume-time curves were derived and the results compared to those from manual segmentation. Improvement over an earlier published version of the method is noted.

Algorithms↗

PubMed2002

3-D freehand echocardiography for automatic left ventricle reconstruction and analysis based on multiple acoustic windows.

A new method is proposed to reconstruct and analyze the left ventricle (LV) from multiple acoustic window three-dimensional (3-D) ultrasound acquired using a transthoracic 3-D rotational probe. Prior research in this area has been based on one acoustic window acquisition. However, the data suffers from several limitations that degrade the reconstruction and reduce the clinical value of interpretation, such as the presence of shadow due to bone (ribs) and air (in the lungs) and motion of the probe during the acquisition. In this paper, we show how to overcome these limitations by automatically fusing information from multiple acoustic window sparse-view acquisitions and using a position sensor to track the probe in real time. Geometric constraints of the object shape, and spatiotemporal information relating to the image acquisition process, are used in new algorithms for 1) grouping endocardial edge cues from an initial image segmentation and 2) defining a novel reconstruction method that utilizes information from multiple acoustic windows. The new method has been validated on a phantom and three real heart data sets. In the phantom study, one finger of a latex glove was scanned from two acoustic windows and reconstructed using the new method. The volume error was measured to be less than 4%. In the clinical case study, 3-D ultrasound and magnetic resonance imaging (MRI) scanning were performed on the same healthy volunteers. Quantitative ejection fractions (EFs) and volume-time curves over a cardiac cycle were estimated using the new method and compared to cardiac MRI measurements. This showed that the new method agrees better with MRI measurements than the previous approach we have developed based on a single acoustic window. The EF errors of the new method with respect to MRI measurements were less than 6%. A more extensive clinical validation is required to establish whether these promising first results translate to a method suitable for routine clinical use.

Algorithms↗