Introduction: Studies of the movement of the chest wall show their potential in the diagnosis of heart diseases. Few studies have focused on mapping these movements especially in the lower inaudible frequency band.Aims: This study evaluates Body Surface Mapping (BSM) as a method for describing mechanical cardiac activity.Methods: The chest wall's velocity was measured with a Laser Doppler Vibrometer (LDV) on six healthy subjects. The measuring procedure was repeated for 30 points positioned in a grid at the subjects chest. An electrocardiogram (ECG) and respiration was measured to support the signal processing. The heart movement were described using amplitude maps, constructed from the integrated LDV signal components of 1-20 Hz.Results: The impact of the cardiac motion on the displacement of the chest wall was shown as a typical pattern of the changes of the amplitude maps as a function of time.Conclusion: The results had a high reproducibility and were in concordance with existing evidence, thus indicating BSM to be a valid method for characterization of the mechanical cardiac activity.
ObjectiveMinimum deformation averaging (MDA) procedures exploit the information contained in inter-individual variations to generate high-resolution, high-contrast models through iterative model building. However, MDA models built from different image contrasts reside in disparate spaces and their complementary information cannot be utilized easily. The aim of this work was to develop an algorithm for the non-linear alignment of two MDA models with different contrasts to create a high-resolution in vivo model of the human hippocampus with a spatial resolution of 300 μm.MethodsA Turbo Spin Echo MDA model covering the hippocampus was contrast matched to a whole-brain MP2RAGE MDA model and aligned using cross-correlation and non-linear transformation. The contrast matching algorithm followed a global voxel location-based approach to estimate the relationship between intensity values of the two models. The performance of the algorithm was evaluated by comparing it to a non-linear registration obtained using mutual information without contrast matching. The complimentary information from both contrasts was then utilized in an automated hippocampal subfield segmentation pipeline.ResultsThe contrast of the Turbo Spin Echo MDA model could successfully be matched to the MP2RAGE MDA model. Registration using cross correlation provided more accurate alignment of the models compared to a mutual information based approach. The segmentation using ASHS resulted in hippocampal subfield delineations that resembled the tissue boundaries observed in the Turbo Spin Echo MDA model.ConclusionThe developed contrast matching algorithm facilitated the creation of a high-resolution multi-modal in vivo MDA model of the human hippocampus. This model can be used to improve algorithms for hippocampal subfield segmentation and could potentially support the early detection of neurodegenerative diseases.
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