Dataset Information

Removing physiological motion from intravital and clinical functional imaging data.

ABSTRACT: Intravital microscopy can provide unique insights into the function of biological processes in a native context. However, physiological motion caused by peristalsis, respiration and the heartbeat can present a significant challenge, particularly for functional readouts such as fluorescence lifetime imaging (FLIM), which require longer acquisition times to obtain a quantitative readout. Here, we present and benchmark Galene, a versatile multi-platform software tool for image-based correction of sample motion blurring in both time resolved and conventional laser scanning fluorescence microscopy data in two and three dimensions. We show that Galene is able to resolve intravital FLIM-FRET images of intra-abdominal organs in murine models and NADH autofluorescence of human dermal tissue imaging subject to a wide range of physiological motions. Thus, Galene can enable FLIM imaging in situations where a stable imaging platform is not always possible and rescue previously discarded quantitative imaging data.

SUBMITTER: Warren SC

PROVIDER: S-EPMC6037484 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Publications

Removing physiological motion from intravital and clinical functional imaging data.

Warren Sean C SC Nobis Max M Magenau Astrid A Mohammed Yousuf H YH Herrmann David D Moran Imogen I Vennin Claire C Conway James Rw JR Mélénec Pauline P Cox Thomas R TR Wang Yingxiao Y Morton Jennifer P JP Welch Heidi Ce HC Strathdee Douglas D Anderson Kurt I KI Phan Tri Giang TG Roberts Michael S MS Timpson Paul P

eLife 20180709

Intravital microscopy can provide unique insights into the function of biological processes in a native context. However, physiological motion caused by peristalsis, respiration and the heartbeat can present a significant challenge, particularly for functional readouts such as fluorescence lifetime imaging (FLIM), which require longer acquisition times to obtain a quantitative readout. Here, we present and benchmark <i>Galene</i>, a versatile multi-platform software tool for image-based correcti ...[more]

PMID: 29985127

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Project description:Differing noise variance across study populations has been shown to cause artifactual group differences in functional connectivity measures. In this study, we investigate the use of short echo time functional MRI data to correct for these noise sources in blood oxygenation level dependent (BOLD)-weighted time series. A dual-echo sequence was used to simultaneously acquire data at both a short (TE=3.3 ms) and a BOLD-weighted (TE=35 ms) echo time. This approach is effectively "free," using dead-time in the pulse sequence to collect an additional echo without affecting overall scan time or temporal resolution. The proposed correction method uses voxelwise regression of the short TE data from the BOLD-weighted data to remove noise variance. In addition to a typical resting state scan, non-compliant behavior associated with patient groups was simulated via increased head motion or physiological fluctuations in 10 subjects. Short TE data showed significant correlations with the traditional motion-related and physiological noise regressors used in current connectivity analyses. Following traditional preprocessing, the extent of significant additional variance explained by the short TE data regressors was significantly correlated with the average head motion across the scan in the resting data (r(2)=0.93, p<0.0001). The reduction in data variance following the inclusion of short TE regressors was also correlated with scan head motion (r(2)=0.48, p=0.027). Task-related data were used to demonstrate the effects of the short TE correction on BOLD activation time series with known temporal structure; the size and strength of the activation were significantly decreased, but it is not clear whether this reflects BOLD contamination in the short TE data or correlated changes in blood volume. Finally, functional connectivity maps of the default mode network were constructed using a seed correlation approach. The effects of short TE correction and low-pass filtering on the resulting correlations maps were compared. Results suggest that short TE correction more accurately differentiates artifactual correlations from the correlations of interest in conditions of amplified noise.

Dataset Information

Removing physiological motion from intravital and clinical functional imaging data.

Publications

Removing physiological motion from intravital and clinical functional imaging data.

Similar Datasets

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