Spatially variant regularization improves diffuse optical tomography

Pogue, Brian W.; McBride, Troy O.; Prewitt, Judith M. S.; Österberg, Ulf L.; Paulsen, Keith D.

doi:10.1364/ao.38.002950

Cited by 270 publications

(202 citation statements)

References 27 publications

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“…Additionally, there have been a variety of methods developed for solving the inverse problem. [40][41][42][43][44][45][46][47][48] We have chosen to follow a Green's function ͑or adjoint͒ method. [15][16][17][18]27,28 The inverse problem, therefore, is formulated in the following way:…”

Section: Figmentioning

confidence: 99%

Three-dimensional diffuse optical mammography with ultrasound localization in a human subject

et al. 2000

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Section: Figmentioning

confidence: 99%

Three-dimensional diffuse optical mammography with ultrasound localization in a human subject

et al. 2000

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“…The general image reconstruction protocol was as follows: 1) Reconstruct for optical properties at the excitation wavelength, µ ax and µ sx ', with frequency domain data, 2) Reconstruct for optical properties at the emission wavelength, µ am and µ sm ', with frequency domain data collected using a laser source at the emission wavelength, 3) Use the reconstructed optical properties and fluorescence intensity data to recover fluorescence yield. The same reconstruction algorithm was used to determine background optical properties in steps (1) and (2) and is based on previously reported work [32,33]. Initial estimates for all parameters were generated using homogenous fitting algorithms which enforce a single value for all nodes.…”

Section: Simulation Studiesmentioning

confidence: 99%

Combined Contrast-Enhanced MRI and Fluorescence Molecular Tomography for Breast Tumor Imaging

Davis¹

2009

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION / AVAILABILITY STATEMENTApproved for Public Release; Distribution Unlimited SUPPLEMENTARY NOTESOriginal contains colored plates: ALL DTIC reproductions will be in black and white. ABSTRACTFluorescence molecular tomography is an emerging technology to image the spatial distribution of fluorescence activity in tissue volumes with the potential to reduce false positive readings and eliminatethe need for stressful biopsy procedures for many women. However, the spatial resolution and quantification capabilities of FMT must be improved before realizing this potential. The funded research aims to improve the imaging capabilities of FMT by incorporating the optical imaging system directly into a clinical MRI for simultaneous image acquisition. Tissue structural information from the MR image is then used to guide the FMT image reconstruction algorithm. This approach is shown to dramatically improve image quality and fluorescence yield quantification in realistic simulated domains and provide noticeable improvements for simple domains in preliminary phantom experiments. Additionally, a newly developed algorithm to quantify exogenous absorption is developed and validated.

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“…A number of authors have proposed methods of incorporating prior information into the tomography problem to improve image reconstructions. Schweiger et al (2005) and Pogue et al (1999) describe spatial regularization approaches that reduce spatial variance in exchange for increased model residual. Applying a cortical constraint to the reconstructions has also been described by Pogue and Paulsen (1998) and Boas and Dale (2005).…”

Section: Prior Information In Dotmentioning

confidence: 99%

Dynamic physiological modeling for functional diffuse optical tomography

et al. 2006

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Diffuse optical tomography (DOT) is a noninvasive imaging technology that is sensitive to local concentration changes in oxy-and deoxyhemoglobin. When applied to functional neuroimaging, DOT measures hemodynamics in the scalp and brain that reflect competing metabolic demands and cardiovascular dynamics. The diffuse nature of near-infrared photon migration in tissue and the multitude of physiological systems that affect hemodynamics motivate the use of anatomical and physiological models to improve estimates of the functional hemodynamic response. In this paper, we present a linear state-space model for DOT analysis that models the physiological fluctuations present in the data with either static or dynamic estimation. We demonstrate the approach by using auxiliary measurements of blood pressure variability and heart rate variability as inputs to model the background physiology in DOT data. We evaluate the improvements accorded by modeling this physiology on ten human subjects with simulated functional hemodynamic responses added to the baseline physiology. Adding physiological modeling with a static estimator significantly improved estimates of the simulated functional response, and further significant improvements were achieved with a dynamic Kalman filter estimator (paired t tests, n = 10, P < 0.05). These results suggest that physiological modeling can improve DOT analysis. The further improvement with the Kalman filter encourages continued research into dynamic linear modeling of the physiology present in DOT. Cardiovascular dynamics also affect the blood-oxygen-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI). This state-space approach to DOT analysis could be extended to BOLD fMRI analysis, multimodal studies and real-time analysis.

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Spatially variant regularization improves diffuse optical tomography

Cited by 270 publications

References 27 publications

Three-dimensional diffuse optical mammography with ultrasound localization in a human subject

Three-dimensional diffuse optical mammography with ultrasound localization in a human subject

Combined Contrast-Enhanced MRI and Fluorescence Molecular Tomography for Breast Tumor Imaging

Dynamic physiological modeling for functional diffuse optical tomography

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