An Explicit Estimated Baseline Model for Robust Estimation of Fluorophores Using Multiple-Wavelength Excitation Fluorescence Spectroscopy.

Spectroscopy is a popular technique for identifying and quantifying fluorophores in fluorescent materials. However, quantifying the fluorophore of interest can be challenging when the material also contains other fluorophores (baseline), particularly if the emission spectrum of the baseline is not well-defined and overlaps with that of the fluorophore of interest. In this work, we propose a method that is free from any prior assumptions about the baseline by utilizing fluorescence signals at multiple excitation wavelengths. Despite the nonlinearity of the model, a closed-form expression of the least squares estimator is also derived. To evaluate our method, we consider the practical case of estimating the contributions of two forms of protoporphyrin IX (PpIX) in a fluorescence signal. This fluorophore of interest is commonly utilized in neuro-oncology operating rooms to distinguish the boundary between healthy and tumor tissue in a type of brain tumor known as glioma. Using a digital phantom calibrated with clinical and experimental data, we demonstrate that our method is more robust than current state-of-the-art methods for classifying pathological status, particularly when applied to images of simulated clinical gliomas. To account for the high variability in the baseline, we are examining various scenarios and their corresponding outcomes. In particular, it maintains the ability to distinguish between healthy and tumor tissue with an accuracy of up to 87%, while the ability of existing methods drops near 0%.

Framework for integrated MRI average of the spinal cord white and gray matter: the MNI-Poly-AMU template.

The field of spinal cord MRI is lacking a common template, as existing for the brain, which would allow extraction of multi-parametric data (diffusion-weighted, magnetization transfer, etc.) without user bias, thereby facilitating group analysis and multi-center studies. This paper describes a framework to produce an unbiased average anatomical template of the human spinal cord. The template was created by co-registering T2-weighted images (N = 16 healthy volunteers) using a series of pre-processing steps followed by non-linear registration. A white and gray matter probabilistic template was then merged to the average anatomical template, yielding the MNI-Poly-AMU template, which currently covers vertebral levels C1 to T6. New subjects can be registered to the template using a dedicated image processing pipeline. Validation was conducted on 16 additional subjects by comparing an automatic template-based segmentation and manual segmentation, yielding a median Dice coefficient of 0.89. The registration pipeline is rapid (~15 min), automatic after one C2/C3 landmark manual identification, and robust, thereby reducing subjective variability and bias associated with manual segmentation. The template can notably be used for measurements of spinal cord cross-sectional area, voxel-based morphometry, identification of anatomical features (e.g., vertebral levels, white and gray matter location) and unbiased extraction of multi-parametric data.

Estimation of metabolite T1 relaxation times using tissue specific analysis, signal averaging and bootstrapping from magnetic resonance spectroscopic imaging data.

OBJECT: A novel method of estimating metabolite T1 relaxation times using MR spectroscopic imaging (MRSI) is proposed. As opposed to conventional single-voxel metabolite T1 estimation methods, this method investigates regional and gray matter (GM)/white matter (WM) differences in metabolite T1 by taking advantage of the spatial distribution information provided by MRSI. MATERIAL AND METHODS: The method, validated by Monte Carlo studies, involves a voxel averaging to preserve the GM/WM distribution, a non-linear least squares fit of the metabolite T1 and an estimation of its standard error by bootstrapping. It was applied in vivo to estimate the T1 of N-acetyl compounds (NAA), choline, creatine and myo-inositol in eight normal volunteers, at 1.5 T, using a short echo time 2D-MRSI slice located above the ventricles. RESULTS: WM-T 1,NAA was significantly (P < 0.05) longer in anterior regions compared to posterior regions of the brain. The anterior region showed a trend of a longer WM T1 compared to GM for NAA, creatine and myo-Inositol. Lastly, accounting for the bootstrapped standard error estimate in a group mean T1 calculation yielded a more accurate T1 estimation. CONCLUSION: The method successfully measured in vivo metabolite T1 using MRSI and can now be applied to diseased brain.

Time-domain semi-parametric estimation based on a metabolite basis set.

A novel and fast time-domain quantitation algorithm--quantitation based on semi-parametric quantum estimation (QUEST)--invoking optimal prior knowledge is proposed and tested. This nonlinear least-squares algorithm fits a time-domain model function, made up from a basis set of quantum-mechanically simulated whole-metabolite signals, to low-SNR in vivo data. A basis set of in vitro measured signals can be used too. The simulated basis set was created with the software package NMR-SCOPE which can invoke various experimental protocols. Quantitation of 1H short echo-time signals is often hampered by a background signal originating mainly from macromolecules and lipids. Here, we propose and compare three novel semi-parametric approaches to handle such signals in terms of bias-variance trade-off. The performances of our methods are evaluated through extensive Monte-Carlo studies. Uncertainty caused by the background is accounted for in the Cramér-Rao lower bounds calculation. Valuable insight about quantitation precision is obtained from the correlation matrices. Quantitation with QUEST of 1H in vitro data, 1H in vivo short echo-time and 31P human brain signals at 1.5 T, as well as 1H spectroscopic imaging data of human brain at 1.5 T, is demonstrated.