(1992). Time Series—A Biostatistical Introduction. Technometrics: Vol. 34, No. 2, pp. 229-230.

Time Series—A Biostatistical Introduction

The microenvironment within tumors is significantly different from that in normal tissues. A major difference is seen in the chaotic vasculature of tumors, which results in unbalanced blood supply and significant perfusion heterogeneities. As a consequence, many regions within tumors are transiently or chronically hypoxic. This exacerbates tumor cells' natural tendency to overproduce acids, resulting in very acidic pH values. The hypoxia and acidity of tumors have important consequences for antitumor therapy and can contribute to the progression of tumors to a more aggressive metastatic phenotype. Over the past decade, techniques have emerged that allow the interrogation of the tumor microenvironment with high resolution and molecularly specific probes. Techniques are available to interrogate perfusion, vascular distribution, pH, and pO(2) nondestructively in living tissues with relatively high precision. Studies employing these methods have provided new insights into the causes and consequences of the hostile tumor microenvironment. Furthermore, it is quite exciting that there are emerging techniques that generate tumor image contrast via ill-defined mechanisms. Elucidation of these mechanisms will yield further insights into the tumor microenvironment. This review attempts to identify techniques and their application to tumor biology, with an emphasis on nuclear magnetic resonance (NMR) approaches. Examples are also discussed using electron MR, optical, and radionuclear imaging techniques.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.10181

MRI of the tumor microenvironment.

Computer executable software and device for guiding brain activity training comprising: logic which takes data corresponding to activity measurements of one or more internal voxels of a brain and determines one or more members of the group consisting of: a) what next stimulus to communicate to the subject, b) what next behavior to instruct the subject to perform, c) when a subject is to be exposed to a next stimulus, d) when the subject is to perform a next behavior, e) one or more activity metrics computed from the measured activity, f) a spatial pattern computed from the measured activity, g) a location of a region of interest computed from the measured activity, h) performance targets that a subject is to achieve computed from the measured activity, i) a performance measure of a subject's success computed from the measured activity, j) a subject's position relative to an activity measurement instrument; and logic for communicating information based on the determinations to the subject in substantially real time relative to when the activity is measured.

Methods for physiological monitoring,training, exercise and regulation

http://web.mit.edu/swg/ImagingPubs/Smoothing/laconte_eval_choices.pdf

The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics.

Purpose: We introduce L2-regularized reconstruction algorithms with closed-form solutions that achieve dramatic computational speed-up relative to state of the art L1- and L2-based iterative algorithms while maintaining similar image quality for various applications in MRI reconstruction. Materials and Methods: We compare fast L2-based methods to state of the art algorithms employing iterative L1- and L2-regularization in numerical phantom and in vivo data in three applications; (i) Fast Quantitative Susceptibility Mapping (QSM), (ii) Lipid artifact suppression in Magnetic Resonance Spectroscopic Imaging (MRSI), and (iii) Diffusion Spectrum Imaging (DSI). In all cases, proposed L2-based methods are compared with the state of the art algorithms, and two to three orders of magnitude speed up is demonstrated with similar reconstruction quality. Results: The closed-form solution developed for regularized QSM allows processing of a three-dimensional volume under 5 s, the proposed lipid suppression algorithm takes under 1 s to reconstruct single-slice MRSI data, while the PCA based DSI algorithm estimates diffusion propagators from undersampled q-space for a single slice under 30 s, all running in Matlab using a standard workstation. Conclusion: For the applications considered herein, closed-form L2-regularization can be a faster alternative to its iterative counterpart or L1-based iterative algorithms, without compromising image quality.

/pdf/fast-image-reconstruction-with-l2-regularization-1tf3ov2ntg.pdf

Fast image reconstruction with L2-regularization.

Echoplanar spectroscopic imaging (EPSI) was introduced as a fast alternative for spectroscopic imaging and has been recently implemented on clinical scanners. With further advances in gradient hardware and processing strategies, EPSI can be used to obtain spectroscopic images whose spatial resolution parallels that of conventional anatomic images within clinically acceptable acquisition time. The present work demonstrates that high-resolution EPSI can be used to derive structural images for applications in which spectroscopic information is beneficial. These applications are chemical shift (fat-water) imaging, narrow bandwidth imaging, and T2* mapping. In this paper, the EPSI sequence design and processing strategies are detailed and experimental results in normal volunteers are presented to illustrate the potential of using EPSI in imaging anatomic structures. J. Magn. Reson Imaging 1999; 10:1‐7. r 1999 Wiley-Liss, Inc.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/%28SICI%291522-2586%28199907%2910%3A1%3C1%3A%3AAID-JMRI1%3E3.0.CO%3B2-C

Applications of high-resolution echoplanar spectroscopic imaging for structural imaging †

A hybrid technique for spectroscopic imaging with reduced truncation artifact.

Activation detection in event-related fMRI data based on spatio-temporal properties.

Nonadditive two-way ANOVA for event-related fMRI data analysis.

A multiscale approach for analyzing in vivo magnetic resonance spectroscopic imaging (SI) data is described in this paper. With this method, fitting is performed at multiple spatial scales in a coarse-to-fine order. Results obtained at one scale are used as prior knowledge in fitting spectra at the next scale. The multiscale approach was validated with simulated data and demonstrated with proton SI datasets of the human brains. The results showed that this method improved the robustness and efficiency of the fitting and facilitated the automatic analysis of in vivo SI data.

Shantanu Sarkar

Papers

Applications of high-resolution echoplanar spectroscopic imaging for structural imaging †

A hybrid technique for spectroscopic imaging with reduced truncation artifact.

Activation detection in event-related fMRI data based on spatio-temporal properties.

Nonadditive two-way ANOVA for event-related fMRI data analysis.

A multiscale approach for analyzing in vivo spectroscopic imaging data.