4D image reconstruction for emission tomography

Summary Magnetic Resonance (MR) is an exceptionally powerful and versatile measurement technique. The basic structure of an MR experiment has remained nearly constant for almost 50 years. Here we introduce a novel paradigm, Magnetic Resonance Fingerprinting (MRF) that permits the non-invasive quantification of multiple important properties of a material or tissue simultaneously through a new approach to data acquisition, post-processing and visualization. MRF provides a new mechanism to quantitatively detect and analyze complex changes that can represent physical alterations of a substance or early indicators of disease. MRF can also be used to specifically identify the presence of a target material or tissue, which will increase the sensitivity, specificity, and speed of an MR study, and potentially lead to new diagnostic testing methodologies. When paired with an appropriate pattern recognition algorithm, MRF inherently suppresses measurement errors and thus can improve accuracy compared to previous approaches.

A method is presented for the generation of parametric images of radioligand-receptor binding using PET. The method is based on a simplified reference region compartmental model, which requires no arterial blood sampling, and gives parametric images of both the binding potential of the radioligand and its local rate of delivery relative to the reference region. The technique presented for the estimation of parameters in the model employs a set of basis functions which enables the incorporation of parameter bounds. This basis function method (BFM) is compared with conventional nonlinear least squares estimation of parameters (NLM), using both simulated and real data. BFM is shown to be more stable than NLM at the voxel level and is computationally much faster. Application of the technique is illustrated for three radiotracers: [11C]raclopride (a marker of the D2 receptor), [11C]SCH 23390 (a marker of the D1 receptor) in human studies, and [11C]CFT (a marker of the dopamine transporter) in rats. The assumptions implicit in the model and its implementation using BFM are discussed. Copyright 1997 Academic Press.

A novel method of reconstruction from single-photon emission computerized tomography data is proposed. This method builds on the expectation-maximization (EM) approach to maximum likelihood reconstruction from emission tomography data, but aims instead at maximum posterior probability estimation, which takes account of prior belief about smoothness in the isotope concentration. A novel modification to the EM algorithm yields a practical method. The method is illustrated by an application to data from brain scans.