Email: jheusser@stc.unm.edu
Phone: 505-272-7908

Magnetic resonance imaging (MRI) is an invaluable technique commonly employed to visualize tissues and identify abnormalities to diagnose pathologies associated with them. Along with neurologists who routinely wield MRI as an accurate tool for detecting tumors, strokes, aneurysms and other problems associated with the brain and spinal cord, radiologists use it to uncover anomalies in various tissues with great precision.  The MRI images are produced by processing the signals generated by the protons in various tissues when subjected to strong electromagnetic pulses. The tissues vary in biomagnetic susceptibility and therefore produce different radio signals and tissue image contrast which are processed to construct high-resolution images by T2*MRI. The varying susceptibilities of different tissues are the underlying source of T2*MRI.  However, the nonlinearity of MRI renders the T2*MRI image map to be an inexact representation of biomagnetic susceptibilities. Due to this, the physiological state of the tissue such as iron deposition and heme-iron perturbation associated with brain activity as shown in a T2*MRI image is not the most truthful image of the physiological condition. Simply, the output MR image acquired by T2*MRI cannot exactly represent the internal magnetic susceptibility source distribution of a tissue physiological state.  Therefore a method to generate the magnetic susceptibility source dataset from an MRI image with the utmost precision for faithfully representing the physiological state is currently needed.

In order to obtain the intact magnetic susceptibility distribution from complexMR image for faithfully representing pathophysiological state, a new model called computed inverse magnetic resonance imaging (CIMRI) has been developed.  This model uses the volumetric phase image acquired by T2*MRI to reconstruct the 3D susceptibility map by reversing the forward MRI procedure by a two-step computation. This is done using the 3D total variation regularized deconvolution method by efficiently implementing split Bregman iteration. Dynamic processes detected by functional MRI (fMRI) can also be enhanced by using the CIMRI model to produce 4D dynamic susceptibility series.  The resulting reconstructed susceptibility map can depict more precisely the structural or functional, static or dynamic physiological states in normal or abnormal humans or animals and can replace the commonly used MR image. In short, this technology is to replace the conventional MR image dataset with the CIMRI-reconstructed magnetic susceptibility dataset, thereby achieving more truthful tissue iron measurement and neuroimaging.

•  Compared to currently employed methods that are based MR images, most truthful magnetic susceptibility data for structural anatomy and functional physiology in research and clinical enterprises can be obtained by this invention (generating susceptibility dataset from complex MR dataset)
• More accurate measurements of iron in organs such as liver, spleen, pancreas, heart, brain
• More accurate depiction of brain injuries and dysfunctions
• More truthful functional imaging done by this novel method can help in diagnostic and prognostic imaging of brain function diseases like Alzheimer’s, schizophrenia, epilepsy
• More truthful nonheme iron deposit measurement in tissues and organs and neurovascular-coupled heme iron measurement for brain functional and neuroimaging