No abstract

Neurological disorders represent major causes of lost years of healthy life and mortality worldwide. Development of their quantitative interdisciplinary in vivo evaluation is required. Compartment modeling (CM) of brain data acquired in vivo using magnetic resonance imaging techniques with clinically available contrast agents can be performed to quantitatively assess brain perfusion. Transport of 1H spins in water molecules across physiological compartmental brain barriers in three different pools was mathematically modeled and theoretically evaluated in this paper and the corresponding theoretical compartment modeling of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data was analyzed. The pools considered were blood, tissue, and cerebrospinal fluid (CSF). The blood and CSF data were mathematically modeled assuming continuous flow of the 1H spins in these pools. Tissue data was modeled using three CMs. Results in this paper show that transport across physiological brain barriers such as the blood to brain barrier, the extracellular space to the intracellular space barrier, or the blood to CSF barrier can be evaluated quantitatively. Statistical evaluations of this quantitative information may be performed to assess tissue perfusion, barriers' integrity, and CSF flow in vivo in the normal or disease-affected brain or to assess response to therapy.

Background: Artificial intelligence (AI) implementation in medicine will increase the efficiency of medical services. Objective: To develop a disease management strategy for the direct and immediate implementation of AI MRI in radiology. Methods: Correlations between selected quantitative MRI parameters available in the literature and the corresponding physio-anatomy were made to build the human MRI physio-anatomical state chart (hMRI_PASC). Pathology can be assessed using the relative-to-normal (RN) values of each MRI parameter for corresponding control-normal (CN) and disease-affected (DA) regions, based on the equation: RN_Parameter(%) = multiply(100, divide(subtract(Parameter DA , Parameter CN ), (Parameter CN ))). The 50% RN_Parameter absolute value threshold for the selected MRI parameters was used to define a medical condition severity staging scale (MCSSS). The disease management strategy is presented for a scenario of DA human MRI organ model: the eye, using the hMRI_PASC, and MCSSS. Results: Inflammation, constriction, stiffness, and/or infiltration of blood or T1 and/or T2 lengthening or shortening agents, macromolecules, calcifications, and iron particles through broken blood vessels or broken blood vessels and blood-to-tissue barriers can be assessed based on the hMRI_PASC. Three levels: infiltration, dynamics and elastography (IDE), seven types, and eighteen stages are defined in the MCSSS. The disease management strategy introduced in this study shows that integrity of the seven affected ocular regions could be regained through therapeutical intervention, possibly followed by surgery targeted to one of the affected ocular regions. Conclusion: The hMRI_PASC, MCSSS, and disease management strategy presented in this study can be implemented immediately and directly in a software for AI-based MRI.

Automated push-button medical analysis using quantitative multiparametric standard scales will offer the possibility of efficient fast medical diagnosis in a precise and accurate manner. Magnetic Resonance Imaging (MRI) offers complex geometrico-physicochemical analysis of the physio-anatomy of interest. Most imaging protocols for the quantitative multiparametric MRI have already been implemented clinically, but flow assessment still remains a problem, especially when evaluated without contrast agents. A simple method for flow quantification using MRI without contrast agents is described in this study. The proposed method has the potential of non-invasive flow quantification, produced offline from MRI images acquired in less than one minute.