NSF Award
9761225
This Small Business Innovation Research Phase I project will develop a computational environment which will enable optimization of magnetic resonance (MR) flow sequences. The optimization (suppression or enhancement) of signal intensity from arteries and veins in magnetic resonance images (MRI's) requires a major effort by MRI sequence developers Currently, costly clinical studies in commercial scanners are used to optimize imaging sequences. The goal of the research will be to combine a Computational Fluid Dynamics (CFD) program with a MR sequence emulation module. The Bloch equation will be integrated using CFD-derived fluid velocities in a finite volume scheme. The net magnetization within the flow field will be summed to generate a simulated echo signal. Multiple echo signals will yield a k-space data which will be 2D Fourier transformed to display the simulated image. The project will also apply Bayesian statistical methods to assess the inverse problem of obtaining important physiologic parameters (e.g. flow rate) given a MR echo signal. The Phase I project will utilize arbitrary sequences within a laminar flow field. Phase II will extend the simulation capabilities to include turbulent flow, injection of contrast agents, and neural net logic for the inverse problem. A number of imaging system manufacturers have expressed a need for a computational optimization tool for MR flow imaging. If the approach is successful, the cost savings could be substantial: currently, one hour on a commercial MR scanner is valued at around $1500, while one hour on a computer workstation is typically valued at around $9. ݾ?%[¤þ[ X <"L7@Úm g M¶' P?¦Ò<"z ?Yf¯?¿$Ì?¾Â!ÌM¾Á·?@ ½?2 $¶ÎGÁ ?¬ ^ ? P}´o:Pp _ ?ð+??úf(??5$´µ?M´µ t ?;{47_¸ ½În.±)wzp rC ÝðA-ÈGÁ·?< ©^¶ ?yNL²[v·9@¾m o:|?ZB?wþHkí!ú"p?ß¹Ìñ»¹? L¾4Nfñ»¹,'PpÔf???¦?µ¼?#Í pÔf?;?ßm?Z)óÔo?$@ó¶Ã?¶9L ¾P ¬.]À _.¸Ãz 'Ý?< ¢ÚOB¶ÙvÕC uo:ßXTõ?ZÎ ÁÏ 'Ý?< ¢ ó[;¸$¯)²O¬ ð¨?¬C¬¨ ^ Ú4 r:[*¶° ?¾ÌYÕ¾´Á·,'PpÔf???¦¹ °w ?]] ¢ LKÒf¥ IÌ8fM LM3p¶9 ¦ ±}3'[ ¦M# JÌM ý
