A predictive software tool for optimal timing in contrast enhanced carotid MR angiography

A clear understanding of the first pass dynamics of contrast agents in the vascular system is crucial in synchronizing data acquisition of 3D MR angiography (MRA) with arrival of the contrast bolus in the vessels of interest. We implemented a computational model to simulate contrast dynamics in the vessels using the theory of linear time-invariant systems. The algorithm calculates a patient-specific impulse response for the contrast concentration from time-resolved images following a small test bolus injection. This is performed for a specific region of interest and through deconvolution of the intensity curve using the long division method. Since high spatial resolution 3D MRA is not time-resolved, the method was validated on time-resolved arterial contrast enhancement in Multi Slice CT angiography. For 20 patients, the timing of the contrast enhancement of the main bolus was predicted by our algorithm from the response to the test bolus, and then for each case the predicted time of maximum intensity was compared to the corresponding time in the actual scan which resulted in an acceptable agreement. Furthermore, as a qualitative validation, the algorithm's predictions of the timing of the carotid MRA in 20 patients with high quality MRA were correlated with the actual timing of those studies. We conclude that the above algorithm can be used as a practical clinical tool to eliminate guesswork and to replace empiric formulae by a priori computation of patient-specific timing of data acquisition for MR angiography.

[1]  Norbert Wiener,et al.  Extrapolation, Interpolation, and Smoothing of Stationary Time Series , 1964 .

[2]  G D Rubin,et al.  Improved uniformity of aortic enhancement with customized contrast medium injection protocols at CT angiography. , 2000, Radiology.

[3]  J. Debatin,et al.  Peripheral magnetic resonance angiography. , 2001, Topics in magnetic resonance imaging : TMRI.

[4]  A. Djamali,et al.  Nephrogenic systemic fibrosis: risk factors and incidence estimation. , 2007, Radiology.

[5]  B. Thiers Gadodiamide-Associated Nephrogenic Systemic Fibrosis: Why Radiologists Should Be Concerned , 2008 .

[6]  D. Fleischmann,et al.  Mathematical analysis of arterial enhancement and optimization of bolus geometry for CT angiography using the discrete fourier transform. , 1999, Journal of computer assisted tomography.

[7]  K. Hayashi,et al.  Aortoiliac and lower extremity arteries: comparison of three-dimensional dynamic contrast-enhanced subtraction MR angiography and conventional angiography. , 1999, Radiology.

[8]  Raimund Erbel,et al.  Nephrogenic systemic fibrosis: pathogenesis, diagnosis, and therapy. , 2009, Journal of the American College of Cardiology.

[9]  T. Leiner Magnetic Resonance Angiography of Abdominal and Lower Extremity Vasculature , 2005, Topics in magnetic resonance imaging : TMRI.

[10]  S. Cowper,et al.  Nephrogenic systemic fibrosis: a population study examining the relationship of disease development to gadolinium exposure. , 2007, Clinical journal of the American Society of Nephrology : CJASN.

[11]  M. Blomley,et al.  Bolus dynamics: theoretical and experimental aspects. , 1997, The British journal of radiology.