Background phase correction for quantitative cardiovascular MRI

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Project Summary/Abstract Alterations in hemodynamics have been linked to wide-ranging cardiac and vascular conditions, including congenital heart disease, valvular abnormalities, aortic atherosclerosis and aneurysm, renal stenosis, portal hypertension due to liver cirrhosis, intracranial aneurysm and stenosis, and peripheral arterial disease. Phase- contrast MRI (PC-MRI) is a noninvasive imaging technique that can potentially provide a comprehensive evaluation of hemodynamics, which can be coupled with other important MRI-derived information on cardiovascular anatomy, function, and tissue characterization. However, the credibility of PC-MRI as a quantitative tool is challenged by the inaccuracies introduced by background phase. Studies have shown that this background phase can introduce significant errors in the quantification of flow. One method that has been proposed to quantify and correct for the background phase is to perform a separate scan using a static phantom. This method, despite being robust, is impractical because of the significant extra time required to perform phantom imaging for each clinical sequence performed. Another widely reported method to correct background phase is based on performing polynomial fitting to the pixels that belong to the static tissue. The accuracy of this method heavily relies on the availability of static tissue in the close vicinity of the region of interest–a requirement that is often not met when imaging the heart or great vessels. To address the issue of background phase that invariably impacts every PC-MRI measurement, we propose a new correction scheme called multi-slice acquisition and processing (mSAP). In mSAP, in addition to the slice of interest, at least one extra slice is collected using the same slice orientation and gradient waveforms but with a different table position. By jointly processing the background phase information from multiple slices, mSAP circumvents the shortcomings associated with existing methods at the cost of slightly prolonged acquisition. In Specific Aim 1, we will develop a data acquisition and processing method for mSAP. We will modify and streamline our current PC-MRI acquisition protocol to minimize the overhead associated with mSAP. To jointly process the multi-slice data, we will develop and implement polynomial regression based on generalized least squares with an ℓ1-norm penalty imposed on the coefficients of the polynomial. This fitting method is completely automated and does not require tuning parameters. In Specific Aim 2, we will validate mSAP using a pulsatile flow phantom and healthy volunteers. By using just one additional slice, we anticipate mSAP to reduce the background phase errors to the level where miscalculation of flow volume is reduced to below 5%. Our preliminary data demonstrate the validity of the primary assumption made in mSAP, i.e., background phase maps collected using the same gradient waveforms but different table positions are identical. We believe the methods developed in this work can be readily utilized in clinical settings to improve the accuracy of an otherwise potent imaging tool.