Improved respiratory self‐navigation for 3D radial acquisitions through the use of a pencil‐beam 2D‐T2‐prep for free‐breathing, whole‐heart coronary MRA

    loading  Checking for direct PDF access through Ovid

Excerpt

In traditional whole‐heart magnetic resonance angiography (MRA), cardiac and respiratory motion make it challenging to both rapidly and accurately acquire high‐resolution images 1. To minimize the effects of cardiac motion, data acquisition is most often timed to coincide with quiescent periods of the cardiac cycle (i.e., end‐systole or mid‐diastole) 4 through electrocardiography (ECG) triggering or self‐gated methods 6. Conversely, minimizing the effects of the respiratory motion is more problematic. Most often, this is achieved by discarding and reacquiring data collected during unfavorable respiratory phases, which are identified through use of diaphragmatic respiratory navigators 13. Although several innovative strategies have been proposed to improve on diaphragmatic navigators 14, navigator‐based approaches nonetheless remain highly inefficient. Additionally, these approaches also suffer from problems such as respiratory drift 17, suboptimal navigator positioning 18, temporal delays 19, hysteresis effects 20, operator dependence, and the fact that diaphragmatic respiratory motion is only an approximation of cardiac respiratory motion 21.
A more accurate and time‐efficient approach may be to directly track the heart's respiratory motion and to then perform motion correction on data acquired during non‐ideal respiratory phases. Because a displacement in image space is equal to a phase shift in k‐space 22, one may compensate for the respiratory displacement of the heart by adding a corresponding phase shift to data acquired during any given respiratory position. In turn, this allows for 100% scan efficiency, as data may be used regardless of respiratory position. Whereas Cartesian imaging nonetheless remains the reference standard, self‐navigated approaches 23 such as these have found widespread application, with several notable developments 27, and even have become routine in certain clinical settings 36.
To identify the aforementioned respiratory motion, one implementation collects a 1D superior–inferior (SI) projection at the start of each ECG‐triggered acquisition window 23. In that approach, the blood pool is identified as a hyperintense structure; by tracking its displacement, respiratory motion can be identified and used for motion correction. Although highly promising, these self‐navigation (SN) techniques nonetheless remain imperfect.
One major issue is that static structures, such as the chest wall and arms, make it difficult to reliably track the position of the blood pool. This may be particularly problematic in female or obese patients in whom increased chest tissue can interfere with blood pool tracking. Even when motion detection is successful, rigid motion correction may subsequently introduce artefacts when applied to static structures. That is, when compensating for respiratory motion, one effectively turns nonmoving structures into moving ones. In Cartesian acquisitions, this artificial motion can result in blurring and ghosting, whereas in radial acquisitions this may also present as streaking 40. This advances the hypothesis that suppressing signal from these static tissues may significantly improve image quality in SN.
Several inner‐volume selection (IVS)/outer‐volume selection (OVS) techniques have been proposed 41, though many rely on lengthy excitation or preparation schemes. Conversely, a 2D‐T2‐Preparation (2D‐T2‐Prep) 46 has recently been introduced, which incorporates a 2D selective excitation pulse into a T2‐Prep module. Prior to imaging, this technique adds T2‐weighting to a cylindrical region of tissue, while simultaneously spoiling signal elsewhere.
Although this technique has been reported for navigator‐based coronary imaging 46, it has not yet been tested with whole‐heart self‐navigated 3D radial MRA. Because whole‐heart MRA is frequently T2‐prepared, replacing a conventional T2‐Prep with this 2D‐T2‐Prep should add no additional time requirements. Additionally, by suppressing signal from static tissues, image quality should improve, as should motion tracking. Thus, the 2D‐T2‐Prep provides a useful tool for exploring the previously unreported idea that outer‐volume suppression can improve self‐navigated image acquisition.

Related Topics

    loading  Loading Related Articles