Dynamic coil selection for real‐time imaging in interventional MRI

Abstract

MR-guided intravascular interventions require image update rates of up to 10 images per second, which can be achieved using parallel imaging. However, parallel imaging requires many coil elements, which increases reconstruction times and thus compromises real-time image reconstruction. In this study a dynamic coil selection (DCS) algorithm is presented that selects a subset of receive coils to reduce image reconstruction times. The center-of-sensitivity coordinates and the relative signal intensities are determined for each coil in a prescan. During the intervention m coils are selected for reconstruction using a coil ranking based on the distance to the current slice or catheter position. In a phantom experiment for m ‫؍‬ 6, an optimal signal-to-background ratio (SBR) was achieved and foldover artifacts were avoided. In three animal experiments involving catheter manipulation in the aorta and the right heart chamber, the anatomy was successfully visualized at frame rates of about 5 Hz using active catheter tracking. Magn Reson Med 56:1156 -1162, 2006.

Real-time MR navigation and localization of an intravascular catheter with ferromagnetic components

“…In order to achieve higher forces, materials with higher saturation magnetization could be used and the catheter-tip design can be improved using more flexible materials for the connecting tube. The image acquisition process could be made faster using real-time parallel imaging techniques [24,25].…”

Automatic coil selection for channel reduction in SENSE-based parallel imaging

“…Coil element elimination could also be applied after digitization to decrease the reconstruction time [11]. Also in this case, the SVD-based coil selection would be an appropriate selection method, because of its short decision times.…”

“…The dynamic coil selection (DCS) algorithm, proposed by Mueller et al [11], selects a subset of receive coils to reduce the image reconstruction time in interventional imaging. This algorithm performs coil ranking based on two criteria: (i) the distance between the location with highest sensitivity for each coil and the current slice and (ii) the signal intensity in each coil.…”

“…These can be realized by a linear combination of the original coil data prior to reception, using an appropriate hardware combiner [7,19], data compression of the sampled data before reconstruction [1,5], or by discarding particular data from coil elements with low signal content (either before or after reception) [9,11]. The concept of a hardware combiner for the realization of the linear combination of the coil signals was presented by King et al [7] and Reykowski et al [19].…”

Magnetic Resonance Sequences and Rapid Acquisition for MR-Guided Interventions

“…A number of algorithms have been proposed for coil selection 43-45 to choose the most suitable subset of coils for reconstruction and array compression 46-49 to combine channels for reducing reconstruction time. Most notably, principle component analysis has been used for array compression without the need for coil sensitivity maps 47 .…”