High spatial and temporal resolution dynamic contrast-enhanced magnetic resonance angiography (CE-MRA) using compressed sensing with magnitude image subtraction

PURPOSE
We propose a compressed-sensing (CS) technique based on magnitude image subtraction for high spatial and temporal resolution dynamic contrast-enhanced MR angiography (CE-MRA).


METHODS
Our technique integrates the magnitude difference image into the CS reconstruction to promote subtraction sparsity. Fully sampled Cartesian 3D CE-MRA datasets from 6 volunteers were retrospectively under-sampled and three reconstruction strategies were evaluated: k-space subtraction CS, independent CS, and magnitude subtraction CS. The techniques were compared in image quality (vessel delineation, image artifacts, and noise) and image reconstruction error. Our CS technique was further tested on seven volunteers using a prospectively under-sampled CE-MRA sequence.


RESULTS
Compared with k-space subtraction and independent CS, our magnitude subtraction CS provides significantly better vessel delineation and less noise at 4× acceleration, and significantly less reconstruction error at 4× and 8× (P < 0.05 for all). On a 1-4 point image quality scale in vessel delineation, our technique scored 3.8 ± 0.4 at 4×, 2.8 ± 0.4 at 8×, and 2.3 ± 0.6 at 12× acceleration. Using our CS sequence at 12× acceleration, we were able to acquire dynamic CE-MRA with higher spatial and temporal resolution than current clinical TWIST protocol while maintaining comparable image quality (2.8 ± 0.5 vs. 3.0 ± 0.4, P = NS).


CONCLUSION
Our technique is promising for dynamic CE-MRA.

From 16th Annual SCMR Scientific Sessions San Francisco, CA, USA. 31 January -3 February 2013 Background Due to limitations in image acquisition speed, dynamic CE-MRA typically has lower spatial resolution than conventional CE-MRA. In a breath-held dynamic CE-MRA acquisition, the subtraction of a pre-contrast mask to all post-contrast frames promotes sparsity of the resulting difference images. This "subtraction sparsity" using direct k-space complex subtraction has previously been shown to benefit parallel imaging as well as compressed sensing but suffers from SNR loss. We propose a novel CS algorithm for dynamic CE-MRA that integrates magnitude subtraction into the reconstruction to avoid direct complex subtraction while taking advantages of the "subtraction sparsity".

Methods
The reconstruction uses a split-Bregman minimization of the sum of the L1 norm of the pixel-wise magnitude difference between two successive temporal frames, i.e. |(|I_2 |-|I_1 |)|_1, a fidelity term and a total variation (TV) sparsity term.
Retrospective study: a full-sampled Cartesian 3D GRE sequence was used for the CE-MRA acquisitions on 6 volunteers with a resolution of 1x1x1.3-2.2 mm 3 . Datasets were retrospectively subsampled and different strategies of reconstruction were quantitatively and qualitatively evaluated: a) magnitude subtraction of the original k-space images; b) separate independent CS reconstructions (IDCS); c) k-space complex subtraction CS reconstruction (KDCS); d) magnitude subtraction CS reconstruction (MDCS).
Prospective study: a sequence was implemented that is capable of prospectively acquire under-sampled 3D dynamic CE-MRA data according to pre-defined sampling mask at 1x1x2.0 mm3 resolution. The net acceleration from random subsampling was set to 12-fold, enabling the acquisition of 6 volumes within a single breath-hold on 2 volunteers.

Results
Proposed novel magnitude-subtraction CS reconstructs images with accurate details (Figure 1) compared to independent CS and less noise than complex-subtraction CS. At 10-fold acceleration, RMSE was lower for MDCS (20.36 %) compared to IDCS (28.93%, p<0.05) and KDCS (25.15%, p=0.07) and qualitative scores confirmed the superiority of MDCS images (2.59±0.51) to KDCS (1±0, p<0.01) and IDCS (1.75±0.45, p<0.05) images. K-space subtraction suffers very poor images quality due to the SNR loss from complex subtraction. Independent CS reconstruction has significantly lower images quality while magnitude subtraction CS images were considered "good" for all subsampling rates.

Conclusions
Based on our developed prospective CS dynamic CE-MRA sequence and our magnitude-subtraction based reconstruction algorithm, high quality dynamic CE-MRA is feasible even at 12.5x acceleration as shown in Figure 2. Figure 1 Thin MIP chest CE-MRA (zoomed, same windowing level). The proposed magnitude subtraction CS (d) reconstructs excellent image quality, close to reference (a) from highly under-sampled data while subtracted images from independent CS reconstruction (b) of each volume suffers from image degradation. Image quality is inferior using k-space subtraction CS reconstruction (c) due to significant SNR loss.

Figure 2
The high image quality of a prospectively subsampled (12.5-fold) dynamic high-resolution CS-CE-MRA allows obtaining both dynamic travel of contrast bolus in the circulatory system and clear delineation of small blood vessels. Thin MIP are reconstructed in coronal planes (after mask subtraction, 10cm thick) and transverse planes (without background subtraction, 6cm thick). The comparison with TWIST MIPs as used clinically in our institution shows the improvement of resolution in the slice direction (2mm instead of 6mm) at closest matching time points. The color-combined MIP (Right) provides both high-resolution vascular structure and dynamic information.
CS-accelerated MRA has the potential to benefit MRA clinical practice. The extension of our algorithm with parallel imaging remains to be explored. The concept of magnitude subtraction can also be extended to many applications like dynamic contrast-enhanced MRI for perfusion imaging.