Strategies for rapid reconstruction in 3D MRI with radial data acquisition: 3D fast Fourier transform vs two-step 2D filtered back-projection

Rapid CNN-based needle localization for automatic slice alignment in MR-guided interventions using 3D undersampled radial white-marker imaging

BACKGROUND

In MR-guided in-bore percutaneous needle interventions, typically 2D interactive real-time imaging is used for navigating the needle into the target. Misaligned 2D imaging planes can result in losing visibility of the needle in the 2D images, which impedes successful targeting. Necessary iterative manual slice adjustment can prolong interventional workflows. Therefore, rapid automatic alignment of the imaging planes with the needle would be preferable to improve such workflows.

PURPOSE

To investigate rapid 3D localization of needles in MR-guided interventions via a convolutional neural network (CNN)-based localization algorithm using an undersampled white-marker contrast acquisition for the purpose of automatic imaging slice alignment.

METHODS

A radial 3D rf-spoiled gradient echo MR pulse sequence with white-marker encoding was implemented and a CNN-based localization algorithm was employed to extract position and orientation of an aspiration needle from the undersampled white-marker images. The CNN was trained using porcine tissue phantoms (257 needle trajectories, four-fold data augmentation, 90%/10% split into training and validation dataset). Achievable localization times and accuracy were evaluated retrospectively in an ex vivo study (109 needle trajectories) for a range of needle orientations between 78° and 90° relative to the B0 field. A proof-of-concept in vivo experiment was performed in two porcine animal models and feasibility of automatic imaging slice alignment was evaluated retrospectively.

RESULTS

Ex vivo needle localization was achieved with a median localization accuracy of 1.9 mm (distance needle tip to detected needle axis) and a median angular deviation of 2.6° for needle orientations between 86° and 90° to the B0 field from fully sampled WM images (resolution of (4 mm)3, 6434 acquired radial k-space spokes, acquisition time of 80.4 s) in a field-of-view of (256 mm)3. Localization accuracy decreased with increasing undersampling and needle trajectory increasingly aligned with B0. For needle orientations between 86° and 90° to the B0 field, a highly accelerated acquisition of only 32 k-space spokes (acquisition time of 0.4 s) yielded a median localization accuracy of 3.1 mm and a median angular deviation of 4.7°. For needle orientations between 78° and 82° to the B0 field, a median accuracy and angular deviation of 3.5 mm and 6.8° could still be achieved with 64 sampled spokes (acquisition time of 0.8 s). In vivo, a localization accuracy of 1.4 mm and angular deviation of 3.4° was achieved sampling 32 k-space spokes (acquisition time of 0.48 s) with the needle oriented at 87.7° to the B0 field. For a needle oriented at 77.6° to the B0 field, localization accuracy of 5.3 mm and angular deviation of 6.8° were still achieved sampling 128 k-space spokes (acquisition time of 1.92 s), allowing for retrospective slice alignment.

CONCLUSION

The investigated approach enables passive biopsy needle localization in 3D. Acceleration of the localization to real-time applicability is feasible for needle orientations approximately perpendicular to B0. The method can potentially facilitate MR-guided needle interventions by enabling automatic imaging slice alignment with the needle.

Line scan-based rapid magnetic resonance imaging of repetitive motion

Two-dimensional (2D) line scan-based dynamic magnetic resonance imaging (MRI) is examined as a means to capture the interior of objects under repetitive motion with high spatiotemporal resolutions. The method was demonstrated in a 9.4-T animal MRI scanner where line-by-line segmented k-space acquisition enabled recording movements of an agarose phantom and quail eggs in different conditions—raw and cooked. A custom MR-compatible actuator which utilized the Lorentz force on its wire loops in the scanner’s main magnetic field effectively induced the required periodic movements of the objects inside the magnet. The line-by-line k-space segmentation was achieved by acquiring a single k-space line for every frame in a motion period before acquisition of another line with a different phase-encode gradient in the succeeding motion period. The reconstructed time-course images accurately represented the objects’ displacements with temporal resolutions up to 5.5 ms. The proposed method can drastically increase the temporal resolution of MRI for imaging rapid periodic motion of objects while preserving adequate spatial resolution for internal details when their movements are driven by a reliable motion-inducing mechanism.

Total variation with modified group sparsity for CT reconstruction under low SNR

BACKGROUND AND OBJECTIVE

Since the stair artifacts may affect non-destructive testing (NDT) and diagnosis in the later stage, an applicable model is desperately needed, which can deal with the stair artifacts and preserve the edges. However, the classical total variation (TV) algorithm only considers the sparsity of the gradient transformed image. The objective of this study is to introduce and test a new method based on group sparsity to address the low signal-to-noise ratio (SNR) problem.

METHODS

This study proposes a weighted total variation with overlapping group sparsity model. This model combines the Gaussian kernel and overlapping group sparsity into TV model denoted as GOGS-TV, which considers the structure sparsity of the image to be reconstructed to deal with the stair artifacts. On one hand, TV is the accepted commercial algorithm, and it can work well in many situations. On the other hand, the Gaussian kernel can associate the points around each pixel. Quantitative assessments are implemented to verify this merit.

RESULTS

Numerical simulations are performed to validate the presented method, compared with the classical simultaneous algebraic reconstruction technique (SART) and the state-of-the-art TV algorithm. It confirms the significantly improved SNR of the reconstruction images both in suppressing the noise and preserving the edges using new GOGS-TV model.

CONCLUSIONS

The proposed GOGS-TV model demonstrates its advantages to reduce stair artifacts especially in low SNR reconstruction because this new model considers both the sparsity of the gradient image and the structured sparsity. Meanwhile, the Gaussian kernel is utilized as a weighted factor that can be adapted to the global distribution.