SMART tracking: Simultaneous anatomical imaging and real-time passive device tracking for MR-guided interventions

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.

Passive needle guide tracking with radial acquisition and phase-only cross-correlation

PURPOSE

Acceleration of a passive tracking sequence based on phase-only cross-correlation (POCC) using radial undersampling.

METHODS

The phase-only cross-correlation (POCC) algorithm allows passive tracking of interventional instruments in real-time. In a POCC sequence, two cross-sectional images of a needle guide with a positive MR contrast are continuously acquired from which the instrument trajectory is calculated. Conventional Cartesian imaging for tracking is very time consuming; here, a higher temporal resolution is achieved using a highly undersampled radial acquisition together with a modified POCC algorithm that incorporates the point-spread-function. Targeting and needle insertion is performed in two phantom experiments with 16 fiducial targets, each using 4 and 16 radial projections for passive tracking. Additionally, targeting of eight deep lying basivertebral veins in the lumbar spines is performed for in vivo proof-of-application with four radial projections for needle guide tracking.

RESULTS

The radially undersampled POCC sequence yielded in the phantom experiments a lateral targeting accuracy of 1.1 ± 0.4 mm and 1.0 ± 0.5 mm for 16 and 4 radial projections, respectively, without any statistically significant difference. In the in vivo application, a mean targeting duration of 62 ± 13 s was measured.

CONCLUSION

Radial undersampling can drastically reduce the acquisition time for passive tracking in a POCC sequences for MR-guided needle interventions without compromising the targeting accuracy.

Automatic needle tracking using Mask R-CNN for MRI-guided percutaneous interventions

PURPOSE

Accurate needle tracking provides essential information for MRI-guided percutaneous interventions. Passive needle tracking using MR images is challenged by variations of the needle-induced signal void feature in different situations. This work aimed to develop an automatic needle tracking algorithm for MRI-guided interventions based on the Mask Region Proposal-Based Convolutional Neural Network (R-CNN).

METHODS

Mask R-CNN was adapted and trained to segment the needle feature using 250 intra-procedural images from 85 MRI-guided prostate biopsy cases and 180 real-time images from MRI-guided needle insertion in ex vivo tissue. The segmentation masks were passed into the needle feature localization algorithm to extract the needle feature tip location and axis orientation. The proposed algorithm was tested using 208 intra-procedural images from 40 MRI-guided prostate biopsy cases, and 3 real-time MRI datasets in ex vivo tissue. The algorithm results were compared with human-annotated references.

RESULTS

In prostate datasets, the proposed algorithm achieved needle feature tip localization error with median Euclidean distance (dxy) of 0.71 mm and median difference in axis orientation angle (dθ) of 1.28°, respectively. In 3 real-time MRI datasets, the proposed algorithm achieved consistent dynamic needle feature tracking performance with processing time of 75 ms/image: (a) median dxy = 0.90 mm, median dθ = 1.53°; (b) median dxy = 1.31 mm, median dθ = 1.9°; (c) median dxy = 1.09 mm, median dθ = 0.91°.

CONCLUSIONS

The proposed algorithm using Mask R-CNN can accurately track the needle feature tip and axis on MR images from in vivo intra-procedural prostate biopsy cases and ex vivo real-time MRI experiments with a range of different conditions. The algorithm achieved pixel-level tracking accuracy in real time and has potential to assist MRI-guided percutaneous interventions.