RACER‐GRASP: Respiratory‐weighted, aortic contrast enhancement‐guided and coil‐unstreaking golden‐angle radial sparse MRI

Feng, Li; Huang, Chenchan; Shanbhogue, Krishna; Sodickson, Daniel K.; Chandarana, Hersh; Otazo, Ricardo

doi:10.1002/mrm.27002

Cited by 35 publications

(63 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This component would be essential in improving the efficiency of routine clinical workflow, given that over 100 dynamic frames can be generated in GRASP‐Pro (e.g., 175 frames in this study) and therefore manual selection of desired contrast phases (e.g., an arterial phase) can be cumbersome and time‐consuming. Moreover, the deep learning‐based artery selection does not rely on specific contrast enhancement pattern . As a result, this will be useful in DCE‐MRI irrespective of contrast agent, dose, and volume, such as the hepatobiliary contrast agent Eovist that typically does not have a quick and sharp first pass dynamics.…”

Section: Discussionmentioning

confidence: 99%

“…However, the application of GRASP‐Pro to moving organs, such as the liver or the lung, will require further optimization, given that respiratory motion is patient‐dependent and therefore the optimal number of PCs ( K ) in GRASP‐Pro may vary from patient to patient. The motion management strategies proposed in the XD‐GRASP and RACER‐GRASP techniques could be further incorporated into GRASP‐Pro to handle respiratory motion, but this will require additional investigation and will be explored in future work.…”

Section: Discussionmentioning

confidence: 99%

“…However, because the purpose of artery segmentation is only to obtain the peak artery enhancement timing, or the peak dynamic frame index, so that clinically relevant contrast phases can be selected automatically, a perfect segmentation of the artery with a high‐level of precision is not required. Besides, we have applied a clustering algorithm to further ensure the accuracy of generating contrast enhancement curves in the artery. Fourth, the GRASP‐Pro reconstruction was performed slice by slice because of limited GPU memory.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

GRASP‐Pro: imProving GRASP DCE‐MRI through self‐calibrating subspace‐modeling and contrast phase automation

Feng

Huang

Tong

et al. 2019

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

Purpose To propose a highly accelerated, high‐resolution dynamic contrast‐enhanced MRI (DCE‐MRI) technique called GRASP‐Pro (golden‐angle radial sparse parallel imaging with imProved performance) through a joint sparsity and self‐calibrating subspace constraint with automated selection of contrast phases. Methods GRASP‐Pro reconstruction enforces a combination of an explicit low‐rank subspace‐constraint and a temporal sparsity constraint. The temporal basis used to construct the subspace is learned from an intermediate reconstruction step using the low‐resolution portion of radial k‐space, which eliminates the need for generating the basis using auxiliary data or a physical signal model. A convolutional neural network was trained to generate the contrast enhancement curve in the artery, from which clinically relevant contrast phases are automatically selected for evaluation. The performance of GRASP‐Pro was demonstrated for high spatiotemporal resolution DCE‐MRI of the prostate and was compared against standard GRASP in terms of overall image quality, image sharpness, and residual streaks and/or noise level. Results Compared to GRASP, GRASP‐Pro reconstructed dynamic images with enhanced sharpness, less residual streaks and/or noise, and finer delineation of the prostate without prolonging reconstruction time. The image quality improvement reached statistical significance (P < 0.05) in all the assessment categories. The neural network successfully generated contrast enhancement curves in the artery, and corresponding peak enhancement indexes correlated well with that from the manual selection. Conclusion GRASP‐Pro is a promising method for rapid and continuous DCE‐MRI. It enables superior reconstruction performance over standard GRASP and allows reliable generation of artery enhancement curve to guide the selection of desired contrast phases for improving the efficiency of GRASP MRI workflow.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

GRASP‐Pro: imProving GRASP DCE‐MRI through self‐calibrating subspace‐modeling and contrast phase automation

Feng

Huang

Tong

et al. 2019

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

show abstract

“…The use of streaking reduction methods in these reconstruction settings is an interesting area for future research. The RACER‐GRASP method for dynamic contrast enhanced imaging with built‐in streaking artifact suppression is an example of one such method. The streaking reduction step in RACER‐GRASP is based on suppressing signal from strongly streaking coils according to the streaking artifacts level and can be used with iterative reconstruction methods.…”

Section: Discussionmentioning

confidence: 99%

Radial streak artifact reduction using phased array beamforming

Mandava

Keerthivasan

Martín

et al. 2019

Magnetic Resonance in Med

View full text Add to dashboard Cite

Purpose A new method for streak artifact reduction in radial MRI based on phased array filtering. Theory Radial imaging in applications that require large fields‐of‐view can be susceptible to streaking artifacts due to gradient nonlinearities. Coil removal methods prune the coils contributing the most to streaking artifacts at the expense of signal loss. Phased array beamforming is a form of spatial filtering used to suppress unwanted signals. The proposed method uses interference covariance generated from the streaking artifact samples which are manually extracted with phased array beamforming to suppress streaking in the images. Methods The performance of the proposed method was evaluated on abdomen radial fast spin echo images acquired on a 1.5T Siemens scanner and compared with previously proposed methods. Results Our results demonstrate that the proposed method can effectively suppress streaking artifacts without any noticeable loss in signal levels. Coil removal methods can suppress streaks as well but they may incur significant signal loss due to coil pruning. Quantitative metrics also demonstrate the superiority of the proposed method over earlier methods. Conclusion The use of interference covariance with phased array beamforming can help reduce streaking artifacts.

show abstract

“…The quasi‐periodic respiratory motion signal was extracted from the centers of stack‐of‐stars k ‐space data, which reflects the change of average image intensity over time. The respiratory motion detection was performed using the principal component analysis (PCA)‐based method previously proposed …”

Section: Methodsmentioning

confidence: 99%

Pancreas deformation in the presence of tumors using feature tracking from free‐breathing XD‐GRASP MRI

Chițiboi

Muckley

Dane

et al. 2019

Magnetic Resonance Imaging

Self Cite

View full text Add to dashboard Cite

Background: Quantifying the biomechanical properties of pancreatic tumors could potentially help with assessment of tumor aggressiveness, prognosis, and prediction of therapy response. Purpose: To quantify respiratory-induced deformation in the pancreas and pancreatic lesions using XD-GRASP (eXtra-Dimensional Golden-angle RAdial Sparse Parallel), MRI. Study Type: Retrospective study where patients undergoing clinically indicated abdominal MRI which included free-breathing radial T1-weighted (T1W) imaging were studied. Subjects: Thirty-two patients (12 male and 20 female) including nine with pancreatic lesions constituted our study cohort. Field Strength/Sequence: 3.0 T with T1WI contrast-enhanced gradient echo radial free-breathing acquisition. Assessment: Using the XD-GRASP imaging technique, the acquired free-breathing radial data were sorted and binned into 10 consecutive respiratory motion states that were jointly reconstructed. 3D deformation fields along the respiratory dimension were computed using an optical flow method and were analyzed in the pancreas. Statistical Tests: The Wilcoxon signed-rank test was used to assess the difference in average displacement across pancreatic regions, while the Wilcoxon rank-sum test was used for displacement differences between patients with and without tumors. The interclass correlation coefficient (ICC) was computed to assess consistency between observers for each image quality measure. Results: There was a significantly larger displacement in the pancreatic tail compared with the head (8.2 ± 3.7 mm > 5.8 ± 2.4 mm; P < 0.001) and body regions (8.2 ± 3.7 mm > 6.6 ± 2.9 mm; P < 0.001). Furthermore, there was reduced normalized average displacement in patients with pancreatic lesions compared with subjects without lesions (0.33 ± 0.1 < 0.69 ± 0.26, P < 0.001 for the head; 0.30 ± 0.1 < 0.84 ± 0.31, P < 0.001 for the body; and 0.44 ± 0.31 < 1.08 ± 0.53, P < 0.001 for the tail, respectively). Data Conclusion: Free-breathing respiratory motion-sorted XD-GRASP MRI has the potential to noninvasively characterize the biomechanical properties of the pancreas by quantifying breathing-induced mechanical displacement.

show abstract

RACER‐GRASP: Respiratory‐weighted, aortic contrast enhancement‐guided and coil‐unstreaking golden‐angle radial sparse MRI

Cited by 35 publications

References 23 publications

GRASP‐Pro: imProving GRASP DCE‐MRI through self‐calibrating subspace‐modeling and contrast phase automation

GRASP‐Pro: imProving GRASP DCE‐MRI through self‐calibrating subspace‐modeling and contrast phase automation

Radial streak artifact reduction using phased array beamforming

Pancreas deformation in the presence of tumors using feature tracking from free‐breathing XD‐GRASP MRI

Contact Info

Product

Resources

About