There is growing awareness of the detrimental effect of radiation associated with diagnostic imaging in pediatric patients. Although in many cases abdominal MRI can serve as a viable alternative to CT, it is underutilized in clinical practice due to a) high sensitivity to motion artifacts, b) need to perform these exams under general anesthesia or deep sedation to obtain acceptable diagnostic image quality, and c) limitations in the diagnostic accuracy, as data cannot be acquired during suspended respiration in patients who are under sedation. In the proposed project, we will develop and evaluate novel motion-robust MRI techniques based on radial k-space acquisition and advanced non-linear reconstruction to overcome these limitations. The developments will complement our existing radial techniques for motion-robust MRI, which provide only fat- saturated T1-weighted contrast and, thus, cannot be utilized for comprehensive diagnostic exams so far. The technical developments include a new fat/water-separation technique for radial 3D multi-gradient-echo acquisitions, which can be used to assess the fat content of lesions and to resolve artifacts currently seen in T1-weighted radial acquisitions with spectral fat saturation. To obtain T2-weighted image contrast, a robust radial 3D TSE sequence will be developed and combined with a tailored compressed-sensing reconstruction to achieve clinically acceptable scan times. In addition, a novel time-efficient image-stabilization technique will be developed based on k-space consistency assessment and adaptive data rescanning. It will be integrated into all our radial sequences to prevent data inconsistencies from bulk patient motion. All developed techniques will be evaluated, first, in sedated pediatric patients with Tuberous Sclerosis and compared to the clinical-imaging standard. The improvement in image quality will be assessed in blinded-reader studies and by quantitatively estimating the lesion detection rate. The techniques will then be used to implement a full-radial diagnostic exam strategy for pediatric abdominal MRI, which will enable motion-robust examination of pediatric patients without the use of deep sedation or general anesthesia. The sedation-free imaging approach will be evaluated in patients with Tuberous Sclerosis and benchmarked in terms of image quality, detection accuracy, and exam time against the clinical standard for pediatric abdominal MRI performed under general anesthesia or with deep sedation. This new simplified exam strategy promises not only to change the way diagnostic body MRI is done in pediatric patients but it will also impact clinical imaging of elderly and sick patients who cannot adequately suspend respiration.

Public Health Relevance

In the proposed project, we will develop novel motion-robust MR imaging techniques based on radial k-space acquisition and advanced non-linear reconstruction, and evaluate them in clinical studies for abdominopelvic imaging of pediatric patients with Tuberous Sclerosis. Technical developments include a robust fat/water- separation method for T1-weighted radial scans, a radial 3D TSE sequence with compressed-sensing reconstruction for T2-weighted contrast, and a novel data-consistency-driven mechanism for additional image stabilization. These new developments will be combined with our existing radial techniques for fat-suppressed T1-weighted MRI into a full-radial diagnostic exam strategy for sedation-free abdominopelvic MRI and compared in terms of image quality, lesion-detection accuracy, and exam time to the conventional pediatric MRI exam scheme performed under general anesthesia.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB018308-04
Application #
9394798
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Liu, Guoying
Project Start
2015-02-01
Project End
2018-12-31
Budget Start
2018-01-01
Budget End
2018-12-31
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
New York University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
121911077
City
New York
State
NY
Country
United States
Zip Code
10010
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Feng, Li; Benkert, Thomas; Block, Kai Tobias et al. (2017) Compressed sensing for body MRI. J Magn Reson Imaging 45:966-987
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Stemkens, Bjorn; Benkert, Thomas; Chandarana, Hersh et al. (2017) Adaptive bulk motion exclusion for improved robustness of abdominal magnetic resonance imaging. NMR Biomed 30:
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