Advanced Hyperpolarized 13 C Metabolic Imaging Protocol for Patients with Gliomas: A Comprehensive Multimodal MRI Approach.
Adam W AutrySana VaziriJeremy W GordonHsin-Yu ChenYaewon KimDuy DangMarisa LafontaineRalph NoeskeRobert BokJavier E Villanueva-MeyerJennifer L ClarkeNancy Ann Oberheim BushSusan M ChangDuan XuJanine M LupoPeder Eric Zufall LarsonDaniel B VigneronYan LiPublished in: Cancers (2024)
This study aimed to implement a multimodal 1 H/HP- 13 C imaging protocol to augment the serial monitoring of patients with glioma, while simultaneously pursuing methods for improving the robustness of HP- 13 C metabolic data. A total of 100 1 H/HP [1- 13 C]-pyruvate MR examinations (104 HP- 13 C datasets) were acquired from 42 patients according to the comprehensive multimodal glioma imaging protocol. Serial data coverage, accuracy of frequency reference, and acquisition delay were evaluated using a mixed-effects model to account for multiple exams per patient. Serial atlas-based HP- 13 C MRI demonstrated consistency in volumetric coverage measured by inter-exam dice coefficients (0.977 ± 0.008, mean ± SD; four patients/11 exams). The atlas-derived prescription provided significantly improved data quality compared to manually prescribed acquisitions ( n = 26/78; p = 0.04). The water-based method for referencing [1- 13 C]-pyruvate center frequency significantly reduced off-resonance excitation relative to the coil-embedded [ 13 C]-urea phantom (4.1 ± 3.7 Hz vs. 9.9 ± 10.7 Hz; p = 0.0007). Significantly improved capture of tracer inflow was achieved with the 2-s versus 5-s HP- 13 C MRI acquisition delay ( p = 0.007). This study demonstrated the implementation of a comprehensive multimodal 1 H/HP- 13 C MR protocol emphasizing the monitoring of steady-state/dynamic metabolism in patients with glioma.
Keyphrases
- contrast enhanced
- randomized controlled trial
- high resolution
- magnetic resonance imaging
- end stage renal disease
- ejection fraction
- newly diagnosed
- electronic health record
- healthcare
- magnetic resonance
- single cell
- big data
- prognostic factors
- computed tomography
- primary care
- high grade
- chronic pain
- deep learning
- mass spectrometry
- photodynamic therapy
- rna seq