Initial Assessment of 3D Magnetic Resonance Fingerprinting (MRF) Towards Quantitative Brain Imaging for Radiation Therapy.

PURPOSE Magnetic resonance fingerprinting (MRF) provides quantitative T1/T2 maps, enabling applications in clinical radiotherapy such as large-scale, multi-center clinical trials for longitudinal assessment of therapy response. We evaluated the feasibility of a quantitative 3D-MRF towards its radiotherapy applications of primary brain tumors. METHODS A fast whole-brain 3D-MRF sequence initially developed for diagnostic radiology was optimized using flexible body coils, which is the typical MR imaging setup for radiotherapy treatment planning and for MRI-guided treatment delivery. Optimization criteria included the accuracy and the precision of T1/T2 quantifications of polyvinylpyrrolidone (PVP) solutions, compared to those from the 3D-MRF using a 32-channel head coil. The accuracy of T1/T2 quantifications from the optimized MRF was first examined in healthy volunteers with two different coil setups. The intra- and inter-scanner variations of image intensity from the optimized sequence were quantified by longitudinal scans of the PVP solutions on two 3T scanners. Using a 3D-printed MRI geometry phantom, susceptibility-induced distortion with the optimized 3D-MRF was quantified as the Dice coefficient of phantom contours, compared to those from CT images. By introducing intentional head motion during 10% of the scan, the robustness of the optimized 3D-MRF towards motion was evaluated through visual inspection of motion artifacts and through quantitative analysis of image sharpness in brain MRF maps. RESULTS The optimized sequence acquired whole-brain T1, T2 and proton density maps and with a resolution of 1.2×1.2×3mm3 in 10 minutes, similar to the total acquisition time of 3D T1- and T2-weighted images of the same resolution. In vivo T1 and T2 values of the white and gray matter were consistent with literature. The intra- and inter-scanner variability of the intensity-normalized MRF T1 was 1.0%±0.7% and 2.3%±1.0% respectively, in contrast to 5.3%±3.8% and 3.2%±1.6% from the normalized T1-weighted MRI. Repeatability and reproducibility of MRF T1 were independent of intensity normalization. Both phantom and human data demonstrated that the optimized 3D-MRF is more robust to subject motion and artifacts from subject-specific susceptibility difference. Compared to CT contours, the Dice coefficient of phantom contours from 3D-MRF was 0.93, improved from 0.87 from the T1-weighted MRI. CONCLUSION Compared to conventional MRI, the optimized 3D-MRF demonstrated improved repeatability across time points and reproducibility across scanners for better tissue quantification, as well as improved robustness to subject-specific susceptibility and motion artifacts under a typical MR imaging setup for radiotherapy. More importantly, quantitative MRF T1/T2 measurements lead to promising potentials towards longitudinal quantitative assessment of treatment response for better adaptive therapy and for large-scale, multi-center clinical trials.

[1]  J. Schenck The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds. , 1996, Medical physics.

[2]  S. Holland,et al.  NMR relaxation times in the human brain at 3.0 tesla , 1999, Journal of magnetic resonance imaging : JMRI.

[3]  Peter Jezzard,et al.  Rapid T1 mapping using multislice echo planar imaging , 2001, Magnetic resonance in medicine.

[4]  G. Collewet,et al.  Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. , 2004, Magnetic resonance imaging.

[5]  Thoralf Niendorf,et al.  Rapid volumetric MRI using parallel imaging with order-of-magnitude accelerations and a 32-element RF coil array: feasibility and implications. , 2005, Academic radiology.

[6]  S. Schoenberg,et al.  Practical approaches to the evaluation of signal‐to‐noise ratio performance with parallel imaging: Application with cardiac imaging and a 32‐channel cardiac coil , 2005, Magnetic resonance in medicine.

[7]  P Jezzard,et al.  Functional changes in CSF volume estimated using measurement of water T2 relaxation , 2009, Magnetic resonance in medicine.

[8]  Robert Jeraj,et al.  Imaging for assessment of radiation-induced normal tissue effects. , 2010, International journal of radiation oncology, biology, physics.

[9]  Yue Cao,et al.  The promise of dynamic contrast-enhanced imaging in radiation therapy. , 2011, Seminars in radiation oncology.

[10]  Adam Johansson,et al.  CT substitute derived from MRI sequences with ultrashort echo time. , 2011, Medical physics.

[11]  Mark W. Woolrich,et al.  FSL , 2012, NeuroImage.

[12]  Bruce Fischl,et al.  Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI , 2012, Magnetic resonance in medicine.

[13]  Khader M Hasan,et al.  Human brain iron mapping using atlas‐based T2 relaxometry , 2012, Magnetic resonance in medicine.

[14]  J Balter,et al.  Patient-induced susceptibility effect on geometric distortion of clinical brain MRI for radiation treatment planning on a 3T scanner , 2013, Physics in medicine and biology.

[15]  M. Barton,et al.  The Potential for an Enhanced Role for MRI in Radiation-therapy Treatment Planning , 2013, Technology in cancer research & treatment.

[16]  Mary Feng,et al.  Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy , 2013, Physics in medicine and biology.

[17]  J. Duerk,et al.  Magnetic Resonance Fingerprinting , 2013, Nature.

[18]  Maxim Zaitsev,et al.  Magnetic properties of materials for MR engineering, micro-MR and beyond. , 2014, Journal of magnetic resonance.

[19]  Jan J W Lagendijk,et al.  MR guidance in radiotherapy , 2014, Physics in medicine and biology.

[20]  Yue Cao,et al.  Clinical applications for diffusion magnetic resonance imaging in radiotherapy. , 2014, Seminars in radiation oncology.

[21]  M. Griswold,et al.  MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout , 2015, Magnetic resonance in medicine.

[22]  Indrin J Chetty,et al.  Technology for Innovation in Radiation Oncology. , 2015, International journal of radiation oncology, biology, physics.

[23]  Catherine Coolens,et al.  Quantitative Imaging in Radiation Oncology: An Emerging Science and Clinical Service. , 2015, Seminars in radiation oncology.

[24]  Andrés Larroza,et al.  Texture Analysis in Magnetic Resonance Imaging: Review and Considerations for Future Applications , 2016 .

[25]  Vishwa Parekh,et al.  Radiomics: a new application from established techniques , 2016, Expert review of precision medicine and drug development.

[26]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[27]  M Griswold,et al.  MR Fingerprinting of Adult Brain Tumors: Initial Experience , 2017, American Journal of Neuroradiology.

[28]  Jing Li,et al.  A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images , 2018, European Radiology.

[29]  Jianhui Zhong,et al.  Robust sliding‐window reconstruction for Accelerating the acquisition of MR fingerprinting , 2017, Magnetic resonance in medicine.

[30]  Jesse I. Hamilton,et al.  MR fingerprinting for rapid quantification of myocardial T1, T2, and proton spin density , 2017, Magnetic resonance in medicine.

[31]  M. Griswold,et al.  Repeatability of magnetic resonance fingerprinting T1 and T2 estimates assessed using the ISMRM/NIST MRI system phantom , 2017, Magnetic resonance in medicine.

[32]  Prateek Prasanna,et al.  Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma , 2018, Scientific Reports.

[33]  G Nair,et al.  Transverse relaxation of cerebrospinal fluid depends on glucose concentration. , 2017, Magnetic resonance imaging.

[34]  J. Hennig,et al.  Pseudo Steady‐State Free Precession for MR‐Fingerprinting , 2017, Magnetic resonance in medicine.

[35]  P. Huang,et al.  Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics. , 2018, International journal of radiation oncology, biology, physics.

[36]  Shaihan J Malik,et al.  Joint system relaxometry (JSR) and Crámer‐Rao lower bound optimization of sequence parameters: A framework for enhanced precision of DESPOT T1 and T2 estimation , 2018, Magnetic resonance in medicine.

[37]  Dan Ma,et al.  Image reconstruction algorithm for motion insensitive MR Fingerprinting (MRF): MORF , 2018, Magnetic resonance in medicine.

[38]  M. Oldham,et al.  Executive summary of AAPM Report Task Group 113: Guidance for the physics aspects of clinical trials , 2018, Journal of applied clinical medical physics.

[39]  Vikas Gulani,et al.  Fast 3D magnetic resonance fingerprinting for a whole‐brain coverage , 2018, Magnetic resonance in medicine.

[40]  Yun Jiang,et al.  Magnetic resonance fingerprinting: a technical review , 2018, Magnetic resonance in medicine.

[41]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[42]  Josef Pfeuffer,et al.  Magnetic resonance field fingerprinting , 2018, Magnetic resonance in medicine.

[43]  Vikas Gulani,et al.  Development of high‐resolution 3D MR fingerprinting for detection and characterization of epileptic lesions , 2018, Journal of magnetic resonance imaging : JMRI.

[44]  Tobias Gauer,et al.  Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type. , 2019, Radiology.

[45]  Pedro A. Gómez,et al.  Multi-site repeatability and reproducibility of MR fingerprinting of the healthy brain at 1.5 and 3.0 T , 2019, NeuroImage.

[46]  Qian Wang,et al.  Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting , 2019, IEEE Transactions on Medical Imaging.

[47]  Vikas Gulani,et al.  Three-dimensional MR Fingerprinting for Quantitative Breast Imaging. , 2019, Radiology.

[48]  Berkman Sahiner,et al.  Deep learning in medical imaging and radiation therapy. , 2018, Medical physics.

[49]  Jianhui Zhong,et al.  Ultrashort echo time magnetic resonance fingerprinting (UTE‐MRF) for simultaneous quantification of long and ultrashort T2 tissues , 2018, Magnetic resonance in medicine.