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Image Acquisition and Reconstruction (pre-2019)

Multi-parametric PET-MR imaging

Project ID: 2014_112

Student: Camila Munoz

1st supervisor: Claudia Prieto, King’s College London
2nd supervisor: Andrew Reader, King’s College London

MRI provides 3D high-resolution anatomical and functional information with an excellent soft-tissue contrast yielding a wide range of diagnostic information. MRI has been shown to allow for highly accurate detection and characterisation of cancer lesions especially in the liver [1]. Commonly T1 and T2 weighted images are obtained to provide anatomical information and diffusion weighted and contrast enhanced sequences can be added to provide functional information [2]. Recently, simultaneous PET-MR systems have been introduced, enabling the acquisition of highly sensitive PET and versatile high-resolution MR data during a single examination [3]. This yields two independent data streams providing complementary information of the same patho-physiological processes and holding promising prospects for new advanced approaches in oncology such as assessment of tumour heterogeneity. Although the feasibility of simultaneous PET-MR has been demonstrated [4] scan time restrictions still remain as major challenges for a widespread clinical application of this new technology.

A whole body PET scan is usually performed by measuring multiple 25 cm large stations each in approx. 8 minutes. In order to achieve an acquisition of multi-parametric MR data (T1, T2, diffusion weighted and contrast enhanced) for each station during this short time, a highly efficient data acquisition is required. In addition a highly accurate attenuation correction (AC) map needs to be obtained with MRI to allow for quantitative PET imaging.
The aim of this project is to develop a highly efficient MR acquisition scheme maximising the information obtained from a PET-MR assessment using the ideas of “MR Fingerprinting (MRF)”. This technique was presented in Nature in 2013 [5]. Acquisition parameters are varied during the MR scan and a library of MR signals is used to identify and distinguish the different tissue types in the final image. With this approach, multi-parametric data can be obtained from one single examination.

MRF has been demonstrated to be highly robust against field inhomogeneities and subject motion in the brain. Here this approach will be assessed for applications in the abdominal area and different approaches to vary the image acquisition parameters during the MR scan will be explored to identify the most robust acquisition scheme. Advanced regularisations in image reconstruction will be utilised to improve the accuracy of the multi-parametric images [6-7]. Furthermore, the image reconstruction will be extended to combine both MR and PET data in one comprehensive image reconstruction making optimal use of the two complimentary data streams.

[1] Schwenzer, N. F.; Schmidt, H. & Claussen, C. D. Whole-body MR/PET: applications in abdominal imaging. Abdom Imaging, 2012, 37, 20-28.
[2] Maniam, S. & Szklaruk, J. Magnetic resonance imaging: Review of imaging techniques and overview of liver imaging. World J Radiol, 2010, 2, 309-322.
[3] Judenhofer, M. S.; Wehrl, H. F.; Newport, D. F.; Catana, C.; Siegel, S. B.; Becker, M.; Thielscher, A.; Kneilling, M.; Lichy, M. P.; Eichner, M.; Klingel, K.; Reischl, G.; Widmaier, S.; Roecken, M.; Nutt, R. E.; Machulla, H.-J.; Uludag, K.; Cherry, S. R.; Claussen, C. D. & Pichler, B. J. Simultaneous PET-MRI: a new approach for functional and morphological imaging. Nat. Med., 2008, 14, 459-465.
[4] Drzezga, A.; Souvatzoglou, M.; Eiber, M.; Beer, A. J.; Fürst, S.; Martinez-Möller, A.; Nekolla, S. G.; Ziegler, S.; Ganter, C.; Rummeny, E. J. & Schwaiger, M.
First clinical experience with integrated whole-body PET/MR: comparison to PET/CT in patients with oncologic diagnoses. J Nucl Med, 2012, 53, 845-855.
[5] Ma, D.; Gulani, V.; Seiberlich, N.; Liu K.; Sunshine, J. L.; Duerk, J.L. & Griswold, M. A.; Magnetic resonance fingerprinting. Nature, 2013, 495, 87-92.
[6] Tran-Gia, J.; Stäb, D.; Wech, T.; Hahn, D. & Köstler, H; Model-based Acceleration of Parameter mapping (MAP) for saturation prepared radially acquired data. Magn Reson Med, 2013, 70, 1524–1534.
[7] Wang, H. & Cao, Y. Spatially regularized T(1) estimation from variable flip angles MRI. Med Phys, 2012, 39, 4139-4148.

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