Development of multi-purpose 3D printed phantoms for MRI

This work describes the development and application of 3D printed MRI phantoms. Unlike traditional phantoms these test objects are made from solid materials which can be imaged directly without filling. The models were manufactured using both MRI visible and invisible materials. The MRI visible materials were imaged on a 3T system to quantify their T 1 and T 2 properties and CT to quantify the electron density. Three phantoms are described: a distortion phantom was imaged on an open bore MRI system to assess distortion over a 30 cm field-of-view; a solid tumour model was imaged using a motion simulator and compared to a standard water phantom to assess reduction in artefacts; finally, a test object created for textural analysis was evaluated on two 3T systems and reproducibility was assessed. Material 1 was the main material used in all phantom models and has a T 1 and T 2 of 152.3  ±  3.7 ms and 56.7  ±  2.5 ms and a CT density of 127.9 HU. Material 2 had a CT density of 115.1 HU and material 3 had a T 1 and T 2 of 149.5  ±  2.9 ms and 68.8  ±  7.8 ms and CT density of 15.3 HU. Image tests demonstrated the suitability and advantage of each phantom over more traditional versions: a high density set of control points enabled a comprehensive measurement of geometric accuracy; sufficient signal with a reduction in artefact was observed in the motion phantom, and the texture model provided reproducible measurements with an ICC  >  0.9 for over 76% of texture features. Three different phantoms have been successfully manufactured and used to demonstrate the application of 3D printable materials for MRI phantoms.

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

[2]  Wei Huang,et al.  Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE‐MRI: Results from a multicenter phantom study , 2018, Magnetic resonance in medicine.

[3]  Peter Gibbs,et al.  Breast lesion analysis of shape technique: Semiautomated vs. manual morphological description , 2006, Journal of magnetic resonance imaging : JMRI.

[4]  Christopher Dean,et al.  Assessment of Geometric Distortion in Six Clinical Scanners Using a 3D-Printed Grid Phantom , 2017, J. Imaging.

[5]  Terry K Koo,et al.  A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. , 2016, Journal Chiropractic Medicine.

[6]  Peter Metcalfe,et al.  Continuous table acquisition MRI for radiotherapy treatment planning: distortion assessment with a new extended 3D volumetric phantom. , 2015, Medical physics.

[7]  Stephan E Maier,et al.  Three‐dimensional printing of MRI‐visible phantoms and MR image‐guided therapy simulation , 2017, Magnetic resonance in medicine.

[8]  Y. Rong,et al.  Fabrication of an anthropomorphic heterogeneous mouse phantom for multimodality medical imaging , 2018, Physics in medicine and biology.

[9]  Andrzej Materka,et al.  Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: an application-oriented study. , 2009, Medical physics.

[10]  Ho-Ling Liu,et al.  Quality Assurance of Clinical MRI Scanners Using ACR MRI Phantom: Preliminary Results , 2004, Journal of Digital Imaging.

[11]  Michael G Jameson,et al.  3D printed phantoms mimicking cortical bone for the assessment of ultrashort echo time magnetic resonance imaging , 2018, Medical physics.

[12]  P Keall,et al.  Imaging performance of a dedicated radiation transparent RF coil on a 1.0 Tesla inline MRI-linac , 2018, Physics in medicine and biology.

[13]  Michael Bock,et al.  MRI compatible head phantom for ultrasound surgery. , 2015, Ultrasonics.

[14]  O. Pardini,et al.  FTIR, 1H‐NMR spectra, and thermal characterization of water‐based polyurethane/acrylic hybrids , 2008 .

[15]  Peter Metcalfe,et al.  MRI distortion: considerations for MRI based radiotherapy treatment planning , 2014, Australasian Physical & Engineering Sciences in Medicine.

[16]  Luc Bidaut,et al.  The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms. , 2011, Medical physics.

[17]  Daniel Güllmar,et al.  3D printing of MRI compatible components: why every MRI research group should have a low-budget 3D printer. , 2014, Medical engineering & physics.

[18]  Matthew F Bieniosek,et al.  Technical Note: Characterization of custom 3D printed multimodality imaging phantoms. , 2015, Medical physics.

[19]  Debiao Li,et al.  Four‐dimensional MRI using three‐dimensional radial sampling with respiratory self‐gating to characterize temporal phase‐resolved respiratory motion in the abdomen , 2016, Magnetic resonance in medicine.

[20]  Ersin Bayram,et al.  Optimization of a novel large field of view distortion phantom for MR‐only treatment planning , 2017, Journal of applied clinical medical physics.

[21]  Milan Hájek,et al.  Phantoms for texture analysis of MR images. Long-term and multi-center study. , 2004, Medical physics.

[22]  L R Schad,et al.  The use of reticulated foam in texture test objects for magnetic resonance imaging. , 1998, Magnetic resonance imaging.

[23]  Steffen Greilich,et al.  An anthropomorphic multimodality (CT/MRI) head phantom prototype for end-to-end tests in ion radiotherapy. , 2015, Zeitschrift fur medizinische Physik.

[24]  Charalampos Tsoumpas,et al.  Recent advances on the development of phantoms using 3D printing for imaging with CT, MRI, PET, SPECT, and ultrasound , 2018, Medical physics.

[25]  P. Choyke,et al.  Performance of a fast and high‐resolution multi‐echo spin‐echo sequence for prostate T2 mapping across multiple systems , 2018, Magnetic resonance in medicine.

[26]  L. Axel,et al.  Quality assurance methods and phantoms for magnetic resonance imaging: report of AAPM nuclear magnetic resonance Task Group No. 1. , 1990, Medical physics.

[27]  Thomas E. Yankeelov,et al.  Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network , 2017, Journal of medical imaging.