Cross-calibration of the Siemens mMR: easily acquired accurate PET phantom measurements, long-term stability and reproducibility
© The Author(s). 2016
Received: 7 April 2016
Accepted: 8 June 2016
Published: 7 July 2016
We present a quick and easy method to perform quantitatively accurate PET scans of typical water-filled PET plastic shell phantoms on the Siemens Biograph mMR PET/MR system.
We perform regular cross-calibrations (Xcal) of our PET systems, including the PET/MR, using a Siemens mCT water phantom.
The mMR calibration stability was evaluated over a 3-year period where 54 cross-calibrations were acquired, showing that the mMR on average underestimated the concentration by 16 %, consistently due to the use of MR-based μ-maps.
The mMR produced the narrowest calibration ratio range with the lowest standard deviation, implying it is the most stable of the six systems in the study over a 3-year period.
mMR accuracy with predefined μ-maps
With the latest mMR software version, VB20P, it is possible to utilize predefined phantom μ-maps. We evaluated both the system-integrated, predefined μ-map of the long mMR water phantom and our own user-defined CT-based μ-map of the mCT water phantom, which is used for cross-calibration.
For seven scans, which were reconstructed with correctly segmented μ-maps, the mMR produced cross-calibration ratios of 1.00–1.02, well within the acceptance range [0.95–1.05], showing high accuracy.
The mMR is the most stable PET system in this study, and the mean underestimation is no longer an issue with the easily accessible μ-map, which resulted in correct cross-calibration ratios in all seven tests. We will share the user-defined μ-map of the mCT phantom and the protocol with interested mMR users.
KeywordsPET/MR Quality control Cross-calibration Calibration PET phantom
Accurate PET measurements on PET/MR systems are problematic with MR-based attenuation correction (MRAC) [9, 11, 12]. For phantoms, the plastic materials in MR-based μ-maps are typically segmented as air and are assigned with a linear attenuation coefficient (LAC) of 0 cm−1 when they ought to have LACs close to that of water [2, 11, 12].
For single phantom scans, one can utilize externally acquired, co-registered, and converted (Hounsfield units (HU) to 511 keV LACs) CT-based μ-maps  or calculated μ-maps . This approach is time-consuming and error-prone, and it requires either the use of external μ-maps or access to an external reconstruction setup. Thus, it is not a suitable approach for regularly repeated phantom scans, e.g., for routine quality control (QC) such as our regularly performed cross-calibrations (Xcal) [1, 6].
In the current work, we firstly present a quick and easy method to perform quantitatively accurate PET scans of a typical water-filled plastic shell cylinder phantom on the Siemens mMR PET/MR system. We describe how to integrate an external, CT-based μ-map into the system software as a user-defined μ-map for routine use.
As a second part of the assessment of the mMR phantom scan performance, we evaluate the results of executing cross-calibrations every 2–3 weeks over a 3-year period, comparing the mMR to five other PET systems to assess the long-term stability and reproducibility.
Material and methods
Test of μ-maps for mMR cross-calibration: concentrations (kBq/ml) and ratios
mMR Xcal concentration ratio
CT-based (+8 mm in x)
CT-based (−10 mm in x)
CT-based (−2 mm in x)
CT-based (no shift)
mMR water (original)
mMR water (original)
mMR water (original)
The mMR PET images were reconstructed on the mMR using OP-OSEM with 4 iterations, 21 subsets, and a 3-mm FWHM Gaussian post-reconstruction filter into 344 × 344 × 127 matrices of 0.83 × 0.83 × 2.03 mm3 voxels.
We used a Veenstra VDC 404 dose calibrator as reference, and all devices in the cross-calibration should measure the same concentration within ±5 % of the reference concentration, i.e., have Xcal concentration ratios in the range [0.95–1.05].
Predefined μ-map options as alternatives to the standard Dixon MR-based μ-maps
The mMR software (version VB20P, available since Q4 2013) offers the choice of four easily accessible predefined μ-maps for phantom reconstructions as alternative to the standard Dixon MR-based μ-maps. Two of the predefined choices are user-defined options, which we use to test our external CT-based μ-map. As an alternative, we also tested the μ-map of the mMR water phantom, which is given as one of the two predefined and build-in μ-maps (Fig. 2c): The mCT and mMR water phantoms are made of the same material and have the same diameter and wall thickness (see Fig. 2). We acquired and reconstructed seven PET scans on the mMR of the mCT phantom using five different predefined μ-maps. In three cases, the μ-maps were shifted up to 10 mm in the x-y plane testing robustness against misregistrations.
Creation of a user-defined CT-based μ-map
After a 120-kVp CT scan of the mCT phantom, the CT image was automatically registered to an mMR PET scan of the mCT phantom using Vinci 2.55 . HU were converted to linear attenuation coefficients at 511 keV in Matlab following the method of Carney et al. , where LAC = 9.6× 105 × (HU + 1000). A 3-mm filter was applied in Vinci, and images were saved in interfile format. Header text files were then generated according to the specifications in . The registration procedure above served mostly to correct any rotational differences because the translational positioning of the μ-map had to be set in the header, specifying a μ-map origin in pixels and an origin offset in millimeters relative to the system patient table origin. This requires fixed phantom positioning for all PET scans performed using this μ-map. Finally, the image and the corresponding header were named User_Defined_n.v(.hdr) (n = 1, 2) and saved in a dedicated folder for predefined μ-maps.
Statistics of the Xcal ratios for six systems over 3 years
PET1: Siemens HRRT
PET3: Siemens Biograph mCT
PET4: Siemens Biograph mCT
PET5: Siemens Biograph True-Point TrueV
PET6: Siemens Biograph True-Point TrueV
PET7: Siemens Biograph mMR
All measurements of the concentrations in the images were performed in Siemens TrueD using a cylindrical volume of interest (VOI) placed centrally in the phantoms, 12–15 cm in diameter and 500–950 cm3 in volume.
Test of μ-maps
Long-term mMR calibration stability
Table 2 shows that the mMR measures activity concentrations are 16 % too low on average due to the use of MR-based μ-maps with the plastic body of the phantom segmented as air. All other systems measure very close to 1.00 on average. We only had ratios out of the acceptance range in 5 out of 290 scans (PET1: 2× 0.94, PET4: 1× 1.06, and PET5: 2× 1.06) and newer twice in a row (see Fig. 5).
The results in Table 2 show that the mMR has a smaller standard deviation (SD) and narrower range of cross-calibration ratios than the other systems, which implies high reproducibility. Scaling the 54 mMR Xcal ratios reconstructed with the MR-based μ-map to a mean of 1.00 by dividing each by the actual mean (0.84) changes the range to [0.97–1.05], which is still narrower than for any other systems in this study, and the SD changes to 0.0170 (still the lowest). Thus, the mMR is the most stable of the systems over a 3-year period, which could be caused by its use of avalanche photodiode (APD) PET detectors instead of conventional photomultiplier tubes (PMTs).
The long-term stability and accuracy of all six PET systems in this study were high with only 5/290 measures out of range (off by 1 % each time and newer off twice in a row) warranting no further actions to ensure the systems measure accurately. The correction would be to redetermine the ECAT calibration factors (ECFs) normally only adjusted when a new [68Ge]-phantom for daily QC, normalization, and setup is put in use (at 1.5-year intervals).
This phantom study is limited to cross-calibrations using [18F]-FDG. But the μ-maps used are tracer-independent, and similar μ-maps could be generated for other phantoms scanned on a regular basis in a fixed position.
Over a 3-year period and 54 cross-calibrations, the mMR showed to be the most stable of the six PET systems evaluated in this study. The Xcal ratios were persistently off by a factor of 16 % due to the use of MR-based μ-maps, a factor that we can now easily eliminate by using correct μ-maps.
We have successfully demonstrated a procedure to perform accurate cross-calibration of the mMR PET/MR system. Both a new CT-based user-defined μ-map of the mCT water phantom and a predefined μ-map of the mMR water phantom resulted in accurate cross-calibration ratios. The μ-maps are available as an easily accessible drop-down option in the system’s user interface.
We will share our user-defined μ-map of the mCT phantom and the protocol with interested mMR users, who wish to employ our method. Following our work, one can also generate user-defined μ-maps for other frequently used phantoms. If compliant with local procedures, the mMR water phantom can also be used across systems for cross-calibrations.
We kindly thank The John and Birthe Meyer Foundation who donated the mMR PET/MR system to Rigshospitalet.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour PET imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54.View ArticlePubMedGoogle Scholar
- Boellaard R, Rausch I, Beyer T, Delso G, Yaqub M, Quick HH, et al. Quality control for quantitative multicenter whole-body PET/MR studies: a NEMA image quality phantom study with three current PET/MR systems. Med Phys. 2015;42:5961–9.View ArticlePubMedGoogle Scholar
- Carney JP, Townsend DW, Rappoport V, Bendriem B. Method for transforming CT images for attenuation correction in PET/CT imaging. Med Phys. 2006;33:976–83.View ArticlePubMedGoogle Scholar
- Delso G, Fürst S, Jakoby B, Ladebeck R, Ganter C, Nekolla SG, et al. Performance measurements of the Siemens mMR integrated whole-body PET/MR scanner. J Nucl Med. 2011;52:1–9.View ArticleGoogle Scholar
- Fenchel M. Support of customer defined hardware μ-maps, vol. 01. Siemens: Whitepaper; 2011.Google Scholar
- Geworski L, Knoop BO, de Wit M, Ivancevic V, Bares R, Munz DL. Multicenter comparison of calibration and cross calibration of PET scanners. J Nucl Med. 2002;43:635–9.PubMedGoogle Scholar
- Jakoby BW, Bercier Y, Conti M, Casey ME, Bendriem B, Townsend DW. Physical and clinical performance of the mCT time-of-flight PET/CT scanner. Phys Med Biol. 2011;56:2375–89.View ArticlePubMedGoogle Scholar
- Keereman V, Mollet P, Fierens Y, Espana S, Vandenberghe S. Design of a realistic PET-CT-MRI phantom. IEEE Nucl Sci Symp Conf Rec. 2011;3173–7.Google Scholar
- Keller SH, Hansen AE, Holm S, Beyer T. Image distortions in clinical PET/MR imaging. In: Carrio I, Ros P, editors. PET/MRI. Heidelberg: Springer; 2014. p. 21–41.View ArticleGoogle Scholar
- Max-Planck Institut für Neurologische Forschung. Vinci Online Resources. 2012. http://www.nf.mpg.de/vinci/index2.html. Accessed 21 June 2016.
- Oprea-Lager DE, Yaqub M, Pieters IC, Reinhard R, van Moorselaar RJA, van den Eertwegh AJM, et al. A clinical and experimental comparison of time of flight PET/MRI and PET/CT systems. Mol Imaging Biol. 2015;17:714–25.View ArticlePubMedPubMed CentralGoogle Scholar
- Ziegler S, Braun H, Ritt P, Hocke C, Kuwert T, Quick HH. Systematic evaluation of phantom fluids for simultaneous PET/MR hybrid imaging. J Nucl Med. 2013;54:1464–71.View ArticlePubMedGoogle Scholar