Impact of Tissue Classification in MRI-Guided Attenuation Correction on Whole-Body Patlak PET/MRI

  • Mingzan Zhuang
  • Nicolas A. Karakatsanis
  • Rudi A. J. O. Dierckx
  • Habib ZaidiEmail author
Research Article



The aim of this work is to investigate the impact of tissue classification in magnetic resonance imaging (MRI)-guided positron emission tomography (PET) attenuation correction (AC) for whole-body (WB) Patlak net uptake rate constant (Ki) imaging in PET/MRI studies.


WB dynamic PET/CT data were acquired for 14 patients. The CT images were utilized to generate attenuation maps (μ-mapCTAC) of continuous attenuation coefficient values (Acoeff). The μ-mapCTAC were then segmented into four tissue classes (μ-map4-classes), namely background (air), lung, fat, and soft tissue, where a predefined Acoeff was assigned to each class. To assess the impact of bone for AC, the bones in the μ-mapCTAC were then assigned a predefined soft tissue Acoeff (0.1 cm−1) to produce an AC μ-map without bones (μ-mapno-bones). Thereafter, both WB static SUV and dynamic PET images were reconstructed using μ-mapCTAC, μ-map4-classes, and μ-mapno-bones (PETCTAC, PET4-classes, and PETno-bones), respectively. WB indirect and direct parametric Ki images were generated using Patlak graphical analysis. Malignant lesions were delineated on PET images with an automatic segmentation method that uses an active contour model (MASAC). Then, the quantitative metrics of the metabolically active tumor volume (MATV), target-to-background (TBR), contrast-to-noise ratio (CNR), peak region-of-interest (ROIpeak), maximum region-of-interest (ROImax), mean region-of-interest (ROImean), and metabolic volume product (MVP) were analyzed. The Wilcoxon test was conducted to assess the difference between PET4-classes and PETno-bones against PETCTAC for all images. The same test was also adopted to compare the differences between SUV, indirect Ki, and direct Ki images for each evaluated AC method.


No significant differences in MATV, TBR, and CNR were observed between PET4-classes and PETCTAC for either SUV or Ki images. PET4-classes significantly overestimated ROIpeak, ROImax, ROImean, as well as MVP scores compared with PETCTAC in both SUV and Ki images. SUV images exhibited the highest median relative errors for PET4-classes with respect to PETCTAC (RE4-classes): 6.91 %, 6.55 %, 5.90 %, and 6.56 % for ROIpeak, ROImax, ROImean, and MVP, respectively. On the contrary, Ki images showed slightly reduced RE4-classes (indirect 5.52 %, 5.95 %, 4.43 %, and 5.70 %, direct 6.61 %, 6.33 %, 5.53 %, and 4.96 %) for ROIpeak, ROImax, ROImean, and MVP, respectively. A higher TBR was observed on indirect and direct Ki images relative to SUV, while direct Ki images demonstrated the highest CNR.


Four-tissue class AC may impact SUV and Ki parameter estimation but only to a limited extent, thereby suggesting that WB Patlak Ki imaging for dynamic WB PET/MRI studies is feasible. Patlak Ki imaging can enhance TBR, thereby facilitating lesion segmentation and quantification. However, patient-specific Acoeff for each tissue class should be used when possible to address the high inter-patient variability of Acoeff distributions.

Key words

Whole-body PET/MRI SUV Patlak analysis Tissue classification Attenuation correction 



The authors would like to thank Prof. Ronald Boellaard, Prof. Qingchun Qiu, and Zemian Chen for their assistance.

Funding Information

This work was supported by the Swiss National Science Foundation under grant no. SNSF 320030_176052 and the Swiss Cancer Research Foundation under Grant KFS-3855-02-2016.

Compliance with Ethical Standards

The study was approved by the local ethics committee.

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

11307_2019_1338_MOESM1_ESM.pdf (436 kb)
ESM 1 (PDF 436 kb)


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Copyright information

© World Molecular Imaging Society 2019

Authors and Affiliations

  1. 1.Department of Nuclear Medicine and Molecular ImagingUniversity of Groningen, University Medical CenterGroningenThe Netherlands
  2. 2.Department of Radiation OncologyAffiliated Hospital of Yangzhou UniversityYangzhouChina
  3. 3.Division of Radiopharmaceutical Sciences, Department of Radiology, Weill Cornell Medical CollegeCornell UniversityNew YorkUSA
  4. 4.Division of Nuclear Medicine and Molecular ImagingGeneva University HospitalGenevaSwitzerland
  5. 5.Geneva University Neurocenter, University of GenevaGenevaSwitzerland
  6. 6.Department of Nuclear MedicineUniversity of Southern DenmarkOdenseDenmark

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