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

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 (K_i) 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-map_CTAC) of continuous attenuation coefficient values (A_coeff). The μ-map_CTAC were then segmented into four tissue classes (μ-map_4-classes), namely background (air), lung, fat, and soft tissue, where a predefined A_coeff was assigned to each class. To assess the impact of bone for AC, the bones in the μ-map_CTAC were then assigned a predefined soft tissue A_coeff (0.1 cm⁻¹) to produce an AC μ-map without bones (μ-map_no-bones). Thereafter, both WB static SUV and dynamic PET images were reconstructed using μ-map_CTAC, μ-map_4-classes, and μ-map_no-bones (PET_CTAC, PET_4-classes, and PET_no-bones), respectively. WB indirect and direct parametric K_i 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 (ROI_peak), maximum region-of-interest (ROI_max), mean region-of-interest (ROI_mean), and metabolic volume product (MVP) were analyzed. The Wilcoxon test was conducted to assess the difference between PET_4-classes and PET_no-bones against PET_CTAC for all images. The same test was also adopted to compare the differences between SUV, indirect K_i, and direct K_i images for each evaluated AC method.

No significant differences in MATV, TBR, and CNR were observed between PET_4-classes and PET_CTAC for either SUV or K_i images. PET_4-classes significantly overestimated ROI_peak, ROI_max, ROI_mean, as well as MVP scores compared with PET_CTAC in both SUV and K_i images. SUV images exhibited the highest median relative errors for PET_4-classes with respect to PET_CTAC (RE_4-classes): 6.91 %, 6.55 %, 5.90 %, and 6.56 % for ROI_peak, ROI_max, ROI_mean, and MVP, respectively. On the contrary, K_i images showed slightly reduced RE_4-classes (indirect 5.52 %, 5.95 %, 4.43 %, and 5.70 %, direct 6.61 %, 6.33 %, 5.53 %, and 4.96 %) for ROI_peak, ROI_max, ROI_mean, and MVP, respectively. A higher TBR was observed on indirect and direct K_i images relative to SUV, while direct K_i images demonstrated the highest CNR.

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