Capturing Bone Signal in Mri of Pelvis, As a Large Fov Region, Using Twist Sequence and Generating a 5-Class Attenuation Map for Prostate Pet/Mri Imaging Publisher Pubmed



Shandiz MS¹ ; Rad HS^{2, 3} ; Ghafarian P^{4, 5} ; Yaghoubi K¹ ; Ay MR^{2, 3} Show Affiliations
Source: Molecular Imaging Published:2018

Abstract

Purpose: Prostate imaging is a major application of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI). Currently, MRI-based attenuation correction (MRAC) for whole-body PET/MRI in which the bony structures are ignored is the main obstacle to successful implementation of the hybrid modality in the clinical work flow. Ultrashort echo time sequence captures bone signal but needs specific hardware–software and is challenging in large field of view (FOV) regions, such as pelvis. The main aims of the work are (1) to capture a part of the bone signal in pelvis using short echo time (STE) imaging based on time-resolved angiography with interleaved stochastic trajectories (TWIST) sequence and (2) to consider the bone in pelvis attenuation map (µ-map) to MRAC for PET/MRI systems. Procedures: Time-resolved angiography with interleaved stochastic trajectories, which is routinely used for MR angiography with high temporal and spatial resolution, was employed for fast/STE MR imaging. Data acquisition was performed in a TE of 0.88 milliseconds (STE) and 4.86 milliseconds (long echo time [LTE]) in pelvis region. Region of interest (ROI)-based analysis was used for comparing the signal-to-noise ratio (SNR) of cortical bone in STE and LTE images. A hybrid segmentation protocol, which is comprised of image subtraction, a Fuzzy-based segmentation, and a dedicated morphologic operation, was used for generating a 5-class µ-map consisting of cortical bone, air cavity, fat, soft tissue, and background (µ-mapMR-5c). A MR-based 4-class µ-map (µ-mapMR-4c) that considered soft tissue rather than bone was generated. As such, a bilinear (µ-mapCT-ref), 5 (µ-mapCT-5c), and 4 class µ-map (µ-mapCT-4c) based on computed tomography (CT) images were generated. Finally, simulated PET data were corrected using µ-mapMR-5c (PET-MRAC5c), µ-mapMR-4c (PET-MRAC4c), µ-mapCT-5c (PET-CTAC5c), and µ-mapCT-ref (PET-CTAC). Results: The ratio of SNRbone to SNRair cavity in LTE images was 0.8, this factor was increased to 4.4 in STE images. The Dice, Sensitivity, and Accuracy metrics for bone segmentation in proposed method were 72.4% ± 5.5%, 69.6% ± 7.5%, and 96.5% ± 3.5%, respectively, where the segmented CT served as reference. The mean relative error in bone regions in the simulated PET images were −13.98% ± 15%, −35.59% ± 15.41%, and 1.81% ± 12.2%, respectively, in PET-MRAC5c, PET-MRAC4c, and PET-CTAC5c where PET-CTAC served as the reference. Despite poor correlation in the joint histogram of µ-mapMR-4c versus µ-mapCT-5c (R2 > 0.78) and PET-MRAC4c versus PET-CTAC5c (R2 = 0.83), high correlations were observed in µ-mapMR-5c versus µ-mapCT-5c (R2 > 0.94) and PET-MRAC5c versus PET-CTAC5c (R2 > 0.96). Conclusions: According to the SNRSTE, pelvic bone, the cortical bone can be separate from air cavity in STE imaging based on TWIST sequence. The proposed method generated an MRI-based µ-map containing bone and air cavity that led to more accurate tracer uptake estimation than MRAC4c. Uptake estimation in hybrid PET/MRI can be improved by employing the proposed method. © The Author(s) 2018.