Effect of Time-of-Flight and Regularized Reconstructions on Quantitative Measurements and Qualitative Assessments in Newly Diagnosed Prostate Cancer With ¹⁸F-Fluorocholine Dual Time Point PET/MRI

Abstract

Recent technical advances in positron emission tomography/magnetic resonance imaging (PET/MRI) technology allow much improved time-of-flight (TOF) and regularized iterative PET reconstruction regularized iterative reconstruction (RIR) algorithms. We evaluated the effect of TOF and RIR on standardized uptake values (maximum and peak SUV [SUVmax and SUVpeak]) and their metabolic tumor volume dependencies and visual image quality for 18F-fluorocholine PET/MRI in patients with newly diagnosed prostate cancer. Fourteen patients were administered with 3 MBq/kg of 18F-fluorocholine and scanned dynamically for 30 minutes. Positron emission tomography images were divided to early and late time points (1-6 minutes summed and 7-30 minutes summed). The values of the different SUVs were documented for dominant PET-avid lesions, and metabolic tumor volume was estimated using a 50% isocontour and SUV threshold of 2.5. Image quality was assessed via visual acuity scoring (VAS). We found that incorporation of TOF or RIR increased lesion SUVs. The lesion to background ratio was not improved by TOF reconstruction, while RIR improved the lesion to background ratio significantly (P < .05). The values of the different VAS were all significantly higher (P < .05) for RIR images over TOF, RIR over non-TOF, and TOF over non-TOF. In conclusion, our data indicate that TOF or RIR should be incorporated into current protocols when available.

Quantitative and Qualitative Improvement of Low-Count [68Ga]Citrate and [90Y]Microspheres PET Image Reconstructions Using Block Sequential Regularized Expectation Maximization Algorithm

“…On the other hand, the penalized likelihood (PL) algorithms [17], where a penalty term is added to the cost function to control the image quality, provide a better way to the tradeoff between image quality and image resolution. Here, in our investigation, we have used the block sequential regularized expectation maximization (BSREM) [18][19][20][21][22][23][24][25] reconstruction method as in the commercialized version (Q.Clear, GE Healthcare, Waukesha, WI), a type of PL algorithm which uses the relative difference penalty [26,27] to reconstruct low-count PET images of [ 90 Y]microspheres and [ 68 Ga]citrate. Although the use of BSREM reconstruction for [ 90 Y]microspheres was reported by Rowley et al on the datasets acquired using a PET/x-ray computed tomography scanner (CT) [28], investigation of BSREM for low-count [ 68 Ga]citrate cancer imaging has not been reported.…”