Optimization of [18F]PSMA-1007 PET-CT using regularized reconstruction in patients with prostate cancer

Abstract

Background: Prostate-specific membrane antigen (PSMA) radiotracers such as [ 18 F]PSMA-1007 used with positron emission tomography-computed tomography (PET-CT) is promising for initial staging and detection of recurrent disease in prostate cancer patients. The block-sequential regularization expectation maximization algorithm (BSREM) is a new PET reconstruction algorithm, which provides higher image contrast while also reducing noise. The aim of the present study was to evaluate the influence of different acquisition times and different noise-suppressing factors in BSREM (β values) in [ 18 F]PSMA-1007 PET-CT regarding quantitative data as well as a visual image quality assessment. We included 35 patients referred for clinical [ 18 F]PSMA-1007 PET-CT. Four megabecquerels per kilogramme were administered and imaging was performed after 120 min. Eighty-four image series per patient were created with combinations of acquisition times of 1-4 min/bed position and β values of 300-1400. The noise level in normal tissue and the contrast-to-noise ratio (CNR) of pathological uptakes versus the local background were calculated. Image quality was assessed by experienced nuclear medicine physicians. Results: The noise level in the liver, spleen, and muscle was higher for low β values and low acquisition times (written as activity time products (ATs = administered activity × acquisition time)) and was minimized at maximum AT (16 MBq/kg min) and maximum β (1400). There was only a small decrease above AT 10. The median CNR increased slowly with AT from approximately 6 to 12 and was substantially lower at AT 4 and higher at AT 14-16. At AT 4-6, many images were regarded as being of unacceptable quality. For AT 8, β values of 700-900 were considered of acceptable quality. Conclusions: An AT of 8 (for example as in our study, 4 MB/kg with an acquisition time of 2 min) with a β value of 700 performs well regarding noise level, CNR, and visual image quality assessment.

Moving the goalposts while scoring―the dilemma posed by new PET technologies

“…• Image noise [75][76][77][78] • Subjective image quality [73,77,[79][80][81][82][83][84] • Prediction of subjective image quality from objectified measures [85] • Lesion SUV [75,86,87] • Conspicuity of pulmonary lesions [69,77,86] • Detection rate in pulmonary lesions [88] • Lesion detectability in pelvic PSMA PET/MRI at low activity [89] • Detection rate of lymph nodes in fluorocholine PET [90] • Normal databases for brain PET in neurodegenerative disease [91,92] • Acquisition time reduction [81,84,89,93] Level 2:…”

“…A potential to reduce acquisition time by 25 to >50% has been reported [81,93]. Other authors have only investigated PL reconstruction at different β values [80,84]. It is generally recognized that a reduction in acquisition time could equally well translate into reduced injected activity [97] and therefore into minimized radiation exposure.…”

Dose-reduced [18F]PSMA-1007 PET is feasible for functional imaging of the renal cortex

“…Reconstruction was performed using the block-sequential regularization expectation maximization algorithm (BSREM), marketed as Q.Clear (GE Healthcare, Milwaukee, WI, USA), using a β-value of 800 [ 14 ]. Kidneys were manually segmented in the PET images using the RECOMIA platform [ 15 ].…”

“…Due to a low image quality in the kidney region, determined visually, no rebinning of the original 30 s/bp images from the first 3 acquisitions into series with shorter acquisition times was performed. Reconstruction was performed using the block-sequential regularization expectation maximization algorithm (BSREM), marketed as Q.Clear (GE Healthcare, Milwaukee, WI, USA), using a β-value of 800 [14]. Kidneys were manually segmented in the PET images using the RECOMIA platform [15].…”

Automated analysis of PSMA-PET/CT studies using convolutional neural networks

“…Images were acquired for 2 min/bed position. The PET images were reconstructed using the commercially available block-sequential regularization expectation maximization algorithm Q.Clear (GE Healthcare, Milwaukee, WI) including the time-of-flight and point spread function modelling with a 256 x 256 matrix (pixel size 2.7 x 2.7 mm2, slice thickness 2.8 mm) and a beta factor of 800 21 .…”