The value of Bayesian penalized likelihood reconstruction for improving lesion conspicuity of malignant lung tumors on 18F-FDG PET/CT: comparison with ordered subset expectation maximization reconstruction incorporating time-of-flight model and point spread function correction

Abstract

Objective To evaluate the value of Bayesian penalized likelihood (BPL) reconstruction for improving lesion conspicuity of malignant lung tumors on 18 F-fluoro-2-deoxy-d-glucose (FDG) positron emission tomography computed tomography (PET/ CT) as compared with the ordered subset expectation maximization (OSEM) reconstruction incorporating time-of-flight (TOF) model and point-spread-function (PSF) correction. Methods Twenty-nine patients with primary or metastatic lung cancers who underwent 18 F-FDG PET/CT were retrospectively studied. PET images were reconstructed with OSEM + TOF, OSEM + TOF + PSF, and BPL with noise penalty strength β-value of 200, 400, 600, and 800. The signal-to-noise ratio (SNR) was determined in normal liver parenchyma. Lung lesion conspicuity was evaluated in 50 lung lesions by using a 4-point scale (0, no visible; 1, poor; 2, good; 3, excellent conspicuity). Two observers were independently asked to choose the most preferred reconstruction for detecting the lung lesions on a per-patient level. The maximum standardized uptake value (SUV max ) was measured in each of the 50 lung lesions. Results Liver SNR on the images reconstructed by BPL with β-value of 600 and 800 (17.8 ± 3.7 and 22.5 ± 4.6, respectively) was significantly higher than that by OSEM + TOF + PSF (15.0 ± 3.4, p < 0.0001). BPL with β-value of 600 was chosen most frequently as the preferred reconstruction algorithm for lung lesion assessment by both observers. The conspicuity score of the lung lesions < 10 mm in diameter on images reconstructed by BPL with β-value of 600 was significantly greater than that with OSEM + TOF + PSF (2.2 ± 0.8 vs 1.6 ± 0.9, p < 0.0001), while the conspicuity score of the lesions ≥ 10 mm in diameter was not significantly different between BPL with β-value of 600 and OSEM + TOF + PSF. The mean SUV max was increased by BPL with β-value of 600 for the lung lesions with < 10 mm in diameter, compared to OSEM + TOF + PSF (3.4 ± 3.1 to 4.2 ± 3.5, p = 0.001). In contrast, BPL with β-value of 600 did not provide increased SUV max for the lesions ≥ 10 mm in diameter. Conclusion BPL reconstruction significantly improves the detection of small inconspicuous malignant tumors in the lung, improving the diagnostic performance of PET/CT.

Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for 18F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom

“…The BPL becomes smoother and less noisy depending on β values. Thus, BPL improved the SNR in the background region more effectively than OSEM and was consistent with prior findings using liver SNR [39,40]. The better image quality of BPL with a β value of 400 in the clinical study compared with OSEM, which was compatible with the findings of our custom-designed phantom.…”

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

“…• Lesion SUV [57,60,[63][64][65][66][67] • Image quality [61] • Image quality and lesion detection in PET/MRI [68] • Conspicuity of malignant lung lesions [69] • Lesion detection in prostate cancer biochemical relapse [70] Level 2:…”

“…• 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:…”

“…Please note that this list is representative but not necessarily exhaustive. Furthermore, studies were included irrespective of positive or negative findings PSF reconstruction Setting Level of evidence Phantom Level 1: • Contrast recovery and SUV [ 52 , 54 , 55 , 60 ] • Noise and image quality [ 61 ] • Reconstructed spatial resolution [ 51 , 52 ] • Quantitative accuracy in microspheres [ 53 ] • Detection of simulated lesions [ 62 ] Patients Level 1: • Lesion SUV [ 57 , 60 , 63 – 67 ] • Image quality [ 61 ] • Image quality and lesion detection in PET/MRI [ 68 ] • Conspicuity of malignant lung lesions [ 69 ] • Lesion detection in prostate cancer biochemical relapse [ 70 ] Level 2: • Diagnostic accuracy in lymph node staging for lung cancer [ 63 ] • Diagnostic accuracy in lymph node staging for rectal cancer [ 64 ] Level 4: • Response assessment in lymphoma [ 71 ] …”

Effects of New Bayesian Penalized Likelihood Reconstruction Algorithm on Visualization and Quantification of Upper Abdominal Malignant Tumors in Clinical FDG PET/CT Examinations

“…Although BPL theoretically affects visual images and quantitative values, little is known about their changes in real clinical situations. There have been several reports regarding the effects of BPL in pulmonary lesions (2)(3)(4)(5)(6). Only limited reports have been available so far as to the effects of BPL focusing on abdominal lesions in 2-deoxy-2-[F-18]fluoro-D-glucose (FDG) PET imaging (7).…”