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Introduction The aim of this study was to evaluate the behavior of a penalized-likelihood image reconstruction method (Q.Clear) under different count statistics and lesion-to-background ratios (LBR) on a BGO scanner, in order to obtain an optimum penalization factor (β value) to study and optimize for different acquisition protocols and clinical goals. Methods Both phantom and patient images were evaluated. Data from an image quality phantom were acquired using different Lesion-to-Background ratios and acquisition times. Then, each series of the phantom was reconstructed using β values between 50 and 500, at intervals of 50. Hot and cold contrasts were obtained, as well as background variability and contrast-to-noise ratio (CNR). Fifteen 18 F-FDG patients (five brain scans and 10 torso acquisitions) were acquired and reconstructed using the same β values as in the phantom reconstructions. From each lesion in the torso acquisition, noise, contrast, and signal-to-noise ratio (SNR) were computed. Image quality was assessed by two different nuclear medicine physicians. Additionally, the behaviors of 12 different textural indices were studied over 20 different lesions. Results Q.Clear quantification and optimization in patient studies depends on the activity concentration as well as on the lesion size. In the studied range, an increase on β is translated in a decrease in lesion contrast and noise. The net product is an overall increase in the SNR, presenting a tendency to a steady value similar to the CNR in phantom data. As the activity concentration or the sphere size increase the optimal β increases, similar results are obtained from clinical data. From the subjective quality assessment, the optimal β value for torso scans is in a range between 300 and 400, and from 100 to 200 for brain scans. For the recommended torso β values, texture indices present coefficients of variation below 10%. Conclusions Our phantom and patients demonstrate that improvement of CNR and SNR of Q.Clear algorithm which depends on the studied conditions and the penalization factor. Using the Q.Clear reconstruction algorithm in a BGO scanner, a β value of 350 and 200 appears to be the optimal value for 18F-FDG oncology and brain PET/CT, respectively.

Gradually, FDG-PET/CT has been strengthening within the diagnostic algorithms of oncological diseases. In many of these, PET/CT has shown to be useful at different stages of the disease: diagnosis, staging or re-staging, treatment response assessment, and recurrence. Some of the advantages of this imaging modality versus CT, MRI, bone scan, mammography, or ultrasound, are based on its great diagnostic capacity since, according to the radiopharmaceutical used, it reflects metabolic changes that often occur before morphological changes and therefore allows us to stage at diagnosis. Moreover, another advantage of this technique is that it allows us to evaluate the whole body so it can be very useful for the detection of distant disease. With regard to breast cancer, FDG-PET/CT has proven to be important when recurrence is suspected or in the evaluation of treatment response. The technological advancement of PET equipment through the development of new detectors and equipment designed specifically for breast imaging, and the development of more specific radiopharmaceuticals for the study of the different biological processes of breast cancer, will allow progress not only in making the diagnosis of the disease at an early stage but also in enabling personalized therapy for patients with breast cancer.