We report our initial clinical experience for image quality and diagnostic performance of a digital PET prototype with time-of-flight (DigitalTF, Philips Healthcare) compared with an analog PET with time-of-flight (GeminiTF PET/CT, Philips Healthcare). Methods: Twenty-one oncologic patients, mean age 58, first underwent clinical FDG PET/CT on the GeminiTF. The scanner table was then withdrawn while the patient remained on the table, and the DigitalTF was inserted between the GeminiTF PET and CT scanner. The patients were scanned for a second time using the same PET field of view with CT from GeminiTF for attenuation correction. Two readers reviewed the two sets of PET/CT images for overall image quality, lesion conspicuity and sharpness. They counted the number of suspicious, FDG avid lesions and provided the TNM staging for the five patients referred for initial staging. Standardized uptake values (SUV) and SUV gradients as a measure of lesion sharpness were obtained. Results: DigitalTF showed better image quality compared with GeminiTF. In side-by-side comparison using a 5-point scale, lesion conspicuity (4.3 ± 0.6), lesion sharpness (4.3 ± 0.6) and diagnostic confidence (3.4 ± 0.7) were better with DigitalTF compared with GeminiTF, P < 0.01. In 52 representative lesions, lesion maximum SUV was 36% higher with DigitalTF than with GeminiTF, lesion-to-blood pool SUV ratio was 59% higher and SUV gradient was 51% higher, with good correlation between the two scanners. Lesions < 1.5 cm showed a greater increase in SUV from GeminiTF to DigitalTF compared to those ≥ 1.5 cm. DigitalTF showed additional eight suspicious lesions in 5 of 21 patients that were not seen at GeminiTF. In the 15 restaging patients, the true negative rate was 100% and true positive rate was 78% for both scanners. In the five patients for initial staging, DigitalTF lead to upstaging in two patients and showed the same staging in the other three patients compared with GeminiTF. Conclusion: DigitalTF provides better image quality, diagnostic confidence and accuracy compared with GeminiTF. It may be most beneficial in detecting small tumor lesions and disease staging.

The aim of this study was to assess the physical performance of a new PET/CT system, the Discovery IQ with 5-ring detector blocks. Methods: Performance was measured using the National Electrical Manufacturers Association NU2-2012 methodology. Image quality was extended by accounting for different acquisition parameters (lesion-to-background ratios [8:1, 4:1, and 2:1] and acquisition times) and reconstruction algorithms (VUE-point HD [VPHD], VPHD with point-spread-function modeling [VPHD-S], and Q.Clear). Tomographic reconstruction was also assessed using a Jaszczak phantom. Additionally, 30 patient lesions were analyzed to account for differences in lesion volume and SUV quantification between reconstruction algorithms. Results: Spatial resolution ranged from 4.2 mm at 1 cm to 8.5 mm at 20 cm. Sensitivity measured at the center and at 10 cm was 22.8 and 20.4 kps/kBq, respectively. The noise-equivalent counting rate peak was 124 kcps at 9.1 kBq/cm³. The scatter fraction was 36.2%. The accuracy of correction for count losses and randoms was 3.9%. In the image quality test, contrast recovery for VPHD, VPHD-S, and Q.Clear ranged from 18%, 18%, and 13%, respectively (hot contrast; 10-mm sphere diameter; ratio, 2:1), to 68%, 67%, and 81%, respectively (cold contrast; 37-mm sphere diameter; ratio, 8:1). Background variability ranged from 3.4%, 3.0%, and 2.1%, respectively (ratio, 2:1), to 5.5%, 4.8%, and 3.7%, respectively (ratio, 8:1). On Q.Clear reconstruction, the decrease in the penalty term (β) increased the contrast recovery coefficients and background variability. With the Jaszczak phantom, image quality increased overall when a reconstruction algorithm modeling the point-spread function was used, and use of Q.Clear increased the signal-to-noise ratio. Lesions analyzed using VPHD-S and Q.Clear had an SUV_mean of 6.5 ± 3 and 7 ± 3, respectively (P < 0.01), and an SUV_max of 11 ± 4.8 and 12 ± 4, respectively (P < 0.01). No significant difference in mean lesion volume was found between algorithms. Conclusion: Among the various Discovery bismuth germanium oxide–based PET/CT scanners, the IQ with 5-ring detector blocks has the highest overall performance, with improved sensitivity and counting rate performance. Q.Clear reconstruction improves the PET image quality, with higher recovery coefficients and lower background variability.

MR imaging and PET/CT are integrated in the work-up of head and neck cancer patients. The hybrid imaging technology (18)F-FDG-PET/MR imaging combining morphological and functional information might be attractive in this patient population. The aim of the study was to compare whole-body (18)F-FDG-PET/MR imaging and (18)F-FDG-PET/CT in patients with head and neck cancer, both qualitatively in terms of lymph node and distant metastases detection and quantitatively in terms of standardized uptake values measured in (18)F-FDG-avid lesions. Fourteen patients with head and neck cancer underwent both whole-body PET/CT and PET/MR imaging after a single injection of (18)F-FDG. Two groups of readers counted the number of lesions on PET/CT and PET/MR imaging scans. A consensus reading was performed in those cases in which the groups disagreed. Quantitative standardized uptake value measurements were performed by placing spheric ROIs over the lesions in 3 different planes. Weighted and unweighted κ statistics, correlation analysis, and the Wilcoxon signed rank test were used for statistical analysis. κ statistics for the number of head and neck lesion lesions counted (pooled across regions) revealed interreader agreement between groups 1 and 2 of 0.47 and 0.56, respectively. Intrareader agreement was 0.67 and 0.63. The consensus reading provided an intrareader agreement of 0.63. For the presence or absence of metastasis, interreader agreement was 0.85 and 0.70. The consensus reading provided an intrareader agreement of 0.72. The correlations between the maximum standardized uptake value in (18)F-FDG-PET/MR imaging and (18)F-FDG-PET/CT for primary tumors and lymph node and metastatic lesions were very high (Spearman r = 1.00, 0.93, and 0.92, respectively). In patients with head and neck cancer, (18)F-FDG-PET/MR imaging and (18)F-FDG-PET/CT provide comparable results in the detection of lymph node and distant metastases. Standardized uptake values derived from (18)F-FDG-PET/MR imaging can be used reliably in this patient population.

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.

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 ¹⁸F‐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.