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Abstract Background Residual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) — a locally adaptive image filter — allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by training a suitable network with the target of reproducing the results of manually adjusted case-specific bilateral filtering. Methods Altogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ( $$[^{18}\text {F}]$$ [ 18 F ] FDG, $$[^{18}\text {F}]$$ [ 18 F ] L-DOPA, $$[^{68}\text {Ga}]$$ [ 68 Ga ] DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the “optimal” filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs. Results The automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal $$\text {SUV}_\text {max}$$ SUV max values of the unfiltered images with a low mean ± SD difference of $$\delta \text {SUV}_\text {max}^{\text {CNN},\text {STD}}$$ δ SUV max CNN , STD = (−3.9 ± 5.2)% and $$\delta \text {SUV}_\text {max}^{\text {BF},\text {STD}}$$ δ SUV max BF , STD = (−4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar $$\text {SUV}_\text {max}$$ SUV max values in the vast majority of cases with an overall average difference of $$\delta \text {SUV}_\text {max}^{\text {CNN},\text {BF}}$$ δ SUV max CNN , BF = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with $$\delta \text {Noise}^{\text {CNN},\text {BF}}$$ δ Noise CNN , BF = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed. Conclusions Our results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning.

Abstract Background The administration of a 166Ho scout dose is available as an alternative to 99mTc particles for pre-treatment imaging in Selective Internal Radiation Therapy (SIRT). It has been reported that the 166Ho scout dose may be more accurate for the prediction of microsphere distribution and the associated therapy planning. The aim of the current study is to compare the scintigraphic imaging characteristics of both isotopes, considering the objectives of the pre-treatment imaging using clinically geared phantoms. Methods Planar and SPECT/CT images were obtained using a NEMA image quality phantom in different phantom setups and another body-shaped phantom with several inserts. The influence of collimator type, count statistics, dead time effects, isotope properties and patient obesity on spatial resolution, contrast recovery and the detectability of small activity accumulations was investigated. Furthermore, the effects of the imaging characteristics on personalized dosimetry are discussed. Results The images with 99mTc showed up to 3 mm better spatial resolution, up to two times higher contrast recovery and significantly lower image noise than those with 166Ho. The contrast-to-noise ratio was up to five times higher for 99mTc than for 166Ho. Only when using 99mTc all activity-filled spheres could be distinguished from the activity-filled background. The measurements mimicking an obese patient resulted in a degraded image quality for both isotopes. Conclusions Our measurements demonstrate better scintigraphic imaging properties for 99mTc compared to 166Ho in terms of spatial resolution, contrast recovery, image noise, and lesion detectability. While the 166Ho scout dose promises better prediction of the microsphere distribution, it is important to consider the inferior imaging characteristics of 166Ho, which may affect individualized treatment planning in SIRT.

Abstract Background PET nuclides can have a considerable influence on the spatial resolution and image quality of PET/CT scans, which can influence diagnostics in oncology, for example. The individual impact of the positron energy of 18F, 68Ga, and 64Cu on spatial resolution and image quality was compared for PET/CT scans acquired using a clinical, digital scanner. Methods A Jaszczak phantom and a NEMA PET body phantom were filled with 18F-FDG, 68Ga-HCl, or 64Cu-HCl, and PET/CT scans were performed on a Siemens Biograph Vision. Acquired images were analyzed regarding spatial resolution and image quality (recovery coefficients (RC), coefficient of variation within the background, contrast recovery coefficient (CRC), contrast–noise ratio (CNR), and relative count error in the lung insert). Data were compared between scans with different nuclides. Results We found that image quality was comparable between 18F-FDG and 64Cu-HCl PET/CT measurements featuring similar maximal endpoint energies of the positrons. In comparison, RC, CRC, and CNR were degraded in 68Ga-HCl data despite similar count rates. In particular, the two smallest spheres of 10 mm and 13 mm diameter revealed lower RC, CRC, and CNR values. The spatial resolution was similar between 18F-FDG and 64Cu-HCl but up to 18% and 23% worse compared with PET/CT images of the NEMA PET body phantom filled with 68Ga-HCl. Conclusions The positron energy of the PET nuclide influences the spatial resolution and image quality of a digital PET/CT scan. The image quality and spatial resolution of 68Ga-HCl PET/CT images were worse than those of 18F-FDG or 64Cu-HCl despite similar count rates.

Abstract Background The analysis aimed to compare the radiotracers [68Ga]-Ga-PSMA-11 and [18F]-F-PSMA-1007 intraindividually in terms of malignant lesions, mi(molecular-imaging)TNM staging and presumable unspecific lesions retrospectively as used in routine clinical practice. Methods A retrospective analysis of 46 prostate cancer patients (median age: 71 years) who underwent consecutive [68Ga]-Ga-PSMA-11- and [18F]-F-PSMA-1007-PET/CT or PET/MRI within a mean of 12 ± 8.0 days was performed. MiTNM staging was performed in both studies by two nuclear medicine physicians who were blinded to the results of the other tracer. After intradisciplinary and interdisciplinary consensus with two radiologists was reached, differences in both malignant and presumable nonspecific tracer accumulation were analyzed. Results Differences in terms of miTNM stages in both studies occurred in nine of the 46 patients (19.6%). The miT stages differed in five patients (10.9%), the miN stages differed in three patients (6.5%), and different miM stages occurred only in one patient who was upstaged in [18F]-F-PSMA-1007 PET. Concordant miTNM stages were obtained in 37 patients (80.4%). There was no significant difference between [18F]-F-PSMA-1007 and [68Ga]-Ga-PSMA-11 in the SUVmax locally (31.5 vs. 32.7; p = 0.658), in lymph node metastases (28.9 vs. 24.9; p = 0.30) or in bone metastases (22.9 vs. 27.6; p = 0.286). In [18F]-F-PSMA-1007 PET, more patients featured presumable unspecific uptake in the lymph nodes (52.2% vs. 28.3%; p:

Abstract Background Quantitative lung computed tomography (CT) provides fundamental information about lung aeration in critically ill patients. We tested a scanning protocol combining reduced number of CT slices and tube current, comparing quantitative analysis and radiation exposure to conventional CT. Methods In pigs, CT scans were performed during breath hold in a model of lung injury with three different protocols: standard spiral with 180 mAs tube current-time product (Spiral180), sequential with 20-mm distance between slices and either 180 mAs (Sequential180) or 50 mAs (Sequential50). Spiral scans of critically ill patients were collected retrospectively, and subsets of equally spaced slices were extracted. The agreement between CT protocols was assessed with Bland–Altman analysis. Results In 12 pigs, there was good concordance between the sequential protocols and the spiral scan (all biases ≤1.9%, agreements ≤±6.5%). In Spiral180, Sequential180 and Sequential50, estimated dose exposure was 2.3 (2.1–2.8), 0.21 (0.19–0.26), and 0.09 (0.07–0.10) mSv, respectively (p