This article presents and validates a newly developed GATE model of the Siemens Inveon trimodal imaging platform. Fully incorporating the positron emission tomography (PET), single-photon emission computed tomography (SPECT), and computed tomography (CT) data acquisition subsystems, this model enables feasibility studies of new imaging applications, the development of reconstruction and correction algorithms, and the creation of a baseline against which experimental results for real data can be compared. Model validation was based on comparing simulation results against both empirical and published data. The PET modality was validated using the NEMA NU-4 standard. Validations of SPECT and CT modalities were based on assessment of model accuracy compared to published and empirical data on the platform. Validation results show good agreement between simulation and empirical data of approximately ± 5%.

Elevated breast density is among the strongest independent predictors of breast cancer. Breast density scores are critical inputs in models used to calculate a patient's lifetime risk of developing breast cancer. Today, the only FDA-cleared technology for assessing breast density uses mammography. An alternative modality for breast density quantification is 3D transmission ultrasound (TU). In this retrospective study, we compared automated breast density calculations derived from TU using quantitative breast density (QBD) and mammography with tomosynthesis using VolparaDensity 3.1 for 225 breasts. Pearson correlation coefficients (r) and intraclass correlation coefficients were compared. Subset analyses of extremely dense breasts, premenopausal, and postmenopausal breasts were also performed. Comparative analysis between radiologist-derived density assessment and objective automated scores was performed. Calculations from TU and mammography with tomosynthesis for breast density, total breast volume (TBV), and fibroglandular volume (FGV) were strongly correlated (r ¼ 0.91, 0.92, and 0.67, respectively). We observed moderate absolute agreement for FGV and breast density, and strong absolute agreement for TBV. A subset of 56 extremely dense breasts showed similar trends, however with lower breast density agreement in the subset than in the full study. No significant difference existed in density correlation between premenopausal and postmenopausal breasts across modalities. QBD calculations from TU were strongly correlated with breast density scores from VolparaDensity. TU systematically measured higher FGV and breast density compared with mammography, and the difference increased with breast density.Impact: TU of the breast can accurately quantify breast density comparable with mammography with tomosynthesis.

This paper addresses 123I and 125I dual isotope SPECT imaging, which can be challenging because of spectrum overlap in the low energy spectrums of these isotopes. We first quantify the contribution of low-energy photons from each isotope using GATE-based Monte Carlo simulations for the MOBY mouse phantom. We then describe and analyze a simple, but effective method that uses the ratio of detected low and high energy 123I activity to separate the mixed low energy 123I and 125I activities. Performance is compared with correction methods used in conventional tissue biodistribution techniques. The results indicate that the spectrum overlap effects can be significantly reduced, if not entirely eliminated, when attenuation and scatter is either absent or corrected for using standard methods. In particular, we show that relative activity levels of the two isotopes can be accurately estimated for a wide range of organs and provide quantitative validation that standard methods for spectrum overlap correction provide reasonable estimates for reasonable corrections in small-animal SPECT/CT imaging.

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Purpose Respiratory motion of patients during positron emission tomography (PET)/computed tomography (CT) imaging affects both image quality and quantitative accuracy. Hardware‐based motion estimation, which is the current clinical standard, requires initial setup, maintenance, and calibration of the equipment, and can be associated with patient discomfort. Data‐driven techniques are an active area of research with limited exploration into lesion‐specific motion estimation. This paper introduces a time‐of‐flight (TOF)‐weighted positron emission particle tracking (PEPT) algorithm that facilitates lesion‐specific respiratory motion estimation from raw listmode PET data. Methods The TOF‐PEPT algorithm was implemented and investigated under different scenarios: (a) a phantom study with a point source and an Anzai band for respiratory motion tracking; (b) a phantom study with a point source only, no Anzai band; (c) two clinical studies with point sources and the Anzai band; (d) two clinical studies with point sources only, no Anzai band; and (e) two clinical studies using lesions/internal regions instead of point sources and no Anzai band. For studies with radioactive point sources, they were placed on patients during PET/CT imaging. The motion tracking was performed using a preselected region of interest (ROI), manually drawn around point sources or lesions on reconstructed images. The extracted motion signals were compared with the Anzai band when applicable. For the purposes of additional comparison, a center‐of‐mass (COM) algorithm was implemented both with and without the use of TOF information. Using the motion estimate from each method, amplitude‐based gating was applied, and gated images were reconstructed. Results The TOF‐PEPT algorithm is shown to successfully determine the respiratory motion for both phantom and clinical studies. The derived motion signals correlated well with the Anzai band; correlation coefficients of 0.99 and 0.94‐0.97 were obtained for the phantom study and the clinical studies, respectively. TOF‐PEPT was found to be 13–38% better correlated with the Anzai results than the COM methods. Maximum Standardized Uptake Values (SUVs) were used to quantitatively compare the reconstructed‐gated images. In comparison with the ungated image, a 14–39% increase in the max SUV across several lesion areas and an 8.7% increase in the max SUV on the tracked lesion area were observed in the gated images based on TOF‐PEPT. The distinct presence of lesions with reduced blurring effect and generally sharper images were readily apparent in all clinical studies. In addition, max SUVs were found to be 4–10% higher in the TOF‐PEPT‐based gated images than in those based on Anzai and COM methods. Conclusion A PEPT‐ based algorithm has been presented for determining movement due to respiratory motion during PET/CT imaging. Gating based on the motion estimate is shown to quantifiably improve the image quality in both a controlled point source phantom study and in clinical data patient studies. The algorithm has the...