Combined positron emission tomography (PET) and magnetic resonance imaging (MRI) is a new tool to study functional processes in the brain. Here we study brain function in response to a barrel-field stimulus simultaneously using PET, which traces changes in glucose metabolism on a slow time scale, and functional MRI (fMRI), which assesses fast vascular and oxygenation changes during activation. We found spatial and quantitative discrepancies between the PET and the fMRI activation data. The functional connectivity of the rat brain was assessed by both modalities: the fMRI approach determined a total of nine known neural networks, whereas the PET method identified seven glucose metabolism-related networks. These results demonstrate the feasibility of combined PET-MRI for the simultaneous study of the brain at activation and rest, revealing comprehensive and complementary information to further decode brain function and brain networks.

No abstract

Deep learning (DL) has been reemerging recently in many fields, including computer vision and speechrecognition, because of big data and groundbreaking GPU performance (LeCun et al 2015, Sze et al 2017). Sophisticated deep neural network (DNN) models were proposed in the competition of ILSVRC (ImageNet Large-Scale Visual Recognition Challenge), such as AlexNet (Krizhevsky et al 2012), VGG Net (Simonyan and Zisserman 2014), Microsoft ResNet (He et al 2015), and GoogLeNet (Szegedy et al 2015). DL is adopted quickly in medical imaging applications for lesion detection (Esteva et al 2017), image segmentation (Ronneberger et al 2015) and registration, and automated diagnosis (Dolz et al 2016). DL has been also used in end-to-end trainings to enhance image quality, such as noise and artifacts reduction, across many medical imaging modalities (Han

Simultaneous PET/MR imaging is an emerging hybrid modality for clinical and preclinical imaging. The static magnetic field of the MR imaging device affects the trajectory of the positrons emitted by the PET radioisotopes. This effect translates into an improvement of the spatial resolution in transaxial images. However, because of the elongation of the positron range distribution along the magnetic field, the axial resolution worsens and shine-through artifacts may appear. These artifacts can lead to misinterpretation and overstaging. The aim of this work was to study the relevance of this effect. Methods: Measurements were performed in a 3-tesla PET/ MR scanner. A 1-cm 2 piece of paper, soaked with a radioisotope and placed in air, was scanned, and the magnitude of the shinethrough was quantified from the PET images for various radioisotopes. Additionally, PET/MR and PET/CT images of the lungs and the larynx with trachea of a deceased swine were obtained after injecting a mixture of NiSO 4 and 68 Ga to simulate hot tumor lesions. Results: For the radioactive paper, shine-through artifacts appeared in the location of the acrylic glass backplane, located 3 cm from the source in the axial direction. The ratio between the activity of the shine-through and the activity reconstructed in the original location ranged from 0.9 ( 18 F) to 5.7 ( 68 Ga). For the larynx-withtrachea images, the magnitude of the artifacts depended on the organ orientation with respect to the magnetic field. The shine-through activity could reach 46% of the reconstructed activity (larynx lesion). The lesion within the trachea produced 2 artifacts, symmetrically aligned with the magnetic field and characterized by artifact-to-lesion volumeof-interest ratios ranging from 21% to 30%. Conclusion: In simultaneous PET/MR imaging, the effect of the magnetic field on positrons may cause severe artifacts in the PET image when the lesions are close to air cavities and high-energy radioisotopes are used. For accurate staging and interpretation, this effect needs to be recognized and adequate compensation techniques should be developed. PET/ MR imaging is a powerful technology that is now expanding worldwide (1); integrated PET/MR imaging systems are already commercially available for clinical applications (2,3). Special attention is thus being paid to the ability of simultaneous PET/MR imaging to provide quantitative information and artifact-free images. A particular phenomenon of this integrated technology is the effect of the static magnetic field on positrons. Because of the Lorentz force, positrons follow helical paths along the magnetic field lines. Consequently, the positron range distribution narrows in the plane perpendicular to the magnetic field, thus improving the transaxial resolution (4-9). On the other hand, little attention has been devoted to the behavior of positrons in the axial direction. The positron range distribution elongates along the magnetic field, and the axial resolution subsequently degrades (9-11). The positron range can rea...

Purpose Imaging of the secondary electron bremsstrahlung (SEB) x rays emitted during particle‐ion irradiation is a promising method for beam range estimation. However, the SEB x‐ray images are not directly correlated to the dose images. In addition, limited spatial resolution of the x‐ray camera and low‐count situation may impede correctly estimating the beam range and width in SEB x‐ray images. To overcome these limitations of the SEB x‐ray images measured by the x‐ray camera, a deep learning (DL) approach was proposed in this work to predict the dose images for estimating the range and width of the carbon ion beam on the measured SEB x‐ray images. Methods To prepare enough data for the DL training efficiently, 10,000 simulated SEB x‐ray and dose image pairs were generated by our in‐house developed model function for different carbon ion beam energies and doses. The proposed DL neural network consists of two U‐nets for SEB x ray to dose image conversion and super resolution. After the network being trained with these simulated x‐ray and dose image pairs, the dose images were predicted from simulated and measured SEB x‐ray testing images for performance evaluation. Results For the 500 simulated testing images, the average mean squared error (MSE) was 2.5 × 10−5 and average structural similarity index (SSIM) was 0.997 while the error of both beam range and width was within 1 mm FWHM. For the three measured SEB x‐ray images, the MSE was no worse than 5.5 × 10−3 and SSIM was no worse than 0.980 while the error of the beam range and width was 2 mm and 5 mm FWHM, respectively. Conclusions We have demonstrated the advantages of predicting dose images from not only simulated data but also measured data using our deep learning approach.

Purpose: Parametric images obtained from kinetic modeling of dynamic positron emission tomography (PET) data provide a new way of visualizing quantitative parameters of the tracer kinetics. However, due to the high noise level in pixel-wise image-driven time-activity curves, parametric images often suffer from poor quality and accuracy. In this study, we propose an indirect parameter estimation framework which aims to improve the quality and quantitative accuracy of parametric images. Methods: Three different approaches related to noise reduction and advanced curve fitting algorithm are used in the proposed framework. First, dynamic PET images are denoised using a kernel-based denoising method and the highly constrained backprojection technique. Second, gradient-free curve fitting algorithms are exploited to improve the accuracy and precision of parameter estimates. Third, a kernel-based post-filtering method is applied to parametric images to further improve the quality of parametric images. Computer simulations were performed to evaluate the performance of the proposed framework. Results and conclusions: The simulation results showed that when compared to the Gaussian filtering, the proposed denoising method could provide better PET image quality, and consequentially improve the quality and quantitative accuracy of parametric images. In addition, gradient-free optimization algorithms (i.e., pattern search) can result in better parametric images than the gradientbased curve fitting algorithm (i.e., trust-region-reflective). Finally, our results showed that the proposed kernel-based post-filtering method could further improve the precision of parameter estimates while maintaining the accuracy of parameter estimates.

No abstract

In this study, we present an image denoising method for diffusion-weighted magnetic resonance imaging (DW-MRI) data. Our aim is to improve the estimation of intravoxel incoherent motion (IVIM) parameters using denoised DW-MRI data. A general-threshold filtering (GTF) reconstruction via total variation minimization has been proposed to improve image quality in few-view computed tomography. Here, we applied the combination of GTF and total difference to image denoising. Voxel-wise IVIM analysis was performed using both real and simulated DW-MRI data. Using an institutional review board-approved protocol with written informed consent, DW-MRI imaging was performed at a 3 T hybrid PET/MR system in 10 patients with Hodgkin lymphoma lesions. A simulated phantom consisting of four organs (liver, pancreas, spleen and kidney) was used to generate noisy DW-MRI data according to the IVIM model at different noise levels. DW-MRI data were denoised before IVIM parameter estimation. The proposed image denoising method was compared with the image denoising method using joint rank and edge constraints (JREC). The results of simulated data show that at the lower signal-to-noise ratios the proposed image denoising method outperformed the JREC method in terms of the accuracy and precision of the IVIM parameter estimates. The experimental results also show that the proposed image denoising method could yield better parametric images than the JREC method in terms of noise reduction and edge preservation.