Feasibility of Dedicated Breast Positron Emission Tomography Image Denoising Using a Residual Neural Network.

Objectives: This study aimed to create a deep learning (DL)-based denoising model using a residual neural network (Res-Net) trained to reduce noise in ring-type dedicated breast positron emission tomography (dbPET) images acquired in about half the emission time, and to evaluate the feasibility and the effectiveness of the model in terms of its noise reduction performance and preservation of quantitative values compared to conventional post-image filtering techniques. Methods: Low-count (LC) and full-count (FC) PET images with acquisition durations of 3 and 7 minutes, respectively, were reconstructed. A Res-Net was trained to create a noise reduction model using fifteen patients' data. The inputs to the network were LC images and its outputs were denoised PET (LC + DL) images, which should resemble FC images. To evaluate the LC + DL images, Gaussian and non-local mean (NLM) filters were applied to the LC images (LC + Gaussian and LC + NLM, respectively). To create reference images, a Gaussian filter was applied to the FC images (FC + Gaussian). The usefulness of our denoising model was objectively and visually evaluated using test data set of thirteen patients. The coefficient of variation (CV) of background fibroglandular tissue or fat tissue were measured to evaluate the performance of the noise reduction. The SUVmax and SUVpeak of lesions were also measured. The agreement of the SUV measurements was evaluated by Bland-Altman plots. Results: The CV of background fibroglandular tissue in the LC + DL images was significantly lower (9.10±2.76) than the CVs in the LC (13.60± 3.66) and LC + Gaussian images (11.51± 3.56). No significant difference was observed in both SUVmax and SUVpeak of lesions between LC + DL and reference images. For the visual assessment, the smoothness rating for the LC + DL images was significantly better than that for the other images except for the reference images. Conclusion: Our model reduced the noise in dbPET images acquired in about half the emission time while preserving quantitative values of lesions. This study demonstrates that machine learning is feasible and potentially performs better than conventional post-image filtering in dbPET denoising.

Amide proton transfer imaging of glioblastoma, neuroblastoma, and breast cancer cells on a 11.7 T magnetic resonance imaging system.

PURPOSE: The purpose of this study was (i) to determine the optimal magnetization transfer (MT) pulse parameter for amide proton transfer (APT) chemical exchange saturation transfer (CEST) imaging on an ultra-high-field magnetic resonance imaging (MRI) system and (ii) to use APT CEST imaging to noninvasively assess brain orthotopic and ectopic tumor cells transplanted into the mouse brain. METHODS: To evaluate APT without the influence of other metabolites, we prepared egg white phantoms. Next, we used 7-11-week-old nude female mice and the following cell lines to establish tumors after injection into the left striatum of mice: C6 (rat glioma, n = 8) as primary tumors and Neuro-2A (mouse neuroblastoma, n = 11) and MDA-MB231 (human breast cancer, n = 8) as metastatic tumors. All MRI experiments were performed on an 11.7 T vertical-bore scanner. CEST imaging was performed at 1 week after injection of Neuro-2A cells and at 2 weeks after injection of C6 and MDA-MB231 cells. The MT pulse amplitude was set at 2.2 µT or 4.4 µT. We calculated and compared the magnetization transfer ratio (MTR) and difference of MTR asymmetry between normal tissue and tumor (ΔMTR asymmetry) on APT CEST images between mouse models of brain tumors. Then, we performed hematoxylin and eosin (HE) staining and Ki-67 immunohistochemical staining to compare the APT CEST effect on tumor tissues and the pathological findings. RESULTS: Phantom study of the amide proton phantom containing chicken egg white, z-spectra obtained at a pulse length of 500 ms showed smaller peaks, whereas those obtained at a pulse length of 2000 ms showed slightly higher peaks. The APT CEST effect on tumor tissues was clearer at a pulse amplitude of 2.2 µT than at 4.4 µT. For all mouse models of brain tumors, ΔMTR asymmetry was higher at 2.2 µT than at 4.4 µT. ΔMTR asymmetry was significantly higher for the Neuro-2A model than for the MDA-MB231 model. HE staining revealed light bleeding in Neuro-2A tumors. Immunohistochemical staining revealed that the density of Ki-67-positive cells was higher in Neuro-2A tumors than in C6 or MDA-MB231 tumors. CONCLUSION: The MTR was higher at 4.4 µT than at 2.2 µT for each concentration of egg white at a pulse length of 500 ms or 2000 ms. High-resolution APT CEST imaging on an ultra-high-field MRI system was able to provide tumor information such as proliferative potential and intratumoral bleeding, noninvasively.

Longitudinal observations of progressive cardiac dysfunction in a cardiomyopathic animal model by self-gated cine imaging based on 11.7-T magnetic resonance imaging.

The purpose of this study was to longitudinally assess left ventricular function and wall thickness in a hamster model of cardiomyopathy using 11.7-T magnetic resonance imaging (MRI). MRI were performed for six cardiomyopathic J2N-k hamsters and six J2N-n hamsters at 5, 10, 15, and 20 weeks of age. Echocardiography was also performed at 20 weeks. The ejection fraction (EF) at 15 and 20 weeks of age in J2N-k hamsters showed a significant decrease compared with those in controls. Conversely, the end-systolic and end-diastolic volumes in cardiomyopathic hamsters showed a significant increase compared with those in controls. Moreover, the heart walls of J2N-k hamsters at 15 and 20 weeks were thicker than those of controls at end-systole; however, there were no significant differences at end-diastole. Optical microscopy with Masson's trichrome staining depicted no fibrosis in the control myocardium, although it showed interstitial fibrosis in the 20-week-old J2N-k cardiomyopathic myocardium. There were no differences in EF and the wall thickness observed on MRI and those observed on echocardiography. These results indicate the presence of systolic dysfunction in cardiomyopathic hamsters. Self-gated cine imaging based on 11.7-T MRI can be used for serial measurements of cardiac function and wall thickness in a cardiomyopathic model.