Prediction of standard-dose brain PET image by using MRI and low-dose brain [ 18 F]FDG PET images: Prediction of standard-dose brain PET image

Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [18F]FDG PET image by using a low-dose brain [18F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image

MRI radiomic features are independently associated with overall survival in soft tissue sarcoma

Purpose: Soft tissue sarcomas (STS) represent a heterogeneous group of diseases, and selection of individualized treatments remains a challenge. The goal of this study was to determine whether radiomic features extracted from magnetic resonance (MR) images are independently associated with overall survival (OS) in STS. Methods and Materials: This study analyzed 2 independent cohorts of adult patients with stage II-III STS treated at center 1 (N = 165) and center 2 (N = 61). Thirty radiomic features were extracted from pretreatment T1-weighted contrast-enhanced MR images. Prognostic models for OS were derived on the center 1 cohort and validated on the center 2 cohort. Clinical-only (C), radiomics-only (R), and clinical and radiomics (C+R) penalized Cox models were constructed. Model performance was assessed using Harrell\u27s concordance index. Results: In the R model, tumor volume (hazard ratio [HR], 1.5) and 4 texture features (HR, 1.1-1.5) were selected. In the C+R model, both age (HR, 1.4) and grade (HR, 1.7) were selected along with 5 radiomic features. The adjusted c-indices of the 3 models ranged from 0.68 (C) to 0.74 (C+R) in the derivation cohort and 0.68 (R) to 0.78 (C+R) in the validation cohort. The radiomic features were independently associated with OS in the validation cohort after accounting for age and grade (HR, 2.4; Conclusions: This study found that radiomic features extracted from MR images are independently associated with OS when accounting for age and tumor grade. The overall predictive performance of 3-year OS using a model based on clinical and radiomic features was replicated in an independent cohort. Optimal models using clinical and radiomic features could improve personalized selection of therapy in patients with STS

PET/MRI of Hepatic 90Y Microsphere Deposition Determines Individual Tumor Response.

PurposeThe purpose of our study is to determine if there is a relationship between dose deposition measured by PET/MRI and individual lesion response to yttrium-90 ((90)Y) microsphere radioembolization.Materials and methods26 patients undergoing lobar treatment with (90)Y microspheres underwent PET/MRI within 66 h of treatment and had follow-up imaging available. Adequate visualization of tumor was available in 24 patients, and contours were drawn on simultaneously acquired PET/MRI data. Dose volume histograms (DVHs) were extracted from dose maps, which were generated using a voxelized dose kernel. Similar contours to capture dimensional and volumetric change of tumors were drawn on follow-up imaging. Response was analyzed using both RECIST and volumetric RECIST (vRECIST) criteria.ResultsA total of 8 hepatocellular carcinoma (HCC), 4 neuroendocrine tumor (NET), 9 colorectal metastases (CRC) patients, and 3 patients with other metastatic disease met inclusion criteria. Average dose was useful in predicting response between responders and non-responders for all lesion types and for CRC lesions alone using both response criteria (p < 0.05). D70 (minimum dose to 70 % of volume) was also useful in predicting response when using vRECIST. No significant trend was seen in the other tumor types. For CRC lesions, an average dose of 29.8 Gy offered 76.9 % sensitivity and 75.9 % specificity for response.ConclusionsPET/MRI of (90)Y microsphere distribution showed significantly higher DVH values for responders than non-responders in patients with CRC. DVH analysis of (90)Y microsphere distribution following treatment may be an important predictor of response and could be used to guide future adaptive therapy trials

Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images

Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance (MR) images do not reflect attenuation coefficients directly. To address this issue, we present deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images. With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods. When both Dixon and zero echo time (ZTE) images are available, we have proposed a modified U-net structure, named GroupU-net, to efficiently make use of both Dixon and ZTE information through group convolution modules when the network goes deeper. Quantitative analysis based on fourteen real patient data sets demonstrates that both network approaches can perform better than the standard methods, and the proposed network structure can further reduce the PET quantification error compared to the U-net structure.Comment: 15 pages, 12 figure