Comparison of amide proton transfer imaging with perfusion imaging of using arterial spin-labeling for evidence of tumor invasion in glioblastoma

Comparison between supervised and physics-informed unsupervised deep neural networks for estimating cerebral perfusion using multi-delay arterial spin labeling MRI

This study aimed to implement a physics-informed unsupervised deep neural network (DNN) to estimate cerebral blood flow (CBF) and arterial transit time (ATT) from multi-delay arterial spin labeling (ASL), and compare its performance with that of a supervised DNN and the conventional method. Supervised and unsupervised DNNs were trained using simulation data. The accuracy and noise immunity of the three methods were compared using simulations and in vivo data. The simulation study investigated the differences between the predicted and ground-truth values and their variations with the noise level. The in vivo study evaluated the predicted values from the original images and noise-induced variations in the predicted values from the synthesized noisy images by adding Rician noise to the original images. The simulation study showed that CBF estimated using the supervised DNN was not biased by noise, whereas that estimated using other methods had a positive bias. Although the ATT with all methods exhibited a similar behavior with noise increase, the ATT with the supervised DNN was less biased. The in vivo study showed that CBF and ATT with the supervised DNN were the most accurate and that the supervised and unsupervised DNNs had the highest noise immunity in CBF and ATT estimations, respectively. Physics-informed unsupervised learning can estimate CBF and ATT from multi-delay ASL signals, and its performance is superior to that of the conventional method. Although noise immunity in ATT estimation was superior with unsupervised learning, other performances were superior with supervised learning.

Exploratory Analysis of Serial 18F-fluciclovine PET-CT and Multiparametric MRI during Chemoradiation for Glioblastoma

Anti-1-amino-3-18fluorine-fluorocyclobutane-1-carboxylic acid (18F-fluciclovine) positron emission tomography (PET) shows preferential glioma uptake but there is little data on how uptake correlates with post-contrast T1-weighted (Gd-T1) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) activity during adjuvant treatment. This pilot study aimed to compare 18F-fluciclovine PET, DCE-MRI and Gd-T1 in patients undergoing chemoradiotherapy for glioblastoma (GBM), and in a parallel pre-clinical GBM model, to investigate correlation between 18F-fluciclovine uptake, MRI findings, and tumour biology. 18F-fluciclovine-PET-computed tomography (PET-CT) and MRI including DCE-MRI were acquired before, during and after adjuvant chemoradiotherapy (60 Gy in 30 fractions with temozolomide) in GBM patients. MRI volumes were manually contoured; PET volumes were defined using semi-automatic thresholding. The similarity of the PET and DCE-MRI volumes outside the Gd-T1 volume boundary was measured using the Dice similarity coefficient (DSC). CT-2A tumour-bearing mice underwent MRI and 18F-fluciclovine PET-CT. Post-mortem mice brains underwent immunohistochemistry staining for ASCT2 (amino acid transporter), nestin (stemness) and Ki-67 (proliferation) to assess for biologically active tumour. 6 patients were recruited (GBM 1-6) and grouped according to overall survival (OS)-short survival (GBM-SS, median OS 249 days) and long survival (GBM-LS, median 903 days). For GBM-SS, PET tumour volumes were greater than DCE-MRI, in turn greater than Gd-T1. For GBM-LS, Gd-T1 and DCE-MRI were greater than PET. Tumour-specific 18F-fluciclovine uptake on pre-clinical PET-CT corresponded to immunostaining for Ki-67, nestin and ASCT2. Results suggest volumes of 18F-fluciclovine-PET activity beyond that depicted by DCE-MRI and Gd-T1 are associated with poorer prognosis in patients undergoing chemoradiotherapy for GBM. The pre-clinical model confirmed 18F-fluciclovine uptake reflected biologically active tumour.