A dual-supervised deformation estimation model (DDEM) for constructing ultra-quality 4D-MRI based on a commercial low-quality 4D-MRI for liver cancer radiation therapy.

BACKGROUND: Most available four-dimensional (4D)-magnetic resonance imaging (MRI) techniques are limited by insufficient image quality and long acquisition times or require specially designed sequences or hardware that are not available in the clinic. These limitations have greatly hindered the clinical implementation of 4D-MRI. PURPOSE: This study aims to develop a fast ultra-quality (UQ) 4D-MRI reconstruction method using a commercially available 4D-MRI sequence and dual-supervised deformation estimation model (DDEM). METHODS: Thirty-nine patients receiving radiotherapy for liver tumors were included. Each patient was scanned using a time-resolved imaging with interleaved stochastic trajectories (TWIST)-lumetric interpolated breath-hold examination (VIBE) MRI sequence to acquire 4D-magnetic resonance (MR) images. They also received 3D T1-/T2-weighted MRI scans as prior images, and UQ 4D-MRI at any instant was considered a deformation of them. A DDEM was developed to obtain a 4D deformable vector field (DVF) from 4D-MRI data, and the prior images were deformed using this 4D-DVF to generate UQ 4D-MR images. The registration accuracies of the DDEM, VoxelMorph (normalized cross-correlation [NCC] supervised), VoxelMorph (end-to-end point error [EPE] supervised), and the parametric total variation (pTV) algorithm were compared. Tumor motion on UQ 4D-MRI was evaluated quantitatively using region of interest (ROI) tracking errors, while image quality was evaluated using the contrast-to-noise ratio (CNR), lung-liver edge sharpness, and perceptual blur metric (PBM). RESULTS: The registration accuracy of the DDEM was significantly better than those of VoxelMorph (NCC supervised), VoxelMorph (EPE supervised), and the pTV algorithm (all, p < 0.001), with an inference time of 69.3 ± 5.9 ms. UQ 4D-MRI yielded ROI tracking errors of 0.79 ± 0.65, 0.50 ± 0.55, and 0.51 ± 0.58 mm in the superior-inferior, anterior-posterior, and mid-lateral directions, respectively. From the original 4D-MRI to UQ 4D-MRI, the CNR increased from 7.25 ± 4.89 to 18.86 ± 15.81; the lung-liver edge full-width-at-half-maximum decreased from 8.22 ± 3.17 to 3.65 ± 1.66 mm in the in-plane direction and from 8.79 ± 2.78 to 5.04 ± 1.67 mm in the cross-plane direction, and the PBM decreased from 0.68 ± 0.07 to 0.38 ± 0.01. CONCLUSION: This novel DDEM method successfully generated UQ 4D-MR images based on a commercial 4D-MRI sequence. It shows great promise for improving liver tumor motion management during radiation therapy.

The impact of video gaming on cognitive functioning of people with schizophrenia (GAME-S): study protocol of a randomised controlled trial.

BACKGROUND: Video gaming is a promising intervention for cognitive and social impairment in patients with schizophrenia. A number of gaming interventions have been evaluated in small-scale studies with various patient groups, but studies on patients with schizophrenia remain scarce and rarely include the evaluation of both clinical and neurocognitive outcomes. In this study, we will test the effectiveness of two interventions with gaming elements to improve cognitive and clinical outcomes among persons with schizophrenia. METHODS: The participants will be recruited from different outpatient units (e.g., outpatient psychiatric units, day hospitals, residential care homes). The controlled clinical trial will follow a three-arm parallel-group design: 1) cognitive training (experimental group, CogniFit), 2) entertainment gaming (active control group, SIMS 4), and 3) treatment as usual. The primary outcomes are working memory function at 3-month and 6-month follow-ups. The secondary outcomes are patients' other cognitive and social functioning, the ability to experience pleasure, self-efficacy, and negative symptoms at 3-month and 6-month follow-ups. We will also test the effectiveness of gaming interventions on neurocognitive outcomes (EEG and 3 T MRI plus rs-fMRI) at a 3-month follow-up as an additional secondary outcome. Data will be collected in outpatient psychiatric services in Hong Kong. Participants will have a formal diagnosis of schizophrenia and be between 18 and 60 years old. We aim to have a total of 234 participants, randomly allocated to the three arms. A sub-sample of patients (N = 150) will be recruited to undergo an EEG. For neuroimaging assessment, patients will be randomly allocated to a subset of patients (N=126). We will estimate the efficacy of the interventions on the primary and secondary outcomes based on the intention-to-treat principle. Behavioural and EEG data will be analysed separately. DISCUSSION: The study will characterise benefits of gaming on patients' health and well-being, and contribute towards the development of new treatment approaches for patients with schizophrenia. TRIAL REGISTRATION: ClinicalTrials.gov NCT03133143 . Registered on April 28, 2017.

B-Value Optimization in the Estimation of Intravoxel Incoherent Motion Parameters in Patients with Cervical Cancer.

OBJECTIVE: This study aimed to find the optimal number of b-values for intravoxel incoherent motion (IVIM) imaging analysis, using simulated and in vivo data from cervical cancer patients. MATERIALS AND METHODS: Simulated data were generated using literature pooled means, which served as reference values for simulations. In vivo data from 100 treatment-naïve cervical cancer patients with IVIM imaging (13 b-values, scan time, 436 seconds) were retrospectively reviewed. A stepwise b-value fitting algorithm calculated optimal thresholds. Feed forward selection determined the optimal subsampled b-value distribution for biexponential IVIM fitting, and simplified IVIM modeling using monoexponential fitting was attempted. IVIM parameters computed using all b-values served as reference values for in vivo data. RESULTS: In simulations, parameters were accurately estimated with six b-values, or three b-values for simplified IVIM, respectively. In vivo data showed that the optimal threshold was 40 s/mm² for patients with squamous cell carcinoma and a subsampled acquisition of six b-values (scan time, 198 seconds) estimated parameters were not significantly different from reference parameters (individual parameter error rates of less than 5%). In patients with adenocarcinoma, the optimal threshold was 100 s/mm², but an optimal subsample could not be identified. Irrespective of the histological subtype, only three b-values were needed for simplified IVIM, but these parameters did not retain their discriminative ability. CONCLUSION: Subsampling of six b-values halved the IVIM scan time without significant losses in accuracy and discriminative ability. Simplified IVIM is possible with only three b-values, at the risk of losing diagnostic information.