Patient centered radiology: investigating 3 Tesla whole body MRI acceptance in cancer patients.

INTRODUCTION: Whole body magnetic resonance imaging (WB-MRI) is a promising emerging imaging technology for detecting bone and soft tissue pathology, especially in the onco-hematological field. This study aims to evaluate cancer patients' experience of WB-MRI performed on a 3T scanner compared to other diagnostic total body examinations. MATERIAL AND METHOD: In this prospective committee-approved study, patients completed a questionnaire in person (n = 134) after undergoing a WB-MRI scan to collect data on their physical and psychological reactions during the scan, the global satisfaction level, and preference for other types of MRI or computed tomography (CT), or positron emission tomography (PET/CT). Of all patients who had performed a CT or PET/CT the previous year, 61.9% had already undergone an MRI. The most common symptoms reported were: 38.1% perceived a localized increase in temperature and 34.4% numbness and tingling of the limbs. The scan time averaged 45 min and was well tolerated by most patients (112, 85.5%). Overall, WB-MRI was appreciated by the majority (121/134-90.3%) of patients who said they would probably undergo the procedure again. Patients preferred the WB-MRI in 68.7% of cases (92/134), followed by CT in 15.7% of cases (21/134) and by PET/CT in 7.4% (10/134), with 8.4% (11/134) of patients without any preference. The preference for imaging modalities was age-dependent (p = 0.011), while (p > 0.05) was independent of sex and a primary cancer site. CONCLUSION: These results demonstrate a high degree of WB-MRI acceptance from a patient's point of view.

A fully automated left atrium segmentation approach from late gadolinium enhanced magnetic resonance imaging based on a convolutional neural network.

BACKGROUND: Several studies suggest that the evaluation of left atrial (LA) fibrosis is a relevant information for the assessment of the appropriate strategy in catheter ablation in atrial fibrillation (AF). Late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI) is a non-invasive technique, which might be employed for the non-invasive quantification of LA myocardial fibrotic tissue in patients with AF. Nowadays, the analysis of LGE MRI relies on manual tracing of LA boundaries and this procedure is time-consuming and prone to high inter-observer variability given the different degrees of observers' experience, LA wall thickness and data resolution. Therefore, an automated segmentation approach of the atrial cavity for the quantification of scar tissue would be highly desirable. METHODS: This study focuses on the design of a fully automated LGE MRI segmentation pipeline which includes a convolutional neural network (CNN) based on the successful architecture U-Net. The CNN was trained, validated and tested end-to-end with the data available from the Statistical Atlases and Computational Modelling of the Heart 2018 Atrial Segmentation Challenge (100 cardiac data). Two different approaches were tested: using both stacks of 2-D axial slices and using 3-D data (with the appropriate changes in the baseline architecture). In the latter approach, thanks to the 3-D convolution operator, all the information underlying 3-D data can be exploited. Once the training was completed using 80 cardiac data, a post-processing step was applied on 20 predicted segmentations belonging to the test set. RESULTS: By applying the 2-D and 3-D approaches, average Dice coefficient and mean Hausdorff distances were 0.896, 0.914, and 8.98 mm, 8.34 mm, respectively. Volumes of the anatomical LA meshes from the automated analysis were highly correlated with the volumes from ground truth [2-D: r=0.978, y=0.94x+0.07, bias=3.5 ml (5.6%), SD=5.3 mL (8.5%); 3-D: r=0.982, y=0.92x+2.9, bias=2.1 mL (3.5%), SD=5.2 mL (8.4%)]. CONCLUSIONS: These results suggest the proposed approach is feasible and provides accurate results. Despite the increase of the number of trainable parameters, the proposed 3-D CNN learns better features leading to higher performance, feasible for a real clinical application.