Fine-Grained MRI Reconstruction Using Attentive Selection Generative Adversarial Networks

Compressed sensing (CS) leverages the sparsity prior to provide the foundation for fast magnetic resonance imaging (fastMRI). However, iterative solvers for ill-posed problems hinder their adaption to time-critical applications. Moreover, such a prior can be neither rich to capture complicated anatomical structures nor applicable to meet the demand of high-fidelity reconstructions in modern MRI. Inspired by the state-of-the-art methods in image generation, we propose a novel attention-based deep learning framework to provide high-quality MRI reconstruction. We incorporate large-field contextual feature integration and attention selection in a generative adversarial network (GAN) framework. We demonstrate that the proposed model can produce superior results compared to other deep learning-based methods in terms of image quality, and relevance to the MRI reconstruction in an extremely low sampling rate diet.Comment: 5 pages, 2 figures, 1 table, 22 reference

Tumour auto-contouring on 2d cine MRI for locally advanced lung cancer: A comparative study

BACKGROUND AND PURPOSE: Radiotherapy guidance based on magnetic resonance imaging (MRI) is currently becoming a clinical reality. Fast 2d cine MRI sequences are expected to increase the precision of radiation delivery by facilitating tumour delineation during treatment. This study compares four auto-contouring algorithms for the task of delineating the primary tumour in six locally advanced (LA) lung cancer patients. MATERIAL AND METHODS: Twenty-two cine MRI sequences were acquired using either a balanced steady-state free precession or a spoiled gradient echo imaging technique. Contours derived by the auto-contouring algorithms were compared against manual reference contours. A selection of eight image data sets was also used to assess the inter-observer delineation uncertainty. RESULTS: Algorithmically derived contours agreed well with the manual reference contours (median Dice similarity index: ⩾0.91). Multi-template matching and deformable image registration performed significantly better than feature-driven registration and the pulse-coupled neural network (PCNN). Neither MRI sequence nor image orientation was a conclusive predictor for algorithmic performance. Motion significantly degraded the performance of the PCNN. The inter-observer variability was of the same order of magnitude as the algorithmic performance. CONCLUSION: Auto-contouring of tumours on cine MRI is feasible in LA lung cancer patients. Despite large variations in implementation complexity, the different algorithms all have relatively similar performance

Iterative Data Refinement for Self-Supervised MR Image Reconstruction

Magnetic Resonance Imaging (MRI) has become an important technique in the clinic for the visualization, detection, and diagnosis of various diseases. However, one bottleneck limitation of MRI is the relatively slow data acquisition process. Fast MRI based on k-space undersampling and high-quality image reconstruction has been widely utilized, and many deep learning-based methods have been developed in recent years. Although promising results have been achieved, most existing methods require fully-sampled reference data for training the deep learning models. Unfortunately, fully-sampled MRI data are difficult if not impossible to obtain in real-world applications. To address this issue, we propose a data refinement framework for self-supervised MR image reconstruction. Specifically, we first analyze the reason of the performance gap between self-supervised and supervised methods and identify that the bias in the training datasets between the two is one major factor. Then, we design an effective self-supervised training data refinement method to reduce this data bias. With the data refinement, an enhanced self-supervised MR image reconstruction framework is developed to prompt accurate MR imaging. We evaluate our method on an in-vivo MRI dataset. Experimental results show that without utilizing any fully sampled MRI data, our self-supervised framework possesses strong capabilities in capturing image details and structures at high acceleration factors.Comment: 5 pages, 2 figures, 1 tabl

Cost-effectiveness of abbreviated-protocol MRI screening for women with mammographically dense breasts in a national breast cancer screening program

INTRODUCTION: Magnetic resonance imaging (MRI) has shown the potential to improve the screening effectiveness among women with dense breasts. The introduction of fast abbreviated protocols (AP) makes MRI more feasible to be used in a general population. We aimed to investigate the cost-effectiveness of AP-MRI in women with dense breasts (heterogeneously/extremely dense) in a population-based screening program. METHODS: A previously validated model (SiMRiSc) was applied, with parameters updated for women with dense breasts. Breast density was assumed to decrease with increased age. The base scenarios included six biennial AP-MRI strategies, with biennial mammography from age 50–74 as reference. Fourteen alternative scenarios were performed by varying screening interval (triennial and quadrennial) and by applying a combined strategy of mammography and AP-MRI. A 3% discount rate for both costs and life years gained (LYG) was applied. Model robustness was evaluated using univariate and probabilistic sensitivity analyses. RESULTS: The six biennial AP-MRI strategies ranged from 132 to 562 LYG per 10,000 women, where more frequent application of AP-MRI was related to higher LYG. The optimal strategy was biennial AP-MRI screening from age 50–65 for only women with extremely dense breasts, producing an incremental cost-effectiveness ratio of € 18,201/LYG. At a threshold of € 20,000/LYG, the probability that the optimal strategy was cost-effective was 79%. CONCLUSION: Population-based biennial breast cancer screening with AP-MRI from age 50–65 for women with extremely dense breasts might be a cost-effective alternative to mammography, but is not an option for women with heterogeneously dense breasts

Dosimetric validation of a magnetic resonance image gated radiotherapy system using a motion phantom and radiochromic film.

PurposeMagnetic resonance image (MRI) guided radiotherapy enables gating directly on the target position. We present an evaluation of an MRI-guided radiotherapy system's gating performance using an MRI-compatible respiratory motion phantom and radiochromic film. Our evaluation is geared toward validation of our institution's clinical gating protocol which involves planning to a target volume formed by expanding 5 mm about the gross tumor volume (GTV) and gating based on a 3 mm window about the GTV.MethodsThe motion phantom consisted of a target rod containing high-contrast target inserts which moved in the superior-inferior direction inside a body structure containing background contrast material. The target rod was equipped with a radiochromic film insert. Treatment plans were generated for a 3 cm diameter spherical planning target volume, and delivered to the phantom at rest and in motion with and without gating. Both sinusoidal trajectories and tumor trajectories measured during MRI-guided treatments were used. Similarity of the gated dose distribution to the planned, motion-frozen, distribution was quantified using the gamma technique.ResultsWithout gating, gamma pass rates using 4%/3 mm criteria were 22-59% depending on motion trajectory. Using our clinical standard of repeated breath holds and a gating window of 3 mm with 10% target allowed outside the gating boundary, the gamma pass rate was 97.8% with 3%/3 mm gamma criteria. Using a 3 mm window and 10% allowed excursion, all of the patient tumor motion trajectories at actual speed resulting in at least 95% gamma pass rate at 4%/3 mm.ConclusionsOur results suggest that the device can be used to compensate respiratory motion using a 3 mm gating margin and 10% allowed excursion results in conjunction with repeated breath holds. Full clinical validation requires a comprehensive evaluation of tracking performance in actual patient images, outside the scope of this study