Evaluation of an MRI-based screening pathway for prostate cancer

In recent years there has been a wealth of debate regarding prostate cancer screening, with a concurrent increase in new imaging techniques for prostate cancer diagnosis. Imaging has been the technique of choice in lung and breast cancer screening programmes but has not been explored for prostate cancer screening. Herein, this thesis explores the role of magnetic resonance imaging (MRI) as a new approach to screen for prostate cancer. Following an introduction to the current screening landscape, my thesis focuses on the development and validation of a fast MRI, known as a prostagram, that could serve as a viable image-based screening test. Evaluation of this new technique is performed within a prospective, population-based, blinded, cohort study which was conducted at seven primary care practices and two imaging centres. A diverse array of performance characteristics of fast MRI are compared to PSA. These encompass biopsy rates, cancer detection rates, diagnostic accuracy and patient reported experience measures. The second half of this thesis focuses on further optimising the fast MRI protocol for screening and exploring methods of integrating it into an alternative screening pathway. The outcomes point towards a pathway which combines a low threshold PSA and a fast MRI as yielding a more acceptable balance between benefits and harms. This is followed by the development of a risk tool to address the challenges of equivocal MRI lesions. Overall my thesis provides a balanced evaluation of fast MRI as a new screening test and the final chapter highlights outstanding challenges that must be addressed for fast MRI to progress as a legitimate screening modality. There is a requirement for all new screening tests to be evaluated in robust randomised controlled trials and the thesis concludes by setting out a phased research framework for fast MRI to enable a full evaluation over the next decade.Open Acces

Swin transformer for fast MRI

Magnetic resonance imaging (MRI) is an important non-invasive clinical tool that can produce high-resolution and reproducible images. However, a long scanning time is required for high-quality MR images, which leads to exhaustion and discomfort of patients, inducing more artefacts due to voluntary movements of the patients and involuntary physiological movements. To accelerate the scanning process, methods by k-space undersampling and deep learning based reconstruction have been popularised. This work introduced SwinMR, a novel Swin transformer based method for fast MRI reconstruction. The whole network consisted of an input module (IM), a feature extraction module (FEM) and an output module (OM). The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers. The RSTB consisted of a series of Swin transformer layers (STLs). The shifted windows multi-head self-attention (W-MSA/SW-MSA) of STL was performed in shifted windows rather than the multi-head self-attention (MSA) of the original transformer in the whole image space. A novel multi-channel loss was proposed by using the sensitivity maps, which was proved to reserve more textures and details. We performed a series of comparative studies and ablation studies in the Calgary-Campinas public brain MR dataset and conducted a downstream segmentation experiment in the Multi-modal Brain Tumour Segmentation Challenge 2017 dataset. The results demonstrate our SwinMR achieved high-quality reconstruction compared with other benchmark methods, and it shows great robustness with different undersampling masks, under noise interruption and on different datasets. The code is publicly available at https://github.com/ayanglab/SwinMR.This work was supported in part by the UK Research and Inno- vation Future Leaders Fellowship [MR/V023799/1], in part by the Medical Research Council [MC/PC/21013], in part by the European Research Council Innovative Medicines Initiative [DRAGON, H2020-JTI-IMI2 101005122], in part by the AI for Health Imaging Award [CHAIMELEON, H2020-SC1-FA-DTS-2019-1 952172], in part by the British Heart Foundation [Project Number: TG/18/5/34111, PG/16/78/32402], in part by the NVIDIA Academic Hardware Grant Program, in part by the Project of Shenzhen International Cooper- ation Foundation [GJHZ20180926165402083], in part by the Bas- que Government through the ELKARTEK funding program [KK- 2020/00049], and in part by the consolidated research group MATHMODE [IT1294-19

Can mammogram readers swiftly and effectively learn to interpret first post-contrast acquisition subtracted (FAST) MRI, a type of abbreviated breast MRI? : a single centre data-interpretation study

To assess whether NHS breast screening programme (NHSBSP) mammogram readers could effectively interpret first post-contrast acquisition subtracted (FAST) MRI, for intended use in screening for breast cancer. Eight NHSBSP mammogram readers from a single centre (four who also read breast MRI (Group 1) and four who do not (Group 2)) were given structured FAST MRI reader training (median 4 h: 32 min). They then prospectively interpreted 125 FAST MRIs (250 breasts: 194 normal and 56 cancer) comprising a consecutive series of screening MRIs enriched with additional cancer cases from 2015, providing 2000 interpretations. Readers were blinded to other readers' opinions and to clinical information. Categorisation followed the NHSBSP MRI reporting categorisation, with categories 4 and 5 considered indicative of cancer. Diagnostic accuracy (reference standard: histology or 2 years' follow-up) and agreement between readers were determined. The accuracy achieved by Group 2 (847/1000 (85%; 95% confidence interval (CI) 82-87%)) was 5% less than that of Group 1 (898/1000 (90%; 95% CI 88-92)). Good inter-reader agreement was seen between both Group 1 readers (κ = 0.66; 95% CI 0.61-0.71) and Group 2 readers (κ = 0.63; 95% CI 0.58-0.68). The median time taken to interpret each FAST MRI was Group 1: 34 s (range 3-351) and Group 2: 77 s (range 11-321). Brief structured training enabled multiprofessional mammogram readers to achieve similar accuracy at FAST MRI interpretation to consultant radiologists experienced at breast MRI interpretation. FAST MRI could be feasible from a training-the-workforce perspective for screening within NHSBSP

Fast MRI methods

Tato práce se zabývá porovnáním rychlých a konvenčních měřících metod v MRI. Jsou zde uvedeny základní rychlé sekvence, z kterých pak vycházejí další a efektivnější metody. Popsané jsou zde metody EPI (Echo Planar Imaging), rychlé gradientní echo GRE, FSE (Fast spin echo) dále pak snapshot-FLASH a FISP (Fast Imaging with Steady Precession). Experimentální část práce se zabývá rychlou metodou FSE (Fast Spin Echo). Speciálně je zde vysvětlen a sestaven algoritmus pro správné sestavení dat z metody FSE. Tento algoritmus nám umožňuje pak vyhodnocovat obrazy z metody FSE. Tato metoda je pak podrobně prozkoumána (z hlediska vlivu parametrů) a porovnána s klasickými konvenčními metodami. Nakonec jsou určeny nejvhodnější parametry pro metodu FSE.This thesis deals with comparison of rapid and conventional methods used in MRI (Magnetic Resonance Imaging). There is a description of imaging methods such as EPI (Echo Planar Imaging), Ultra-fast GRE, FSE (Fast spin echo) as well as a snapshot-FLASH and FISP (Fast Imaging with Steady Precession). Experimental part of this thesis deals with the rapid FSE (Fast Spin Echo) method. Especially is explained and assembled an algorithm for proper compilation of data from the FSE method. This algorithm allows us to evaluate the images from the FSE method. This method is examined in detail (in terms of impact parameters) and compared with traditional conventional methods. Finally, the individual images are evaluated, and the best parameters for FSE method are identified.

One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time. The scan time can be significantly reduced through k-space undersampling but the introduced artifacts need to be removed in image reconstruction. Although deep learning (DL) has emerged as a powerful tool for image reconstruction in fast MRI, its potential in multiple imaging scenarios remains largely untapped. This is because not only collecting large-scale and diverse realistic training data is generally costly and privacy-restricted, but also existing DL methods are hard to handle the practically inevitable mismatch between training and target data. Here, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. For a 2D image, the reconstruction is separated into many 1D basic problems and starts with the 1D data synthesis, to facilitate generalization. We demonstrate that training DL models on synthetic data, integrated with enhanced learning techniques, can achieve comparable or even better in vivo MRI reconstruction compared to models trained on a matched realistic dataset, reducing the demand for real-world MRI data by up to 96%. Moreover, our PISF shows impressive generalizability in multi-vendor multi-center imaging. Its excellent adaptability to patients has been verified through 10 experienced doctors' evaluations. PISF provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.Comment: 22 pages, 9 figures, 1 tabl