A supervised learning approach for diffusion MRI quality control with minimal training data

Quality control (QC) is a fundamental component of any study. Diffusion MRI has unique challenges that make manual QC particularly difficult, including a greater number of artefacts than other MR modalities and a greater volume of data. The gold standard is manual inspection of the data, but this process is time-consuming and subjective. Recently supervised learning approaches based on convolutional neural networks have been shown to be competitive with manual inspection. A drawback of these approaches is they still require a manually labelled dataset for training, which is itself time-consuming to produce and still introduces an element of subjectivity. In this work we demonstrate the need for manual labelling can be greatly reduced by training on simulated data, and using a small amount of labelled data for a final calibration step. We demonstrate its potential for the detection of severe movement artefacts, and compare performance to a classifier trained on manually-labelled real data

Scanner Invariant Representations for Diffusion MRI Harmonization

Purpose: In the present work we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation. Theory and Methods: Pooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi-site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory-based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures. To implement this, we use a deep learning method based on variational auto-encoders (VAE) to construct scanner invariant encodings of the imaging data. Results: To evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusion: As imaging studies continue to grow, the use of pooled multi-site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data

Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution

In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as super-resolution (SR) and image synthesis. However, the highly ill-posed nature of such problems results in inevitable ambiguity in the learning of networks. We propose to account for intrinsic uncertainty through a per-patch heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference in the form of variational dropout. We show that the combined benefits of both lead to the state-of-the-art performance SR of diffusion MR brain images in terms of errors compared to ground truth. We further show that the reduced error scores produce tangible benefits in downstream tractography. In addition, the probabilistic nature of the methods naturally confers a mechanism to quantify uncertainty over the super-resolved output. We demonstrate through experiments on both healthy and pathological brains the potential utility of such an uncertainty measure in the risk assessment of the super-resolved images for subsequent clinical use.Comment: Accepted paper at MICCAI 201

Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning

Deep learning (DL) has emerged as a leading approach in accelerating MR imaging. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MR imaging involves physics-based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data-driven approaches. Our review will introduce the significant challenges faced by such knowledge-driven DL approaches in the context of fast MR imaging along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.Comment: 46 pages, 5figures, 1 tabl

Automatic Multi-Class Cardiovascular Magnetic Resonance Image Quality Assessment using Unsupervised Domain Adaptation in Spatial and Frequency Domains

Population imaging studies rely upon good quality medical imagery before downstream image quantification. This study provides an automated approach to assess image quality from cardiovascular magnetic resonance (CMR) imaging at scale. We identify four common CMR imaging artefacts, including respiratory motion, cardiac motion, Gibbs ringing, and aliasing. The model can deal with images acquired in different views, including two, three, and four-chamber long-axis and short-axis cine CMR images. Two deep learning-based models in spatial and frequency domains are proposed. Besides recognising these artefacts, the proposed models are suitable to the common challenges of not having access to data labels. An unsupervised domain adaptation method and a Fourier-based convolutional neural network are proposed to overcome these challenges. We show that the proposed models reliably allow for CMR image quality assessment. The accuracies obtained for the spatial model in supervised and weakly supervised learning are 99.41+0.24 and 96.37+0.66 for the UK Biobank dataset, respectively. Using unsupervised domain adaptation can somewhat overcome the challenge of not having access to the data labels. The maximum achieved domain gap coverage in unsupervised domain adaptation is 16.86%. Domain adaptation can significantly improve a 5-class classification task and deal with considerable domain shift without data labels. Increasing the speed of training and testing can be achieved with the proposed model in the frequency domain. The frequency-domain model can achieve the same accuracy yet 1.548 times faster than the spatial model. This model can also be used directly on k-space data, and there is no need for image reconstruction.Comment: 21 pages, 9 figures, 7 table

Diffusion-tensor MRI methods to study and evaluate muscle architecture

The thesis describes the development of various approaches for measuring muscle architectural parameters using Diffusion Tensor MR Imaging (DTI). It also illustrates how to apply them to study changes in muscle architecture after an injury prevention program.In Chapter 2, because manual segmentation of muscles is cumbersome, we validated a semi-automatic framework for estimating DTI indices in upper leg muscles. This method reduced segmentation time by a factor of three in a cross-sectional study design and can be used fully automatically in a longitudinal assessment of changes in DTI indices.Chapter 3 was a feasibility study measuring fiber orientation changes with DTI in calf muscles and sub-compartments of the Soleus and Tibialis Anterior during plantarflexion and dorsiflexion. Differences in fiber orientations corresponded to the known agonist-antagonist function of the muscles. This shows that DTI can be utilized to assess changes in muscle orientation due to posture or training.In Chapter 4, we compared DTI fiber tractography for Vastus Lateralis fiber architecture assessment with 3D ultrasonography (3D-US). We discovered that both methods have their advantages and disadvantages, with the agreement between the two techniques being moderate.Finally, in Chapter 5, we examined the effects of a hamstring injury prevention exercise on the muscle architectural parameters of basketball players. DTI was employed to quantify changes in fiber orientation and length using tractography and fiber orientation maps. It was observed that the Semitendinosus fascicle length increased after the Nordics exercise, while the Biceps Femoris long head fiber orientation decreased following the Divers intervention

Deep learning‐based method for reducing residual motion effects in diffusion parameter estimation

PURPOSE: Conventional motion-correction techniques for diffusion MRI can introduce motion-level-dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects. METHODS: The data-rejection approach was adopted in which motion-corrupted data are discarded before model-fitting. A deep learning-based parameter estimation algorithm, using a hierarchical convolutional neural network (H-CNN), was combined with motion assessment and corrupted volume rejection. The method was designed to overcome the limitations of existing methods of this kind that produce parameter estimations whose quality depends strongly on a proportion of the data discarded. Evaluation experiments were conducted for the estimation of diffusion kurtosis and diffusion-tensor-derived measures at both the individual and group levels. The performance was compared with the robust approach of iteratively reweighted linear least squares (IRLLS) after motion correction with and without outlier replacement. RESULTS: Compared with IRLLS, the H-CNN-based technique is minimally sensitive to motion effects. It was tested at severe motion levels when 70% to 90% of the data are rejected and when random motion is present. The technique had a stable performance independent of the numbers and schemes of data rejection. A further test on a data set from children with attention-deficit hyperactivity disorder shows the technique can potentially ameliorate spurious group-level difference caused by head motion. CONCLUSION: This method shows great potential for reducing residual motion effects in motion-corrupted diffusion-weighted-imaging data, bringing benefits that include reduced bias in derived metrics in individual scans and reduced motion-level-dependent bias in population studies employing diffusion MRI