Accelerated Motion Correction with Deep Generative Diffusion Models

Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality, but unfortunately suffers from long scan times which, aside from increasing operational costs, can lead to image artifacts due to patient motion. Motion during the acquisition leads to inconsistencies in measured data that manifest as blurring and ghosting if unaccounted for in the image reconstruction process. Various deep learning based reconstruction techniques have been proposed which decrease scan time by reducing the number of measurements needed for a high fidelity reconstructed image. Additionally, deep learning has been used to correct motion using end-to-end techniques. This, however, increases susceptibility to distribution shifts at test time (sampling pattern, motion level). In this work we propose a framework for jointly reconstructing highly sub-sampled MRI data while estimating patient motion using diffusion based generative models. Our method does not make specific assumptions on the sampling trajectory or motion pattern at training time and thus can be flexibly applied to various types of measurement models and patient motion. We demonstrate our framework on retrospectively accelerated 2D brain MRI corrupted by rigid motion

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR's computational overhead compared to standard methods and observe improved reconstruction accuracy in presence of affine motion artifacts of approximately 30% compared to conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. With these experiments we demonstrate successful application of PVR motion compensation to the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical Imaging. v2: wadded funders acknowledgements to preprin

Temporal Interpolation via Motion Field Prediction

Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables high contrast 4D MR imaging during free breathing and provides in-vivo observations for treatment planning and guidance. Navigator slices are vital for retrospective stacking of 2D data slices in this method. However, they also prolong the acquisition sessions. Temporal interpolation of navigator slices an be used to reduce the number of navigator acquisitions without degrading specificity in stacking. In this work, we propose a convolutional neural network (CNN) based method for temporal interpolation via motion field prediction. The proposed formulation incorporates the prior knowledge that a motion field underlies changes in the image intensities over time. Previous approaches that interpolate directly in the intensity space are prone to produce blurry images or even remove structures in the images. Our method avoids such problems and faithfully preserves the information in the image. Further, an important advantage of our formulation is that it provides an unsupervised estimation of bi-directional motion fields. We show that these motion fields can be used to halve the number of registrations required during 4D reconstruction, thus substantially reducing the reconstruction time.Comment: Submitted to 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, The Netherland

Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

Diagnostic accuracy of 3.0-T magnetic resonance T1 and T2 mapping and T2-weighted dark-blood imaging for the infarct-related coronary artery in Non-ST-segment elevation myocardial infarction

Background: Patients with recent non–ST‐segment elevation myocardial infarction commonly have heterogeneous characteristics that may be challenging to assess clinically. Methods and Results: We prospectively studied the diagnostic accuracy of 2 novel (T1, T2 mapping) and 1 established (T2‐weighted short tau inversion recovery [T2W‐STIR]) magnetic resonance imaging methods for imaging the ischemic area at risk and myocardial salvage in 73 patients with non–ST‐segment elevation myocardial infarction (mean age 57±10 years, 78% male) at 3.0‐T magnetic resonance imaging within 6.5±3.5 days of invasive management. The infarct‐related territory was identified independently using a combination of angiographic, ECG, and clinical findings. The presence and extent of infarction was assessed with late gadolinium enhancement imaging (gadobutrol, 0.1 mmol/kg). The extent of acutely injured myocardium was independently assessed with native T1, T2, and T2W‐STIR methods. The mean infarct size was 5.9±8.0% of left ventricular mass. The infarct zone T1 and T2 times were 1323±68 and 57±5 ms, respectively. The diagnostic accuracies of T1 and T2 mapping for identification of the infarct‐related artery were similar (P=0.125), and both were superior to T2W‐STIR (P<0.001). The extent of myocardial injury (percentage of left ventricular volume) estimated with T1 (15.8±10.6%) and T2 maps (16.0±11.8%) was similar (P=0.838) and moderately well correlated (r=0.82, P<0.001). Mean extent of acute injury estimated with T2W‐STIR (7.8±11.6%) was lower than that estimated with T1 (P<0.001) or T2 maps (P<0.001). Conclusions: In patients with non–ST‐segment elevation myocardial infarction, T1 and T2 magnetic resonance imaging mapping have higher diagnostic performance than T2W‐STIR for identifying the infarct‐related artery. Compared with conventional STIR, T1 and T2 maps have superior value to inform diagnosis and revascularization planning in non–ST‐segment elevation myocardial infarction. Clinical Trial Registration: URL: http://www.clinicaltrials.gov. Unique identifier: NCT02073422