Automated tracking of a passive intramyocardial needle with off-resonance MRI: a feasibility study

Direct intramyocardial therapies aimed at treating myocardial regions affected by severe ischemia may benefit from CMR-guided interventional procedures. Although interventional MR approaches using active devices are considered to be the method of choice, potential tissue heating and altered mechanical properties are some of their limitations. Methods that have the capacity to visualize MR-compatible passive devices may overcome many of these obstacles. Recently, an off-resonance-based real-time positive contrast method (FLAPS) was used to visualize the passage of an intramyocardial needle (PIN) through the aorta and into the heart of swine [1,2]. We envision this procedure may benefit from computer assisted strategies that track the needle's location throughout the MR procedure. However, the feasibility of real-time automated tracking of a PIN has not been established

Automated tracking of a passive endomyocardial stiletto catheter with dephased FLAPS MRI: a feasibility study

Automated tracking of a passive stiletto catheter for regenerative myocardial therapy under the MR environment may improve the accuracy ofthe procedure. We report successful implementation of automated computer-assisted tracking for this purpose in a controlled phantom study

Acute reperfusion intramyocardial hemorrhage leads to regional chronic iron deposition in the heart

Intramyocardial hemorrhage commonly occurs in large reperfused myocardial infarctions. However, its long-term fate remains unexplored. We hypothesized that acute reperfusion intramyocardial hemorrhage leads to chronic iron deposition

Factorised spatial representation learning: application in semi-supervised myocardial segmentation

The success and generalisation of deep learning algorithms heavily depend on learning good feature representations. In medical imaging this entails representing anatomical information, as well as properties related to the specific imaging setting. Anatomical information is required to perform further analysis, whereas imaging information is key to disentangle scanner variability and potential artefacts. The ability to factorise these would allow for training algorithms only on the relevant information according to the task. To date, such factorisation has not been attempted. In this paper, we propose a methodology of latent space factorisation relying on the cycle-consistency principle. As an example application, we consider cardiac MR segmentation, where we separate information related to the myocardium from other features related to imaging and surrounding substructures. We demonstrate the proposed method's utility in a semi-supervised setting: we use very few labelled images together with many unlabelled images to train a myocardium segmentation neural network. Specifically, we achieve comparable performance to fully supervised networks using a fraction of labelled images in experiments on ACDC and a dataset from Edinburgh Imaging Facility QMRI. Code will be made available at https://github.com/agis85/spatial_factorisation.Comment: Accepted in MICCAI 201

A fully-automated statistical method for characterization of flow artifact presence in cardiac MRI

Flow artifacts in MR images can appear as ghosts within and outside the body cavity. Current approaches for optimizing sequences for suppressing such artifacts rely on expert scoring or on semi-automated methods for evaluation

Unsupervised Myocardial Segmentation for Cardiac BOLD

A fully automated 2-D+time myocardial segmentation framework is proposed for cardiac magnetic resonance (CMR) blood-oxygen-level-dependent (BOLD) data sets. Ischemia detection with CINE BOLD CMR relies on spatio-temporal patterns in myocardial intensity, but these patterns also trouble supervised segmentation methods, the de facto standard for myocardial segmentation in cine MRI. Segmentation errors severely undermine the accurate extraction of these patterns. In this paper, we build a joint motion and appearance method that relies on dictionary learning to find a suitable subspace.Our method is based on variational pre-processing and spatial regularization using Markov random fields, to further improve performance. The superiority of the proposed segmentation technique is demonstrated on a data set containing cardiac phase resolved BOLD MR and standard CINE MR image sequences acquired in baseline and is chemic condition across ten canine subjects. Our unsupervised approach outperforms even supervised state-of-the-art segmentation techniques by at least 10% when using Dice to measure accuracy on BOLD data and performs at par for standard CINE MR. Furthermore, a novel segmental analysis method attuned for BOLD time series is utilized to demonstrate the effectiveness of the proposed method in preserving key BOLD patterns

Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction

Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time. In neuroimaging, DL methods can reconstruct high-quality images from undersampled data. However, it is essential to consider fairness in DL algorithms, particularly in terms of demographic characteristics. This study presents the first fairness analysis in a DL-based brain MRI reconstruction model. The model utilises the U-Net architecture for image reconstruction and explores the presence and sources of unfairness by implementing baseline Empirical Risk Minimisation (ERM) and rebalancing strategies. Model performance is evaluated using image reconstruction metrics. Our findings reveal statistically significant performance biases between the gender and age subgroups. Surprisingly, data imbalance and training discrimination are not the main sources of bias. This analysis provides insights of fairness in DL-based image reconstruction and aims to improve equity in medical AI applications.Comment: Accepted for publication at FAIMI 2023 (Fairness of AI in Medical Imaging) at MICCA

In vivo contrast free chronic myocardial infarction characterization using diffusion-weighted cardiovascular magnetic resonance.

BackgroundDespite the established role of late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) in characterizing chronic myocardial infarction (MI), a significant portion of chronic MI patients are contraindicative for the use of contrast agents. One promising alternative contrast free technique is diffusion weighted CMR (dwCMR), which has been shown ex vivo to be sensitive to myocardial fibrosis. We used a recently developed in vivo dwCMR in chronic MI pigs to compare apparent diffusion coefficient (ADC) maps with LGE imaging for infarct characterization.MethodsIn eleven mini pigs, chronic MI was induced by complete occlusion of the left anterior descending artery for 150 minutes. LGE, cine, and dwCMR imaging was performed 8 weeks post MI. ADC maps were derived from three orthogonal diffusion directions (b = 400 s/mm2) and one non-diffusion weighted image. Two semi-automatic infarct classification methods, threshold and full width half max (FWHM), were performed in both LGE and ADC maps. Regional wall motion (RWM) analysis was performed and compared to ADC maps to determine if any observed ADC change was significantly influenced by bulk motion.ResultsADC of chronic MI territories was significantly increased (threshold: 2.4 ± 0.3 μm2/ms, FWHM: 2.4 ± 0.2 μm2/ms) compared to remote myocardium (1.4 ± 0.3 μm2/ms). RWM was significantly reduced (threshold: 1.0 ± 0.4 mm, FWHM: 0.9 ± 0.4 mm) in infarcted regions delineated by ADC compared to remote myocardium (8.3 ± 0.1 mm). ADC-derived infarct volume and location had excellent agreement with LGE. Both LGE and ADC were in complete agreement when identifying transmural infarcts. Additionally, ADC was able to detect LGE-delineated infarcted segments with high sensitivity, specificity, PPV, and NPV. (threshold: 0.88, 0.93, 0.87, and 0.94, FWHM: 0.98, 0.97, 0.93, and 0.99, respectively).ConclusionsIn vivo diffusion weighted CMR has potential as a contrast free alternative for LGE in characterizing chronic MI