78 research outputs found
Review of data types and model dimensionality for cardiac DTI SMS-related artefact removal
As diffusion tensor imaging (DTI) gains popularity in cardiac imaging due to
its unique ability to non-invasively assess the cardiac microstructure, deep
learning-based Artificial Intelligence is becoming a crucial tool in mitigating
some of its drawbacks, such as the long scan times. As it often happens in
fast-paced research environments, a lot of emphasis has been put on showing the
capability of deep learning while often not enough time has been spent
investigating what input and architectural properties would benefit cardiac DTI
acceleration the most. In this work, we compare the effect of several input
types (magnitude images vs complex images), multiple dimensionalities (2D vs 3D
operations), and multiple input types (single slice vs multi-slice) on the
performance of a model trained to remove artefacts caused by a simultaneous
multi-slice (SMS) acquisition. Despite our initial intuition, our experiments
show that, for a fixed number of parameters, simpler 2D real-valued models
outperform their more advanced 3D or complex counterparts. The best performance
is although obtained by a real-valued model trained using both the magnitude
and phase components of the acquired data. We believe this behaviour to be due
to real-valued models making better use of the lower number of parameters, and
to 3D models not being able to exploit the spatial information because of the
low SMS acceleration factor used in our experiments.Comment: 11 pages, 3 tables, 1 figure. To be published at the STACOM workshop,
MICCAI 202
High-Resolution Reference Image Assisted Volumetric Super-Resolution of Cardiac Diffusion Weighted Imaging
Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is the only in vivo
method to non-invasively examine the microstructure of the human heart. Current
research in DT-CMR aims to improve the understanding of how the cardiac
microstructure relates to the macroscopic function of the healthy heart as well
as how microstructural dysfunction contributes to disease. To get the final
DT-CMR metrics, we need to acquire diffusion weighted images of at least 6
directions. However, due to DWI's low signal-to-noise ratio, the standard voxel
size is quite big on the scale for microstructures. In this study, we explored
the potential of deep-learning-based methods in improving the image quality
volumetrically (x4 in all dimensions). This study proposed a novel framework to
enable volumetric super-resolution, with an additional model input of
high-resolution b0 DWI. We demonstrated that the additional input could offer
higher super-resolved image quality. Going beyond, the model is also able to
super-resolve DWIs of unseen b-values, proving the model framework's
generalizability for cardiac DWI superresolution. In conclusion, we would then
recommend giving the model a high-resolution reference image as an additional
input to the low-resolution image for training and inference to guide all
super-resolution frameworks for parametric imaging where a reference image is
available.Comment: Accepted by SPIE Medical Imaging 202
Coronary MRA at 3 T using 3d multi-interleaved multi-echo acquisition with varpro fat-water separation
Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation
Stain variation is a unique challenge associated with automated analysis of
digital pathology. Numerous methods have been developed to improve the
robustness of machine learning methods to stain variation, but comparative
studies have demonstrated limited benefits to performance. Moreover, methods to
handle stain variation were largely developed for H&E stained data, with
evaluation generally limited to classification tasks. Here we propose Stain
Consistency Learning, a novel framework combining stain-specific augmentation
with a stain consistency loss function to learn stain colour invariant
features. We perform the first, extensive comparison of methods to handle stain
variation for segmentation tasks, comparing ten methods on Masson's trichrome
and H&E stained cell and nuclei datasets, respectively. We observed that stain
normalisation methods resulted in equivalent or worse performance, while stain
augmentation or stain adversarial methods demonstrated improved performance,
with the best performance consistently achieved by our proposed approach. The
code is available at: https://github.com/mlyg/stain_consistency_learnin
Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration
Diffusion tensor based cardiac magnetic resonance (DT-CMR) is a method
capable of providing non-invasive measurements of myocardial microstructure.
Image registration is essential to correct image shifts due to intra and inter
breath-hold motion. Registration is challenging in DT-CMR due to the low
signal-to-noise and various contrasts induced by the diffusion encoding in the
myocardial and surrounding organs. Traditional deformable registration destroys
the texture information while rigid registration inefficiently discards frames
with local deformation. In this study, we explored the possibility of deep
learning-based deformable registration on DT- CMR. Based on the noise
suppression using low-rank features and diffusion encoding suppression using
variational auto encoder-decoder, a B-spline based registration network
extracted the displacement fields and maintained the texture features of
DT-CMR. In this way, our method improved the efficiency of frame utilization,
manual cropping, and computational speed.Comment: 4 pages, 4 figures, conferenc
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Correlation of DTI tractography with electroanatomic mapping in normal and infarcted myocardium
Diffusion tensor MRI of the human heart In Vivo with a navigator based free breathing approach
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