13,540 research outputs found
Real-time Assessment of Right and Left Ventricular Volumes and Function in Children Using High Spatiotemporal Resolution Spiral bSSFP with Compressed Sensing
Background: Real-time (RT) assessment of ventricular volumes and function
enables data acquisition during free-breathing. However, in children the
requirement for high spatiotemporal resolution requires accelerated imaging
techniques. In this study, we implemented a novel RT bSSFP spiral sequence
reconstructed using Compressed Sensing (CS) and validated it against the
breath-hold (BH) reference standard for assessment of ventricular volumes in
children with heart disease.
Methods: Data was acquired in 60 children. Qualitative image scoring and
evaluation of ventricular volumes was performed by 3 clinical cardiac MR
specialists. 30 cases were reassessed for intra-observer variability, and the
other 30 cases for inter-observer variability.
Results: Spiral RT images were of good quality, however qualitative scores
reflected more residual artefact than standard BH images and slightly lower
edge definition. Quantification of Left Ventricular (LV) and Right Ventricular
(RV) metrics showed excellent correlation between the techniques with narrow
limits of agreement. However, we observed small but statistically significant
overestimation of LV end-diastolic volume, underestimation of LV end-systolic
volume, as well as a small overestimation of RV stroke volume and ejection
fraction using the RT imaging technique. No difference in inter-observer or
intra-observer variability were observed between the BH and RT sequences.
Conclusions: Real-time bSSFP imaging using spiral trajectories combined with
a compressed sensing reconstruction is feasible. The main benefit is that it
can be acquired during free breathing. However, another important secondary
benefit is that a whole ventricular stack can be acquired in ~20 seconds, as
opposed to ~6 minutes for standard BH imaging. Thus, this technique holds the
potential to significantly shorten MR scan times in children
GPU-based Iterative Cone Beam CT Reconstruction Using Tight Frame Regularization
X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinical
concern in most image guided radiation therapy procedures. It is the goal of
this paper to develop a fast GPU-based algorithm to reconstruct high quality
CBCT images from undersampled and noisy projection data so as to lower the
imaging dose. For this purpose, we have developed an iterative tight frame (TF)
based CBCT reconstruction algorithm. A condition that a real CBCT image has a
sparse representation under a TF basis is imposed in the iteration process as
regularization to the solution. To speed up the computation, a multi-grid
method is employed. Our GPU implementation has achieved high computational
efficiency and a CBCT image of resolution 512\times512\times70 can be
reconstructed in ~5 min. We have tested our algorithm on a digital NCAT phantom
and a physical Catphan phantom. It is found that our TF-based algorithm is able
to reconstrct CBCT in the context of undersampling and low mAs levels. We have
also quantitatively analyzed the reconstructed CBCT image quality in terms of
modulation-transfer-function and contrast-to-noise ratio under various scanning
conditions. The results confirm the high CBCT image quality obtained from our
TF algorithm. Moreover, our algorithm has also been validated in a real
clinical context using a head-and-neck patient case. Comparisons of the
developed TF algorithm and the current state-of-the-art TV algorithm have also
been made in various cases studied in terms of reconstructed image quality and
computation efficiency.Comment: 24 pages, 8 figures, accepted by Phys. Med. Bio
Blur resolved OCT: full-range interferometric synthetic aperture microscopy through dispersion encoding
We present a computational method for full-range interferometric synthetic
aperture microscopy (ISAM) under dispersion encoding. With this, one can
effectively double the depth range of optical coherence tomography (OCT),
whilst dramatically enhancing the spatial resolution away from the focal plane.
To this end, we propose a model-based iterative reconstruction (MBIR) method,
where ISAM is directly considered in an optimization approach, and we make the
discovery that sparsity promoting regularization effectively recovers the
full-range signal. Within this work, we adopt an optimal nonuniform discrete
fast Fourier transform (NUFFT) implementation of ISAM, which is both fast and
numerically stable throughout iterations. We validate our method with several
complex samples, scanned with a commercial SD-OCT system with no hardware
modification. With this, we both demonstrate full-range ISAM imaging, and
significantly outperform combinations of existing methods.Comment: 17 pages, 7 figures. The images have been compressed for arxiv -
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Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of
moving objects in sequences of images. Optimising the interpretation of dynamic
biological systems requires accurate and precise motion tracking as well as
efficient representations of high-dimensional motion trajectories so that these
can be used for prediction tasks. Here we use image sequences of the heart,
acquired using cardiac magnetic resonance imaging, to create time-resolved
three-dimensional segmentations using a fully convolutional network trained on
anatomical shape priors. This dense motion model formed the input to a
supervised denoising autoencoder (4Dsurvival), which is a hybrid network
consisting of an autoencoder that learns a task-specific latent code
representation trained on observed outcome data, yielding a latent
representation optimised for survival prediction. To handle right-censored
survival outcomes, our network used a Cox partial likelihood loss function. In
a study of 302 patients the predictive accuracy (quantified by Harrell's
C-index) was significantly higher (p < .0001) for our model C=0.73 (95 CI:
0.68 - 0.78) than the human benchmark of C=0.59 (95 CI: 0.53 - 0.65). This
work demonstrates how a complex computer vision task using high-dimensional
medical image data can efficiently predict human survival
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