1,866 research outputs found
Retrospective Motion Correction in Magnetic Resonance Imaging of the Brain
Magnetic Resonance Imaging (MRI) is a tremendously useful diagnostic imaging modality that provides outstanding soft tissue contrast. However, subject motion is a significant unsolved problem; motion during image acquisition can cause blurring and distortions in the image, limiting its diagnostic utility. Current techniques for addressing head motion include optical tracking which can be impractical in clinical settings due to challenges associated with camera cross-calibration and marker fixation. Another category of techniques is MRI navigators, which use specially acquired MRI data to track the motion of the head.
This thesis presents two techniques for motion correction in MRI: the first is spherical navigator echoes (SNAVs), which are rapidly acquired k-space navigators. The second is a deep convolutional neural network trained to predict an artefact-free image from motion-corrupted data.
Prior to this thesis, SNAVs had been demonstrated for motion measurement but not motion correction, and they required the acquisition of a 26s baseline scan during which the subject could not move. In this work, a novel baseline approach is developed where the acquisition is reduced to 2.6s. Spherical navigators were interleaved into a spoiled gradient echo sequence (SPGR) on a stand-alone MRI system and a turbo-FLASH sequence (tfl) on a hybrid PET/MRI system to enable motion measurement throughout image acquisition. The SNAV motion measurements were then used to retrospectively correct the image data.
While MRI navigator methods, particularly SNAVs that can be acquired very rapidly, are useful for motion correction, they do require pulse sequence modifications. A deep learning technique may be a more general solution. In this thesis, a conditional generative adversarial network (cGAN) is trained to perform motion correction on image data with simulated motion artefacts. We simulate motion in previously acquired brain images and use the image pairs (corrupted + original) to train the cGAN.
MR image data was qualitatively and quantitatively improved following correction using the SNAV motion estimates. This was also true for the simultaneously acquired MR and PET data on the hybrid system. Motion corrected images were more similar than the uncorrected to the no-motion reference images. The deep learning approach was also successful for motion correction. The trained cGAN was evaluated on 5 subjects; and artefact suppression was observed in all images
preliminary clinical evaluation of the ASTRA4D algorithm
Objectives. To propose and evaluate a four-dimensional (4D) algorithm for joint motion elimination and spatiotemporal noise reduction in low-dose dynamic myocardial computed tomography perfusion (CTP).
Methods. Thirty patients with suspected or confirmed coronary artery disease were prospectively included und underwent dynamic contrast-enhanced 320-row CTP. The presented deformable image registration method ASTRA4D identifies a low-dimensional linear model of contrast propagation (by principal component analysis, PCA) of the ex-ante temporally smoothed time-intensity curves (by local polynomial regression). Quantitative (standard deviation, signal-to-noise ratio (SNR), temporal variation, volumetric deformation) and qualitative (motion, contrast, contour sharpness; 1, poor; 5, excellent) measures of CTP quality were assessed for the original and motion-compensated volumes (without and with temporal filtering, PCA/ASTRA4D). Following visual myocardial perfusion deficit detection by two readers, diagnostic accuracy was evaluated using 1.5T magnetic resonance (MR) myocardial perfusion imaging as the reference standard in 15 patients.
Results. Registration using ASTRA4D was successful in all 30 patients and resulted in comparison with the benchmark PCA in significantly (p<0.001) reduced noise over time (-83%, 178.5 vs 29.9) and spatially (-34%, 21.4 vs 14.1) as well as improved SNR (+47%, 3.6 vs 5.3) and subjective image quality (motion, contrast, contour sharpness: +1.0, +1.0, +0.5). ASTRA4D resulted in significantly improved per-segment sensitivity of 91% (58/64) and similar specificity of 96% (429/446) compared with PCA (52%, 33/64; 98%, 435/446; p=0.011) and the original sequence (45%, 29/64; 98%, 438/446; p=0.003) in the visual detection of perfusion deficits.
Conclusions. The proposed functional approach to temporal denoising and morphologic alignment was shown to improve quality metrics and sensitivity of 4D CTP in the detection of myocardial ischemia.Zielsetzung. Die Entwicklung und Bewertung einer Methode zur simultanen Rauschreduktion und Bewegungskorrektur für niedrig dosierte dynamische CT Myokardperfusion.
Methoden. Dreißig prospektiv eingeschlossene Patienten mit vermuteter oder bestätigter koronarer Herzkrankheit wurden einer dynamischen CT Myokardperfusionsuntersuchung unterzogen.
Die präsentierte Registrierungsmethode ASTRA4D ermittelt ein niedrigdimensionales Modell des Kontrastmittelflusses (mittels einer Hauptkomponentenanalyse, PCA) der vorab zeitlich geglätteten Intensitätskurven (mittels lokaler polynomialer Regression). Quantitative (Standardabweichung, Signal-Rausch-Verhältnis (SNR), zeitliche Schwankung, räumliche Verformung) und qualitative (Bewegung, Kontrast, Kantenschärfe; 1, schlecht; 5, ausgezeichnet) Kennzahlen der unbearbeiteten und bewegungskorrigierten Perfusionsdatensätze (ohne und mit zeitlicher Glättung PCA/ASTRA4D) wurden ermittelt. Nach visueller Beurteilung von myokardialen Perfusionsdefiziten durch zwei Radiologen wurde die diagnostische Genauigkeit im Verhältnis zu 1.5T Magnetresonanztomographie in 15 Patienten ermittelt.
Resultate. Bewegungskorrektur mit ASTRA4D war in allen 30 Patienten erfolgreich und resultierte im Vergleich mit der PCA Methode in signifikant (p<0.001) verringerter zeitlicher Schwankung (-83%, 178.5 gegenüber 29.9) und räumlichem Rauschen (-34%, 21.4 gegenüber 14.1) sowie verbesserter SNR (+47%, 3.6 gegenüber 5.3) und subjektiven Qualitätskriterien (Bewegung, Kontrast, Kantenschärfe: +1.0, +1.0, +0.5). ASTRA4D resultierte in signifikant verbesserter segmentweiser Sensitivität 91% (58/64) und ähnlicher Spezifizität 96% (429/446) verglichen mit der PCA Methode (52%, 33/64; 98%, 435/446; p=0.011) und dem unbearbeiteten Perfusionsdatensatz (45%, 29/64; 98%, 438/446; p=0.003) in der visuellen Beurteilung von myokardialen Perfusionsdefiziten.
Schlussfolgerungen. Der vorgeschlagene funktionale Ansatz zur simultanen Rauschreduktion und Bewegungskorrektur verbesserte Qualitätskriterien und Sensitivität von dynamischer CT Perfusion in der visuellen Erkennung von Myokardischämie
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Wavelets and sparse methods for image reconstruction and classification in neuroimaging
This dissertation contributes to neuroimaging literature in the fields of compressed sensing magnetic resonance imaging (CS-MRI) and image-based detection of Alzheimer’s disease (AD). It consists of three main contributions, based on wavelets and sparse methods.
The first contribution is a method for wavelet packet basis optimisation for sparse approximation and compressed sensing reconstruction of magnetic resonance (MR) images of the brain. The proposed method is based on the basis search algorithm developed by Coifman and Wickerhauser, with a cost function designed specifically for compressed sensing. It is tested on MR images available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
The second contribution consists of evaluating and comparing several sparse classification methods in an application to detection of AD based on positron emission tomography (PET) images of the brain. This comparison includes univariate feature selection, feature clustering and classifiers that automatically select a small subset of features due to their mathematical or algorithmic construction. The evaluation is based on PET images available from ADNI.
The third contribution is proposing an extension of wavelet-based scattering networks (originally proposed by Mallat and Bruna) to three-dimensional tomographic images. The proposed extension is evaluated as a feature representation in an application to detection of AD based on MR images available from ADNI.
There are several possible extensions of the work presented in this dissertation. The wavelet packet basis search method proposed in the first contribution can be improved to take into account the coherence between the sparse approximation basis and the sensing basis. The evaluation presented in the second contribution can be extended with additional algorithms to make it more comprehensive. The three-dimensional scattering networks that are the core part of the third contribution can be combined with other machine learning methods, such as manifold learning or deep convolutional neural networks.
As a whole, the methods proposed in this dissertation contribute to the work towards efficient screening for Alzheimer’s disease, by making MRI scans of the brain faster and helping to automate image analysis for AD detection.
The first contribution is a method for wavelet packet basis optimisation for sparse approximation and compressed sensing reconstruction of magnetic resonance (MR) images of the brain. The proposed method is based on the basis search algorithm developed by Coifman and Wickerhauser, with a cost function designed specifically for compressed sensing. It is tested on MR images available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
The second contribution consists of evaluating and comparing several sparse classification methods in an application to detection of AD based on positron emission tomography (PET) images of the brain. This comparison includes univariate feature selection, feature clustering and classifiers that automatically select a small subset of features due to their mathematical or algorithmic construction. The evaluation is based on PET images available from ADNI.
The third contribution is proposing an extension of wavelet-based scattering networks (originally proposed by Mallat and Bruna) to three-dimensional tomographic images. The proposed extension is evaluated as a feature representation in an application to detection of AD based on MR images available from ADNI.
There are several possible extensions of the work presented in this dissertation. The wavelet packet basis search method proposed in the first contribution can be improved to take into account the coherence between the sparse approximation basis and the sensing basis. The evaluation presented in the second contribution can be extended with additional algorithms to make it more comprehensive. The three-dimensional scattering networks that are the core part of the third contribution can be combined with other machine learning methods, such as manifold learning or deep convolutional neural networks.
This dissertation contributes to neuroimaging literature in the fields of compressed sensing magnetic resonance imaging (CS-MRI) and image-based detection of Alzheimer’s disease (AD). It consists of three main contributions, based on wavelets and sparse methods.
The first contribution is a method for wavelet packet basis optimisation for sparse approximation and compressed sensing reconstruction of magnetic resonance (MR) images of the brain. The proposed method is based on the basis search algorithm developed by Coifman and Wickerhauser, with a cost function designed specifically for compressed sensing. It is tested on MR images available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
The second contribution consists of evaluating and comparing several sparse classification methods in an application to detection of AD based on positron emission tomography (PET) images of the brain. This comparison includes univariate feature selection, feature clustering and classifiers that automatically select a small subset of features due to their mathematical or algorithmic construction. The evaluation is based on PET images available from ADNI.
The third contribution is proposing an extension of wavelet-based scattering networks (originally proposed by Mallat and Bruna) to three-dimensional tomographic images. The proposed extension is evaluated as a feature representation in an application to detection of AD based on MR images available from ADNI.
There are several possible extensions of the work presented in this dissertation. The wavelet packet basis search method proposed in the first contribution can be improved to take into account the coherence between the sparse approximation basis and the sensing basis. The evaluation presented in the second contribution can be extended with additional algorithms to make it more comprehensive. The three-dimensional scattering networks that are the core part of the third contribution can be combined with other machine learning methods, such as manifold learning or deep convolutional neural networks.
As a whole, the methods proposed in this dissertation contribute to the work towards efficient screening for Alzheimer’s disease, by making MRI scans of the brain faster and helping to automate image analysis for AD detection.Open Acces
LEGO-Net: Learning Regular Rearrangements of Objects in Rooms
Humans universally dislike the task of cleaning up a messy room. If machines
were to help us with this task, they must understand human criteria for regular
arrangements, such as several types of symmetry, co-linearity or
co-circularity, spacing uniformity in linear or circular patterns, and further
inter-object relationships that relate to style and functionality. Previous
approaches for this task relied on human input to explicitly specify goal
state, or synthesized scenes from scratch -- but such methods do not address
the rearrangement of existing messy scenes without providing a goal state. In
this paper, we present LEGO-Net, a data-driven transformer-based iterative
method for learning regular rearrangement of objects in messy rooms. LEGO-Net
is partly inspired by diffusion models -- it starts with an initial messy state
and iteratively "de-noises'' the position and orientation of objects to a
regular state while reducing the distance traveled. Given randomly perturbed
object positions and orientations in an existing dataset of
professionally-arranged scenes, our method is trained to recover a regular
re-arrangement. Results demonstrate that our method is able to reliably
rearrange room scenes and outperform other methods. We additionally propose a
metric for evaluating regularity in room arrangements using number-theoretic
machinery.Comment: Project page: https://ivl.cs.brown.edu/projects/lego-ne
Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data. A performance gain was achieved in both binary classification and multiclass classification of AD. The advantages and limitations of the proposed framework are discussed
An automated machine learning approach to predict brain age from cortical anatomical measures
The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree-based Pipeline Optimisation Tool (TPOT) which uses a tree-based representation of ML pipelines and conducts a genetic programming-based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ± .124 years) and a relevance vector regression (MAE 5.474 ± .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guessing. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as patient-specific biomarker trends. The submission system remains open via the website https://tadpole.grand-challenge.org, while code for submissions is being collated by TADPOLE SHARE: https://tadpole-share.github.io/. Our work suggests that current prediction algorithms are accurate for biomarkers related to clinical diagnosis and ventricle volume, opening up the possibility of cohort refinement in clinical trials for Alzheimer's disease
Clinical quantitative cardiac imaging for the assessment of myocardial ischaemia
Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality
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