924 research outputs found
Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images
The demons algorithm is a fast non-parametric non-rigid registration method. In recent years great efforts have been made to improve the approach; the state of the art version yields symmetric inverse-consistent largedeformation diffeomorphisms. However, only limited work has explored inter-modal similarity metrics, with no practical evaluation on multi-modality data. We present a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser. We report the first qualitative and quantitative assessment of the demons for inter-modal registration. Experiments to spatially normalise real MR images, and to recover simulated deformation fields, demonstrate (i) similar accuracy from NMI-demons and classical demons when the latter may be used, and (ii) similar accuracy for NMI-demons on T1w-T1w and T1w-T2w registration, demonstrating its potential in multi-modal scenarios
Longitudinal multivariate tensor- and searchlight-based morphometry using permutation testing
Tensor based morphometry [1] was used to detect
statistically significant regions of neuroanatomical
change over time in a comparison between 36 probable
Alzheimer's Disease patients and 20 age- and sexmatched
controls. Baseline and twelve-month repeat
Magnetic Resonance images underwent tied spatial
normalisation [10] and longitudinal high-dimensional
warps were then estimated. Analyses involved univariate
and multivariate data derived from the longitudinal
deformation fields. The most prominent findings were
expansion of the fluid spaces, and contraction of the
hippocampus and temporal region. Multivariate measures
were notably more powerful, and have the potential to
identify patterns of morphometric difference that would
be overlooked by conventional mass-univariate analysis
Diffeomorphic Demons using Normalised Mutual Information, Evaluation on Multi-Modal Brain MR Images
The demons algorithm is a fast non-parametric non-rigid registration method. In recent years great efforts have been made to improve the approach; the state of the art version yields symmetric inverse-consistent large-deformation diffeomorphisms. However, only limited work has explored inter-modal similarity metrics, with no practical evaluation on multi-modality data. We present a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser. We report the first qualitative and quantitative assessment of the demons for inter-modal registration. Experiments to spatially normalise real MR images, and to recover simulated deformation fields, demonstrate (i) similar accuracy from NMI-demons and classical demons when the latter may be used, and (ii) similar accuracy for NMI-demons on T1w-T1w and T1w-T2w registration, demonstrating its potential in multi-modal scenarios
Longitudinal segmentation of age-related white matter hyperintensities
Although white matter hyperintensities evolve in the course of ageing, few solutions exist to consider the lesion segmentation problem longitudinally. Based on an existing automatic lesion segmentation algorithm, a longitudinal extension is proposed. For evaluation purposes, a longitudinal lesion simulator is created allowing for the comparison between the longitudinal and the cross-sectional version in various situations of lesion load progression. Finally, applied to clinical data, the proposed framework demonstrates an increased robustness compared to available cross-sectional methods and findings are aligned with previously reported clinical patterns
Combined Reconstruction and Registration of Digital Breast Tomosynthesis
Digital breast tomosynthesis (DBT) has the potential to en-
hance breast cancer detection by reducing the confounding e ect of su-
perimposed tissue associated with conventional mammography. In addi-
tion the increased volumetric information should enable temporal datasets
to be more accurately compared, a task that radiologists routinely apply
to conventional mammograms to detect the changes associated with ma-
lignancy. In this paper we address the problem of comparing DBT data
by combining reconstruction of a pair of temporal volumes with their reg-
istration. Using a simple test object, and DBT simulations from in vivo
breast compressions imaged using MRI, we demonstrate that this com-
bined reconstruction and registration approach produces improvements
in both the reconstructed volumes and the estimated transformation pa-
rameters when compared to performing the tasks sequentially
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Background and objective:
Processing of medical images such as MRI or CT presents different challenges compared to RGB images typically used in computer vision. These include a lack of labels for large datasets, high computational costs, and the need of metadata to describe the physical properties of voxels. Data augmentation is used to artificially increase the size of the training datasets. Training with image subvolumes or patches decreases the need for computational power. Spatial metadata needs to be carefully taken into account in order to ensure a correct alignment and orientation of volumes.
Methods:
We present TorchIO, an open-source Python library to enable efficient loading, preprocessing, augmentation and patch-based sampling of medical images for deep learning. TorchIO follows the style of PyTorch and integrates standard medical image processing libraries to efficiently process images during training of neural networks. TorchIO transforms can be easily composed, reproduced, traced and extended. Most transforms can be inverted, making the library suitable for test-time augmentation and estimation of aleatoric uncertainty in the context of segmentation. We provide multiple generic preprocessing and augmentation operations as well as simulation of MRI-specific artifacts.
Results:
Source code, comprehensive tutorials and extensive documentation for TorchIO can be found at http://torchio.rtfd.io/. The package can be installed from the Python Package Index (PyPI) running pip install torchio. It includes a command-line interface which allows users to apply transforms to image files without using Python. Additionally, we provide a graphical user interface within a TorchIO extension in 3D Slicer to visualize the effects of transforms.
Conclusion:
TorchIO was developed to help researchers standardize medical image processing pipelines and allow them to focus on the deep learning experiments. It encourages good open-science practices, as it supports experiment reproducibility and is version-controlled so that the software can be cited precisely. Due to its modularity, the library is compatible with other frameworks for deep learning with medical images
Spatial calibration of a 2D/3D ultrasound using a tracked needle
PURPOSE: Spatial calibration between a 2D/3D ultrasound and a pose tracking system requires a complex and time-consuming procedure. Simplifying this procedure without compromising the calibration accuracy is still a challenging problem. METHOD: We propose a new calibration method for both 2D and 3D ultrasound probes that involves scanning an arbitrary region of a tracked needle in different poses. This approach is easier to perform than most alternative methods that require a precise alignment between US scans and a calibration phantom. RESULTS: Our calibration method provides an average accuracy of 2.49Â mm for a 2D US probe with 107Â mm scanning depth, and an average accuracy of 2.39Â mm for a 3D US with 107Â mm scanning depth. CONCLUSION: Our method proposes a unified calibration framework for 2D and 3D probes using the same phantom object, work-flow, and algorithm. Our method significantly improves the accuracy of needle-based methods for 2D US probes as well as extends its use for 3D US probes
A Multi-Path Approach to Histology Volume Reconstruction
This paper presents a method for correcting erratic pairwise registrations when reconstructing a volume from 2D histology slices. Due to complex and unpredictable alterations of the content of histology images, a pairwise rigid registration between two adjacent slices may fail systematically. Conversely, a neighbouring registration, which potentially involves one of these two slices, will work. This grounds our approach: using correct spatial correspondences established through neighbouring registrations to account for direct failures. We propose to search the best alignment of every couple of adjacent slices from a finite set of transformations that involve neighbouring slices in a transitive fashion. Using the proposed method, we obtained reconstructed volumes with increased coherence compared to the classical pairwise approach, both in synthetic and real data
Forward-Backward Splitting in Deformable Image Registration: A Demons Approach
Efficient non-linear image registration implementations are
key for many biomedical imaging applications. By using the
classical demons approach, the associated optimization problem
is solved by an alternate optimization scheme consisting
of a gradient descent step followed by Gaussian smoothing.
Despite being simple and powerful, the solution of the underlying
relaxed formulation is not guaranteed to minimize
the original global energy. Implicitly, however, this second
step can be recast as the proximal map of the regularizer.
This interpretation introduces a parallel to the more general
Forward-Backward Splitting (FBS) scheme consisting of a
forward gradient descent and proximal step. By shifting entirely
to FBS, we can take advantage of the recent advances in
FBS methods and solve the original, non-relaxed deformable
registration problem for any type of differentiable similarity
measure and convex regularization associated with a tractable
proximal operator. Additionally, global convergence to a
critical point is guaranteed under weak restrictions. For the
first time in the context of image registration, we show that
Tikhonov regularization breaks down to the simple use of
B-Spline filtering in the proximal step. We demonstrate the
versatility of FBS by encoding spatial transformation as displacement
fields or free-form B-Spline deformations. We use
state-of-the-art FBS solvers and compare their performance
against the classical demons, the recently proposed inertial
demons and the conjugate gradient optimizer. Numerical experiments
performed on both synthetic and clinical data show
the advantage of FBS in image registration in terms of both
convergence and accuracy
Interventional multispectral photoacoustic imaging of the epidural space
Injections of anaesthetics into the epidural space are widely performed to relieve pain. Here, for the first time, we investigated the use of photoacoustic imaging to identify the epidural space using an interventional multispectral photoacoustic (IMPA) imaging system. Excitation light was delivered through an optical fibre positioned within a needle to illuminate the epidural space in a swine model. Spectral unmixing of the images revealed prominent distributions of lipids and haemoglobin in the epidural space at a depth of 35 mm. We conclude that IMPA could be a useful imaging modality to guide placement of needles into the epidural space
- âŠ