737 research outputs found
Registration and analysis of dynamic magnetic resonance image series
Cystic fibrosis (CF) is an autosomal-recessive inherited metabolic disorder that affects all organs in the human body. Patients affected with CF suffer particularly from chronic inflammation and obstruction of the airways. Through early detection, continuous monitoring methods, and new treatments, the life expectancy of patients with CF has been increased drastically in the last decades. However, continuous monitoring of the disease progression is essential for a successful treatment. The current state-of-the-art method for lung disease detection and monitoring is computed tomography (CT) or X-ray. These techniques are ill-suited for the monitoring of disease progressions because of the ionizing radiation the patient is exposed during the examination. Through the development of new magnetic resonance imaging (MRI) sequences and evaluation methods, MRI is able to measure physiological changes in the lungs. The process to create physiological maps, i.e. ventilation and perfusion maps, of the lungs using MRI can be split up into three parts: MR-acquisition, image registration, and image analysis. In this work, we present different methods for the image registration part and the image analysis part. We developed a graph-based registration method for 2D dynamic MR image series of the lungs in order to overcome the problem of sliding motion at organ boundaries. Furthermore, we developed a human-inspired learning-based registration method. Here, the registration is defined as a sequence of local transformations. The sequence-based approach combines the advantage of dense transformation models, i.e. large space of transformations, and the advantage of interpolating transformation models, i.e. smooth local transformations. We also developed a general registration framework called Autograd Image Registration Laboratory (AIRLab), which performs automatic calculation of the gradients for the registration process. This allows rapid prototyping and an easy implementation of existing registration algorithms. For the image analysis part, we developed a deep-learning approach based on gated recurrent units that are able to calculate ventilation maps with less than a third of the number of images of the current method. Automatic defect detection in the estimated MRI ventilation and perfusion maps is essential for the clinical routine to automatically evaluate the treatment progression. We developed a weakly supervised method that is able to infer a pixel-wise defect segmentation by using only a continuous global label during training. In this case, we directly use the lung clearance index (LCI) as a global weak label, without any further manual annotations. The LCI is a global measure to describe ventilation inhomogeneities of the lungs and is obtained by a multiple breath washout test
Implementation and evaluation of various demons deformable image registration algorithms on GPU
Online adaptive radiation therapy (ART) promises the ability to deliver an
optimal treatment in response to daily patient anatomic variation. A major
technical barrier for the clinical implementation of online ART is the
requirement of rapid image segmentation. Deformable image registration (DIR)
has been used as an automated segmentation method to transfer tumor/organ
contours from the planning image to daily images. However, the current
computational time of DIR is insufficient for online ART. In this work, this
issue is addressed by using computer graphics processing units (GPUs). A
grey-scale based DIR algorithm called demons and five of its variants were
implemented on GPUs using the Compute Unified Device Architecture (CUDA)
programming environment. The spatial accuracy of these algorithms was evaluated
over five sets of pulmonary 4DCT images with an average size of 256x256x100 and
more than 1,100 expert-determined landmark point pairs each. For all the
testing scenarios presented in this paper, the GPU-based DIR computation
required around 7 to 11 seconds to yield an average 3D error ranging from 1.5
to 1.8 mm. It is interesting to find out that the original passive force demons
algorithms outperform subsequently proposed variants based on the combination
of accuracy, efficiency, and ease of implementation.Comment: Submitted to Physics in Medicine and Biolog
Methodology for registration of distended recutms in pelvic CT studies
Purpose: Accurate delineation of the rectum is of high importance in off-line adaptive radiation therapy since it is a major dose-limiting organ in prostate cancer radiotherapy. The intensity-based deformable image registration (DIR) methods cannot create a correct spatial transformation if there is no correspondence between the template and the target images. The variation of rectal filling, gas, or feces, creates a noncorrespondence in image intensities that becomes a great obstacle for intensity-based DIR.
Methods: In this study the authors have designed and implemented a semiautomatic method to create a rectum mask in pelvic computed tomography (CT) images. The method, that includes a DIR based on the demons algorithm, has been tested in 13 prostate cancer cases, each comprising of two CT scans, for a total of 26 CT scans.
Results: The use of the manual segmentation in the planning image and the proposed rectum mask method (RMM) method in the daily image leads to an improvement in the DIR performance in pelvic CT images, obtaining a mean value of overlap volume index = 0.89, close to the values obtained using the manual segmentations in both images.
Conclusions: The application of the RMM method in the daily image and the manual segmentations in the planning image during prostate cancer treatments increases the performance of the registration in presence of rectal fillings, obtaining very good agreement with a physician's manual contours
Recommended from our members
Advanced H-1 Lung Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) is one of the widely used medical imaging modality, since it can provide both structural and functional assessment in a single imaging session. However, two major challenges should be considered by using MRI for lung imaging. The first challenge is the intrinsic low SNR of H-1 lung MRI due to the low proton density as well as the fast decay of the lung parenchyma signal. And the second challenge is subject motion. To achieve high resolution structural image, MRI requires a long scan time, usually a few minutes or even longer, which make MRI sensitive to subject motion. To address the first challenge, ultra-short echo time (UTE) MRI sequence is used to capture the lung parenchyma signal before decay. As for subject motion, two major strategies are widely used. One strategy is fast breath-holding scan, the subjects are asked to hold their breaths for a short duration, and the fast 3D MR sequence would be used to acquire data within that duration. This dissertation proposes a new acquisition scheme based on the standard UTE sequence, which largely increases the encoding efficiency and improves the breath-holding scan images. The other is free breathing scan with motion correction. The subjects are allowed to breathe during the MR acquisition. After the acquisition, the motion corrupted data would go through the motion correction step to reconstruct the motion free images. In this dissertation, two novel motion corrected reconstruction strategies are proposed to incorporate the motion modeling and compensation into the reconstruction to get high SNR motion corrected 3D and 4D images. When translating the developed techniques to the clinical studies, specifically for pediatric and neonatal studies, more practical problems need to be considered, such as smaller but finer anatomy to image, the different respiratory patterns of the young subjects etc. This dissertation proposes a 5-minute free breathing UTE MRI strategy to achieve a 3D high resolution motion free lung image for pediatric and neonatal studies
An ITK Implementation of the Symmetric Log-Domain Diffeomorphic Demons Algorithm
To be filled INThis article provides an implementation of the symmetric log-domain diffeomorphic image registration algorithm, or symmetric demons algorithm for short. It generalizes Thirion's demons and the diffeo- morphic demons algorithm. The main practical advantages of the symmetric demons with respect to the other demons variants is that is provides the inverse of the spatial transformation at no additional computational cost and ensures that the registration of image A to image B provides the inverse of the registration from image B to image A. The algorithm works completely in the log-domain, i.e. it uses a stationary velocity field to encode the spatial transformation as its exponential. Within the Insight Toolkit (ITK), the classical demons algorithm is implemented as part of the finite difference solver framework. Our code reuses and extends this generic framework. The source code is composed of a set of reusable ITK filters and classes together with their unit tests. We also provide a small example program that al- lows the user to compare the different variants of the demons algorithm. This paper gives an overview of the algorithm, an overview of its implementation and a small user guide to ease the use of the registration executable
Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images
Medical image registration is a challenging task involving the estimation of
spatial transformations to establish anatomical correspondence between pairs or
groups of images. Recently, deep learning-based image registration methods have
been widely explored, and demonstrated to enable fast and accurate image
registration in a variety of applications. However, most deep learning-based
registration methods assume that the deformation fields are smooth and
continuous everywhere in the image domain, which is not always true, especially
when registering images whose fields of view contain discontinuities at
tissue/organ boundaries. In such scenarios, enforcing smooth, globally
continuous deformation fields leads to incorrect/implausible registration
results. We propose a novel discontinuity-preserving image registration method
to tackle this challenge, which ensures globally discontinuous and locally
smooth deformation fields, leading to more accurate and realistic registration
results. The proposed method leverages the complementary nature of image
segmentation and registration and enables joint segmentation and pair-wise
registration of images. A co-attention block is proposed in the segmentation
component of the network to learn the structural correlations in the input
images, while a discontinuity-preserving registration strategy is employed in
the registration component of the network to ensure plausibility in the
estimated deformation fields at tissue/organ interfaces. We evaluate our method
on the task of intra-subject spatio-temporal image registration using
large-scale cinematic cardiac magnetic resonance image sequences, and
demonstrate that our method achieves significant improvements over the
state-of-the-art for medical image registration, and produces high-quality
segmentation masks for the regions of interest
- …