Computational atlases based on nonrigid registration have found much use in the medical imaging community.
To avoid bias to any single element of the training set, there are two main approaches: using a (random) subject
to serve as an initial reference and posteriorly removing bias, and a true groupwise registration with a constraint
of zero average transformation for direct computation of the atlas. Major drawbacks are the possible selection
of an outlier on one side, and an initialization with an invalid instance on the other. In both cases there is great
potential for affecting registration performance, and producing a final average image in which the structure of
interest deviates from the central anatomy of the population under study.
We propose an inexpensive means of reference selection based on a groupwise correspondence measure, which
avoids the selection of an outlier and is independent from the atlas construction approach that follows. Thus,
it improves tractability of reference selection and robustness of automated atlas construction. We illustrate the
method using a set of 20 cardiac multislice computed tomography volumes.
KEYWORDS: Dual energy x-ray absorptiometry, Bone, 3D image reconstruction, 3D modeling, 3D image processing, Data modeling, Statistical analysis, Image registration, Minerals, In vitro testing
Area Bone Mineral Density (aBMD) measured by Dual-energy X-ray Absorptiometry (DXA) is an established
criterion in the evaluation of hip fracture risk. The evaluation from these planar images, however, is limited
to 2D while it has been shown that proper 3D assessment of both the shape and the Bone Mineral Density
(BMD) distribution improves the fracture risk estimation. In this work we present a method to reconstruct both
the 3D bone shape and 3D BMD distribution of the proximal femur from a single DXA image. A statistical
model of shape and a separate statistical model of the BMD distribution were automatically constructed from
a set of Quantitative Computed Tomography (QCT) scans. The reconstruction method incorporates a fully
automatic intensity based 3D-2D registration process, maximizing the similarity between the DXA and a digitally
reconstructed radiograph of the combined model. For the construction of the models, an in vitro dataset of
QCT scans of 60 anatomical specimens was used. To evaluate the reconstruction accuracy, experiments were
performed on simulated DXA images from the QCT scans of 30 anatomical specimens. Comparisons between
the reconstructions and the same subject QCT scans showed a mean shape accuracy of 1.2mm, and a mean
density error of 81mg/cm3. The results show that this method is capable of accurately reconstructing both the
3D shape and 3D BMD distribution of the proximal femur from DXA images used in clinical routine, potentially
improving the diagnosis of osteoporosis and fracture risk assessments at a low radiation dose and low cost.
This paper proposes a new pairwise registration algorithm called Large Diffeomorphic Free-Form Deformation
(LDFFD). In the LDFFD algorithm, the diffeomorphic mapping is represented as a composition of Free-Form
Deformation (FFD) transformations at each time step, all time steps being jointly optimized. The fact that the
transformation at one time step influences all subsequent time steps, naturally enforces temporal consistency in
the transformation compared to other existing diffeomorphic registration algorithms and does not restrict the
space of solutions to stationary displacement fields over all time steps. A multiresolution strategy is presented
to find the optimal number of time steps and solve efficiently the joint optimization of all transformations in the
registration chain. Accuracy of the algorithm in the presence of large deformations is illustrated and compared
to other standard or diffeomorphic registration algorithms. Our algorithm is applied here in the context of
monitoring disease progression by image-based quantification of aneurysmal growth post endovascular treatment.
In this context, aneurysm volume changes occurring after embolization are quantified by integrating the Jacobian
of the diffeomorphic transformation between 3D rotational angiography images acquired at subsequent followups.
This paper combines different parallelization strategies for speeding up motion and deformation computation by
non-rigid registration of a sequence of images. The registration is performed in a two-level acceleration approach:
(1) parallelization of each registration process using MPI and/or threads, and (2) distribution of the sequential
registrations over a cluster.
On a 24-node double quad-core Intel Xeon (2.66 GHz CPU, 16 GB RAM) cluster, the method is demonstrated
to efficiently compute the deformation of a cardiac sequence reducing the computation time from more than 3
hours to a couple of minutes (for low downsampled images). It is shown that the distribution of the sequential
registrations over the cluster together with the parallelization of each pairwise registration by multithreading
lowers the computation time towards values compatible with clinical requirements (a few minutes per patient).
The combination of MPI and multithreading is only advantageous for large input data sizes.
Performances are assessed for the specific scenario of aligning cardiac sequences of taggedMagnetic Resonance
(tMR) images, with the aim of comparing strain in healthy subjects and hypertrophic cardiomyopathy (HCM)
patients. In particular, we compared the distribution of systolic strain in both populations. On average, HCM
patients showed lower average values of strain with larger deviation due to the coexistence of regions with
impaired deformation and regions with normal deformation.
Knowledge-based vascular segmentation methods typically rely on a pre-built training set of segmented images,
which is used to estimate the probability of each voxel to belong to a particular tissue. In 3D Rotational Angiography
(3DRA) the same tissue can correspond to different intensity ranges depending on the imaging device,
settings and contrast injection protocol. As a result, pre-built training sets do not apply to all images and
the best segmentation results are often obtained when the training set is built specifically for each individual
image. We present an Image Intensity Standardization (IIS) method designed to ensure a correspondence between
specific tissues and intensity ranges common to every image that undergoes the standardization process.
The method applies a piecewise linear transformation to the image that aligns the intensity histogram to the
histogram taken as reference. The reference histogram has been selected from a high quality image not containing
artificial objects such as coils or stents. This is a pre-processing step that allows employing a training set
built on a limited number of standardized images for the segmentation of standardized images which were not part of the training set. The effectiveness of the presented IIS technique in combination with a well-validated knowledge-based vasculature segmentation method is quantified on a variety of 3DRA images depicting cerebral arteries and intracranial aneurysms. The proposed IIS method offers a solution to the standardization of tissue classes in routine medical images and effectively improves automation and usability of knowledge-based vascular segmentation algorithms.
Endovascular treatment of intracranial aneurysms is a minimally-invasive technique recognized as a valid alternative
to surgical clipping. However, endovascular treatment can be associated to aneurysm recurrence, either
due to coil compaction or aneurysm growth. The quantification of coil compaction or aneurysm growth is usually
performed by manual measurements or visual inspection of images from consecutive follow-ups. Manual measurements
permit to detect large global deformation but might have insufficient accuracy for detecting subtle or
more local changes between images. Image inspection permits to detect a residual neck in the aneurysm but do
not differentiate aneurysm growth from coil compaction. In this paper, we propose to quantify independently coil
compaction and aneurysm growth using non-rigid image registration. Local changes of volume between images
at successive time points are identified using the Jacobian of the non-rigid transformation.
Two different non-rigid registration strategies are applied in order to explore the sensitivity of Jacobian-based
volume changes against the registration method, FFD registration based on mutual information and Demons.
This volume-variation measure has been applied to four patients of which a series of 3D Rotational Angiography
(3DRA) images obtained at different controls separated from two months to two years were available. The
evolution of coil and aneurysm volumes along the period has been obtained separately, which allows distinguishing
between coil compaction and aneurysm growth. On the four cases studied in this paper, aneurysm recurrence
was always associated to aneurysm growth, as opposed to strict coil compaction.
Hemodynamics, and in particular Wall Shear Stress (WSS), is thought to play a critical role in the progression
and rupture of intracranial aneurysms. Wall motion is related to local biomechanical properties of the aneurysm,
which in turn are associated with the amount of damage undergone by the tissue. The underlying hypothesis
in this work is that injured regions show differential motion with respect to normal ones, allowing a connection
between local wall biomechanics and a potential mechanism of wall injury such as elevated WSS. In a previous
work, a novel method was presented combining wall motion estimation using image registration techniques with
Computational Fluid Dynamics (CFD) simulations in order to provide realistic intra-aneurysmal flow patterns.
It was shown that, when compared to compliant vessels, rigid models tend to overestimate WSS and produce
smaller areas of elevated WSS and force concentration, being the observed differences related to the magnitude
of the displacements. This work aims to further study the relationships between wall motion, flow patterns and
risk of rupture in aneurysms. To this end, four studies containing both 3DRA and DSA studies were analyzed,
and an improved version of the method developed previously was applied to cases showing wall motion. A
quantification and analysis of the displacement fields and their relationships to flow patterns are presented. This
relationship may play an important role in understanding interaction mechanisms between hemodynamics, wall
biomechanics, and the effect on aneurysm evolution mechanisms.
Intensity Modulated Radiotherapy is a new technique enabling the sculpting of the 3D radiation dose. It enables
to modulate the delivery of the dose inside the malignant areas and constrain the radiation plan for protecting
important functional areas. It also raises the issues of adequacy and accuracy of the selection and delineation
of the target volumes. The delineation in the patient image of the tumor volume is highly time-consuming and
requires considerable expertise. In this paper we focus on atlas based automatic segmentation of head and neck
patients and compare two non-rigid registration methods: B-Spline and Morphons. To assess the quality of each
method, we took a set of four 3D CT patient's images previously segmented by a doctor with the organs at
risk. After a preliminary affine registration, both non-rigid registration algorithms were applied to match the
patient and atlas images. Each deformation field, resulted from the non-rigid deformation, was applied on the
masks corresponding to segmented regions in the atlas. The atlas based segmentation masks were compared to
manual segmentations performed by an expert. We conclude that Morphons has performed better for matching
all structures being considered, improving in average 11% the segmentation.
A new fast non rigid registration algorithm is presented. The algorithm estimates a dense deformation field by optimizing a criterion that measures image similarity by mutual information and regularizes with a linear elastic energy term. The optimal deformation field is found using a Simultaneous Perturbation Stochastic Approximation to the gradient. The implementation is parallelized for symmetric multi-processor architectures.
This algorithm was applied to capture non-rigid brain deformations that occur during neurosurgery. Segmentation of the intra-operative data is not required but preoperative segmentation of the brain allows the algorithm to be robust to artifacts due to the craniotomy.
Intra-Operative MR imaging is an emerging tool for image guided (neuro)surgery. Due to the small size of the magnets and the short acquisition time, the images produced by such devices are often subject to distortions. In this work, we show the particular case of images provided by an ODIN device (Odin Medical Technologies, Newton, MA 02458, USA). Such images suffer from geometric distortions and an important bias field in the luminance. In order to simultaneously correct these deformations, we propose to register a preoperative ODIN image with a diagnosis MR high resolution image while compensating the bias field.
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