It has been shown that the tumour microenvironment plays a crucial role in regulating tumour progression by a number of different mechanisms, including the remodeling of collagen fibres in tumour-associated stroma. It is still unclear, however, if these stromal changes are of benefit to the host or the tumour. We hypothesise that stromal maturity is an important reflection of tumour biology, and thus can be used to predict prognosis. The aim of this study is to develop a texture analysis methodology which will automatically classify stromal regions from images of hematoxylin and eosin-stained (H and E) sections into two categories: mature and immature. Subsequently we will investigate whether stromal maturity could be used as a predictor of survival and also as a means to better understand the relationship between the radiological imaging signal and the underlying tissue microstructure. We present initial results for 118 regions-of-interest from a dataset of 39 patients diagnosed with invasive breast cancer.
Biomechanical modelling enables large deformation simulations of breast tissues under different loading conditions to be performed. Such simulations can be utilised to transform prone Magnetic Resonance (MR) images into a different patient position, such as upright or supine. We present a novel integration of biomechanical modelling with a surface registration algorithm which optimises the unknown material parameters of a biomechanical model and performs a subsequent regularised surface alignment. This allows deformations induced by effects other than gravity, such as those due to contact of the breast and MR coil, to be reversed. Correction displacements are applied to the biomechanical model enabling transformation of the original pre-surgical images to the corresponding target position. <p> </p>The algorithm is evaluated for the prone-to-supine case using prone MR images and the skin outline of supine Computed Tomography (CT) scans for three patients. A mean target registration error (TRE) of 10:9 mm for internal structures is achieved. For the prone-to-upright scenario, an optical 3D surface scan of one patient is used as a registration target and the nipple distances after alignment between the transformed MRI and the surface are 10:1 mm and 6:3 mm respectively.
There is currently an increasing interest in combining the information obtained from radiology and histology with the intent of gaining a better understanding of how different tumour morphologies can lead to distinctive radiological signs which might predict overall treatment outcome. Relating information at different resolution scales is challenging. Reconstructing 3D volumes from histology images could be the key to interpreting and relating the radiological image signal to tissue microstructure. The goal of this study is to determine the minimum sampling (maximum spacing between histological sections through a fixed surgical specimen) required to create a 3D reconstruction of the specimen to a specific tolerance. We present initial results for one lumpectomy specimen case where 33 consecutive histology slides were acquired.
In biomechanical simulations of the human breast, the analysed geometry is often reconstructed from in vivo medical imaging procedures. For example in dynamic contrast enhanced magnetic resonance imaging, the acquired geometry of the patient's breast when lying in the prone position represents a deformed configuration that is pre-stressed by typical in vivo conditions and gravity. Thus, physically realistic simulations require consideration of this loading and, hence, establishing the undeformed configuration is an important task for accurate and reliable biomechanical modelling of the breast. We compare three different numerical approaches to recover the unloaded configuration from the loaded geometry given patient-specific biomechanical models built from prone and supine MR images. The algorithms compared are:(i) the simple inversion of gravity without the consideration of pre-stresses, (ii) an inversefinite deformation approach and (iii) afixed point type iterative approach which uses only forward simulations. It is shown that the iterative and the inverse approach produce similar zero-gravity estimates, where as the simple inversion of gravity is only appropriate for small or highly constrained deformations.
This paper presents a novel approach to X-ray mammography - MRI registration. The proposed method uses
an intensity-based technique and an affine transformation matrix to approximate the 3D deformation of the
breast resulting from the compression applied during mammogram acquisition. The registration is driven by a
similarity measure that is calculated at each iteration of the algorithm between the target X-ray mammogram and
a simulated X-ray image, created from the MR volume. Although the similarity measure is calculated in 2D, we
compute a 3D transformation that is updated at each iteration. We have performed two types of experiments.
In the first set, we used simulated X-ray target data, for which the ground truth deformation of the volume
was known and thus the results could be validated. For this case, we examined the performance of 4 different
similarity measures and we show that Normalized Cross Correlation and Gradient Difference perform best. The
calculated mean reprojection error was for both similarity measures 4mm, for an initial misregistration of 14mm.
In the second set of experiments, we present the initial results of registering real X-ray mammograms with MR
volumes. The results indicate that the breast boundaries were registered well and the volume was deformed in
3D in a similar way to the deformation of the breast during X-ray mammogram acquisition. The experiments
were carried out on five patients.
Stable features under simulated mammographic compressions, which will become candidate landmarks for a temporal
mammographic feature-based registration algorithm, are discussed in this paper. Using these simulated mammograms,
we explore the extraction of features based on standard intensity projection images and local phase projection images.
One approach to establishing corresponding features is by template matching using a similarity measure. Simulated
mammographic projections from deformed MR volumes are employed, as the mean projected 3D displacements are
computed and therefore validation of the technique is possible. Tracking is done by template matching using normalized
cross correlation as the similarity measure. The performance of standard projection images and local phase projection
images is compared. The preliminary results reveal that although the majority of the points within the breast are difficult
to track, a small number may be successfully tracked, which is indicative of their stability and thus their suitability as
candidate landmarks. Whilst matching using the standard projection images achieves an overall error of 14.46mm, this
error increases to 22.7mm when computing local phase of the projection images. These results suggest that using local
phase alone does not improve template matching. For the identification of stable landmarks for feature-based
mammogram registration, we conclude that intensity based template matching using normalized correlation is a feasible
approach for identifying stable features.
This paper describes a novel method for registering multimodal breast images. The method is based on guiding
initial alignment by a 3D statistical deformation model (SDM) followed by a standard non-rigid registration
method for fine alignment. The method was applied to the problem of compensating for large breast compressions,
namely registering magnetic resonance (MR) images to tomosynthesis images and X-ray mammograms. The
SDM was based on simulating plausible breast compressions for a population of 20 subjects via finite element
models created from segmented 3D MR breast images. Leave-one-out tests on simulated data showed that using
SDM guided registration rather than affine registration for the initial alignment led on average to lower mean
registration errors, namely 3.2 mm versus 4.2 mm for MR to tomosynthesis images (17.1 mm initially) and
5.0 mm versus 6.2 mm for MR to X-ray mammograms (15.0 mm initially).