Iterative simulations to estimate the elastic properties from a series of MRI images followed by MRI-US validation

The modeling of breast deformations is of interest in medical applications such as image-guided biopsy, or image registration for diagnostic purposes. In order to have such information, it is needed to extract the mechanical properties of the tissues. In this work, we propose an iterative technique based on finite element analysis that estimates the elastic modulus of realistic breast phantoms, starting from MRI images acquired in different positions (prone and supine), when deformed only by the gravity force. We validated the method using both a single-modality evaluation in which we simulated the effect of the gravity force to generate four different configurations (prone, supine, lateral, and vertical) and a multi-modality evaluation in which we simulated a series of changes in orientation (prone to supine). Validation is performed, respectively, on surface points and lesions using as ground-truth data from MRI images, and on target lesions inside the breast phantom compared with the actual target segmented from the US image. The use of pre-operative images is limited at the moment to diagnostic purposes. By using our method we can compute patient-specific mechanical properties that allow compensating deformations. Graphical Abstract Workflow of the proposed method and comparative results of the prone-to-supine simulation (red volumes) validated using MRI data (blue volumes).

A new idea for visualization of lesions distribution in mammogram based on CPD registration method

BACKGROUND

Mammography is currently the most effective technique for breast cancer. Lesions distribution can provide support for clinical diagnosis and epidemiological studies.

OBJECTIVE

We presented a new idea to help radiologists study breast lesions distribution conveniently. We also developed an automatic tool based on this idea which could show visualization of lesions distribution in a standard mammogram.

METHODS

Firstly, establishing a lesion database to study; then, extracting breast contours and match different women's mammograms to a standard mammogram; finally, showing the lesion distribution in the standard mammogram, and providing the distribution statistics. The crucial process of developing this tool was matching different women's mammograms correctly. We used a hybrid breast contour extraction method combined with coherent point drift method to match different women's mammograms.

RESULTS

We tested our automatic tool by four mass datasets of 641 images. The distribution results shown by the tool were consistent with the results counted according to their reports and mammograms by manual. We also discussed the registration error that was less than 3.3 mm in average distance.

CONCLUSIONS

The new idea is effective and the automatic tool can provide lesions distribution results which are consistent with radiologists simply and conveniently.

Temporal mammogram image registration using optimized curvilinear coordinates

Registration of mammograms plays an important role in breast cancer computer-aided diagnosis systems. Radiologists usually compare mammogram images in order to detect abnormalities. The comparison of mammograms requires a registration between them. A temporal mammogram registration method is proposed in this paper. It is based on the curvilinear coordinates, which are utilized to cope both with global and local deformations in the breast area. Temporal mammogram pairs are used to validate the proposed method. After registration, the similarity between the mammograms is maximized, and the distance between manually defined landmarks is decreased. In addition, a thorough comparison with the state-of-the-art mammogram registration methods is performed to show its effectiveness.

A review of biomechanically informed breast image registration

Breast radiology encompasses the full range of imaging modalities from routine imaging via x-ray mammography, magnetic resonance imaging and ultrasound (both two- and three-dimensional), to more recent technologies such as digital breast tomosynthesis, and dedicated breast imaging systems for positron emission mammography and ultrasound tomography. In addition new and experimental modalities, such as Photoacoustics, Near Infrared Spectroscopy and Electrical Impedance Tomography etc, are emerging. The breast is a highly deformable structure however, and this greatly complicates visual comparison of imaging modalities for the purposes of breast screening, cancer diagnosis (including image guided biopsy), tumour staging, treatment monitoring, surgical planning and simulation of the effects of surgery and wound healing etc. Due primarily to the challenges posed by these gross, non-rigid deformations, development of automated methods which enable registration, and hence fusion, of information within and across breast imaging modalities, and between the images and the physical space of the breast during interventions, remains an active research field which has yet to translate suitable methods into clinical practice. This review describes current research in the field of breast biomechanical modelling and identifies relevant publications where the resulting models have been incorporated into breast image registration and simulation algorithms. Despite these developments there remain a number of issues that limit clinical application of biomechanical modelling. These include the accuracy of constitutive modelling, implementation of representative boundary conditions, failure to meet clinically acceptable levels of computational cost, challenges associated with automating patient-specific model generation (i.e. robust image segmentation and mesh generation) and the complexity of applying biomechanical modelling methods in routine clinical practice.

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

PURPOSE

To evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy.

METHODS

Breast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation.

RESULTS

DRAMMS had the smallest landmark errors (6.05 ± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 ± 3.86 mm), NMI-FFD (8.21 ± 3.81 mm), SSD-FFD (9.46 ± 4.55 mm), Demons (10.76 ± 6.01 mm), and Diffeomorphic Demons (10.82 ± 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups.

CONCLUSIONS

The DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment.

Breast lesion co-localisation between X-ray and MR images using finite element modelling

This paper presents a novel X-ray and MR image registration technique based on individual-specific biomechanical finite element (FE) models of the breasts. Information from 3D magnetic resonance (MR) images was registered to X-ray mammographic images using non-linear FE models subject to contact mechanics constraints to simulate the large compressive deformations between the two imaging modalities. A physics-based perspective ray-casting algorithm was used to generate 2D pseudo-X-ray projections of the FE-warped 3D MR images. Unknown input parameters to the FE models, such as the location and orientation of the compression plates, were optimised to provide the best match between the pseudo and clinical X-ray images. The methods were validated using images taken before and during compression of a breast-shaped phantom, for which 12 inclusions were tracked between imaging modalities. These methods were then applied to X-ray and MR images from six breast cancer patients. Error measures (such as centroid and surface distances) of segmented tumours in simulated and actual X-ray mammograms were used to assess the accuracy of the methods. Sensitivity analysis of the lesion co-localisation accuracy to rotation about the anterior-posterior axis was then performed. For 10 of the 12 X-ray mammograms, lesion localisation accuracies of 14 mm and less were achieved. This analysis on the rotation about the anterior-posterior axis indicated that, in cases where the lesion lies in the plane parallel to the mammographic compression plates, that cuts through the nipple, such rotations have relatively minor effects.This has important implications for clinical applicability of this multi-modality lesion registration technique, which will aid in the diagnosis and treatment of breast cancer.

Deformable registration using edge-preserving scale space for adaptive image-guided radiation therapy

Incorporating of daily cone-beam computer tomography (CBCT) image into online radiation therapy process can achieve adaptive image-guided radiation therapy (AIGRT). Registration of planning CT (PCT) and daily CBCT are the key issues in this process. In our work, a new multiscale deformable registration method is proposed by combining edge-preserving scale space with the multilevel free-form deformation (FFD) grids for CBCT-based AIGRT system. The edge-preserving scale space, which is able to select edges and contours of images according to their geometric size, is derived from the total variation model with the L1 norm (TV-L1). At each scale, despite the noise and contrast resolution differences between the PCT and CBCT, the selected edges and contours are sufficiently strong to drive the deformation using the FFD grid, and the edge-preserving property ensures more meaningful spatial information for mutual information (MI)-based registration. At last, the deformation fields are gained by a coarse to fine manner. Furthermore, in consideration of clinical application we designed an optimal estimation of the TV-L1 parameters by minimizing the defined offset function for automated registration. Six types of patients are studied in our work, including rectum, prostate, lung, H&N (head and neck), breast, and chest cancer patients. The experiment results demonstrate the significance of the proposed method both quantitatively with ground truth known and qualitatively with ground truth unknown. The applications for AIGRT, including adaptive deformable recontouring and redosing, and DVH (dose volume histogram) analysis in the course of radiation therapy are also studied.

Localized fibroglandular tissue as a predictor of future tumor location within the breast

BACKGROUND

Mammographic density (MD) is a strong marker of breast cancer risk, but it is unclear whether tumors arise specifically within dense tissue.

METHODS

In 231 British women diagnosed with breast cancer after at least one negative annual screening during a mammographic screening trial, we assessed whether tumor location was related to localized MD 5 years prior to diagnosis. Radiologists identified tumor locations on digitised films. We used a validated algorithm to align serial images from the same woman to locate the corresponding point on the prediagnostic film. A virtual 1 cm square grid was overlaid on prediagnostic films and MD calculated for each square within a woman's breast (mean = 271 squares/film). Conditional logistic regression, matching on a woman's breast, was used to estimate the odds of a tumor arising in a square in relation to its prediagnostic square-specific MD.

RESULTS

Median (interquartile range) prediagnostic MD was 98.2% (46.8%-100%) in 1 cm-squares that subsequently contained the tumor and 41.0% (31.5%-53.9%) for the whole breast. The odds of a tumor arising in a 1 cm-square were, respectively, 6.1 (95% CI: 1.9-20.1), 16.6 (5.2-53.2), and 25.5-fold (8.1-80.3) higher for squares in the second, third, and fourth quartiles of prediagnostic MD relative to those in the lowest quartile within that breast (P(trend) < 0.001). The corresponding odds ratios were 2.3 (1.3-4.0), 3.9 (2.3-6.4), and 4.6 (2.8-7.6) if a 3 cm-square grid was used.

CONCLUSION

Tumors arise predominantly within the radiodense breast tissue.

IMPACT

Localized MD may be used as a predictor of subsequent tumor location within the breast.