VERDICT MRI validation in fresh and fixed prostate specimens using patient-specific moulds for histological and MR alignment.

The VERDICT framework for modelling diffusion MRI data aims to relate parameters from a biophysical model to histological features used for tumour grading in prostate cancer. Validation of the VERDICT model is necessary for clinical use. This study compared VERDICT parameters obtained ex vivo with histology in five specimens from radical prostatectomy. A patient-specific 3D-printed mould was used to investigate the effects of fixation on VERDICT parameters and to aid registration to histology. A rich diffusion data set was acquired in each ex vivo prostate before and after fixation. At both time points, data were best described by a two-compartment model: the model assumes that an anisotropic tensor compartment represents the extracellular space and a restricted sphere compartment models the intracellular space. The effect of fixation on model parameters associated with tissue microstructure was small. The patient-specific mould minimized tissue deformations and co-localized slices, so that rigid registration of MRI to histology images allowed region-based comparison with histology. The VERDICT estimate of the intracellular volume fraction corresponded to histological indicators of cellular fraction, including high values in tumour regions. The average sphere radius from VERDICT, representing the average cell size, was relatively uniform across samples. The primary diffusion direction from the extracellular compartment of the VERDICT model aligned with collagen fibre patterns in the stroma obtained by structure tensor analysis. This confirmed the biophysical relationship between ex vivo VERDICT parameters and tissue microstructure from histology.

Microstructural models for diffusion MRI in breast cancer and surrounding stroma: an ex vivo study.

The diffusion signal in breast tissue has primarily been modelled using apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and diffusion tensor (DT) models, which may be too simplistic to describe the underlying tissue microstructure. Formalin-fixed breast cancer samples were scanned using a wide range of gradient strengths, durations, separations and orientations. A variety of one- and two-compartment models were tested to determine which best described the data. Models with restricted diffusion components and anisotropy were selected in most cancerous regions and there were no regions in which conventional ADC or DT models were selected. Maps of ADC generally related to cellularity on histology, but maps of parameters from more complex models suggest that both overall cell volume fraction and individual cell size can contribute to the diffusion signal, affecting the specificity of ADC to the tissue microstructure. The areas of coherence in diffusion anisotropy images were small, approximately 1 mm, but the orientation corresponded to stromal orientation patterns on histology.

Histo-MRI map study protocol: a prospective cohort study mapping MRI to histology for biomarker validation and prediction of prostate cancer.

INTRODUCTION: Multiparametric MRI (mpMRI) is now widely used to risk stratify men with a suspicion of prostate cancer and identify suspicious regions for biopsy. However, the technique has modest specificity and a high false-positive rate, especially in men with mpMRI scored as indeterminate (3/5) or likely (4/5) to have clinically significant cancer (csPCa) (Gleason ≥3+4). Advanced MRI techniques have emerged which seek to improve this characterisation and could predict biopsy results non-invasively. Before these techniques are translated clinically, robust histological and clinical validation is required. METHODS AND ANALYSIS: This study aims to clinically validate two advanced MRI techniques in a prospectively recruited cohort of men suspected of prostate cancer. Histological analysis of men undergoing biopsy or prostatectomy will be used for biological validation of biomarkers derived from Vascular and Extracellular Restricted Diffusion for Cytometry in Tumours and Luminal Water imaging. In particular, prostatectomy specimens will be processed using three-dimension printed patient-specific moulds to allow for accurate MRI and histology mapping. The index tests will be compared with the histological reference standard to derive false positive rate and true positive rate for men with mpMRI scores which are indeterminate (3/5) or likely (4/5) to have clinically significant prostate cancer (csPCa). Histopathological validation from both biopsy and prostatectomy samples will provide the best ground truth in validating promising MRI techniques which could predict biopsy results and help avoid unnecessary biopsies in men suspected of prostate cancer. ETHICS AND DISSEMINATION: Ethical approval was granted by the London-Queen Square Research Ethics Committee (19/LO/1803) on 23 January 2020. Results from the study will be presented at conferences and submitted to peer-reviewed journals for publication. Results will also be available on ClinicalTrials.gov. TRIAL REGISTRATION NUMBER: NCT04792138.

Automated Classification of Breast Cancer Stroma Maturity From Histological Images.

OBJECTIVE: The tumor microenvironment plays a crucial role in regulating tumor progression by a number of different mechanisms, in particular, the remodeling of collagen fibers in tumor-associated stroma, which has been reported to be related to patient survival. The underlying motivation of this work is that remodeling of collagen fibers gives rise to observable patterns in hematoxylin and eosin (H&E) stained slides from clinical cases of invasive breast carcinoma that the pathologist can label as mature or immature stroma. The aim of this paper is to categorise and automatically classify stromal regions according to their maturity and show that this classification agrees with that of skilled observers, hence providing a repeatable and quantitative measure for prognostic studies. METHODS: We use multiscale basic image features and local binary patterns, in combination with a random decision trees classifier for classification of breast cancer stroma regions-of-interest (ROI). RESULTS: We present results from a cohort of 55 patients with analysis of 169 ROI. Our multiscale approach achieved a classification accuracy of 84%. CONCLUSION: This work demonstrates the ability of texture-based image analysis to differentiate breast cancer stroma maturity in clinically acquired H&E-stained slides at least as well as skilled observers.

3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging.

PURPOSE: In breast imaging, radiological in vivo images, such as x-ray mammography and magnetic resonance imaging (MRI), are used for tumor detection, diagnosis, and size determination. After excision, the specimen is typically sliced into slabs and a small subset is sampled. Histopathological imaging of the stained samples is used as the gold standard for characterization of the tumor microenvironment. A 3D volume reconstruction of the whole specimen from the 2D slabs could facilitate bridging the gap between histology and in vivo radiological imaging. This task is challenging, however, due to the large deformation that the breast tissue undergoes after surgery and the significant undersampling of the specimen obtained in histology. In this work, we present a method to reconstruct a coherent 3D volume from 2D digital radiographs of the specimen slabs. METHODS: To reconstruct a 3D breast specimen volume, we propose the use of multiple target neighboring slices, when deforming each 2D slab radiograph in the volume, rather than performing pairwise registrations. The algorithm combines neighborhood slice information with free-form deformations, which enables a flexible, nonlinear deformation to be computed subject to the constraint that a coherent 3D volume is obtained. The neighborhood information provides adequate constraints, without the need for any additional regularization terms. RESULTS: The volume reconstruction algorithm is validated on clinical mastectomy samples using a quantitative assessment of the volume reconstruction smoothness and a comparison with a whole specimen 3D image acquired for validation before slicing. Additionally, a target registration error of 5 mm (comparable to the specimen slab thickness of 4 mm) was obtained for five cases. The error was computed using manual annotations from four observers as gold standard, with interobserver variability of 3.4 mm. Finally, we illustrate how the reconstructed volumes can be used to map histology images to a 3D specimen image of the whole sample (either MRI or CT). CONCLUSIONS: Qualitative and quantitative assessment has illustrated the benefit of using our proposed methodology to reconstruct a coherent specimen volume from serial slab radiographs. To our knowledge, this is the first method that has been applied to clinical breast cases, with the goal of reconstructing a whole specimen sample. The algorithm can be used as part of the pipeline of mapping histology images to ex vivo and ultimately in vivo radiological images of the breast.

Apparatus for Histological Validation of In Vivo and Ex Vivo Magnetic Resonance Imaging of the Human Prostate.

This article describes apparatus to aid histological validation of magnetic resonance imaging studies of the human prostate. The apparatus includes a 3D-printed patient-specific mold that facilitates aligned in vivo and ex vivo imaging, in situ tissue fixation, and tissue sectioning with minimal organ deformation. The mold and a dedicated container include MRI-visible landmarks to enable consistent tissue positioning and minimize image registration complexity. The inclusion of high spatial resolution ex vivo imaging aids in registration of in vivo MRI and histopathology data.

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.

Symmetric Biomechanically Guided Prone-to-Supine Breast Image Registration.

Prone-to-supine breast image registration has potential application in the fields of surgical and radiotherapy planning, image guided interventions, and multi-modal cancer diagnosis, staging, and therapy response prediction. However, breast image registration of three dimensional images acquired in different patient positions is a challenging problem, due to large deformations induced to the soft breast tissue caused by the change in gravity loading. We present a symmetric, biomechanical simulation based registration framework which aligns the images in a central, virtually unloaded configuration. The breast tissue is modelled as a neo-Hookean material and gravity is considered as the main source of deformation in the original images. In addition to gravity, our framework successively applies image derived forces directly into the unloading simulation in place of a subsequent image registration step. This results in a biomechanically constrained deformation. Using a finite difference scheme avoids an explicit meshing step and enables simulations to be performed directly in the image space. The explicit time integration scheme allows the motion at the interface between chest and breast to be constrained along the chest wall. The feasibility and accuracy of the approach presented here was assessed by measuring the target registration error (TRE) using a numerical phantom with known ground truth deformations, nine clinical prone MRI and supine CT image pairs, one clinical prone-supine CT image pair and four prone-supine MRI image pairs. The registration reduced the mean TRE for the numerical phantom experiment from initially 19.3 to 0.9 mm and the combined mean TRE for all fourteen clinical data sets from 69.7 to 5.6 mm.

NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics.

PURPOSE: NiftySim, an open-source finite element toolkit, has been designed to allow incorporation of high-performance soft tissue simulation capabilities into biomedical applications. The toolkit provides the option of execution on fast graphics processing unit (GPU) hardware, numerous constitutive models and solid-element options, membrane and shell elements, and contact modelling facilities, in a simple to use library. METHODS: The toolkit is founded on the total Lagrangian explicit dynamics (TLEDs) algorithm, which has been shown to be efficient and accurate for simulation of soft tissues. The base code is written in C[Formula: see text], and GPU execution is achieved using the nVidia CUDA framework. In most cases, interaction with the underlying solvers can be achieved through a single Simulator class, which may be embedded directly in third-party applications such as, surgical guidance systems. Advanced capabilities such as contact modelling and nonlinear constitutive models are also provided, as are more experimental technologies like reduced order modelling. A consistent description of the underlying solution algorithm, its implementation with a focus on GPU execution, and examples of the toolkit's usage in biomedical applications are provided. RESULTS: Efficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages. CONCLUSION: The NiftySim toolkit provides high-performance soft tissue simulation capabilities using GPU technology for biomechanical simulation research applications in medical image computing, surgical simulation, and surgical guidance applications.

MRI to X-ray mammography intensity-based registration with simultaneous optimisation of pose and biomechanical transformation parameters.

Determining corresponding regions between an MRI and an X-ray mammogram is a clinically useful task that is challenging for radiologists due to the large deformation that the breast undergoes between the two image acquisitions. In this work we propose an intensity-based image registration framework, where the biomechanical transformation model parameters and the rigid-body transformation parameters are optimised simultaneously. Patient-specific biomechanical modelling of the breast derived from diagnostic, prone MRI has been previously used for this task. However, the high computational time associated with breast compression simulation using commercial packages, did not allow the optimisation of both pose and FEM parameters in the same framework. We use a fast explicit Finite Element (FE) solver that runs on a graphics card, enabling the FEM-based transformation model to be fully integrated into the optimisation scheme. The transformation model has seven degrees of freedom, which include parameters for both the initial rigid-body pose of the breast prior to mammographic compression, and those of the biomechanical model. The framework was tested on ten clinical cases and the results were compared against an affine transformation model, previously proposed for the same task. The mean registration error was 11.6±3.8mm for the CC and 11±5.4mm for the MLO view registrations, indicating that this could be a useful clinical tool.

Development of patient-specific biomechanical models for predicting large breast deformation.

Physically realistic simulations for large breast deformation are of great interest for many medical applications such as cancer diagnosis, image registration, surgical planning and image-guided surgery. To support fast, large deformation simulations of breasts in clinical settings, we proposed a patient-specific biomechanical modelling framework for breasts, based on an open-source graphics processing unit-based, explicit, dynamic, nonlinear finite element (FE) solver. A semi-automatic segmentation method for tissue classification, integrated with a fully automated FE mesh generation approach, was implemented for quick patient-specific FE model generation. To solve the difficulty in determining material parameters of soft tissues in vivo for FE simulations, a novel method for breast modelling, with a simultaneous material model parameter optimization for soft tissues in vivo, was also proposed. The optimized deformation prediction was obtained through iteratively updating material model parameters to maximize the image similarity between the FE-predicted MR image and the experimentally acquired MR image of a breast. The proposed method was validated and tested by simulating and analysing breast deformation experiments under plate compression. Its prediction accuracy was evaluated by calculating landmark displacement errors. The results showed that both the heterogeneity and the anisotropy of soft tissues were essential in predicting large breast deformations under plate compression. As a generalized method, the proposed process can be used for fast deformation analyses of soft tissues in medical image analyses and surgical simulations.

Establishing spatial correspondence for the analysis of images from highly deforming anatomy.

This invited presentation summarizes recent advances in the incorporation of knowledge of the geometry, tissue mechanical properties and imaging characteristics in establishing spatial correspondence between multiple images of highly deforming, soft tissue structures. Spatial correspondence is used to aid diagnosis and in the extraction of quantitative parameters for disease detection, monitoring disease progression and assessing therapeutic response. The work is illustrated through clinical examples of multi-modal imaging of the breast, assessment of small bowel motility and polyp detection in the large bowel.

MRI to X-ray mammography registration using a volume-preserving affine transformation.

X-ray mammography is routinely used in national screening programmes and as a clinical diagnostic tool. Magnetic Resonance Imaging (MRI) is commonly used as a complementary modality, providing functional information about the breast and a 3D image that can overcome ambiguities caused by the superimposition of fibro-glandular structures associated with X-ray imaging. Relating findings between these modalities is a challenging task however, due to the different imaging processes involved and the large deformation that the breast undergoes. In this work we present a registration method to determine spatial correspondence between pairs of MR and X-ray images of the breast, that is targeted for clinical use. We propose a generic registration framework which incorporates a volume-preserving affine transformation model and validate its performance using routinely acquired clinical data. Experiments on simulated mammograms from 8 volunteers produced a mean registration error of 3.8±1.6mm for a mean of 12 manually identified landmarks per volume. When validated using 57 lesions identified on routine clinical CC and MLO mammograms (n=113 registration tasks) from 49 subjects the median registration error was 13.1mm. When applied to the registration of an MR image to CC and MLO mammograms of a patient with a localisation clip, the mean error was 8.9mm. The results indicate that an intensity based registration algorithm, using a relatively simple transformation model, can provide radiologists with a clinically useful tool for breast cancer diagnosis.