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Biophysical models are a promising means for interpreting diffusion weighted magnetic resonance imaging (DW-MRI) data, as they can provide estimates of physiologically relevant parameters of microstructure including cell size, volume fraction, or dispersion. However, their application in cardiac microstructure mapping (CMM) has been limited. This study proposes seven new two-compartment models with combination of restricted cylinder models and a diffusion tensor to represent intra- and extracellular spaces, respectively. Three extended versions of the cylinder model are studied here: cylinder with elliptical cross section (ECS), cylinder with Gamma distributed radii (GDR), and cylinder with Bingham distributed axes (BDA). The proposed models were applied to data in two fixed mouse hearts, acquired with multiple diffusion times, q-shells and diffusion encoding directions. The cylinderGDR-pancake model provided the best performance in terms of root mean squared error (RMSE) reducing it by 25% compared to diffusion tensor imaging (DTI). The cylinderBDA-pancake model represented anatomical findings closest as it also allows for modelling dispersion. High-resolution 3D synchrotron X-ray imaging (SRI) data from the same specimen was utilized to evaluate the biophysical models. A novel tensor-based registration method is proposed to align SRI structure tensors to the MR diffusion tensors. The consistency between SRI and DW-MRI parameters demonstrates the potential of compartment models in assessing physiologically relevant parameters.

Purpose Structure-guided total variation is a recently introduced prior that allows reconstruction of images using knowledge of the location and orientation of edges in a reference image. In this work, we demonstrate the advantages of a variant of structure-guided total variation known as directional total variation (DTV), over traditional total variation (TV), in the context of compressed-sensing reconstruction and super-resolution. Methods We compared TV and DTV in retrospectively undersampled ex vivo diffusion tensor imaging and diffusion spectrum imaging data from healthy, sham, and hypertrophic rat hearts. Results In compressed sensing at an undersampling factor of 8, the RMS error of mean diffusivity and fractional anisotropy relative to the fully sampled ground truth were 44% and 20% lower in DTV compared with TV. In super-resolution, these values were 29% and 14%, respectively. Similarly, we observed improvements in helix angle, transverse angle, sheetlet elevation, and sheetlet azimuth. The RMS error of the diffusion kurtosis in the undersampled data relative to the ground truth was uniformly lower (22% on average) with DTV compared to TV. Conclusion Acquiring one fully sampled non-diffusion-weighted image and 10 diffusion-weighted images at 8× undersampling would result in an 80% net reduction in data needed. We demonstrate efficacy of the DTV algorithm over TV in reducing data sampling requirements, which can be translated into higher apparent resolution and potentially shorter scan times. This method would be equally applicable in diffusion MRI applications outside the heart.

Background Diffusion tensor imaging (DTI) is widely used to assess tissue microstructure non-invasively. Cardiac DTI enables inference of cell and sheetlet orientations, which are altered under pathological conditions. However, DTI is affected by many factors, therefore robust validation is critical. Existing histological validation is intrinsically flawed, since it requires further tissue processing leading to sample distortion, is routinely limited in field-of-view and requires reconstruction of three-dimensional volumes from two-dimensional images. In contrast, synchrotron radiation imaging (SRI) data enables imaging of the heart in 3D without further preparation following DTI. The objective of the study was to validate DTI measurements based on structure tensor analysis of SRI data. Methods One isolated, fixed rat heart was imaged ex vivo with DTI and X-ray phase contrast SRI, and reconstructed at 100 μm and 3.6 μm isotropic resolution respectively. Structure tensors were determined from the SRI data and registered to the DTI data. Results Excellent agreement in helix angles (HA) and transverse angles (TA) was observed between the DTI and structure tensor synchrotron radiation imaging (STSRI) data, where HADTI-STSRI = −1.4° ± 23.2° and TADTI-STSRI = −1.4° ± 35.0° (mean ± 1.96 standard deviation across all voxels in the left ventricle). STSRI confirmed that the primary eigenvector of the diffusion tensor corresponds with the cardiomyocyte long-axis across the whole myocardium. Conclusions We have used STSRI as a novel and high-resolution gold standard for the validation of DTI, allowing like-with-like comparison of three-dimensional tissue structures in the same intact heart free of distortion. This represents a critical step forward in independently verifying the structural basis and informing the interpretation of cardiac DTI data, thereby supporting the further development and adoption of DTI in structure-based electro-mechanical modelling and routine clinical applications. Electronic supplementary material The online version of this article (doi:10.1186/s12968-017-0342-x) contains supplementary material, which is available to authorized users.

While high-field magnetic resonance imaging promises improved image quality and faster scan time, it is affected by non-uniform flip angle distributions and unsafe specific absorption rate levels within the patient, as a result of the complicated radiofrequency (RF) field-tissue interactions. This numerical study explored the possibility of using a single mechanically rotating RF coil for RF shimming and specific absorption rate management applications at 7 T. In particular, this new approach (with three different RF coil element arrangements) was compared against both an 8-channel parallel coil array and a birdcage volume coil, with and without RF current optimisation. The evaluation was conducted using an in-house developed and validated finite-difference time-domain method in conjunction with a tissue-equivalent human head model. It was found that, without current optimisation, the rotating RF coil method produced a more uniform flip angle distribution and a lower maximum global and local specific absorption rate compared to the 8-channel parallel coil array and birdcage resonator. In addition, due to the large number of degrees of freedom in the form of rotated sensitivity profiles, the rotating RF coil approach exhibited good RF shimming and specific absorption rate management performance. This suggests that the proposed method can be useful in the development of techniques that address contemporary RF issues associated with high-field magnetic resonance imaging.

Cardiac architecture is fundamental to cardiac function and can be assessed non-invasively with diffusion tensor imaging (DTI). Here, we aimed to overcome technical challenges in ex vivo DTI in order to extract fine anatomical details and to provide novel insights in the 3D structure of the heart. An integrated set of methods was implemented in ex vivo rat hearts, including dynamic receiver gain adjustment, gradient system scaling calibration, prospective adjustment of diffusion gradients, and interleaving of diffusion-weighted and non-diffusion-weighted scans. Together, these methods enhanced SNR and spatial resolution, minimised orientation bias in diffusion-weighting, and reduced temperature variation, enabling detection of tissue structures such as cell alignment in atria, valves and vessels at an unprecedented level of detail. Improved confidence in eigenvector reproducibility enabled tracking of myolaminar structures as a basis for segmentation of functional groups of cardiomyocytes. Ex vivo DTI facilitates acquisition of high quality structural data that complements readily available in vivo cardiac functional and anatomical MRI. The improvements presented here will facilitate next generation virtual models integrating micro-structural and electro-mechanical properties of the heart.

This thesis presents a novel set of CAD (computer assisted detection/diagnosis) tools to assist the radiologist with the increasingly complex task of detecting and characterising breast lesions using magnetic resonance imaging (MRI). Commercial CAD systems presently fall short of automatically locating and classifying malignant lesions. Instead they automate many of the image processing and analysis functions that would otherwise have to be performed manually and visualise the data to aid interpretation. It is perhaps not surprising, therefore, that a recent meta-study concluded that existing breast MRI CAD does not improve the sensitivity and specificity of experienced radiologists and their interpretation remains essential. A recent review of breast MRI and MR spectroscopy concluded that what is needed are “quantitative features extracted preferably from the automatically segmented 3D lesion” and a more comprehensive assessment of lesions based on features/measurements “derived from MR multi-parametric acquisitions”. This then was the motivation for the objectives of this thesis: (i) to develop an automatic 3D lesion segmentation algorithm for multi-modal breast MRI data; (ii) to develop features that quantitatively characterise the lesion (morphology, microvasculature, and microstructure) and other breast cancer signs from this data; and (iii) to evaluate these CAD tools using clinical breast MRI data. With regard to (i) a novel fully automatic method for segmentation (i.e., detection and delineation) of suspicious tissue in breast MRI is presented and evaluated. The method is based on mean-shift clustering and graph-cuts on a region adjacency graph. To the author's knowledge it is the first fully automatic method for breast lesion detection and delineation in breast MRI. The method was tested on a total of 102 lesions from two different vendors' scanner systems. The regions of interest identified by the method were compared with the ground truth (manually delineated by an experienced radiographer) and the detection and delineation accuracies quantitatively evaluated. One hundred percent of the lesions were detected with a mean of 4.5±1.2 false positives per subject. This false-positive rate is nearly 50% better than previously reported for a fully automatic breast lesion detection system. The median Dice coefficient was 0.76 (interquartile range, 0.17), and 0.75 (interquartile range, 0.16) for the two scanner systems respectively. With regard to (ii) several new features for breast MRI CAD—derived from anatomical T1w and T2w images, DCE-MRI, and DW-MRI—are presented. They include features that characterise vascularity, blooming, and centripetal/centrifugal enhancement; and features extracted from the repartition of a lesion into mean-shift clusters. A new method for the fully automatic segmentation and measurement of the internal mammary vessels is also presented and evaluated. This was motivated by recent findings that the vascular cross-sectional area of internal mammary vessels is significantly larger on the side of breast cancer compared to the contralateral side. The method was developed on clinical MR data from 15 subjects, and tested on a further 145. The results do not indicate any significant difference in cross-sectional area between subjects with malignant lesions and those without (p > 0.05). With regard to (iii) the efficacy of the proposed features was evaluated using both manual and automatic segmentations of 74 malignant and 47 benign lesions. A superset of proposed and state-of-the-art features was computed for each lesion. Random forest classification was used to estimate classification performance—area under the receiver operating characteristic curve (AUC)—and to identify the most important features. This was done independently for mass-like lesions, non-mass-like lesions, and a combination of both. The results show that the AUCs for the two segmentation approaches are on a par; the best AUC is for mass-like lesions: 0.874±0.038 (manual) and 0.873±0.039 (automatic); the worst AUC is for non-mass-like lesions: 0.677±0.081 and 0.663±0.082 respectively; and the proportion of new features in the top 10 features ranges from 20-60%. Results also show that the combination of T2w, DCE-MRI, and DW-MRI features yields the best performance; i.e. T1w features offer little if any improvement in performance. In summary the CAD tools developed in this thesis permit fully automatic detection and delineation of suspicious lesions, the extraction of lesion features—including several new features characterising vascularity, blooming, and centripetal/centrifugal enhancement—from multi-modal MR images, and the classification of lesions as benign or malignant. The experimental results show that classification performance based on automatically segmented lesions is as good as that for manually segmented lesions. This suggests that it is indeed possible for fully automatic CAD to achieve the sensitivity/specificity of an experienced radiologist.

In this paper we evaluate the suitability of multiple instance learning (MIL) for the classification of T2 weighted magnetic resonance images (MRI) of the breast. Specifically, we compare the performance of citation-kNN against traditional kNN and a random forest (RF) classifier. We utilise both (generic) tile-based features and (domain specific) region-of-interest (ROI) based features We perform experiments on two datasets consisting of A) mass-like lesions and B) both mass-like and non-mass-like lesions. The performance of citation-kNN as both a diagnostic and screening tool is evaluated using the area under the receiver operating characteristics curve (AUC), estimated over 10-fold cross-validation. Results demonstrate that citation- kNN has equivalent performance to traditional kNN and RF. However, the tile-based approach used by citation-kNN does not require the domain specific ROI-based features typically used in breast MRI. This not only makes citation-kNN robust to inaccuracies in the delineation of suspicious lesions, but also makes it suitable for use as a screening tool, where the aim is to discriminate lesions from normal tissue.

This paper presents an investigation of the apparent diffusion coefficient (ADC) for improving the discrimination of benign and malignant lesions in breast magnetic resonance imaging (MRI). In particular a method is presented for automatically selecting hyper intense tumour voxels in dynamic contrast enhanced (DCE) MRI data and evaluating their average ADC in the corresponding diffusion-weighted (DW) MRI data. The method was applied to ten breast MRI datasets obtained from routine clinical practice. The results demonstrate that the combination of the relative signal increase (DCE-MRI) with the apparent diffusion coefficient (DW-MRI) leads to better discrimination than with either feature alone. The results also suggest that it is important to acquire the DWMRI data in a consistent fashion, i.e. either before or after the acquisition of the DCE-MRI data.