The impact of signal-to-noise ratio, diffusion-weighted directions and image resolution in cardiac diffusion tensor imaging - insights from the ex-vivo rat heart

Background: Cardiac diffusion tensor imaging (DTI) is limited by scan time and signal-to-noise (SNR) restrictions. This invariably leads to a trade-off between the number of averages, diffusion-weighted directions (ND), and image resolution. Systematic evaluation of these parameters is therefore important for adoption of cardiac DTI in clinical routine where time is a key constraint. Methods: High quality reference DTI data were acquired in five ex-vivo rat hearts. We then retrospectively set 2 ≤ SNR ≤ 97, 7 ≤ ND ≤ 61, varied the voxel volume by up to 192-fold and investigated the impact on the accuracy and precision of commonly derived parameters. Results: For maximal scan efficiency, the accuracy and precision of the mean diffusivity is optimised when SNR is maximised at the expense of ND. With typical parameter settings used clinically, we estimate that fractional anisotropy may be overestimated by up to 13% with an uncertainty of ±30%, while the precision of the sheetlet angles may be as poor as ±31°. Although the helix angle has better precision of ±14°, the transmural range of helix angles may be under-estimated by up to 30° in apical and basal slices, due to partial volume and tapering myocardial geometry. Conclusions: These findings inform a baseline of understanding upon which further issues inherent to in-vivo cardiac DTI, such as motion, strain and perfusion, can be considered. Furthermore, the reported bias and reproducibility provides a context in which to assess cardiac DTI biomarkers

Validation of diffusion tensor MRI measurements of cardiac microstructure with structure tensor synchrotron radiation imaging.

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

Mapping cardiac microstructure of rabbit heart in different mechanical states by high resolution diffusion tensor imaging: A proof-of-principle study

Myocardial microstructure and its macroscopic materialisation are fundamental to the function of the heart. Despite this importance, characterisation of cellular features at the organ level remains challenging, and a unifying description of the structure of the heart is still outstanding. Here, we optimised diffusion tensor imaging data to acquire high quality data in ex vivo rabbit hearts in slack and contractured states, approximating diastolic and systolic conditions. The data were analysed with a suite of methods that focused on different aspects of the myocardium. In the slack heart, we observed a similar transmural gradient in helix angle of the primary eigenvector of up to 23.6°/mm in the left ventricle and 24.2°/mm in the right ventricle. In the contractured heart, the same transmural gradient remained largely linear, but was offset by up to +49.9° in the left ventricle. In the right ventricle, there was an increase in the transmural gradient to 31.2°/mm and an offset of up to +39.0°. The application of tractography based on each eigenvector enabled visualisation of streamlines that depict cardiomyocyte and sheetlet organisation over large distances. We observed multiple V- and N-shaped sheetlet arrangements throughout the myocardium, and insertion of sheetlets at the intersection of the left and right ventricle. This study integrates several complementary techniques to visualise and quantify the heart’s microstructure, projecting parameter representations across different length scales. This represents a step towards a more comprehensive characterisation of myocardial microstructure at the whole organ level

Filosofia e dialogo: Habermas e il ruolo della struttura dialogica nella costituzione del sapere filosofico

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

Improving the discrimination of benign and malignant breast MRI lesions using the apparent diffusion coefficient

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 hyperintense 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 DW-MRI data in a consistent fashion; i.e. either before or after the acquisition of the DCE-MRI data. © 2010 Crown Copyright

A novel method for automatic extraction of apparent diffusion coefficients in breast MRI

Diffusion weighted (DW) MRI—and in particular the apparent diffusion coefficient (ADC)—shows potential for improving the characterization and classification of enhancing breast lesions identified using dynamic contrast-enhanced (DCE) MRI. Nevertheless, to date there does not exist a well defined and objective method for computing a representative ADC value for such lesions. Typically an average ADC is computed for a manually selected region of interest (ROI) [1]. This is problematic for two reasons. Firstly the choice of ROI is subjective. Differences in ROI selection between individuals, as well as the reproducibility of selection for a given individual, can lead to variation in the mean ADC. In addition ROIs are often defined to be circular or elliptical which imposes an arbitrary geometry on the ROI [2]. Secondly, given the heterogeneity in breast lesions, an ensemble average of ADC may not provide a truly representative value. It is assumed that a representative ADC will be present in the area of neovascularisation, as indicated by rapid contrast enhancement. In order to improve the objectivity, reproducibility and efficiency of representative ADC computation, we propose an automated method based on the selection of hypo-intense areas on the ADC map corresponding to regions of greatest initial contrast enhancement identified in the DCE-MRI data. We also present an evaluation of the method using routine clinical data

Additional file 1: of The impact of signal-to-noise ratio, diffusion-weighted directions and image resolution in cardiac diffusion tensor imaging – insights from the ex-vivo rat heart

This spreadsheet contains look-up tables for Figs. 4 and 5. (XLSX 67 kb