Embracing Uncertainty Flexibility: Harnessing a Supervised Tree Kernel to Empower Ensemble Modelling for 2D Echocardiography-Based Prediction of Right Ventricular Volume

The right ventricular (RV) function deterioration strongly predicts clinical outcomes in numerous circumstances. To boost the clinical deployment of ensemble regression methods that quantify RV volumes using tabular data from the widely available two-dimensional echocardiography (2DE), we propose to complement the volume predictions with uncertainty scores. To this end, we employ an instance-based method which uses the learned tree structure to identify the nearest training samples to a target instance and then uses a number of distribution types to more flexibly model the output. The probabilistic and point-prediction performances of the proposed framework are evaluated on a relatively small-scale dataset, comprising 100 end-diastolic and end-systolic RV volumes. The reference values for point performance were obtained from MRI. The results demonstrate that our flexible approach yields improved probabilistic and point performances over other state-of-the-art methods. The appropriateness of the proposed framework is showcased by providing exemplar cases. The estimated uncertainty embodies both aleatoric and epistemic types. This work aligns with trustworthy artificial intelligence since it can be used to enhance the decision-making process and reduce risks. The feature importance scores of our framework can be exploited to reduce the number of required 2DE views which could enhance the proposed pipeline's clinical application.Comment: In the Proceedings of the 16th International Conference of Machine Vision (ICMV 2023), November 15-18, Yerevan, Armeni

On the averaging of cardiac diffusion tensor MRI data: the effect of distance function selection

Diffusion tensor magnetic resonance imaging (DT-MRI) allows a unique insight into the microstructure of highly-directional tissues. The selection of the most proper distance function for the space of diffusion tensors is crucial in enhancing the clinical application of this imaging modality. Both linear and nonlinear metrics have been proposed in the literature over the years. The debate on the most appropriate DT-MRI distance function is still ongoing. In this paper, we presented a framework to compare the Euclidean, affine-invariant Riemannian and log-Euclidean metrics using actual high-resolution DT-MRI rat heart data. We employed temporal averaging at the diffusion tensor level of three consecutive and identically-acquired DT-MRI datasets from each of five rat hearts as a means to rectify the background noise-induced loss of myocyte directional regularity. This procedure is applied here for the first time in the context of tensor distance function selection. When compared with previous studies that used a different concrete application to juxtapose the various DT-MRI distance functions, this work is unique in that it combined the following: (i) Metrics were judged by quantitative - rather than qualitative – criteria, (ii) the comparison tools were non-biased, (iii) a longitudinal comparison operation was used on a same-voxel basis. The statistical analyses of the comparison showed that the three DT-MRI distance functions tend to provide equivalent results. Hence, we came to the conclusion that the tensor manifold for cardiac DT-MRI studies is a curved space of almost zero curvature. The signal to noise ratio dependence of the operations was investigated through simulations. Finally, the “swelling effect” occurrence following Euclidean averaging was found to be too unimportant to be worth consideration

Heterogeneity of fractional anisotropy and mean diffusivity measurements by in vivo diffusion tensor imaging in normal human hearts

Background: Cardiac diffusion tensor imaging (cDTI) by cardiovascular magnetic resonance has the potential to assess microstructural changes through measures of fractional anisotropy (FA) and mean diffusivity (MD). However, normal variation in regional and transmural FA and MD is not well described. Methods: Twenty normal subjects were scanned using an optimised cDTI sequence at 3T in systole. FA and MD were quantified in 3 transmural layers and 4 regional myocardial walls. Results: FA was higher in the mesocardium (0.46 ±0.04) than the endocardium (0.40 ±0.04, p≤0.001) and epicardium (0.39 ±0.04, p≤0.001). On regional analysis, the FA in the septum was greater than the lateral wall (0.44 ±0.03 vs 0.40 ±0.05 p = 0.04). There was a transmural gradient in MD increasing towards the endocardium (epicardium 0.87 ±0.07 vs endocardium 0.91 ±0.08×10-3 mm2/s, p = 0.04). With the lateral wall (0.87 ± 0.08×10-3 mm2/s) as the reference, the MD was higher in the anterior wall (0.92 ±0.08×10-3 mm2/s, p = 0.016) and septum (0.92 ±0.07×10-3 mm2/s, p = 0.028). Transmurally the signal to noise ratio (SNR) was greatest in the mesocardium (14.5 ±2.5 vs endocardium 13.1 ±2.2, p<0.001; vs epicardium 12.0 ± 2.4, p<0.001) and regionally in the septum (16.0 ±3.4 vs lateral wall 11.5 ± 1.5, p<0.001). Transmural analysis suggested a relative reduction in the rate of change in helical angle (HA) within the mesocardium. Conclusions: In vivo FA and MD measurements in normal human heart are heterogeneous, varying significantly transmurally and regionally. Contributors to this heterogeneity are many, complex and interactive, but include SNR, variations in cardiac microstructure, partial volume effects and strain. These data indicate that the potential clinical use of FA and MD would require measurement standardisation by myocardial region and layer, unless pathological changes substantially exceed the normal variation identified

Rapid automatic segmentation of abnormal tissue in late gadolinium enhancement cardiovascular magnetic resonance images for improved management of long-standing persistent atrial fibrillation

Background: Atrial fibrillation (AF) is the most common heart rhythm disorder. In order for late Gd enhancement cardiovascular magnetic resonance (LGE CMR) to ameliorate the AF management, the ready availability of the accurate enhancement segmentation is required. However, the computer-aided segmentation of enhancement in LGE CMR of AF is still an open question. Additionally, the number of centres that have reported successful application of LGE CMR to guide clinical AF strategies remains low, while the debate on LGE CMR’s diagnostic ability for AF still holds. The aim of this study is to propose a method that reliably distinguishes enhanced (abnormal) from non-enhanced (healthy) tissue within the left atrial wall of (pre-ablation and 3 months post-ablation) LGE CMR data-sets from long-standing persistent AF patients studied at our centre. Methods: Enhancement segmentation was achieved by employing thresholds benchmarked against the statistics of the whole left atrial blood-pool (LABP). The test-set cross-validation mechanism was applied to determine the input feature representation and algorithm that best predict enhancement threshold levels. Results: Global normalized intensity threshold levels T PRE = 1 1/4 and T POST = 1 5/8 were found to segment enhancement in data-sets acquired pre-ablation and at 3 months post-ablation, respectively. The segmentation results were corroborated by using visual inspection of LGE CMR brightness levels and one endocardial bipolar voltage map. The measured extent of pre-ablation fibrosis fell within the normal range for the specific arrhythmia phenotype. 3D volume renderings of segmented post-ablation enhancement emulated the expected ablation lesion patterns. By comparing our technique with other related approaches that proposed different threshold levels (although they also relied on reference regions from within the LABP) for segmenting enhancement in LGE CMR data-sets of AF patients, we illustrated that the cut-off levels employed by other centres may not be usable for clinical studies performed in our centre. Conclusions: The proposed technique has great potential for successful employment in the AF management within our centre. It provides a highly desirable validation of the LGE CMR technique for AF studies. Inter-centre differences in the CMR acquisition protocol and image analysis strategy inevitably impede the selection of a universally optimal algorithm for segmentation of enhancement in AF studies

Solving the Inverse Radon Transform For Vector Field Tomographic Data.

It is widely recognised that the most popular manner of image representation is obtained by using an energy-preserving transform, like the Fourier transform. However, since the advent of computerised tomography in the 70s, another manner of image representation has also entered the center of interest. This new type is the projection space representation, obtained via the Radon transform. Methods to invert the Radon transform have resulted in a wealth of tomographic applications in a wide variety of disciplines. Functions that are reconstructed by inverting the Radon transform are scalar functions. However, over the last few decades there has been an increasing need for similar techniques that would perform tomographic reconstruction of a vector field when having integral information. Prior work at solving the reconstruction problem of 2-D vector field tomography in the continuous domain showed that projection data alone are insufficient for determining a 2-D vector field entirely and uniquely. This thesis treats the problem in the discrete domain and proposes a direct algebraic reconstruction technique that allows one to recover both components of a 2-D vector field at specific points, finite in number and arranged in a grid, of the 2-D domain by relying only on a finite number of line-integral data. In order to solve the reconstruction problem, the method takes advantage of the redundancy in the projection data, as a form of employing regularisation. Such a regularisation helps to overcome the stability deficiencies of the examined inverse problem. The effects of noise are also examined. The potential of the introduced method is demonstrated by presenting examples of complete reconstruction of static electric fields. The most practical sensor configuration in tomographic reconstruction problems is the regular positioning along the domain boundary. However, such an arrangement does not result in uniform distribution in the Radon parameter space, which is a necessary requirement to achieve accurate reconstruction results. On the other hand, sampling the projection space uniformly imposes serious constraints of space or time. In this thesis, motivated by the Radon transform theory, we propose to employ either interpolated data obtained at virtual sensors (that correspond to uniform sampling of the projection space) or probabilistic weights with the purpose of approximating uniformity in the projection space parameters. Simulation results demonstrate that when these two solutions are employed, about 30% decrease in the reconstruction error may be achieved. The proposed methods also increase the resilience to noise. On top of these findings, the method that employs weights offers an attractive solution because it does not increase the reconstruction time, since the weight calculation can be performed off-line.This thesis also looks at the 2-D vector field reconstruction problem from the aspect of sampling. To address sampling issues, the standard parallel scanning is treated. By using sampling theory, the limits to the sampling steps of the Radon parameters, so that no integral information is lost, are derived. Experiments show that when the proposed sampling bounds are violated, the reconstruction accuracy of the 2-D vector field deteriorates over the case where the proposed sampling criteria are imposed. It is shown that the employment of a scanning geometry that satisfies the proposed sampling requirements also increases the resilience to noise

Solving the Inverse Radon Transform For Vector Field Tomographic Data.

It is widely recognised that the most popular manner of image representation is obtained by using an energy-preserving transform, like the Fourier transform. However, since the advent of computerised tomography in the 70s, another manner of image representation has also entered the center of interest. This new type is the projection space representation, obtained via the Radon transform. Methods to invert the Radon transform have resulted in a wealth of tomographic applications in a wide variety of disciplines. Functions that are reconstructed by inverting the Radon transform are scalar functions. However, over the last few decades there has been an increasing need for similar techniques that would perform tomographic reconstruction of a vector field when having integral information. Prior work at solving the reconstruction problem of 2-D vector field tomography in the continuous domain showed that projection data alone are insufficient for determining a 2-D vector field entirely and uniquely. This thesis treats the problem in the discrete domain and proposes a direct algebraic reconstruction technique that allows one to recover both components of a 2-D vector field at specific points, finite in number and arranged in a grid, of the 2-D domain by relying only on a finite number of line-integral data. In order to solve the reconstruction problem, the method takes advantage of the redundancy in the projection data, as a form of employing regularisation. Such a regularisation helps to overcome the stability deficiencies of the examined inverse problem. The effects of noise are also examined. The potential of the introduced method is demonstrated by presenting examples of complete reconstruction of static electric fields. The most practical sensor configuration in tomographic reconstruction problems is the regular positioning along the domain boundary. However, such an arrangement does not result in uniform distribution in the Radon parameter space, which is a necessary requirement to achieve accurate reconstruction results. On the other hand, sampling the projection space uniformly imposes serious constraints of space or time. In this thesis, motivated by the Radon transform theory, we propose to employ either interpolated data obtained at virtual sensors (that correspond to uniform sampling of the projection space) or probabilistic weights with the purpose of approximating uniformity in the projection space parameters. Simulation results demonstrate that when these two solutions are employed, about 30% decrease in the reconstruction error may be achieved. The proposed methods also increase the resilience to noise. On top of these findings, the method that employs weights offers an attractive solution because it does not increase the reconstruction time, since the weight calculation can be performed off-line.This thesis also looks at the 2-D vector field reconstruction problem from the aspect of sampling. To address sampling issues, the standard parallel scanning is treated. By using sampling theory, the limits to the sampling steps of the Radon parameters, so that no integral information is lost, are derived. Experiments show that when the proposed sampling bounds are violated, the reconstruction accuracy of the 2-D vector field deteriorates over the case where the proposed sampling criteria are imposed. It is shown that the employment of a scanning geometry that satisfies the proposed sampling requirements also increases the resilience to noise