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The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review of knee (anterior cruciate ligament, meniscus, and cartilage) injury detection papers using deep learning. The systematic review was carried out following the PRISMA guidelines on several databases, including PubMed, Cochrane Library, EMBASE, and Google Scholar. Appropriate metrics were chosen to interpret the results. The prediction accuracy of the deep-learning models for the identification of knee injuries ranged from 72.5–100%. Deep learning has the potential to act at par with human-level performance in decision-making tasks related to the MRI-based diagnosis of knee injuries. The limitations of the present deep-learning approaches include data imbalance, model generalizability across different centers, verification bias, lack of related classification studies with more than two classes, and ground-truth subjectivity. There are several possible avenues of further exploration of deep learning for improving MRI-based knee injury diagnosis. Explainability and lightweightness of the deployed deep-learning systems are expected to become crucial enablers for their widespread use in clinical practice.

Background: Adults with repaired tetralogy of Fallot die prematurely from ventricular tachycardia (VT) and sudden cardiac death. Inducible VT predicts mortality. Ventricular scar, the key substrate for VT, can be noninvasively defined with late gadolinium enhancement (LGE) cardiovascular magnetic resonance but whether this relates to inducible VT is unknown. Methods: Sixty-nine consecutive repaired tetralogy of Fallot patients (43 male, mean 40±15 years) clinically scheduled for invasive programmed VT-stimulation were prospectively recruited for prior 3-dimensional LGE cardiovascular magnetic resonance. Ventricular LGE was segmented and merged with reconstructed cardiac chambers and LGE volume measured. Results: VT was induced in 22 (31%) patients. Univariable predictors of inducible VT included increased RV LGE (odds ratio [OR], 1.15; P =0.001 per cm 3 ), increased nonapical vent LV LGE (OR, 1.09; P =0.008 per cm 3 ), older age (OR, 1.6; P =0.01 per decile), QRS duration ≥180 ms (OR, 3.5; P =0.02), history of nonsustained VT (OR, 3.5; P =0.02), and previous clinical sustained VT (OR, 12.8; P =0.003); only prior sustained VT (OR, 8.02; P =0.02) remained independent in bivariable analyses after controlling for RV LGE volume (OR, 1.14; P =0.003). An RV LGE volume of 25 cm 3 had 72% sensitivity and 81% specificity for predicting inducible VT (area under the curve, 0.81; P 10 cm 3 was 100% sensitive and >36 cm 3 was 100% specific for predicting inducible VT. Conclusions: Three-dimensional LGE cardiovascular magnetic resonance-defined scar burden is independently associated with inducible VT and may help refine patient selection for programmed VT-stimulation when applied to an at least intermediate clinical risk cohort.

BACKGROUND Cardiomyocytes are organized in microstructures termed sheetlets that reorientate during left ventricular thickening. Diffusion tensor cardiac magnetic resonance (DT-CMR) may enable noninvasive interrogation of in vivo cardiac microstructural dynamics. Dilated cardiomyopathy (DCM) is a condition of abnormal myocardium with unknown sheetlet function. OBJECTIVES This study sought to validate in vivo DT-CMR measures of cardiac microstructure against histology, characterize microstructural dynamics during left ventricular wall thickening, and apply the technique in hypertrophic cardiomyopathy (HCM) and DCM. METHODS In vivo DT-CMR was acquired throughout the cardiac cycle in healthy swine, followed by in situ and ex vivo DT-CMR, then validated against histology. In vivo DT-CMR was performed in 19 control subjects, 19 DCM, and 13 HCM patients. RESULTS In swine, a DT-CMR index of sheetlet reorientation (E2A) changed substantially (E2A mobility ~46°). E2A changes correlated with wall thickness changes (in vivo r2 = 0.75; in situ r2 = 0.89), were consistently observed under all experimental conditions, and accorded closely with histological analyses in both relaxed and contracted states. The potential contribution of cyclical strain effects to in vivo E2A was ~17%. In healthy human control subjects, E2A increased from diastole (18°) to systole (65°; p < 0.001; E2A mobility = 45°). HCM patients showed significantly greater E2A in diastole than control subjects did (48 ; p < 0.001) with impaired E2A mobility (23°; p < 0.001). In DCM, E2A was similar to control subjects in diastole, but systolic values were markedly lower (40° ; p < 0.001) with impaired E2A mobility (20°; p < 0.001). CONCLUSIONS Myocardial microstructure dynamics can be characterized by in vivo DT-CMR. Sheetlet function was abnormal in DCM with altered systolic conformation and reduced mobility, contrasting with HCM, which showed reduced mobility with altered diastolic conformation. These novel insights significantly improve understanding of contractile dysfunction at a level of noninvasive interrogation not previously available in humans. (J Am Coll Cardiol 2017;69:661–76) Published by Elsevier on behalf of the American College of Cardiology Foundation.

The long-standing high blood pressure (also known as hypertension) overworks the heart. Microstructural remodeling is a key factor of hypertensive heart disease progression. Diffusion tensor magnetic resonance imaging (DT-MRI) is a powerful tool for the rapid noninvasive nondestructive delineation of the cardiomyocyte organization. The spontaneously hypertensive rat (SHR) is a well-established model of genetic hypertension. The goal of this study was to employ high-resolution DT-MRI and the SHR animal model to assess the transmural layer-specific remodeling of myocardial microstructure associated with hypertension. Ex vivo experiments were performed on excised formalin-fixed hearts of aged SHRs (n = 4) and age-matched controls (n = 4). The DT-MRI-derived fractional anisotropy (FA), longitudinal diffusivity (λ L ), transversal diffusivity (λ T ), and mean diffusivity (MD) served as the readout parameters investigated at three transmural zones (i.e., endocardium, mesocardium, and epicardium). The helix angles (HAs) of the aggregated cardiomyocytes and the orientation of laminar sheetlets were also studied. Compared with controls, the SHRs exhibited decreased epicardial FA, while FA changes in the other two transmural regions were insignificant. No substantial differences were observed in the diffusivity parameters and the transmural course of HAs between the two groups. A consistent distribution pattern of laminar sheetlet orientation was not identified for either group. Our findings are in line with the known cellular microstructure from early painstaking histological studies. Biophysical explanations of the study outcomes are provided. In conclusion, our experimental findings indicate that the epicardial microstructure is more vulnerable to high blood pressure leading to more pronounced changes in this region during remodeling. DT-MRI is well-suited for elucidating these alterations. The revealed transmural nonuniformity of myocardial reorganization may shed light on the mechanisms of the microstructure-function relationship in hypertension progression. Our results provide insights into the management of patients with systemic arterial hypertension, thus prevent the progression toward heart failure. The findings of this study should be acknowledged by electromechanical models of the heart that simulate the specific cardiac pathology.

Previous ex vivo diffusion tensor imaging (DTI) studies on formalin-fixed myocardial tissue assumed that, after some initial changes in the first 48 hr since the start of fixation, DTI parameters remain stable over time. Prolonged preservation of cardiac tissue in formalin prior to imaging has been seen many times in the DTI literature as it is considered orderly. Our objective is to define the effects of the prolonged cardiac tissue exposure to formalin on tissue microanatomical organization, as this is assessed by DTI parameters. DTI experiments were conducted on eight excised rodent hearts that were fixed by immersion in formalin. The samples were randomly divided into two equinumerous groups corresponding to shorter (∼2 weeks) and more prolonged (∼6-8 weeks) durations of tissue exposure to formalin prior to imaging. We found that when the duration of cardiac tissue exposure to formalin before imaging increased, water diffusion became less restricted, helix angle (HA) histograms flattened out and exhibited heavier tails (even though the classic HA transmural variation was preserved), and a significant loss of inter-voxel primary diffusion orientation integrity was introduced. The prolonged preservation of cardiac tissue in formalin profoundly affected its microstructural organization, as this was assessed by DTI parameters. The accurate interpretation of diffusivity profiles necessitates awareness of the pitfalls of prolonged cardiac tissue exposure duration to formalin. The acquired knowledge works to the advantage of a proper experimental design of DTI studies of fixed hearts. Anat Rec, 299:878-887, 2016. © 2016 Wiley Periodicals, Inc.

Systemic hypertension is a causative factor in left ventricular hypertrophy (LVH). This study is motivated by the potential to reverse or manage the dysfunction associated with structural remodeling of the myocardium in this pathology. Using diffusion tensor magnetic resonance imaging, we present an analysis of myocardial fiber and laminar sheet orientation in ex vivo hypertrophic (6 SHR) and normal (5 WKY) rat hearts using the covariance of the diffusion tensor. First, an atlas of normal cardiac microstructure was formed using the WKY b0 images. Then, the SHR and WKY b0 hearts were registered to the atlas. The acquired deformation fields were applied to the SHR and WKY heart tensor fields followed by the preservation of principal direction (PPD) reorientation strategy. A mean tensor field was then formed from the registered WKY tensor images. Calculating the covariance of the registered tensor images about this mean for each heart, the hypertrophic myocardium exhibited significantly increased myocardial fiber derangement (p=0.017p=0.017) with a mean dispersion of 38.7 deg, and an increased dispersion of the laminar sheet normal (p=0.030p=0.030) of 54.8 deg compared with 34.8 deg and 51.8 deg, respectively, in the normal hearts. Results demonstrate significantly altered myocardial fiber and laminar sheet structure in rats with hypertensive LVH.

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.

Cardiac magnetic resonance (CMR) is regarded as the reference examination for cardiac morphology in tetralogy of Fallot (ToF) patients allowing images of high spatial resolution and high contrast. The detailed knowledge of the right ventricular anatomy is critical in ToF management. The segmentation of the right ventricle (RV) in CMR images from ToF patients is a challenging task due to the high shape and image quality variability. In this paper we propose a fully automatic deep learning-based framework to segment the RV from CMR anatomical images of the whole heart. We adopt a 3D multi-scale deep convolutional neural network to identify pixels that belong to the RV. Our robust segmentation framework was tested on 26 ToF patients achieving a Dice similarity coefficient of 0.8281±0.1010 with reference to manual annotations performed by expert cardiologists. The proposed technique is also computationally efficient, which may further facilitate its adoption in the clinical routine.

The tomographic mapping of a 2-D vector field from line-integral data in the discrete domain requires the uniform sampling of the continuous Radon domain parameter space. In this paper we use sampling theory and derive limits for the sampling steps of the Radon parameters, so that no information is lost. It is shown that if Δx is the sampling interval of the reconstruction region and x max? is the maximum value of domain parameter x, the steps one should use to sample Radon parameters ? and ? should be: $\Delta\rho\leq\Delta x/\sqrt{2}$ and $\Delta\theta\leq\Delta x/((\sqrt{2}+2)|x_{\max}|)$ . Experiments show that when the proposed sampling bounds are violated, the reconstruction accuracy of the vector field deteriorates. We further demonstrate that the employment of a scanning geometry that satisfies the proposed sampling requirements also increases the resilience to noise.

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. 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. 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. 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.

Background In recent years, diffusion tensor imaging (DTI) has emerged as a powerful noninvasive tool that infers the cardiac tissue organization through measuring water molecule motility.[1] Previous DTI studies [2] on normal hearts largely assumed that the measured diffusion magnitude along the 3 preferred directions and the measured degree of diffusion anisotropy remain the same over the endo-, meso-, and epi-cardial layers of the left ventricular (LV) wall. Our goal is to test the validity of this assumption.

Myocardial disarray is a fiber distribution that deviates away from the tightly organized, parallel alignment of myocardial fibers that characterizes the normal myocardium. This coherently-organized distribution of the myofibers results in the twisting contraction of the normal left ventricle (LV). With myofiber disarray, the fibers have random directionality, either locally or globally, within the LV.

International audience; Left ventricular hypertrophy induced by systemic hypertension is generally regarded a morphological precursor of unfortunate cardiovascular events. Myocardial fiber disarray has been long recognized as a prevalent hallmark of this pathology. In this chapter, ex vivo diffusion tensor magnetic resonance imaging is employed to delineate the regional loss of myocardial organization that is present in the excised heart of a spontaneously hypertensive rat, as opposed to a control. Fiber tracking results are provided that illustrate in great detail the alterations in the integrity of cardiac muscle microstructure due to the disease. A quantitative analysis is also performed. Another contribution of this chapter is the model-based assessment of the role of the myofiber disarray in modulating the mechanical properties of the myocardium. The results of this study improve our understanding of the structural remodeling mechanisms that are associated with hypetensive left ventricular hypertrophy and their role.

We consider the application of tomography to the reconstruction of two-dimensional vector fields. The most practical sensor configuration in such problems is the regular positioning along the boundary of the reconstruction domain. However, such a configuration 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 on space or time. In this paper, we propose to place the sensors regularly along the boundary of the reconstruction domain and employ probabilistic weights with the purpose of compensating for the lack of uniformity in the distribution of projection space parameters. Simulation results demonstrate that, when the proposed probabilistic weights are employed, an average 27% decrease in the reconstruction error may be achieved, over the case that projection measurements are not weighed (e.g., in one case the error reduces from 3.7% to 2.6%). When compared with the case where actual uniform sampling of the projection space is employed, the proposed method achieves a 90 times reduction in the number of the required sensors or 180 times reduction in the total scanning time, with only 7% increase in the error with which the vector field is estimated.

Dynamic cardiac single photon emission computed tomography (SPECT) offers an effective way for observing fundamental physiological functions of organs and could aid in the early diagnosis of cardiovascular disease, in particular, for those patients with minimal disease. This would improve the chances of recovery by initiating appropriate therapy and an altered life style. To make dynamic cardiac SPECT viable with present clinical scanners methods need to be developed that reconstruct time activity curves from dynamically moving organs representing the change of tracer concentration as a function of time from projection data acquired from slowly rotating gamma cameras. This type of data analysis faces the challenge of modeling both rigid and non-rigid body deformation as well as modeling of a time varying tracer concentration. In the work presented here, we develop methods for segmenting the beating heart using an approach based upon level sets, which can deal naturally with topological changes. A variational formulation of the level set method was implemented. This allowed the inclusion of a priori information and was computationally efficient. The algorithm was first evaluated with simulated dynamic cardiac image data. The MCAT phantom was used to generate data containing 32 time frames over one cardiac cycle. Each frame had a matrix size of 64×64×32 voxels with a resolution of 6.25 mm. Starting with an initial estimate of the boundary, the algorithm then converged to an accurate segmentation of the deforming heart. The initial estimate was not important and we could segment simultaneously both interior and exterior boundaries. This algorithm forms the foundation for the segmentation of the boundary of the deforming myocardium directly from projection data.

This chapter reviews the principles of conductivity imaging with an emphasis on clinical applications. We also present the methodology used to create tomographic images from the obtained measurements, showing that the backprojection method and the inverse generalized Radon transform method are effectively the same. The limitations and advantages of this type of imaging are discussed in addition to the various application areas beyond medical imaging.

This paper describes a method that allows one to recover both components of a 2-D vector field based on boundary information only, by solving a system of linear equations. The analysis is carried out in the digital domain and takes advantage of the redundancy in the boundary data, since these may be viewed as weighted sums of the local vector field's Cartesian components. Furthermore, a sampling of lines is used in order to combine the available measurements along continuous tracing lines with the digitised 2-D space where the solution is sought. A significant enhancement in the performance of the proposed algorithm is achieved by using, apart from real data, also boundary data obtained at virtual sensors. The potential of the proposed method is demonstrated by presenting an example of vector field reconstruction.

Background Diffusion tensor imaging (DTI) has been shown [1] to be extremely promising for characterizing the hierarchical microstructure of myocardium. DTI studies are hampered by lengthy acquisition times, needed for high spatial resolution and/or improved SNR. Compressed sensing (CS) algorithms recover data from under-sampled acquisitions, and have been used [2] to reduce scan time in MRI. The fast composite splitting algorithm (FCSA) [3] impressively outperforms other classical CS reconstruction methods by providing more accurate results in less CPU time. In this study, we investigate the feasibility of applying FCSA CS to DTI of an excised human heart. To our knowledge, this is the first time CS reconstruction has been applied to DTI of a human heart.

We consider the application of tomography to the reconstruction of 2-D vector fields. The most convenient sensor configuration in such problems is the regular positioning along the domain boundary. However, the most accurate reconstructions are obtained by sampling uniformly the Radon parameter domain rather than the border of the reconstruction domain. This dictates a prohibitively large number of sensors and impractical sensor positioning. In this paper, we propose uniform placement of the sensors along the boundary of the reconstruction domain and interpolation of the measurements for the positions that correspond to uniform sampling in the Radon domain. We demonstrate that when the cubic spline interpolation method is used, a 60 times reduction in the number of sensors may be achieved with only about 10% increase in the error with which the vector field is estimated. The reconstruction error by using the same sensors and ignoring the necessity of uniform sampling in the Radon domain is in fact higher by about 30%. The effects of noise are also examined.

Background: In vivo cardiac diffusion tensor imaging (cDTI) is uniquely capable of interrogating laminar myocardial dynamics non-invasively. A comprehensive dataset of quantative parameters and comparison with subject anthropometrics is required.\ud \ud \ud \ud Methods: cDTI was performed at 3T with a diffusion weighted STEAM sequence. Data was acquired from the mid left ventricle in 43 subjects during the systolic and diastolic pauses. Global and regional values were determined for fractional anisotropy (FA), mean diffusivity (MD), helix angle gradient (HAg, degrees/%depth) and the secondary eigenvector angulation (E2A). Regression analysis was performed between global values and subject anthropometrics.\ud \ud \ud \ud Results: All cDTI parameters displayed regional heterogeneity. The RR interval had a significant, but clinically small effect on systolic values for FA, HAg and E2A. Male sex and increasing left ventricular end diastolic volume were associated with increased systolic HAg. Diastolic HAg and systolic E2A were both directly related to left ventricular mass and body surface area. There was an inverse relationship between E2A mobility and both age and ejection fraction.\ud \ud \ud \ud Conclusions: Future interpretations of quantitative cDTI data should take into account anthropometric variations observed with patient age, body surface area and left ventricular measurements. Further work determining the impact of technical factors such as strain and SNR is required.