The Assessment of left ventricular Function in MRI using the detection of myocardial borders and optical flow approaches: A Review

The evaluation of left ventricular wall motion in Magnetic Resonance Imaging (MRI) clinical practice is based on a visual assessment of cine-MRI sequences. In fact, clinical interpreters (radiologists) proceed with a global visual evaluation of multiple cine-MRI sequences acquired in the three standard views. In addition, some functional parameters are quantified following a manual or a semi-automatic contouring of the myocardial borders. Although these parameters give information about the functional state of the left ventricle, they are not able to provide the location and the extent of wall motion abnormalities, which are associated with many cardiovascular diseases. In the past years, several approaches were developed to overcome the limitations of the classical evaluation techniques of left ventricular function. The aim of this article is to present an overview of the different methods and to summarize the relevant techniques based on myocardial contour detection and optical flow for regional assessment of left ventricular abnormalities

Parametric Imaging for the Assessment of Cardiac Motion: A Review

The assessment of wall motion abnormalities such as hypokinesia or dyskinesia and the identification of their extent as well as their degree of severity allow an accurate evaluation of several ischemic heart diseases and an early diagnosis of heart failure. These dysfunctions are usually revealed by a drop of contraction indicating a regional hypokinesia or a total absence of the wall motion in case of akinesia. The discrimination between these contraction abnormalities plays also a significant role in the therapeutic decision through the differentiation between the infarcted zones, which have lost their contractile function, and the stunned areas that still retain viable myocardial tissues. The lack of a reliable method for the evaluation of wall motion abnormalities in cardiac imaging presents a major limitation for a regional assessment of the left ventricular function. In the past years, several techniques were proposed as additional tools for the local detection of wall motion deformation. Among these approaches, the parametric imaging is likely to represent a promising technique for the analysis of a local contractile function. The aim of this paper is to review the most recent techniques of parametric imaging computation developed in cardiac imaging and their potential contributions in clinical practice

Interpretation of cardiac wall motion from cine-MRI combined with parametric imaging based on the Hilbert transform

Object: The aim of this study was to test and validate the clinical impact of parametric amplitude images obtained using the Hilbert transform on the regional interpretation of cardiac wall motion abnormalities from cine-MR images by non-expert radiologists compared with expert consensus. Materials and methods: Cine-MRI short-axis images obtained in 20 patients (10 with myocardial infarction, 5 with myocarditis and 5 with normal function) were processed to compute a parametric amplitude image for each using the Hilbert transform. Two expert radiologists blindly reviewed the cine-MR images to define a gold standard for wall motion interpretation for each left ventricular sector. Two non-expert radiologists reviewed and graded the same images without and in combination with parametric images. Grades assigned to each segment in the two separate sessions were compared with the gold standard. Results: According to expert interpretation, 264/320 (82.5%) segments were classified as normal and 56/320 (17.5%) were considered abnormal. The accuracy of the non-expert radiologistsâ grades compared to the gold standard was significantly improved by adding parametric images (from 87.2 to 94.6%) together with sensitivity (from 64.29 to 84.4%) and specificity (from 92 to 96.9%), also resulting in reduced interobserver variability (from 12.8 to 5.6%). Conclusion: The use of parametric amplitude images based on the Hilbert transform in conjunction with cine-MRI was shown to be a promising technique for improvement of the detection of left ventricular wall motion abnormalities in less expert radiologists

SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review

International audienceObjective: SARS-CoV-2 is a worldwide health emergency with unrecognized clinical features. This paper aims to review the most recent medical Imaging techniques used for the diagnosis of SARS-CoV-2 and their potential contributions to attenuate the pandemic. Recent researches, including Artificial Intelligence tools, will be described. Methods We review the main clinical features of SARS-CoV-2 revealed by different medical imaging techniques. First, we present the clinical findings of each technique. Then, we describe several artificial intelligence approaches introduced for the SARS-CoV-2 diagnosis. Results CT is the most accurate diagnostic modality of SARS-CoV-2. Additionally, ground-glass opacities and consolidation are the most common signs of SARS-CoV-2 in CT images. However, other findings such as reticular pattern, and crazy paving could be observed. We also found that pleural effusion and pneumothorax features are less common in SARS-CoV-2. According to the literature, the B lines artifacts and pleural line irregularities are the common signs of SARS-CoV-2 in ultrasound images. We have also stated the different studies, focusing on artificial intelligence tools, to evaluate the SARS-CoV-2 severity. We found that most of the reported works based on deep learning focused on the detection of SARS-CoV-2 from medical images while the challenge for the radiologists is how to differentiate between SARS-CoV-2 and other viral infections with the same clinical features. Conclusion The identification of SARS-CoV-2 manifestations on medical images is a key step in radiological workflow for the diagnosis of the virus and could be useful for researchers working on computer-aided diagnosis of pulmonary infections

Data Analysis to investigate the correlation between myocardial injury and SARS-CoV-2 mortality rate

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Assessment of the relationship between regional wall motion abnormality score revealed by parametric imaging and the extent of LGE with CMR

: The aim of this study was to assess the relationship between left ventricular (LV) regional myocardial wall motion abnormality (WMA), revealed by visual interpretation of cardiac magnetic resonance (CMR) cine images together with the computed wall motion parametric image, and the transmural scar extent, as assessed by Late gadolinium Enhancement (LGE), in 40 patients. Each cine CMR short-axis loop was processed to compute a parametric image where each pixel represents the amplitude of the Hilbert transform of videointensity over time. Two expert radiologists blindly interpreted the cine CMR images in combination with the corresponding parametric image to assign a WMA score for each of the 16 myocardial sectors in which the LV myocardium was subdivided. Such score was compared per sector to the level of transmural scar extent obtained by LGE images. A total of 592 myocardial segments were analyzed. A significant decrease in regional wall motion was observed in sectors with LGE transmural hyperenhancement > 75% of tissue, as well as a correlation between parametric image amplitude and peak radial and circumferential strain, computed by feature tracking. The results showed a reduction in prediction error Lambda of WMA from LGE of 65%, and of LGE from WMA of 63%. In particular, the estimated probability of correct prediction of WMA from LGE was 76%, while that of LGE from WMA was 75%. The interpretation of myocardial viability by LGE images combined with the WMA information, derived from cine CMR and parametric images, could improve the clinical decision making process

Lack of AI-based method for pneumocystis pneumonia classification in radiological diagnosis of SARS-CoV-2

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Left ventricular MRI wall motion assessment by monogenic signal amplitude image computation

Background: Cardiac Magnetic Resonance Imaging (MRI) is the commonly used technique for the assessment of left ventricular (LV) function. Apart manually or semi-automatically contouring LV boundaries for quantification of By visual interpretation of cine images, assessment of regional wall motion is performed by visual interpretation of cine images, thus relying on an experience-dependent and subjective modality. Objective: The aim of this work is to describe a novel algorithm based on the computation of the monogenic amplitude image to be utilized in conjunction with conventional cine-MRI visualization to assess LV motion abnormalities and to validate it against gold standard expert visual interpretation. Methods: The proposed method uses a recent image processing tool called “monogenic signal” to decompose the MR images into features, which are relevant for motion estimation. Wall motion abnormalities are quantified locally by measuring the temporal variations of the monogenic signal amplitude. The new method was validated by two non-expert radiologists using a wall motion scoring without and with the computed image, and compared against the expert interpretation. The proposed approach was tested on a population of 40 patients, including 8 subjects with normal ventricular function and 32 pathological cases (20 with myocardial infarction, 9 with myocarditis, and 3 with dilated cardiomyopathy). Results: The results show that, for both radiologists, sensitivity, specificity and accuracy of cine-MRI alone were similar and around 59%, 77%, and 71%, respectively. Adding the proposed amplitude image while visualizing the cine MRI images significantly increased both sensitivity, specificity and accuracy up to 75%, 89%, and 84%, respectively. Conclusion: Accuracy of wall motion interpretation adding amplitude image to conventional visualization was proven feasible and superior to standard image interpretation on the considered population, in inexperienced observers. Adding the amplitude images as a diagnostic tool in clinical routine is likely to improve the detection of myocardial segments presenting a cardiac dysfunction

Parametric Methods for the Regional Assessment of Cardiac Wall Motion Abnormalities: Comparison Study

International audienceLeft ventricular (LV) dysfunction is mainly assessed by global contractile indices such as ejection fraction and LV Volumes in cardiac MRI. While these indices give information about the presence or not of LV alteration, they are not able to identify the location and the size of such alteration. The aim of this study is to compare the performance of three parametric imaging techniques used in cardiac MRI for the regional quantification of cardiac dysfunction. The proposed approaches were evaluated on 20 patients with myocardial infarction and 20 subjects with normal function. Three parametric images approaches: covariance analysis, parametric images based on Hilbert transform and those based on the monogenic signal were evaluated using cine-MRI frames acquired in three planes of views. The results show that parametric images generated from the monogenic signal were superior in term of sensitivity (89.69%), specificity (86.51%) and accuracy (89.06%) to those based on covariance analysis and Hilbert transform in the detection of contractile dysfunction related to myocardial infarction. Therefore, the parametric image based on the monogenic signal is likely to provide additional regional indices about LV dysfunction and it may be used in clinical practice as a tool for the analysis of the myocardial alterations