Improved strain measuring using fast strain-encoded cardiac MR

The strain encoding (SENC) technique encodes regional strain of the heart into the acquired MR images and produces two images with two different tunings so that longitudinal strain, on the short-axis view, or circumferential strain on the long-axis view, are measured. Interleaving acquisition is used to shorten the acquisition time of the two tuned images by 50%, but it suffers from errors in the strain calculations due to inter-tunings motion of the heart, which is the motion between two successive acquisitions. In this work, a method is proposed to correct for the inter-tunings motion by estimating the motion induced shift in the spatial frequency of the encoding pattern, which depends on the strain rate. Numerical data is generated to test the proposed method and real images of human subjects are used for validation The results show an improvement in strain calculations so as to relax the imaging constraints on spatial and temporal resolutions and improve image quality

Strain correction in interleaved strain-encoded (SENC) cardiac MR

The strain encoding (SENC) technique directly encodes regional strain of the heart into the acquired MR images and produces two images with two different tunings so that longitudinal strain, on the short-axis view, or circumferential strain on the long-axis view, are measured. Interleaving acquisition is used to shorten the acquisition time of the two tuned images by 50%, but it suffers from errors in the strain calculations due to inter-tunings motion of the heart. In this work, we propose a method to correct for the inter-tunings motion by estimating the motion-induced shift in the spatial frequency of the encoding pattern, which depends on the strain rate. Numerical data was generated to test the proposed method and real images of human subjects were used for validation. The proposed method corrected the measured strain values so they became nearly identical to the original ones. The results show an improvement in strain calculations so as to relax the imaging constraints on spatial and temporal resolutions and improve image quality

Different regions identification in composite strain encoded (C-SENC) images using machine learning techniques

Different heart tissue identification is important for therapeutic decision-making in patients with myocardial infarction (MI), this provides physicians with a better clinical decision-making tool. Composite Strain Encoding (C-SENC) is an MRI acquisition technique that is used to acquire cardiac tissue viability and contractility images. It combines the use of blackblood delayed-enhancement (DE) imaging to identify the infracted (dead) tissue inside the heart muscle and the ability to image myocardial deformation from the strain-encoding (SENC) imaging technique. In this work, various machine learning techniques are applied to identify the different heart tissues and the background regions in the C-SENC images. The proposed methods are tested using numerical simulations of the heart C-SENC images and real images of patients. The results show that the applied techniques are able to identify the different components of the image with a high accuracy

Automated cardiac-tissue identification in composite strain encoded (C-SENC) images using fuzzy K-means and Bayesian classifier

Composite Strain Encoding (C-SENC) is an MRI acquisition technique for simultaneous acquisition of cardiac tissue viability and contractility images. It combines the use of black-blood delayed-enhancement imaging to identify the infracted (dead) tissue inside the heart wall muscle and the ability to image myocardial deformation (MI) from the strain-encoding (SENC) imaging technique. In this work, we propose an automatic image processing technique to identify the different heart tissues. This provides physicians with a better clinical decision-making tool in patients with myocardial infarction. The technique is based on using Bayesian classifier to identify the background regions in the C-SENC images, and fuzzy clustering technique to identify the different types of the heart tissues. The proposed method is tested using numerical simulations of the heart C-SENC images with MI and real images of patients. The results show that the proposed technique is able to identify the different components of the image with a high accuracy

Strain correction in interleaved strain-encoded (SENC) cardiac MR

The strain encoding (SENC) technique directly encodes regional strain of the heart into the acquired MR images and produces two images with two different tunings so that longitudinal strain, on the short-axis view, or circumferential strain on the long-axis view, are measured. Interleaving acquisition is used to shorten the acquisition time of the two tuned images by 50%, but it suffers from errors in the strain calculations due to inter-tunings motion of the heart. In this work, we propose a method to correct for the inter-tunings motion by estimating the motion-induced shift in the spatial frequency of the encoding pattern, which depends on the strain rate. Numerical data was generated to test the proposed method and real images of human subjects were used for validation. The proposed method corrected the measured strain values so they became nearly identical to the original ones. The results show an improvement in strain calculations so as to relax the imaging constraints on spatial and temporal resolutions and improve image quality

Cardiac MRI steam images denoising using Bayesian classifier

Imaging of the heart anatomy and function using magnetic resonance imaging (MRI) is an important diagnosis tool for heart diseases. Several techniques have been developed to increase the contrast-to-noise ratio (CNR) between myocardium and background. Recently, a technique that acquires cine cardiac images with black-blood contrast has been proposed. Although the technique produces cine sequence of high contrast, it suffers from elevated noise which limits the CNR. In this paper, we study the performance and efficiency of applying a Bayes classifier to remove background noise. Real MRI data is used to test and validate the proposed method; In addition, a quantitative comparison is done between the proposed method and other thresholding-based classifications techniques

Accelerated self-gated UTE MRI of the murine heart

We introduce a new protocol to obtain radial Ultra-Short TE (UTE) MRI Cine of the beating mouse heart within reasonable measurement time. The method is based on a self-gated UTE with golden angle radial acquisition and compressed sensing reconstruction. The stochastic nature of the retrospective triggering acquisition scheme produces an under-sampled and random kt-space filling that allows for compressed sensing reconstruction, hence reducing scan time. As a standard, an intragate multislice FLASH sequence with an acquisition time of 4.5 min per slice was used to produce standard Cine movies of 4 mice hearts with 15 frames per cardiac cycle. The proposed self-gated sequence is used to produce Cine movies with short echo time. The total scan time was 11 min per slice. 6 slices were planned to cover the heart from the base to the apex. 2X, 4X and 6X under-sampled k-spaces cine movies were produced from 2, 1 and 0.7 min data acquisitions for each slice. The accelerated cine movies of the mouse hearts were successfully reconstructed with a compressed sensing algorithm. Compared to the FLASH cine images, the UTE images showed much less flow artifacts due to the short echo time. Besides, the accelerated movies had high image quality and the undersampling artifacts were effectively removed. Left ventricular functional parameters derived from the standard and the accelerated cine movies were nearly identical. Keywords: Mouse, Cardiac MRI, UTE, Compressed Sensin

Different regions identification in composite strain encoded (C-SENC) images using machine learning techniques

Different heart tissue identification is important for therapeutic decision-making in patients with myocardial infarction (MI), this provides physicians with a better clinical decision-making tool. Composite Strain Encoding (C-SENC) is an MRI acquisition technique that is used to acquire cardiac tissue viability and contractility images. It combines the use of blackblood delayed-enhancement (DE) imaging to identify the infracted (dead) tissue inside the heart muscle and the ability to image myocardial deformation from the strain-encoding (SENC) imaging technique. In this work, various machine learning techniques are applied to identify the different heart tissues and the background regions in the C-SENC images. The proposed methods are tested using numerical simulations of the heart C-SENC images and real images of patients. The results show that the applied techniques are able to identify the different components of the image with a high accuracy