Ultrasonic Assessment of Myocardial Microstructure

The overall goal of this procedure is to provide a surrogate measure of myocardial microstructure using an ultrasound based imaging algorithm and open access image analysis software. This is accomplished by first selecting and formatting a digital echocardiographic image. The second step is to select the myocardial region of interest and a pericardial reference area.

Next, the data is processed upon application of the algorithm using the Image J software program. The final step is processing final values and producing output in the form of myocardial to pericardial ratios. Ultimately, the ultrasound signal from a selected echocardiographic region of interest is processed to yield a measure of myocardial density that provides information regarding a surrogate measure of tissue microstructure.

The main advantage of this technique over existing methods like traditional, linear or two-dimensional ultrasound based assessments of cardiac structure is that it allows echocardiographic analysis to include quantification of myocardial tissue microstructure in addition to quantification of macroscopic cardiac parameters. Furthermore, it utilizes open source software After obtaining marine or human echocardiographic B mode images in the parasternal long axis view. Use image J to open the DICOM file of the image to be analyzed and convert the file to an eight bit image file.

The inferolateral left ventricular wall should appear at the base of the frame frames must display the entirety of the left ventricular myocardium and pericardium resolution must be high enough to demarcate the pericardial border, the myocardial wall, and the endocardial border of the left ventricle. Discard any images with excess dropout or image artifacts. Scroll through consecutive frames of the cardiac cycle using the R wave of the ECG tracing to identify the frames closest to end diastole.

Then identify the single frame that best captures the maximal internal dimension of the left ventricle. Consider this single frame, the end diastolic frame. Next, select the pericardial region of interest, aiming to capture the heterogeneity of the pericardial tissue.

Do this by assessing the gray scale range and consistency of the overall pericardium and select a region of interest that is representative of these attributes. Adjust the image, brightness, and contrast for the region of interest selection as needed. Using image j's rectangle drawing tool, select a rectangle with a length approximating the middle third of the basal inferolateral pericardial wall.

Resize the rectangular region of interest to span the width of the pericardium using the ROI sizing tool. Next, use image J'S rotate tool to rotate the region of interest to lie within the pericardial region. If needed, select the manual adjustment tool to adjust the corners of the pericardial region of interest when the adjustments are complete.

The pericardial region of interest should lie within the middle third of the pericardial wall and include the width of the pericardial wall with extending into the myocardial or extra cardiac regions. Aim to capture the same relative location and percentage of total pericardial area for all measures made in a given study. To generate a histogram of intensity values, install an image J histogram macro called get histogram values.

Do text run the histogram macro to preview the distribution of signal intensity values within the selected pericardial region of interest. The macro records the signal density values for the region of interest and to signs and intensity value from zero units for the darkest to 255 units for the brightest to each pixel within the selection to produce a distribution of signal intensity. The macro reports, the 20th, 50th, and 80th percentile for the distribution.

When selecting the myocardial region of interest, also aim to capture the heterogeneity of the myocardial tissue within the middle third of the basal inferolateral myocardial wall. As before, adjust the image, brightness and contrast as needed. Select a rectangle that spans the width of the myocardial wall, excluding the endocardium and epicardium.

Do not include areas of papillary muscle within the selection area. Positioning the region of interest is highly user dependent. To ensure success, the user needs to make an estimation of the major boundaries, Rotate the myocardial region of interest such that it lies adjacent to and parallel to the pericardial selection.

Make any necessary adjustments to the corners of the myocardial region of interest, isolate a final myocardial region of interest that lies within the middle third of the myocardial wall and captures the width of the wall without extending into the pericardial or intraluminal regions as shown earlier. Use the image J histogram macro to preview the distribution of signal intensity values within the myocardial region of interest. The next step is to normalize the myocardial intensities using the previously obtained pericardial reference data begin by dividing the myocardial percentile values of intensity by the corresponding pericardial percentile.

Values of intensity report values for the normalized myocardial to pericardial values for the 20th percentile, 50th percentile, and 80th percentile values. Apply this analysis to myocardial selections through consecutive frames of the DICOM file with special attention paid to end systolic and end diastolic frames. The sonographic signal intensity varies throughout the cardiac cycle as shown here for control mice, the cyclical variability was assessed using three cut points, 20th percentile, 50th percentile, and 80th percentile.

The cyclic variability is more pronounced in mice that had a band placed around the aorta to increase outflow resistance. Note that the relative cyclical variability is higher for the 80th percentile values than for the lower cut point values. This histogram displays distributions of signal intensity derived from the myocardium of a control mouse at seven weeks after sham surgery.

The blue vertical lines denote the 20th percentile, 50th percentile and 80th percentile values. The distribution of signal intensities shows a rightward to higher intensities in an aortic banded mouse at seven weeks after surgery. Signal intensities in a normal intensive human and a hypertensive human are shown here in both mice and humans.

The distributions of signal intensity are right shifted and are larger in range for the subjects with chronic afterload stress as compared to controls shown here is a comparison between a sham operated and an aortic banded mouse and between a normotensive and hypertensive human. The myocardial to pericardial signal intensity ratio was determined using three analytic methods. The ratio of 20th percentile values the ratio of 50th percentile values and the ratio of 80th percentile values.

The greatest difference between controls and after load stress cases is demonstrated by using ratios of the 80th percentile values of signal intensity. Shown here are masson's tri chrome stain sections of the left ventricle at seven weeks post-surgery for a mouse that underwent sham surgery and a mouse that underwent aortic banding. Substantial collagen deposition and interstitial fibrosis can be seen in the ventricle of the aortic banded mouse Once mastered and if performed properly, this technique can be done in five to 10 minutes per file.