Improving Chest Monitoring through Magnetic Resonance Angiogram Image Contrast Enhancement

Abstract

Magnetic resonance angiography is a medical procedure used to offer an image of the blood vessels and organs of the body. Given the worldwide spread of cardiovascular diseases, more and more resources are invested in treating them. One of the most modern treatments involves the acquisition of images of the heart. Sometimes the contrast of these images is not satisfactory. Injecting invasive enhancement substances to obtain a better view of the cardiac route is not advisable. However, software algorithms can solve the problem. This study proposes and tests a local adaptive contrast-adjustment algorithm using the dual-tree complex wavelet transform. The method has been tested with medical data from a public database to allow comparisons to other methods. The selected algorithm further improved the contrast of images. The performances are given for evaluation, both visually (to help doctors make accurate diagnoses) and in parametric form (to show engineers which parts of the algorithm might need improvement). Compared to other contrast enhancement methods, the proposed wavelet algorithm shows good results and greater stability. Thus, we aim to avoid future pointless complications due to unnecessary contrast substances.

Keywords: angiography; dual-tree discrete wavelet transform; filters.

Figure 1

Angiogram image.

Figure 2

Fixed window on the left side (as in Fourier analysis) allows a poor interpretation of the studied data. Adapted-size window on the right side (as in wavelet analysis) allows a better understanding of the analyzed data at the selectivity increasing with each frequency considered.

Figure 3

General view and data flow of the proposed wavelet algorithm.

Figure 4

The variability of the data displayed in Table 1, Table 2 and Table 3 shows greater stability for the wavelet-based CE method, with little deviations, implying that this method can be used to find the optimal way to treat all types of underexposed angiograms.

Figure 5

Original angiogram E1154S7I064. Despite the use of contrast substances (gadolinium contrast enhancement), the visibility is reduced.

Figure 6

The image was obtained by denoising the angiogram E1154S7I064 using the CLAHE method. The contrast is already improved, but the whole areas of the image are too luminous, details might escape.

Figure 7

The result of the wavelet-based CE method applied to the angiogram E1154S7I064 with visible physiological features to improve diagnosis.

Figure 8

Different results to show the contrast improvements given by different CE algorithms. Left image: local contrast of the absolute value of the DT-CWT image corresponding to the angiogram E1154S7I064. Right images: zoom in the same region of the original image (up), result of wavelet-based CE method (middle), and result of CLAHE method (bottom).

Figure 9

Original angiogram E1154S7I001 (left) and wavelet-based CE method result (right).

Figure 10

Original angiogram E1154S7I026 (left) and wavelet-based CE method result (right).

Figure 11

Original angiogram E1154S7I053 (left) and wavelet-based CE method result (right).

Figure 12

Original angiogram E1154S7I075 (left) and wavelet-based CE method result (right).