Introduction to Medical Imaging Week 5: Introduction to Medical Imaging Week 5: MRI (part II) and Denoising (part I) Guy Gilboa Course

Signal Intensity and SNR

Image characteristics (5.20)

Characteristics (cont’) Contrast to noise ◦ Contrast is based on T1, T2, PD scans. ◦ Can be manipulated by choices of TR, TE. ◦ For small lesions, the contrast is increased by having higher spatial resolution to minimize partial volume artifacts.

MR contrast agents Positive ◦ Paramagnetic contrast agents, shorten the T1 of tissue in which they accumulate. ◦ Based on gadolinium ion (Gd). ◦ Used to detect tumors, lesions in the central nervous system (brain and spine). Negative ◦ Superprparamgnetic (iron oxides), reduce T2 relaxation time. ◦ Used in detection of liver lesions.

Examples – brain Comparison of PD, T1, T2 and angiography.

Cardiology 4 chamber view MR angiography of the chest (18 sec scan time)

Some clinical applications of MRI Used widely to scan almost every organ in the body, popular uses are: Neurological applications ◦ Can diagnose both acute and chronic neurological deseases. ◦ Method of choice for brain tumor detection. ◦ Most protocols involve administration of Gd. ◦ Many pathological conditions in the brain result in increased water content, which gives high signal intensity on T2-weighted sequences.

Clinical apps (cont’) Liver and Muscoloskeletal ◦ Can diagnose well lesions in fatty liver. Also iron overload, liver cysts, several lesions. ◦ Muscle-skeleton system. Knee scans to diagnose arthritis (joint inflammation). Cardiology ◦ To reduce motion artifacts - scans are gated according to the cardiac cycle, based on electrocardiograms (ECG). ◦ Detects myocardial infarcts, can measure left ventricular volume and ejection fraction. Good contrast between blood and myocardial wall. ◦ Diagnose coronary artery stenosis using angiography.

MRI summary

MRI vs CT – Brain image Better contrast in MRI for soft tissues, easy to distinguish between gray and white matter.

Comparison between MRI and CT CTMRI Ionizing radiationYesNo CostlowerHigher (x3?) Speed10-30 s (full scan min). Several minutes (full scan 30-60min) Data modesFewMany 3D imagesYes Resolution~7 lp/cm~3 lp/cm Work with metal in the body YesNo SNR increases asRadiation increases, or body is smaller. Primary magnet is stronger (also acquisition time)

Linear Filtering Bilateral filter Non-local means Total variation Spectral TV (briefly) Other popular methods (explained in graduate courses): ◦ Wavelet thresholding ◦ Sparse rep. K-SVD ◦ BM3D

The Denoising Problem Problem: recover g from f.

White noise – Gaussian vs. Poisson Poisson – shot noise – common noise in medical imaging. A common procedure in order to use the Gaussian model: ◦ Transforming the data using the Anscombe transform ◦ Processing ◦ Inverting the data back. Standard model in denoising – additive white Gaussian noise.

Linear Filtering

Gaussian smoothing example (sigma=2) g n f, PSNR=28.1dB u, PSNR=23.8dB f-u g-u

Problems of linear filtering Strong attenuation of high frequencies: Smoothing of edges. Degrades textures (for medical imaging, not always critical, depends on modality and application). Some filters produce oscillatory artifacts (such as Gibbs).

Nonlinear Edge Preserving Filtering Many edge preserving filters were suggested, we will review just a few very popular ones: Today: ◦ Bilateral filters ◦ Non-local means Next lecture: ◦ Total variation denoising (next lecture) ◦ Spectral TV (briefly)

Bilateral Filter Tomasi, C., and Manduchi, R., "Bilateral filtering for gray and color images.", IEEE Sixth International Conference on Computer Vision, 1998.

Denoising a Step Function outputinput reproduced from [Durand 02]

Bilateral filtering example g n f, PSNR=28.1dB u, PSNR=37.8dB f-u g-u

Nonlocal Means Can be viewed as an extension of Bilateral filtering to patches. Uses patch-based distances, instead of simple pixel distances for the weight computation (based on Efros & Leung). Much more informative, can also handle quite well textures. Buades, Antoni, Bartomeu Coll, and J-M. Morel. "A non-local algorithm for image denoising." Computer Vision and Pattern Recognition, CVPR IEEE Computer Society Conference on. Vol. 2. IEEE, 2005.

Patch difference based weights Prof. Guy Gilboa, EE, Technion –IIT, 2013

Non-local Means Filter

NLM parameters

Nonlocal means example g n f, PSNR=28.1dB u, PSNR=40.6dB f-u g-u