1. Tissue Classification Based on Spin Relaxation Environments Jordan Guinn*, Joseph Hornak, Willem Windig Center For Imaging Science Rochester Institute of Technology Rochester, New York

2. Goal of the Project Discussion of How the classification method DECRA algorithm works Examples of DECRA with synthetic images Examples of DECRA with actual magnetic resonance images Discussion Conclusion Overview

3. Radiologist MRI Neuro Physiologist EMG Physician Physician Diagnosis Chemist NeurologistPathologist Cardiologist EEG ECG CT US UV TLC HPLC ISE FFM

4. Radiologist MRI Neuro Physiologist EMG Physician Physician Diagnosis Chemist NeurologistPathologist Cardiologist EEG ECG US UV TLC HPLC ISE FFM

5. Classification

6. Every tissue has a unique T 2 and p value

7. DECRA DECRA tries to resolve individual T 2 and p times T 2,1 p 1 T 2,2 p 2 T 2,3 p 3 Multi-exponential curve

8. *,,……………, *, Exponential curve 1 Exponential curve 2 Exponential curve 3 *,,……………, Creation of Synthetic Images

9. Experiment continued.. This was then run through DECRA to give the following results

10. Experiment continued.. Random noise was added and run through DECRA.

11. 0 10000 20000 30000 40000 50000 60000 70000 0.020000.040000.060000.080000.0 Number of pixels of component 2 Zero One Two Number of pixels of component 1 Regions where DECRA has the ability to Resolve

12. T 2 series of test Objects Hardboiled egg Normal egg gelatin ice Milk (2%) 10 20 30 40 50 60 70 80 90 100 times in ms

13. T 2,1 T 2,2 After the images where run through DECRA

14. T 2 series of only the hardboiled egg Hardboiled egg 10 20 30 40 50 60 70 80 90 100 times in ms

15. Resolved Images

16. T 2 series of eggs only Hardboiled eggRaw egg 10 20 30 40 50 60 70 80 90 100

17. Resolved Images

18. DECRA can resolve components DECRA cannot resolve all the components due to noise in the image As a new concept it has much promise for future application One possible approach would be to weight the images for a particular exponential decay value to specifically resolve that value Discussion

19. DECRA has the ability to segment noiseless systems The addition of small amounts of noise causes DECRA to fail When similar components are present in the images, the signal is greater than the noise resulting in more resolved components Conclusions