Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation

References 58

1. 1.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

2. 2.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

3. 3.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

4. 4.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

5. 5.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

6. 6.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

7. 7.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

8. 8.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

9. 9.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

10. 10.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

11. 11.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

12. 12.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

13. 13.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

14. 14.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

15. 15.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

16. 16.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

17. 17.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

18. 18.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

19. 19.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

20. 20.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

21. 21.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

22. 22.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

23. 23.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

24. 24.
    Deep learning for photoacoustic tomography from sparse data · 2018

    No notice
25. 25.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

26. 26.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

27. 27.
    Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography · 2018

    No notice
28. 28.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

29. 29.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

30. 30.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

31. 31.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

32. 32.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

33. 33.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

34. 34.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

35. 35.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

36. 36.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

37. 37.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

38. 38.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

39. 39.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

40. 40.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

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