1. Observer Study of Reconstruction Strategies for Detection of Solitary Pulmonary Nodules Using Hybrid NeoTect SPECT Images Xiaoming Zheng, PhD. 20 October, 2004

2. Outlines Lung Cancer and SPECT/PET NeoTect in Lung SPECT Image Reconstructions: RBI vs FBP Hybrid Images: Clinical Reality Observer Studies: Human vs Numerical ROC: Receiver Operating Characteristics Results and Conclusions

3. Leading Causes of Cancer Deaths

4. NSCLC: Non-Small-Cell-Lung- Cancer Surgery is providing the best chance of cure if tumor can be re-sected completely. If cancer has spread to contra-lateral lymph nodes or beyond the chest surgery alone is not useful. Chemotherapy and/or radiotherapy are usually applied. These measures are rarely curative

5. SPN: Solitary Pulmonary Nodule Approx. 30% of new cases of lung cancer are found as an SPN An SPN is defined as: –single pulmonary lesion –well defined borders –mean diameter not more than 3 cm Found in 1 : 500 chest X-rays

6. SPECT and PET (With chest X- Ray) NeoTect- SPECT FDG - PET Patients11489 Sensitivity97%98% Specificity73%69% Accuracy91%89%

7. NeoTect/SPECT vs FDG/PET

8. NeoTect ( 99m Tc-Depreotide) Binds to Somatostatin receptors, which are over- expressed in lung cancer (NSCLC and SCLC) Has a negative predictive value of up to 98% in combination with CT or chest X-ray for SPN Procedure is non-invasive 99m Tc-labelled - readily available Procedure is easy Can be used wherever SPECT is available

9. Hcy-Val (N-Me)Phe-Tyr Lys D-Trp ONN H 2 NS NH O O NH NH 2 O H 2 N O NH 2 Tc O Binding region for SSTR* - a small synthetic peptide - 10 amino acids, mol. wt. 1358 Da - binding region for the somatostatin receptor - radio-labeled with 99mTc NeoTect

10. How NeoTect Works –Malignant tumors over-express somatostatin receptors (SSTRs) –NeoTect binds to and detects SSTRs –Most benign lesions do not over- express SSTRs

11. Normal Transaxial SPECT Images

12. CT 72 yr female smoker, complaining of weight loss; chest x-ray: 2.5 cm LUL lesion; CT: LUL 2.0 cm spiculated mass; Histopathology (CT guided FNA biopsy): poorly-differentiated adenocarcinoma Coronal SPECT Transaxial SPECT

13. Aims of This Work Use hybrid images of lung tumor imaging agent Tc-99m NeoTect in Localization Receiver Operating Characteristic (LROC) studies to determine reconstruction parameters and whether iterative reconstruction with attenuation, scatter, and distant resolution compensation should replace FBP clinically.

14. Why Hybrid Images The Optimization of reconstruction parameters, and determination of whether iterative reconstruction should replace FBP clinically should be based on tasks which closely approximate the clinical application of the images The use of hybrid images or studies represents a practical alternative to the use of purely clinical acquisitions for observer studies.

15. How Hybrid Images Were Created Simulated lesions are added to know normal clinical acquisitions Monte Carlo simulation package SIMIND was used to simulate lesions. Nine normal patient’s projection data were used to create 162 tumors randomly distributed within the lung regions. Tumors were 1 cm in diameter which is the smallest tumor could be detected by CT.

16. NeoTect Projections From Clinical 9 Patients

17. Tumor Source Projections From Monte Carlo Simulation

18. Images Reconstructions Iterative Reconstruction: Rescaled Block Iterative Algorithm including attenuation, scatter, and distance resolution compensation. Parameters tested: iteration 1,3,5,7,10 and post Gaussian filter FWHM 0,1,2,3,4 pixels Filtered Back-Projection: Parameters tested: Butterworth filter cut-off frequencies: 0.10, 0.15, 0.20, 0.25 and 0.30 pixel -1

19. Filtered Back-Projection

20. Butterworth Filter and Cutoff Frequency

21. FBP Reconstructed Images

22. Iterative Reconstruction

23. Rescaled Block Iterative Reconstruction Algorithm

24. Attenuation Compensation

25. Scatter Compensation

26. Resolution Compensation

27. RBI Reconstructed Images

28. Receiver Operating Characteristics

29. Images for Observers

30. Numerical Observers

31. Types of Channels

32. Human Observer Interface RBI

33. Human Observer Interface FBP

34. Numerical Observer Results: RBI

35. Numerical Observer Results: FBP

36. Human Observer Results

37. Conclusions Iterative RBI-EM including all corrections performs better than that of FBP. The best performance reconstruction strategy is RBI-EM with 5 iteration and 1 pixel FWHM in Gaussian post-filtering. Numerical observer with and without mean background subtraction set the upper and lower bounds achievable by human observer.

38. Acknowlegements This work was supported by a Charles Sturt University Special Study grant and a NIH research grant. The co-authors of this work are Prof Mike King, Dr Howard Gifford and Dr Hennie Pretorius at the University of Massachusetts Medical School.