SPECTにおける撮像時間短縮の研究

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2 SPECT(single photon emission computed tomography) SPECT (single photon emission computed tomography) SPECT OS-EM (ordered subsets-expectation maximization) OS-EM SNR (signal to noise ratio) SPECT 3

3 16 OS-EM SNR OS-EM OS-EM 64 3/4 48 1/2 32 OS-EM 1/2 1/2 OS-EM 3/4 3/4 OS-EM (sensitivity) (specificity) ROC(receiver operating characteristic)

4 SPECT SPECT 2 SPECT OS-EM ( ordered subsets-expectation maximization) 3 OS-EM SNR (signal to noise ratio) SNR (steak free area) OS-EM subset iteration

5 1 1-1 (radioisotope : RI) RI (in vivo) (in vitro) RI RI RI RI SPECT (single photon emission tomography) RI 1896 Becquerel 1913 Hevesy 1

6 1925 Blumgart RI RI 1931 Lawrence 1934 Joliot 1965 Segre Seaborg ( 99m Tc) RI RI X CT (computed tomography) MRI (magnetic resonance imaging) 1) Cassen GM NaI(Tl) Anger 2

7 Fig.1(a) 1962 Kuhl SPECT 3 RI 2 RI Hounsfiled X X CT SPECT 2 3 mm 1mm 3

8 2) Fig.1(b) GE (General Electric) Starcam4000i SPECT 1-3 SPECT SPECT Fig. 2 2 Fig ) 1-4 SPECT SPECT SPECT SPECT X CT MD (multi-detector row)ct 4

9 X CT 10 SPECT RI SPECT SPECT 10 SPECT 5

10 Larsson 4) (M) M ð 2 N (1) N pixel 64 N=64 M=100 M= Bieszk 5) 1 SPECT Cao 6) SPECT

11 SPECT SPECT Cao (filtered back projection: FBP) streak artifact Fig.3(a) (d) (line source) OS-EM ( ordered subsetsexpectation maximization ) Fig.3(e) (h) SPECT OS-EM OS-EM 7

12 1 2 SPECT 2-1 SPECT X CT Fig.4 RI RI Ramachandran Butterworth RI (aliasing) 8

13 20 SPECT 2-2 OS-EM (ordered subsets-expectation maximization) (pixel),, (ML-EM: maximum likelihood-expectation maximization ) RI OS-EM 9

14 ( ordered subsets- expectation maximization ) ML-EM (subset) Fig.5 24 subset 6 subset 4 1 subset 2 subset 4 subset 1 1 ML-EM subset subset (iteration) OS-EM subset iteration SPECT Fig.6 subset iteration subset iteration Fig.6 subset=8 iteration=4 OS-EM subset=8 iteration=4 10

15 OS-EM (1) (2) SNR(signal to noise ratio) (3) (4) 3) 7) (2) (3) OS-EM 3 OS-EM SPECT SPECT 11

16 3 X 0.01mm X CT 0.5mm SPECT 10mm 15mm (point spread function) (full width at half maximum : FWHM) 1/10 (full width at tenth maximum : FWTM) FWHM 1/2 FWTM 1/10 8) SPECT (line source) FWHM FWTM 1mm ( 99m Tc ) GE entegra 12

17 SPECT Fig.7(a) (d) (low energy general purpose: LEGP) (1pixel=5.40mm), SFOV(scan field of view) Fig.7(a) (d) OS-EM Fig.7(a) (b) FWHM OS-EM Fig.7(c) (d) FWTM OS-EM 64 X Y p<0.05 p< X p< X p<0.01 OS-EM OS-EM FWTM (1) SPECT 9) Joseph 10) SPECT 13

18 pixel Fig.2 d D 1 D 2 (C) C = (D 2 -D 1 )/D 1 (2) SPECT IB-10 Fig.8 99m Tc 12) Fig.9(a) Fig.9(b) D 2 D 1 (3) Fig.9 16 OS-EM OS-EM 16 14

19 SPECT SPECT (coefficient of variation : CV) CV= /m (3) m 1) 1 projection count (reconstruction count) Fig (count/pixel) (count/pixel) 32 40(count/pixel) 15

20 CV Fig.11 CV OS-EM CV OS-EM 20 25% OS-EM 16 OS-EM CV OS-EM subset 8 1 subset 2 iteration= SNR (signal to noise ratio) 3 trade-off SNR(signal to noise ratio) 16

21 SNR SNR=(D 2 -D 1 )/ (4) D 1 D 2 8) SNR 20cm 8cm 99m Tc 1/2 99m Tc SPECT SPECT 1/2 (5) SNR Fig.12 OS-EM SNR SNR OS-EM Table

22 SPECT SNR 13) n n 2 2 nc 2 =n(n-1)/2 S j S k S j t R jt R jt t R j S k R j R j R k R j -R k 18

23 R j R k = Z jk + 2 j 2 k 2r jk j k (5) R j R k S j S k Z jk R j R k p(r j>k ) j k R jt R kt r jk R jt R kt (5) p(r j R k ) Z jk 6 (Table 2) 1 100count/pixel OS-EM Butterworth 0.50cycle/cm OS-EM Butterworth 0.50cycle/cm /4 32 1/2 Fig.13 6 Fuji FM-DPL (Fuji DI-ALc) 19

24 9 14 SPECT Z jk Fig OS-EM OS-EM OS-EM /4 1/2 OS-EM 3-3 SNR SNR 3-2 SNR SNR Fig.15 SNR SNR 20

25 OS-EM SNR 48 OS-EM (streak free area) Bieszk 5) (N) N=2 RV m (6) R V m SPECT R=14(cm) Butterworth V m =0.50(cycle/cm) N 44 SPECT R=20(cm) V m =0.45(cycle/cm) N 50 21

26 1 64 (6) Fig.3 Fig (6) subtraction image Fig

27 OS-EM OS-EM

28 14) Fig OS-EM ML-EM Fig.6 64 subset 32 iteration 1 subset 64 subset 8 subset 16 4 iteration 4-4 OS-EM subset i teration OS-EM subset iteration 24

29 subset iteration subset 1 subset iteration 5 SPECT OS-EM FWHM OS-EM FWTM OS-EM OS-EM 16 OS-EM subset=8 iteration=4 OS-EM 25

30 20 25% SNR OS-EM 64 OS-EM /4 93 OS-EM /2 32 OS-EM 41 0 OS-EM 3/4 1/2 OS-EM OS-EM SPECT SPECT 6 26

31 OS-EM 64 ROC (receiver operating characteristic) OS-EM 27

32 1) (2002) 2) (2001) 3) (2001) 4) S. Larsson, A. Israelsson Consideration on system design, implementation and computer processing in SPECT, IEEE Transactions on nuclear science, , NS-29 (4), (1982) 5) J. A. Bieszk, E. G. Hawman Evaluation of SPECT Angular Sampling Effects - Continuous Versus Step-and-Shoot Acquisition-, The Journal of Nuclear Medicine, 28, , (1987). 6) Zong Jian Cao, Lawrence E. Holder and Charles C. Chen Optimal Number of Views in 360 SPECT Imaging: J Nucl Med 37: , (1996) 28

33 7) OS-EM (ordered subsets-expectation maximization) 57(5) (2001) 8) Bruce H. Hasegawa The physics of MEDICAL X-RAY IMAGING MEDICAL PHYSICS PUBLISHING Wisconsin (1991) 9) SPECT (2001) 10) Peter M. Joseph, Raymond A. Schultz View sampling requirements in fan beam computed tomography : Med. Phys. 7(6) : , (1980) 12) 13) (2002) 14) SPECT 7-28 (1990) 29

34 Table 1 (FBP) OS-EM FBP OS-EM 16 (subset=8 iteration=4 ) SNR

35 Table 2 6 Sample View A FPP 64 1 B FBP 48 3/4 C FBP 32 1/2 D OS-EM 64 1 E OS-EM 48 3/4 F OS-EM 32 1/2

36 (a) (b) Fig.1 (a) (b) SPECT

37 1 2 Fig.2 SPECT 1 d pixel 2 360

38 (a) (b) (c) (d) (e) (f) (g) (h) Fig.3 OS-EM (a) e 64 (b) (f) 48 (c) (g) 32 (d) (h) 16

39 吸収散乱のない理想的系中央部付近の計数値が大きくなりすぎる投影 RI の無いところに計数があらわれる逆投影 Fig.4 SPECT

40 S2 S1 S6 S3 S4 S5 S4 S5 S6 S3 S2 S1 S2 S3 S1 S6 S4 S5 S6 S1 S2 S3 S4 S5 初期値逐次近似 1 回 S1 S4 S6 S5 S3 逐次近似 2 回 S1 S4 S6 S5 S3 逐次近似 3 回 S1 S4 S6 S5 S3 24 投影データを 6 つの Subset に分割 Fig.5 OS-EM subset iteration

41 OS-EM OS-EM Fig.6 SPECT subset iteration 64 subset=8 iteration=4 (FBP) OS-EM subset iteration

42 (a) (b) (a) (b) (c) (d) (c) (d) Fig.7 FBP OS-EM X Y (a) X FWHM (b) Y FWHM (c) X FWTM (d) Y FWTM

43 Fig.8 SPECT IB mm 152mm 70mm

44 D1 D1 D2 D2 (a) (b) (c) Fig.9 (a) (b) (c) 5

45 RECONSTRUCTION COUNT (COUNT/PIXEL) view 48view 32view 16view PROJECTION COUNT (COUNT/PIXEL) Fig.10 (projection count) (reconstruction count)

46 25 CV (%) OS-EM FBP FBP 64VIEW FBP 48VIEW FBP 32VIEW FBP 16VIEW OS-EM 64VIEW OS-EM 48VIEW OS-EM 32VIEW OS-EM 16VIEW COUNT/PIXEL Fig.11 (FBP) OS-EM CV

47 SNR (signal to noise ratio) OS-EM Pos. OS-EM Neg. 4 OS-EM Average FBP Pos. 2 FBP Neg. FBP Average VIEW NUMBER Fig.12 SNR (positive) (negative) SNR /4 32 1/2 20cm 10cm

48 (A) (B) (C) (D) (E) (F) Fig.13 SPECT (A), ( B), ( C) (D), (E), (F ) OS-EM

49 SCALE VALUE OF PAIRED COMPARISON A B C D E F SAMPLE Fig.14 SPECT Sample A, B, C D, E, F OS-EM

50 100 A D B 80 SCALE VALUE OF PAIRED COMPARISON F E 20 C SNR (signal to noise ratio) Fig.15 Fig.12 SNR Fig.13 SNR SNR A F Table 2

51 RLATIVE COUNT (%) VIEW 32VIEW 48VIEW 64VIEW PIXEL NUMBER RLATIVE COUNT (%) VIEW 32VIEW 48VIEW 64VIEW PIXEL NUMBER Fig.16

FBP: 64view 32view 16view (a) (b) Subtracted image: 32-64

Subtracted image: 32-64 view 16-64view Fig.

52 FBP: 64view 32view 16view (a) (b) Subtracted image: view 16-64view (c) OS-EM: 64view 32view 16view (d) Subtracted image: view 16-64view Fig.17 (a) 64 (a) 32 (a) 16 (b) (b) (c) 64 OS-EM (c) 32 (c) 16 (d) (d) 16 64

53 FBP OS-EM 64view 48view 32view 16view 4view Fig.18 4 ML-EM

スライド 1

スライド 1 OSEM ( Ordered Subsets Expectation Maximization ) OSEM フォルダ内の OSEM.exe を起動する Contribution Ratio ボタンを押すと逐次近似法における確率分布の計算を行う ( やや時間がかかる場合がある ) Contribution Ratio による処理を行い画像が表示された後 Read Sino ボタンを押し脳 PET