SPECTにおける撮像時間短縮の研究
Contents
1. Pe 00000000000 000000000000000000000000 0000 00000000000 0 000 0 SPECT 00000000000000000000000 00000000000000000000000 00000000000000000000000 coefficient of variation CV HHHHHH 0 3 HHHHHHHHHHHHHHHprojection J reconstruction 219 10 0000000000000 0 l 0 000000000001000000000000 0 00000000000000000000000 50 rcount pixel U 0000 64HHHHHHHHHHHHHHH 100 count pixel H R a IH B iH H H OOO 40 00000000000000 0000000000000000000000000 0000000000000000000000000 L 15 70 219 11 000000 0000000000000000 CVHHHHHHHHH 0 000000000000000000000000000 OOO OS EMHH2. OD mm poopy HHHHHHHHHHHHHHHHHHHHHHHHH 200 Fig 1 b H 0000000000000 GE General Electric Starcam4000i 0 0 0 0 SPECT 0000000000000 1 3 5 SPECT HHHHHFig9 2000000000000000 0 0000000000000000000000000000 000000 20000000000000000 219 200 000000 10000000 0 00000000000000000000000000 0090000000000000000000000000 000000000000000000000000 360 00000000000000000000000000 0 000000000000000000000000000 000000000000000000000000000 20 1 4 5 SPECTHHHHHHHHHHHHH 00000 SPECTHHHHHHH 5 0000000000000 MD multi detector rowCT HHHHHHHHHHHHHHHHHHHHHHHH HHHHHHHHHHSPECT HHHHHHHHHHHHHH HHHHHHHHHHHHHHHHHHHHHHHHHHHL HHHHHHHH 20000 300000000000000 0 0000000000000000000000 000 000000000000000000000000000
3. 5 00000000000 34000000000 HHHHH172HHHHHHHHHHHH 05 Ul 5 HHHHHHHHHHHHHH 0000000000000000000000 5 A 0 0000000000000000000000 SPECT 000 HHHHHHHHHHHH 1 E ps wm OO 060 26 0 000000000000000000000000000 64 00000000000000000000000 0 000000000000000000000000000 0 000000000000000000000000000 CET 0 00000000000000000 0000 0000 000000000000000000000000000 0000000000000 0000 0100000080 gt 60 000000000000 ROC receiver operating OS EM HHHHHHHHHHHHHHHHHHHHHHH HHHHHHHHHHHHHHHHHHHHHHHHHHHH UU 0000000000000000000000000 00000000000000000000000000 0 0000000000
4. OS EMHHHH OOO 200 25 10000000000000000000 5 HHHHHHHHHHHHHHHHHH HHHHHHHHHHHHHHHH 160 5 0000 04000000000 subsetQ O00 80000100 subset 20 EB 4 000000 0 000000000000000000000 3 1 2 SNR signal to noise ratio 10 trade off SNR signal to noise 16 SNR HHHHHHHHHHHHHHHHHHHHHHHHH SNR D D o 000000000 4 00 00000060 000000000000000 000000000 20 SNR HHHHHHHHHHHHHHHHHHHH 000 20cm HHHHHHHHHHHHH 8cmHHH wg HHHHHHHHHUIHHHH 1 2 00000001 00000000000000000000000 0000000000000 SPECT 000000000 0 000000000000000 000000 0 000000000000000000000000 1 200000000000000000 5510000000 SNR 0000000000 219 120 0 00000000000005 0000000000 0000000000000 SNR 0000000000
5. 5 subset iterationHHHHHHHHHHHHHHH HHHHHHHHHHHHH D B amp 8 AAM u AAM u c d Fig 7 000000000000000 0000 OS EM 0000 X000 0000000000 000 FWHMO X000 FWTM J dY 000 FWTM 0000 HH 000 000 19 8 5 18 10900 00000 204 0 152mm 70 0 000 BRAIN INNC 3DF 2002 05 17 17 58 99 0 PROFILEZ BR D2 boning 28 58 Location pixels 14 CONTRAST m CO P OS N 0 16 32 48 64 80 VIEWNUMBER Fig9 000000000000 0 00000000000 0000000 500000000000 RECONSTRUCTION COUNT COUNT PIXEL 8 40 48view 20 E 1OMew 0 50 10 10 20 20 30 400 COUNT PIXEL 219 10 000000000 projection count O0 0000000 reconstruction court 64VIEW 48VIEW FBP 32VIEW FBP 16VIEW OS 64VIEW OS EM 48VIEW OS 32VIEW o OS EM 16VIEW 50 100 150 200 COUNT PIXEL Fig 11 000000000 FBP 00000000000 CVHHHH SNR signal to noise ratio 18 16 14 12 10 OS Pos OS OS Average FBP Pos FBP
6. ks Sapa 0 000000000000000000000000000 ET EL 850 1 4 000000 5 0 0000000000000 0 6080 1000000000000000000000000000 000000000000000000000000000 000000000000000000000000000 000000000000000 5 000000000000000000 0 000000000000000000000000000 OOo E 5 LR mi a EJ HHHHHHHHHHHHHHHHHHHLarssonH M lt N 000000000 1 00 00000000000000 pixel 000000 0 00000000000000 64 0000 64 0 100 00000000000000000000 600 0 6400000000000 00 00 100 0 000000000000000000000000 0 000000000000000000000000 0 000000000000000 52 HHHHHHHHHHHHHHHHHH B tP E H t 0 000000000000000000000000 0 0000000000000000 0000000000000 SPECT HHHHHHHH 0 00000000000000000000000 0 0000000000000 SPECT 180 HHHHHHHHHHHHHHHHHH 6400 32000000000000000000 0 000000000000000000000000 64000 33000000 180
7. 160 000000 subset 8 iteration 0 000 SNR IIHHHHHHHHHHH Table2 0000600000 Sample View 0000000 64 1 48 3 4 32 1 2 5 64 1 5 48 3 4 5 32 1 2 0000000 00000 91 0 0000000000000000 0 000 SPECTHHHHHHHHHHHHHHHHHHH 000 19 2 HHHHHHHHHHHHHHYHHHHH 10000001 HHHHHHHHHHHHHHHHHHHHHHHAdHHI HHHHHHHHpixeHHHHHHH 0 090000000000002000000000001 HHHHHHHHHHHHHHHHHHHHHHH 300000 000000000 Fig3HHHHHHHHHHHHHHHHHHHHHHHHHH 0000000 90 5 1000000000000 lI OI 64 000 90 48000 90 32000 ME 1600 0000 Fig4 000000000000 SPECTHHHHHH 00000 53 aE 54 ZAA E 55 51 54 gt 56 55 53 56 2 gt S 1 54 565553 51 3 gt S 1 54 gt 56 55 53 52 56 24 6 Subset Fig 5 OS EM 0 0 O O subset iteration 0 iterations Fig 6 subset iteration HHHHHHHHHHHHHHHH 6400000000 subset 8 iteration
8. SNR 000000000 OS EM 000000000000000000 0 000000000000000000000 Table 10 000 ES T ET EE 3 2 0000000000000000000 17 SPECT SNR 00000000000 0000000000000000000000000 0 00000000000000000000000000 0000000000000000000000000 0 0000000000000000000000000 0000000000000000 10000000 200000000000000 00000000000 20000000000000 0 0000000000000000000000000 EP ti nu AO 1 2 0000000000000000000000 a 00000000000000000000000000 0 0000000000000000000 0 5 000 00000 50000000000 0000000 000 0000000000000000000 BHH H TH B THB 0 og HHHHHHHHHHHSkHHHHHHHHHHHRHH RIRkHHHHHHHHHHHHHHHHHHHHHH HHHHHHHRj RkHHHHHHHHH O 18 R 7 10 00000000 5 00000000 RERA TO 50 SIHHHHHHHHHHHHHHHHH 20 00000 0000 00 000 0 0 000000000 00000000 5 HHHHHHHHHHHHHHHH
9. 2 SNR signal to noise ratio 3 h Rutpa 000000000000000000000000000 Ban g BH 05 000000000000000000000000000 030 00000000000 05 3 100000 3 1 1 0000000000000000 HHHHHHHHHHHHHHHSPECT 00000000 0 000000000000000000000000000 0 000000000000000000000000000 0 000000000000000000000000000 a a 0 00000000000000000000000 5 000000 3000000000000000000 0000000000 0000000 0 01mml 0 5 0 00000 10mm 15 00000000000000000000000 00000000000000000000000000 point spread functiong 0000000000 000000000000000000000000 0000000000000000000000000 0000000000000000000000000 0000000000000000000000000 full width at half maximum 1 10 full width at tenth maximum WTM J J FWHM 1 2 00 0017000000000000
10. 000000000000000000 000 000 000 4 3 000 00000 00000 00000 0 0000000000000000000000 0 00000000000000000000000 0 00000000000000000000000 0 00000000000000000000000 0000 20000000000000000000 23 LT 0 o o o Ep ET il LT o LT O 0O 000000000000000000000000 19 18 000000000000000000000000 320 5 160000 kapas 00000000000 4HHHHHHML EM 00000 719 60 00 64000000000000 subset 32 00000000000000000 iteration 0000000000000000100 subset 0 000000000000000000000000000 subset 00000008 subset 160000000 400000000000000000 0 0000000000000000 00000000000 4 40S EM subset iteration 1 J 5 subset iteration Gao GO 000000000000000000000000000 00000000000000000000000
11. e FBP Average 16 32 48 64 80 VIEW NUMBER HHHHHHHHHHHHH HHHHHHHHHHHHHH 000 Fig 12 00000000 SNRHHHHHHHH positive negative SNRHHHHHHH 00000 4000000000000 480000 3 40 32000 U 120000 00000000000000000000000000000 HHHHHHHHHHHHHH 20970 0000 1090000 000 D E F Fig 13HHHHHHHHHHHHHHH SPECTO OO HHHHHHHHHHHHHHHHHHHHH 6400048000 32 D 00000 OS EM 00000 64000 480010 32000000000 99 100 100 93 80 65 lt 2 60 gt lt i 56 lt va 20 0 0 19 14 0000000 SPECTHHHHHHHHHHHHHHHH 0000 Sample 640 OS EM HHHHHHH 6400048000 3220000000000 100 8 SCALE VALUE OF PAIRED COMPARISON 20 5 7 9 1 13 SNR Signal to noise ratio Fig 15 219 12 00000 SNRQ 219 13 000000000 00000000 SNRHHHHHHHHHHHHHH SNR 0 Tabe2 000000 RLATIVE COUNT 94 RLATIVE COUNT 94 100 16VIEW 32VIEW CO 5 20 0 nh 0 20 4 60 80 PIXEL NUMBE Um 10 16VIEW 32VIEW 48VIEW 64VIEW 6 5 4 3 2 1 74 RM 0 20 40 60 80 PIXEL NUMBER F
12. 5 CTHHHH Fig 4 RI 000000000 RI HHHHHHHHHHHHH Ramachandran UU ED oP EGET EP OD ButterworthHHHHHHHHHHHHHHHHHHHH HHHHHHHHHHRIHHHHHHHHHHHHH RH H TU H H A A TH DB U aliasing i 0 00000000000000000000000000 0 00000000000000000000 2000000 0 0000000000000000000 5 HHHH 000000000000000000 2 2 05 ordered subsets expectation maximization O O 0 000000000000000000000000 0 000000000000000000000000 0 0 000000000000000000000000 0 0000000000000000 pixel 000 0
13. 050 000 25 0 60 00000 26 OOOO 28 0000 1 10000000000 radioisotope RI 00000000000000000000000000 00000000000000000000000000 RI 00000000000000000000000000 viv in vitro 00000 0000000040 000000000000000 RIHHHHHHHyYH Rt yl fe a i Vl Pt HUHH RIHHHHHHHHHHHHHHHHHHHH 00000000000 RIHHHHHHHHHHHH SPECT single photon emission tomography 000000 00000000000000000000000000 00000000000000000000000000 000000 iU H 0000 0 1896 00000000000 1913 Hevesy E 0000 19250 00 RIHHHHHHHHHH UD 0 1931 LawrencelHHHHH 0000000 1934000 119 0000000000000 000000000000000000000000000 000000000000000000000000000 1965 Segre Seaborg HHHHHHHHRIHHHHHHHHHHHHHHHHH HHHHHUHHHHHHHHH
14. 00000000 0 00000000000000 6400000000 Tea 0O OA SPECT 00000000 0 0 0 180 000000 00000000000000000000000 180 HHHHHHHHHHHHHHH 00000000000000000000000 0 0000000000000000000 OD filtered back projection 00000000000000000000000 0000 streak artifactd 0 00000000000 00000000000000000000000 00000000000000000000000 a 000000000000000 Fig 3 a 0 9 0 0 line source 000000000000 5 ordered subsets expectation maximization 0 00000000000000000000000 19 3 OOO 000000000000 SPECT HHHHHHH 00000000000000 00000000000 000000000000000000000000000 0000000000000 00000000000 VET a a a 0 000000000000000000000000000 0 000000000000000000000 020 SPECTHHHHHHHHHHH 2r HB
15. 000 00000000000000000000 000000000000000000000000 000000000000000000000000 000000000000000000000000 0 000 00000000000000000000 0000000000000 EAE EL 0 MEE M maximum likelihood expectation maximization RIHHHHHHHHHHHHHHHH 05 ordered subsets expectation maximization 000000000000000000000000000 0 65 00000000000000000000 Fig9 5HHHHHHHHHHHHHH 240 subset 60000 subset 40000000000000001000 HHHHHHHHHHHHHHHHHHHHz2HHH subset 4HHHHHHHHHHHHHHHHHHHHHHHHH subset 10000000000000000000100 ML EM HHHHHHHHH subset HHHHHHHHHHHHHHHHHHHHHH HHHHHHHHHsubsetHHHHHHHHHHHHHH O iterationf 000000000000000000000 OS EM subset Q iteration SPECTHHHHHHHH Fig 6 0 0 subset iteration subset iterationHHHHHHHHHHHHHHHHHHHH HHHHHUHHHHHHHHHHHHHHHHHHHHHHHH HHHHHHHFig 6 subset 8H iteration 4 5 HHHHHHHHHHHHHH HHHHHUHHHHHHHHHHHHHHHHHHHHHHHH subset 8 iteration 4 10 5
16. HHHHHHHHHHHHHHHHHHHHHHHHHHH 20 SPECT Cine a a 00000000000000000000000000 000 0000000 1mm 0000 HHHHHCPTcHHH HHHHHHHHHHHHHHH HHHHHHHHHHHHHHHHHHHHHeE eNTEGRA O 12 SPECTHHHHHHHHH 7 d energy general purpose 64 64 QE 1pixel 5 40mm 0 0000000000000000 00000000000000000000000 00 SFOV scan field of view 19 7 0 0 90 0000000000000 5 0000000000000000 7 0 FWHM OS EM 00000000000000000000000000 0 000000000000000 0719 7 9 0 HOS EMHHHHHHHHHHHHHHHHHHHHHHH 0 640000 lt 050 p lt 0 01 00000000000 4800 lt 0 01 3200 p lt 0 010 000000 0 0000000000000000000000000 00000
17. 0 0 00000000000000000000000 6 0 0 0 0000000000000 4 2 000000000000000000000 OD ODD 6 jj EVEL DDD 320 1600000000 subtraction imageg 0000000000 000 Fig17 000000000000000 OA 000000000000 0 16 6400 0000000 032 64 22 o E O O CI 000 ED ED EY HOON HHHHHHHL HHHHHHHL 41000000 00000000 00000000 00000000 00000000 00000 00000 00000 00000 eh 00000 00000 21 00000 00000 a 5 HHHHHHHHHHHHHHHHH 0 000000000000000 0 000000000000000 OS EM
18. 0000 000000000000000000000000000 24 subset iteration 1000 400 HHHHHHHHHHHHHHHHHHHHHHHHHHH HHHHHHHHHHHHHHHHHHHHHH subset 100 subsetHHHHHHHHHHHHHHH Hiteration HHHHHHHHHHHHHHHHHHHH 00000000000000000000000000 00000000000000000000000000 00000000 050 000 5 OS EM a OS EM 000000000000000000000000000 000000 95 0 Do Ooo Boo 8 UD 000000000000000000000000000 101600000 OS EM subset 8 iteration 4 000 000000000000000000000000000 0 000000000000000000095 00000 25 h Gy Hi 298 256 Be BP E Th HHHHHHHHHHHHHH SNR 00000000000 HHHHHHHHHHHHHHHOS EM 000000000 HHHHHHHHHHHHHHH 000000000000000000000000 0 00000000000000000000000000 640 OS EM 100000000000000000000 488 000000000 64 000 9300000000000000 OS EM 6SHHHHHHHHHH 64000 12 320 5 HHHHH 41000000000000
19. 000000000 6401000000000 000000000000000000000000 3 41 00000 480 000000000000000000000000 2 32HHHHHHOS EM 000000 0000000000000000000000000 12000000 12000000000000 OS EM 3 40 00000 3400000000000000 0000000000000000000000000 OS EM 00000000000000000 000000000000000000000000000 OO OD 0 0 0 0 sensitivity Specificity H 0 000 ROC receiver operating characteristic o 00000000 00000000000000 SEER ES UDC MM i IIHHHHHHHH HHHHHHHHHHH SPECTHHHHHHHHH 000000 1 0 20 8 0 0 0000000000 0 0 OS EM ordered subsets expectation maximization 0 30 00000000000 11 0 0 0 0 0 EEE OA ee Ea 0 0 0 SNR signal to noise ratio HHHHHHHHHHHHHHHHHH SNRHIHHHHHHHHHHHH Mao EE g sas 21 0 0 000000000000 steak free area 0 0 000000000000000000000 0 0 OE 0 0 59 subset iterationg
20. 00000000000000000 27 HOODOO HHHHHHHHHHHHHHHHHHHHHHHH 2002 H 2 HHHHHHHHHHHHHHHHHHHHHHHHHH 2001 0 HHHHHHHHHHHHHHHHHHHHHHHHH 2001 2 5 Larsson A Israelsson Consideration on system design implementation and computer processing in SPECT IEEE Transactions on nuclear science 1221 1342 NS 29 4 1982 gt Bieszk G Evaluation of SPECT Angular Sampling Effects Continuous Versus Step and Shoot Acquisition The J ournal of Nuclear Medicine 28 1308 1314 1987 9 Zong Jian Lawrence Holder and Charles Optimal Number of Views in 360 SPECT Imaging Nucl Med 37 1740 1744 1996 28 70000 OS EM ordered subsets expectation maximization 57 5 523 5290 2001 8 Bruce Hasegawa The physics of MEDICAL X RAY MEDICAL PHYSICS PUBLISHING Wisconsin 1991 Oa iB 78 7900 00000000000 0 0 2001 10 Peter oseph Raymond 2 View sampling requirements fan beam computed tomography Med Phys 7 6 692 702 1980 0000000000000000 gt HHHHHHHHHHHHH 271 2800 000 0 0 2002 00000 5 7 280 0000 0 1990 29 Table1 5 5 00000
21. 00000000000000000000 OS EM 5 00000 00000000000000000 Ha SPECTHHHHHHHHHHHHHHHHH HHHHHHHHHHHHHHHHHHHHHHHHH 13 8 HHHHHHHHHHHHHH Fig 2 AdHHHHHHHHHHHHHHHHHHHHHH 0000000000 0 000000000000000000000000 0 000000000000000000000000 0 000000000000000000000000 0 000000000000000000000000 HHHHHHHHHHHHHHH DiHHHHHHHHH 00000000000 C D D D 000000000 2 0 00000000000000 SPECT 000000 1 1000000000000000000000000 19 800000000000000000000000 HHHHHHHHHHHHHHHHHHHHHHHHHH 0000 19 9 HHHHHHHHHHHHHHHHHHFig 9 b H 0 00000000000 6 0000 3 OO HHHHHHHHHHHHHHHHFig99HHHHHHHH 16 HHHHOS EM Ez OS EM 1600000000000 00000000000000000000000000 14 0
22. HHHHHHHHHH 00000 p RiHRJHHHH 400600 B B 36 2 0000001000000 100count pixel 000000000000000000 320 480 640 00000 00 000 0 00 0 0000 0000000 05 0000000000000000 HHHHHHHHHHHHHHHHHHHHHHHHHHH 000000000 ButterworthH D D D D 0 50cycle cmH 05 HHHHHHHHHHHHHH Butterworth 0 0 0 50cycle cm l 000000 00000000000 64HHHHHHHHHHHHHHH 48000000 34000000 320000000 1 2 000000000000000000000000000 HHHHHHHH 2719 13 0000000 600000000 HHHHHHHHHHFuji FM DPLHHHHHHHHH Fuji DI ALcyHHHHHHHHHHHHHHHHHHHHHHHH O 19 ED SENET SE 0 000000000000000000000000000 SPECTHHHHHHHHHHHHHHHHHHHH HHHHHHHHHHHHHZkHHHHHHHHHHHHH 0000000000000000000000 100000 000000 0000000000000000 219 14 0000000000000 64000 OS EMHHHHH 1000 640 0000000000000000 99000000 0000000000 480000000000000000 U 930048000 OS EMHHHHH 6450000000032 OOO 6500000000000000 320000000000000000000 3 400000000000000000 0000000 172 ee 0 0000000000000 3 3 SNR 3 2 1000000000000000 5 3 2000 0 0000000000000000000000000000 SNR 00000000000000 SNR 00000 00000000000 19 15 0000540000000 HHHHHHHH
23. HHHHHHHHHHHHHHH CT computed tomography MRI magnetic resonance imaging HHHHHHHHHHHHHHHHHH METEU 1 2 000000000000 1951 Cassen 0 0 cM 0000000000000 HHHHHHHHHHHHHHHHHHH NaI TDHHHHHH 1960 19700000000000 00000000000 195700 00000000 0 000000000000000000000000000 YOO A 0 000000000000000000000000000 7 00000 00000 Fig 1 Ga 000000000 OOOO AU 0 00000000000000000 0 00000000000000000000 000 0 000000000000000000 00000 0 0000000000000000000000 SE SEB EN AEP TEL El CEP EP 1962 HKuhIHHHHHHHHHHHHHHHHHHH SPECTHHHHHHHHHHHHHH HHHHHHH 30000 RIHHHH 20000000 RIHHHHHH OOOO OO 000000000 00C000 0 00000000000000000000 CTHH 0 0000000000000000000000000000 SPECT HHHHHHHHHHHHHHHHHHHHHHHH HHHHHHHH2H3 HHHHHHHHHHHHHHHHH 0 000000000000000000000000000 Bea
24. HHHHHHHHHHHHSNR 00000 20 a HHHHHH 64000 480000000000000 SNR 0 000000000000000000000000000 0000000 0 480000 10 0 000000000000000000000000000 HI 4 1 streak free area Ooo Bieszk 0 TLD CaS 000000000000 00000000000 2 RV 00000000 6 8 0 0000000000000000000000000 14 Butterworth O 0 50 0 00 N 44 SPECT O O 20 0 45 cycle cm 21 pus 64000000000000000 60 0 0 000000000000000000000000000 HOOD 0 0000000000000000000 19 3000000000000000000000000 0 000000000000000000000000000 19 16 00000000000000 1600 HHHHHHHHHH 320000000000000 480 64 00000000000000000000000000
25. SPECT Si Photon En ssi on Conput ed Tonogr aphy Pk 00 031 800135 5 0000000 0000000 HHHHHHHHHHHHHHHHHHHH 00 00 160 120 SPECT single photon emission computed tomography IIHHHHHHHHHHHH HO uuu 0000 HHHHHHHHHHH 031 8001355 5 000 U SPECT single photon emission computed tomography 000000000020000 3000000 SPECT A a a RR Bt a 0 0 sO a Ft a 000000000000000000000000000 0000 05 ordered subsets expectation maximization J OS EM SNR signal to noise 6400 160 000000 0 0 0000000000000000000000000000 0000000000000 30000000000000 000000000000000000000000000 160000000000000000000 OA 5 5 0000000000000000 000000000000000
26. ig 16HHHHHHHHHHHHHHHHHHHHHH 16view 16 64view OS EM 64view 32view 16view Subtracted image 32 64 view 16 64view Fig 17 60 0000000000000 32 160 000 32000 640000000 000 400000000 0000000 640 OS EMHHHHH 00000 3200000000 161 9 0 0 32000 640000000 Oo 16000 640000000 HHHHHHHHHHHHHHH 5 90 i Fig 18 4HHHML EMHHHHHHH CO
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