ᙧᗘ㸻 S A
6.2.2 ᥦᡭἲࡢᴫせ
DWI࠾ࡅࡿ⾲♧᮲௳ㄪ⠇ᡭἲࡢᴫせࢆᅗ6.1♧ࡍ㸬ᮏᡭἲࡣ㸪๓ฎ⌮ࡋ
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
69
-ᅗ6.1 ⬻DWI࠾ࡅࡿ⾲♧᮲௳ㄪ⠇ᡭἲࡢᴫせ
࡚㸪ධຊࡋࡓb0⏬ീࡢ㝵ㄪࢆṇつࡋ㸪2್ฎ⌮ࣛ࣋ࣜࣥࢢฎ⌮ࢆ⏝࠸࡚⬻
ᐇ㉁㒊ࢆᢳฟࡋࡓ㸬ḟ㸪⬻ࡢṇ୰▮≧⥺ࡢഴࡁࢆᅇ㌿ฎ⌮ࡼࡾಟṇࡋ㸪⬻ᐇ
㉁㒊ࡢ㔜ᚰࢆồࡵ࡚⏬ീ୰ᚰ⛣ືࡉࡏࡓ㸬ࡑࡢᚋ㸪⬻ᐇ㉁㒊㡿ᇦࡢ࿘ᅖࡢᗙᶆ
ࢆ⏝ࡋ࡚୧ഃࡢどᗋ⨨ࢆỴᐃࡋ㸪⬻ᐇ㉁㒊ෆࡢ⃰ᗘࣄࢫࢺࢢ࣒ࣛࡢ᭱㢖ᗘ
࡞ࡿ⏬⣲್ࢆ⏝ࡋ୍࡚᪉ࡢどᗋࢆ㑅ᢥࡋࡓ㸬᭱ᚋ㸪ࡑࡢ⨨ࢆ୰ᚰROIࢆ タᐃࡋ࡚ᖹᆒ⏬⣲್ࢆồࡵ㸪ᚓࡽࢀࡓᖹᆒ⏬⣲್ࢆ⏝ࡋ࡚㸪DWIࡢ⾲♧᮲௳ࢆ
ㄪ⠇ࡋࡓ㸬
6.2.3 ⬻ᐇ㉁㒊ࡢᢳฟฎ⌮
⬻ᐇ㉁㒊ࡢᢳฟฎ⌮㛵ࡋ࡚㸪⌧ᅾ㸪Statistical Parametric Mapping㸦SPM㸧[3]
ࡸFunctional Magnetic Resonance Imaging of the Brain㸦FMRIB㸧[4][5]ࢯࣇࢺ࢚࢘
ࢆ⏝࠸ࡓ᪉ἲ࡞ [6]ࡀᏑᅾࡍࡿ㸬ࢃࢀࢃࢀࡣ㸪ࢩࢫࢸ࣒ࡢฎ⌮㏿ᗘࡸ」㞧ᛶ㸪
࠾ࡼࡧࢥࢫࢺᛶࢆ⪃៖ࡋ㸪ᗈࡃ⏝ࡉࢀ࡚࠸ࡿ2್ฎ⌮ࣛ࣋ࣜࣥࢢฎ⌮ࢆ⏝
࠸࡚⬻ᐇ㉁㒊ࢆᢳฟࡋࡓ㸬ࡲࡎ㸪ീࡉࢀࡓ⬻b0⏬ീ㸦matrix size: 256×256, gray scale: 12 bits, pixel size: 0.820㹼0.937 mm㸧ࢆࢥࣥࣆ࣮ࣗࢱㄞࡳ㎸ࡳ㸪๓ฎ⌮ࡋ
࡚㸪b0⏬ീࡢ᭱ᑠ⏬⣲್࠾ࡼࡧ᭱⏬⣲್ࢆồࡵ㸪⥺ᙧ㝵ㄪኚฎ⌮ࢆ⏝࠸࡚8
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
ᅗ6.2 ⬻ᐇ㉁㒊ࡢᢳฟฎ⌮㸬㸦a㸧b0⏬ീ㸪㸦b㸧2್ฎ⌮ࢆࡋࡓ⏬ീ㸪㸦c㸧ࣛ
࣋ࣜࣥࢢฎ⌮✰ᇙࡵฎ⌮ࢆࡋࡓ2್⏬ീ㸪㸦d㸧ࣛ࣋ࣜࣥࢢฎ⌮✰ᇙࡵฎ
⌮ࢆࡋࡓཎ⏬ീ
bits㸦0㹼255㸧㝵ㄪኚࡋࡓ㸬ࡇࡢ⌮⏤ࡣ㸪MRI ⨨㛫࠾ࡼࡧ⿕᳨⪅㛫࡛ീ
ࡉࢀࡓb0⏬ീࡢ⏬⣲್ᕪ␗ࡀ⏕ࡌࡿࡓࡵ㸪⬻ᐇ㉁㒊ࢆᢳฟࡍࡿ㝿ᅔ㞴ࢆక࠺
᥎ ࡋࡓࡽ࡛࠶ࡿ㸬ḟ㸪㝵ㄪኚࡋࡓ⏬ീᑐࡋ㸪2 ್ฎ⌮ࢆ⾜ࡗࡓ㸬 ࡋࡁ࠸್タᐃࡣ㸪30ࡢᏛ⩦⏝ࢆ⏝ࡋ࡚ࡋࡁ࠸್ࢆ㡰ḟኚࡉࡏ㸪2ྡࡢ ᨺᑕ⥺⛉་ࡀྜ㆟ࡢࡶ࣐࣮࢟ࣥࢢࡋࡓ⬻ᐇ㉁㒊㡿ᇦෆྵࡲࢀࡿಙྕ⏬⣲ᩘ
ࡢྜࢆ⟬ฟࡋ࡚㸪⬻ᐇ㉁㒊ࡀ࡛ࡁࡿ㝈ࡾṇ☜ᢳฟ࡛ࡁ㸪ୟࡘࡍ࡚ࡢ࡛
୍ᐃࡢྜ࡞ࡿ⏬⣲್ࢆᐃࡵࡓ㸬ᅗ6.2㸦a㸧b0⏬ീ㸪㸦b㸧2್ฎ⌮ࢆ
ࡋࡓ⏬ീࢆ♧ࡍ㸬ࡑࡢᚋ㸪᫂ࡽᑠࡉ࠸㝜ᙳࢆ㝖ཤࡍࡿࡓࡵ㸪ࣛ࣋ࣜࣥࢢฎ⌮
ࢆ⏝࠸࡚㝜ᙳࡢ㠃✚ࢆィ⟬ࡋ㸪㠃✚ࡀ⏬⣲ᩘࡢ15 %௨ୗ࡛࠶ࡿࡶࡢࡣ㝖እࡋࡓ㸬 ࡑࡋ࡚㸪ࣛ࣋ࣜࣥࢢฎ⌮ࢆ⏝࠸ࡓ✰ᇙࡵฎ⌮ࢆࡋ࡚b0⏬ീࡢ⬻ᐇ㉁㒊ࢆᢳฟࡋ ࡓ㸬ᅗ6.2㸦c㸧㸪㸦d㸧⬻ᐇ㉁㒊ࢆᢳฟࡋࡓ2್⏬ീ࠾ࡼࡧཎ⏬ീࢆࡑࢀࡒࢀ♧
ࡍ㸬
⬻ᐇ㉁㒊ࡢᢳฟ⢭ᗘࡣ㸪ᘧ㸦1㸧♧ࡍ㸪ᨺᑕ⥺⛉་ࡼࡗ࡚࣐࣮࢟ࣥࢢࡉࢀࡓ
⬻ᐇ㉁㒊㡿ᇦࡢJaccardࡢ୍⮴⋡㸦jaccard similarity coefficient㸸JSC㸧[7]ࡼࡗ
࡚ホ౯ࡋࡓ㸬
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
71
-ᅗ6.3 どᗋ⨨ࡢỴᐃᡭἲ㸬㸦a㸧どᗋࡢ㡿ᇦࢆ⾲♧ࡋࡓb0⏬ീ㸪㸦b㸧ᅇ㌿⛣ື
⿵ṇࢆࡋࡓb0⏬ീ㸪㸦c㸧⬻ᐇ㉁㒊㡿ᇦ࠾࠸࡚ᕥྑ୧ഃࡢ᭱ࡶእഃ࡞ࡿ⨨㸪 㔜ᚰࡢ⨨㸪࠾ࡼࡧึᮇẁ㝵࡛Ỵᐃࡉࢀࡓ୧ഃࡢどᗋ⨨ࢆ⾲♧ࡋࡓb0⏬ീ㸪㸦d㸧
⬻ᐇ㉁㒊㡿ᇦ࠾࠸࡚๓ᚋ୧ഃࡢ᭱ࡶእഃ࡞ࡿ⨨㸪㔜ᚰࡢ⨨㸪࠾ࡼࡧ⿵ṇ ࡉࢀࡓ୧ഃࡢどᗋ⨨ࢆ⾲♧ࡋࡓb0⏬ീ
㸦1㸧
ࡇࡇ࡛㸪Gࡣṇゎ⏬ീ㸪Rࡣᢳฟࡉࢀࡓ⏬ീ࡛࠶ࡿ㸬
6.2.4 どᗋ⨨Ỵᐃᡭἲ
ᅗ 6.3㸦a㸧㸪2 ྡࡢᨺᑕ⥺⛉་ࡀྜ㆟ࡢࡶ࣐࣮࢟ࣥࢢࡋࡓどᗋ㡿ᇦࢆⅬ
⥺࡛⾲♧ࡋࡓb0⏬ീࢆ♧ࡍ㸬b0⏬ീࡢどᗋ㡿ᇦࡣ㸪࿘㎶㒊ࡢ⏬ീࢥࣥࢺࣛࢫ ࢺࡀ㠀ᖖࢃࡎ࡞ῐ࠸㝜ᙳ࡛࠶ࡿࡓࡵ㸪ࢃࢀࢃࢀࡣ㸪⬻ᐇ㉁㒊㡿ᇦࡢ⨨ሗ
ᇶ࡙ࡃ≉ᚩ㔞ࢆ⏝ࡋ㸪どᗋ⨨ࢆỴᐃࡍࡿᡭἲࢆ㛤Ⓨࡋࡓ㸬
ᮏ◊✲࡛ࡣ㸪⬻ᐇ㉁㒊㡿ᇦࡢ࿘ᅖ࠾ࡅࡿᕥྑ୧ഃࡢ᭱ࡶእഃ࡞ࡿ⨨ࡢᗙ ᶆ㸪⬻ᐇ㉁㒊ࡢ㔜ᚰᗙᶆࢆ⤖ࡪ⥺ୖどᗋࡀᏑᅾࡍࡿ௬ᐃࡋࡓ㸬ᐇ㝿㸪ࡇ ࡢ௬ᐃࡣ㸪ከࡃࡢ⏬ീࢆほᐹࡋ࡚ᚓࡽࢀࡓ▱㆑ᇶ࡙࠸࡚࠸ࡿ㸬ࡑࡇ࡛㸪どᗋ
⨨ࡢỴᐃ࠾࠸࡚㸪⬻ࡢṇ୰▮≧⥺ࡢഴࡁࡀ㞀ᐖ࡞ࡿࡓࡵ㸪ࡲࡎ㸪⬻ᐇ㉁㒊ࢆ
R G
R JSC G
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
ᢳฟࡋࡓཎ⏬ീᑐࡋ㸪⬻ࡢṇ୰▮≧⥺ࡀb0⏬ീࡢᆶ┤୰ᚰ⥺୍⮴ࡍࡿࡼ࠺
ᅇ㌿ࡉࡏ࡚ᙳయࡢഴࡁࢆ⿵ṇࡋࡓᚋ㸪b0 ⏬ീࡢ⬻ᐇ㉁㒊ࡽ㔜ᚰࢆồࡵ࡚㸪 b0⏬ീࡢ୰ᚰ⛣ືࡉࡏࡓ㸬࡞࠾㸪ṇ୰▮≧⥺ࡣ㸪┠どࡼࡗ࡚ᡭື࡛タᐃࡋࡓ㸬 ᅗ6.3㸦b㸧㸪ᅇ㌿࣭⛣ື⿵ṇࢆࡋࡓb0⏬ീࢆ♧ࡍ㸬ḟ㸪b0⏬ീࡢ⬻ᐇ㉁
㒊㡿ᇦࡢ࿘ᅖࡢྑ࠾ࡼࡧᕥഃࡢ᭱ࡶእഃ࡞ࡿ⨨ࡢᗙᶆࢆỴᐃࡋ㸪㔜ᚰࡲ࡛ࡢ
┤⥺㊥㞳ࢆࡑࢀࡒࢀ D1㸪D2 ࡋࡓࡁ㸪ࡇࡢ┤⥺ୖ࡛⬻ᐇ㉁㒊ࡢ㔜ᚰࡽ 0.2
D1㸪0.2D2ࡢ㊥㞳࡞ࡿᗙᶆࢆ㸪ึᮇẁ㝵ࡋ࡚ࡢどᗋ⨨Ỵᐃࡋࡓ㸬ᅗ
6.3㸦c㸧㸪ồࡵࡓ⬻ᐇ㉁㒊㡿ᇦ࿘ᅖࡢᕥྑ୧ഃࡢ᭱ࡶእഃ࡞ࡿ⨨㸦༳㸧㸪
㔜ᚰࡢ⨨㸦ۑ༳㸧㸪࠾ࡼࡧึᮇẁ㝵࡛Ỵᐃࡋࡓ୧ഃࡢどᗋ⨨㸦༑Ꮠ༳㸧ࢆ♧ࡍ㸬
ึᮇẁ㝵࡛Ỵᐃࡋࡓどᗋ⨨ࡣ㸪ᢳฟࡋࡓ⬻ᐇ㉁㒊ࡢᙧ≧ࡀ༸ᙧ࡛㸪ࡘ⬻ᐇ㉁
㒊ࡢ㛗㍈᪉ྥࡀ㛗࠸࡛ࡣ㸪どᗋࡢ㡿ᇦᑐࡋ࡚๓㢌㒊ഃኚືࡍࡿഴྥࡀぢ
ࡽࢀࡓ㸬ࡑࡢࡓࡵ㸪⬻ᐇ㉁㒊㡿ᇦ࿘ᅖࡢ๓࠾ࡼࡧᚋഃࡢ᭱ࡶእഃ࡞ࡿ⨨ࡢᗙ ᶆࢆỴᐃࡋ㸪㔜ᚰࡲ࡛ࡢ┤⥺㊥㞳ࢆࡑࢀࡒࢀD3㸪D4ࡋࡓࡁ㸪D3ࡀD4௨ୖ㸪 D3ࡀD1㸪D2௨ୖ㸪D3㸭D4ࡀ1.07௨ୗࡢ᮲௳ࢆ‶ࡓࡍሙྜ㸪௨ୗ♧ࡍᘧ㸦2㸧
ᘧ㸦3㸧ࡽ㸪ྑ࠾ࡼࡧᕥࡢどᗋ⨨ࢆṇ୰▮≧⥺ᖹ⾜ᚋ㢌㒊ഃ⛣ືࡉࡏ
ࡿ⏬⣲ᩘNR㸪NLࢆồࡵ㸪୧ഃࡢどᗋ⨨ࢆಟṇࡋࡓ㸬
㸦2㸧
㸦3㸧
ࡇࡇ࡛㸪㹨R㸪㹨Lࡣ㸪ࡑࢀࡒࢀึᮇẁ㝵࡛Ỵᐃࡋࡓྑᕥࡢどᗋ⨨࠾ࡅࡿᆶ
┤⥺ୖࡢᗙᶆ࡛࠶ࡿ㸬࡞࠾㸪どᗋ⨨ࡢึᮇỴᐃ࠾ࡼࡧ᭱⤊ಟṇ⏝ࡍࡿ᮲௳
ࡸಀᩘ࡞ࡢࣃ࣓࣮ࣛࢱࡢタᐃࡣ㸪Ꮫ⩦⏝ࢆ⏝࠸࡚㸪ಀᩘࢆኚࡉࡏ࡚Ỵᐃ ࡋࡓどᗋ⨨㸪ᨺᑕ⥺⛉་ࡼࡗ࡚࣐࣮࢟ࣥࢢࡉࢀࡓどᗋ㡿ᇦࡽồࡵࡓ㔜ᚰ
ࡢ㊥㞳ࢆồࡵ㸪㛫࠾࠸࡚᭱ᑠࡢ㊥㞳࡞ࡿ್ࢆᐃࡵࡓ㸬ᅗ6.3㸦d㸧㸪
⬻ᐇ㉁㒊㡿ᇦ࿘ᅖࡢ๓ᚋ୧ഃࡢ᭱ࡶእഃ࡞ࡿ⨨㸦༳㸧㸪㔜ᚰࡢ⨨㸦ۑ༳㸧㸪
࠾ࡼࡧ⿵ṇࡋࡓ୧ഃࡢどᗋ⨨㸦༑Ꮠ༳㸧ࢆ♧ࡍ㸬
6.2.5 どᗋ㑅ᢥᡭἲ
ASIST-Japan ࡼࡾ⪃ࡉࢀࡓ᪉ἲࡣ㸪b0 ⏬ീࡢどᗋࡢಙྕᙉᗘࢆ⏝ࡋ࡚㸪
DWIࡢ⾲♧᮲௳ࢆᶆ‽ࡍࡿ᪉ἲ࡛࠶ࡿ㸬ࡋࡋ㸪ಙྕᙉᗘࢆィ ࡍࡿどᗋ㝞 ᪧᛶᝈࡀᏑᅾ [8][9]ࡋ㸪ࡇࢀࡽࡢᝈࢆ᭷ࡍࡿどᗋࢆ⏝ࡋ࡚DWIࡢ⾲♧᮲௳
ࡀㄪ⠇ࡉࢀࡓሙྜࡣ㸪ಙྕᙉᗘࡸ⏬ീࢥࣥࢺࣛࢫࢺࡀࡁࡃኚࡍࡿ㸬ࡑࡇ࡛㸪
R
R j
D D
N D 0.28
3 1 3
L
L j
D D
N D 0.28
3 2 3
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
73
-b0 ⏬ീࡢᕥྑ୧ഃࡢどᗋࡽṇᖖ࡞どᗋࡢಙྕᙉᗘ㏆࠸ഃࡢどᗋࢆ㑅ᢥࡍࡿ
ᡭἲࢆ㛤Ⓨࡋࡓ㸬
ࢃࢀࢃࢀࡣ㸪ࡲࡎ㸪Ỵᐃࡉࢀࡓ୧ഃࡢどᗋ⨨ࢆ୰ᚰ㸪┤ᚄࢆ2㸪4㸪8㸪12㸪 16㸪22㸪28㸪࠾ࡼࡧ34 pixel8ẁ㝵ኚࡉࡏࡓᙧROIࢆタᐃࡋ㸪b0⏬ീ
࠾ࡅࡿ ROI ෆࡢᖹᆒ⏬⣲್ࢆồࡵࡓ㸬ࡇࡢ⌮⏤ࡣ㸪ROI ࡢタᐃ࠾࠸࡚㸪
ASIST-Japanࡼࡿ᪉ἲ࡛ࡣ㸪ᙧROIࡀ⏝ࡉࢀ࡚࠸ࡿࡀ㸪ᑍἲ㛵ࡍࡿᇶ‽
ࡣỴࡵࡽࢀ࡚࠸࡞࠸ࡓࡵ࡛࠶ࡿ㸬ᮏ◊✲࡛ࡣ㸪ྛᏛ⩦⏝ࡽᚓࡽࢀࡓᖹᆒ⏬
⣲್ࢆᖹᆒࡋ㸪┤ᚄࡢቑຍ࠾࠸࡚ኚࡀㄆࡵࡽࢀ࡞࠸ࡢ┤ᚄ࡞ࡿROIࡀ12
pixel ࡛࠶ࡗࡓࡓࡵ㸪ࡇࢀࢆ⏝࠸࡚どᗋ⨨࠾ࡅࡿᖹᆒ⏬⣲್ࢆồࡵࡓ㸬ḟ㸪
⬻ᐇ㉁㒊㡿ᇦࡢ⃰ᗘࣄࢫࢺࢢ࣒ࣛࢆồࡵ㸪᭱ࡶ㢖ᗘࡀ㧗ࡃ࡞ࡿ⏬⣲್ࢆồࡵࡓ㸬 ࡑࡋ࡚㸪᭱㢖ᗘ࡞ࡿ⏬⣲್㸪ᕥྑ୧ഃࡢどᗋ⨨࡛ࡢᖹᆒ⏬⣲್ࡢᕪࡢ⤯
ᑐ್ࢆồࡵ㸪ᕥྑࡑࢀࡒࢀࡢ⤯ᑐ್ࡢ࠺ࡕᑠࡉ࠸᪉ࡢどᗋࢆ㑅ᢥࡋࡓ㸬ࡇࡢ⌮⏤
ࡣ㸪⃰ᗘࣄࢫࢺࢢ࣒ࣛࡢ᭱㢖ᗘ࡞ࡿ⏬⣲್ࡀṇᖖ࡞⬻ᐇ㉁㒊࠾ࡅࡿᖹᆒⓗ࡞
⏬⣲್ࢆ♧ࡋ࡚࠾ࡾ㸪ࡲࡓ㸪どᗋᝈࡀ࠶ࡿሙྜࡣ㸪どᗋࡢ⏬⣲್ࡀᖜ
పಙྕࡲࡓࡣ㧗ಙྕࢆ࿊ࡍࡿࡓࡵ࡛࠶ࡿ㸬
6.2.6 DWIࡢ⾲♧᮲௳ࡢㄪ⠇
ീࡉࢀࡓ⬻DWI㸦matrix size: 256×256, gray scale: 12 bits, pixel size: 0.820㹼
0.937 mm㸧ࢆࢥࣥࣆ࣮ࣗࢱㄞࡳ㎸ࡳ㸪㑅ᢥࡉࢀࡓどᗋ⨨࠾ࡅࡿᖹᆒ⏬⣲್
ࢆWWタᐃࡋ࡚㸪௨ୗ♧ࡍᘧ㸦4㸧ࡼࡾ㸪DWIࢆ8 bits㸦0㹼255㸧㝵ㄪኚ
ࡋࡓ㸬
㸦4㸧
ࡇࡇ࡛㸪g ( i , j ) ࡣኚฎ⌮ࡋࡓ⏬ീࡢ⏬⣲್㸪f ( i , j ) ࡣཎ⏬ീࡢ⏬⣲್࡛࠶ࡿ㸬
࡞࠾㸪g ( i , j )ࡀ256௨ୖࡢሙྜࡣ㸪⏬⣲್ࢆ255ࡋࡓ㸬
6.2.7 ࢩࢫࢸ࣒ࡢ≉ᛶホ౯
ࢩࢫࢸ࣒㛤Ⓨ⏝ࡋࡓᏛ⩦⏝⏝ࡋ࡚࠸࡞࠸ࢸࢫࢺ⏝ᑐࡋ㸪
ASIST-Japan ࡼࡾ⪃ࡉࢀࡓ᪉ἲࢆ 2 ྡࡢᨺᑕ⥺⛉་ࡢྜ㆟ࡢࡶᡭື࡛ᐇ
ࡋ㸦ᡭື᪉ἲ㸧㸪ࡉࡽ㸪㛤Ⓨࡋࡓᮏࢩࢫࢸ࣒ࢆᐇ⾜ࡉࡏ࡚㸦ᮏᡭἲ㸧㸪ࡑࢀࡒࢀ
ᡭື᪉ἲ࠾ࡼࡧᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓ DWI ࡢ⏬⣲ࡽồࡵࡓᖹᆒ⏬⣲್ࢆ
⏬ീホ౯ᣦᶆࡋ࡚㸪ࡈẚ㍑ホ౯ࡋࡓ㸬࡞࠾㸪ᡭື᪉ἲ࠾ࡅࡿどᗋ
ࡢROIタᐃࡣ㸪ᨺᑕ⥺⛉་ࡢ┠ど࡚ࡈ㐺ࡋࡓᑍἲࡢᙧROIࡀ㓄⨨ࡉ WW
j i j f
i
g ,
255 ,
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
ᅗ6.4 ␗࡞ࡿMRI⨨࡛ീࡉࢀࡓb0⏬ീ㸦ୖẁ㸧⬻ᐇ㉁㒊ࡢᢳฟฎ⌮ࢆ
ࡋࡓ2್⏬ീ㸦ୗẁ㸧
ᅗ 6.5 Ỵᐃࡉࢀࡓどᗋ⨨࠾࠸࡚ ROIࡢ┤ᚄࢆኚࡉࡏ࡚ồࡵࡓ┦ᑐᖹᆒ⏬
⣲್
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
75
-ࢀࡓ㸬⏬ീホ౯ᣦᶆࢆ⏝࠸ࡓẚ㍑ホ౯ࡣ㸪⤫ィⓗ᭷ពᕪ᳨ᐃ㸦୧ഃ࣮࣌ࢻ t ᳨ ᐃ㸧ࡢ㸪௨ୗ♧ࡍᘧ㸦5㸧ࡽ೫ᕪ⋡ DR㸦%㸧ࢆồࡵ㸪⢭ᗘ࠾ࡼࡧ≉ᛶ
ࡘ࠸᳨࡚ウࡋࡓ㸬
㸦5㸧
ࡇࡇ࡛㸪Vmanual㸪Vauto ࡣ㸪ࡑࢀࡒࢀᡭື᪉ἲ࠾ࡼࡧᮏᡭἲࡼࡾᚓࡽࢀࡓ⏬ീホ ౯ᣦᶆ್࡛࠶ࡿ㸬
6.3 ⤖ᯝ
ᅗ6.4㸪3ᶵ✀ࡢMRI⨨࡛ീࡉࢀࡓb0⏬ീ㸦ୖẁ㸧㸪ᢳฟࡉࢀࡓ⬻ᐇ
㉁㒊ࡢ2್⏬ീ㸦ୗẁ㸧ࢆ♧ࡍ㸬ࢃࢀࢃࢀࡣ㸪30ࡢᏛ⩦⏝ࢆ⏝ࡋ࡚⏬
⣲್ࢆ 26 ᐃࡵࡓ㸬ᢳฟࡉࢀࡓ⬻ᐇ㉁㒊ࡣ㸪MRI ⨨ࡢ㐪࠸㛵ࢃࡽࡎ㸪ᐇ㝿 ࡢ⬻ᐇ㉁㒊㡿ᇦࡢ⨨‶㊊࡛ࡁࡿ⛬ᗘ୍⮴ࡋ࡚࠾ࡾ㸪┿ࡢ⬻ᐇ㉁㒊㡿ᇦẚ
㍑ࡋ࡚㛫࡛ᖹᆒ0.95ࡢ୍⮴⋡ࡀᚓࡽࢀࡓ㸬
ᅗ6.5㸪ᙧROIࡢ┤ᚄࢆኚࡉࡏ࡚ồࡵࡓᖹᆒ⏬⣲್ࢆ♧ࡍ㸬࡞࠾㸪⾲♧
ࡋࡓᖹᆒ⏬⣲್ࡣ㸪ྛᏛ⩦⏝ࡽᚓࡽࢀࡓᖹᆒ⏬⣲್ࢆᖹᆒࡋ㸪┤ᚄ2 pixel ࡢᖹᆒ⏬⣲್࡛ṇつࡋࡓ್࡛࠶ࡿ㸬⤖ᯝࡼࡾ㸪┤ᚄ12 pixelࡲ࡛ኚࡣぢࡽࢀ
࡞࠸ࡀ㸪┤ᚄࡢቑຍࡶୖ᪼ࡍࡿഴྥࡀぢࡽࢀࡓࡓࡵ㸪ᮏ◊✲࡛ࡣ㸪┤ᚄ12
pixelࡢROIࢆ⏝࠸࡚どᗋ⨨ࡢᖹᆒ⏬⣲್ࢆồࡵࡓ㸬
ᅗ 6.6 㸪ᮏࢩࢫࢸ࣒ࡼࡾỴᐃࡉࢀࡓどᗋ⨨ࢆ༑Ꮠ࣐࣮ࢡ࡛ฟຊࡋࡓ㸪8
ࡢࢸࢫࢺ⏝ࡢb0⏬ീࢆ♧ࡍ㸬࡞࠾㸪b0⏬ീࡢᐇ⥺ࡣ㸪2ྡࡢᨺᑕ⥺⛉་ࡀ
ྜ㆟ࡢࡶ࣐࣮࢟ࣥࢢࡋࡓどᗋ㡿ᇦ࡛࠶ࡿ㸬b0⏬ീ࠾࠸࡚㸪ᮏࢩࢫࢸ࣒ࡼࡾ
Ỵᐃࡉࢀࡓどᗋ⨨ࡣ㸪8 ࡍ࡚ࡢࢸࢫࢺ⏝࠾࠸࡚どᗋࡢ㍯㒌ෆྵࡲ
ࢀ࡚࠾ࡾ㸪ṇᖖ࡞どᗋഃࡀ㑅ᢥࡉࢀ࡚࠸ࡓ㸬30ࡢᏛ⩦⏝࠾ࡼࡧࡢ 22 ࡢࢸࢫࢺ⏝࠾࠸࡚ࡶྠᵝ࡞⤖ᯝ࡞ࡗࡓ㸬
30 ࡢᏛ⩦⏝ࡘ࠸࡚ᡭື᪉ἲᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓDWIࡢ⏬⣲
ࡽồࡵࡓᖹᆒ⏬⣲್ࢆ⏝࠸࡚㸪ࡈ೫ᕪ⋡ࢆồࡵࡓ⤖ᯝ㸪㛫ࡢᖹᆒ
್ࡣ2.28s1.83 %࡞ࡾ㸪್᭱ࡣ6.30 %࡛࠶ࡗࡓ㸬ࡲࡓ㸪ᡭື᪉ἲ࠾ࡼࡧᮏᡭ
ἲ㛫࠾࠸࡚ᖹᆒ್ࡢᕪࢆ᳨ᐃࡋࡓ⤖ᯝ㸪༴㝤⋡ࡀ0.96᭷ពỈ‽0.05ࡼࡾ㧗ࡃ
࡞ࡗࡓ㸦t㸻0.050㸪df㸻58㸧㸬
⾲ 6.1 ࡣ㸪30 ࡢࢸࢫࢺ⏝࠾ࡅࡿᡭື᪉ἲᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓ DWIࡢ⏬⣲ࡽồࡵࡓᖹᆒ⏬⣲್㸪ࡇࢀࡽࡢ್ࢆ⏝࠸࡚ồࡵࡓ೫ᕪ⋡ࢆ♧ࡍ㸬
100
manual auto manual
V V DR V
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
ᅗ 6.6 ᮏࢩࢫࢸ࣒ࡼࡾỴᐃࡉࢀࡓどᗋ⨨ࢆ⾲♧ࡋࡓ 8 ࡢࢸࢫࢺ⏝ࡢ
b0⏬ീ㸬b0⏬ീࡢᐇ⥺ࡣどᗋࡢ㡿ᇦ࡛࠶ࡿ㸬
⾲6.1 ࢸࢫࢺ⏝࠾ࡅࡿᡭື࠾ࡼࡧᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓDWIࡢ⏬⣲
ࡽồࡵࡓᖹᆒ⏬⣲್㸪ࡑࢀࡒࢀࡢᖹᆒ⏬⣲್ࢆ⏝࠸࡚ࡈồࡵࡓ೫ᕪ
⋡
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
77
-ᅗ6.7 3 ࡢࢸࢫࢺ⏝ DWIࡢẚ㍑㸬㸦a㸧࣭㸦b㸧࣭㸦c㸧ᡭື᪉ἲࡼࡾㄪ⠇ࡉ
ࢀࡓDWI㸪㸦d㸧࣭㸦e㸧࣭㸦f㸧ᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓDWI㸬
೫ᕪ⋡ࡢᖹᆒ್ࡣ1.95s1.37 %࡞ࡾ㸪್᭱ࡣ5.16 %࡛࠶ࡗࡓ㸬᭷ពᕪ᳨ᐃ⤖
ᯝࡣ㸪༴㝤⋡ࡀ0.67࡞ࡾ㸪Ꮫ⩦⏝ࡢ⤖ᯝྠᵝ㸪᭷ពỈ‽0.05ࡼࡾࡁࡃ
࡞ࡗࡓ㸦t㸻0.423㸪df㸻58㸧㸬
ᅗ6.7㸪ᡭື᪉ἲᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓ㸪3ࡢࢸࢫࢺ⏝ࡢDWIࢆ
♧ࡍ㸬ᅗ 6.7㸦a㸧㸪㸦b㸧࠾ࡼࡧ㸦c㸧ࡣᡭື᪉ἲ㸪ᅗ 6.7㸦d㸧㸪㸦e㸧࠾ࡼࡧ㸦f㸧ࡣ ᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓDWI࡛࠶ࡿ㸬࡞࠾㸪ྛ࠾࠸࡚㸪ㄪ⠇ࡉࢀࡓ DWI ࡢ⏬⣲ࡽồࡵࡓᖹᆒ⏬⣲್ࢆ⏝࠸࡚ồࡵࡓ೫ᕪ⋡㸦DR㸧ࡶేࡏ࡚♧ࡍ㸬ᮏࢩ ࢫࢸ࣒ࢆ⏝࠸࡚⾲♧᮲௳ࢆㄪ⠇ࡋࡓ DWI ࡣ㸪ಙྕᙉᗘࡸ⏬ീࢥࣥࢺࣛࢫࢺ࠾
࠸࡚ᡭືࡼࡾㄪ⠇ࡉࢀࡓ DWI ほⓗᴟࡵ࡚㢮ఝࡋ࡚࠸ࡓ㸬ࡢ࠾
࠸࡚ࡶྠᵝ࡞⤖ᯝ࡛࠶ࡗࡓ㸬 6.4 ⪃ᐹ
ᮏ◊✲࡛ࡣ㸪ASIST-Japanࡼࡾ⪃ࡉࢀࡓ᪉ἲࢆᐇࡍࡿMRI᳨ᰝᢸᙜ⪅ࡸ
་ᖌࡼࡿࣄ࣮࣐࢚࣮ࣗࣥࣛࡢ㜵Ṇ㸪సᴗ㛫࠾ࡼࡧປຊࡢ㍍ῶຍ࠼㸪་ᖌ ࡀ⾑ᰦ⁐ゎ⒪ἲࡢ㐺ᛂࢆྍ⬟࡞㝈ࡾ᪩ࡃṇ☜Ỵᐃ࡛ࡁࡿࡇࢆ┠ⓗ㸪DWIࡢ
⾲♧᮲௳ࢆỴᐃࡍࡿࢩࢫࢸ࣒ࢆ㛤Ⓨࡋࡓ㸬
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
ᅗ6.8 どᗋࡢ⨨⿵ṇࢆᚲせࡋࡓࢸࢫࢺ⏝㸬㸦a㸧࣭㸦b㸧ึᮇẁ㝵࡛Ỵᐃࡉ
ࢀࡓどᗋࡢ⨨ࢆ⾲♧ࡋࡓb0⏬ീ㸪㸦c㸧࣭㸦d㸧⿵ṇࡉࢀࡓどᗋࡢ⨨ࢆ⾲♧ࡋࡓ b0⏬ീ㸬
ᮏࢩࢫࢸ࣒࡛ࡣ㸪ࡲࡎ㸪⥺ᙧ㝵ㄪኚฎ⌮ࢆ⏝࠸࡚b0⏬ീࡢ㝵ㄪࢆṇつࡋ㸪 ࡑࡢᚋ㸪2 ್ฎ⌮㸪ࣛ࣋ࣜࣥࢢฎ⌮࠾ࡼࡧ✰ᇙࡵฎ⌮ࢆ⏝࠸࡚⬻ᐇ㉁㒊ࢆᢳฟ ࡋࡓ㸬MR ⏬ീࡣ㸪⨨ࡢᶵ✀ࡀ␗࡞ࡿ⏬⣲್ࡀᖜኚࡍࡿ㸬ᮏᡭἲ࡚
ᢳฟࡉࢀࡓ⬻ᐇ㉁㒊ࡣ㸪MRI⨨ࡢᶵ✀ࡢ㐪࠸㛵ࢃࡽࡎ㸪ᐇ㝿ࡢ⬻ᐇ㉁㒊㡿ᇦ ࡢ⨨‶㊊࡛ࡁࡿ⛬ᗘ୍⮴ࡋ࡚࠸ࡓ㸬ࡲࡓ㸪ᨺᑕ⥺⛉་ࡼࡗ࡚࣐࣮࢟ࣥࢢ ࡉࢀࡓ┿ࡢ⬻ᐇ㉁㒊㡿ᇦᑐࡋ࡚㧗࠸୍⮴⋡ࡀᚓࡽࢀࡓ㸬ࡋࡓࡀࡗ࡚㸪ᮏ◊✲
࠾ࡅࡿ⬻ᐇ㉁㒊ࡢᢳฟᡭἲࡣ㸪どᗋ⨨ࡢỴᐃ⏝ࡍࡿ⬻ᐇ㉁㒊㡿ᇦࡢ⨨
ሗᇶ࡙ࡃ≉ᚩ㔞ࡢṇ☜࡞Ỵᐃࢆྍ⬟ࡋࡓ㸬
ࢃࢀࢃࢀࡣ㸪⬻ᐇ㉁㒊㡿ᇦ࿘ᅖ࠾ࡅࡿᕥྑ୧ഃࡢ᭱ࡶእഃ࡞ࡿ⨨ࡢᗙᶆ
㸪⬻ᐇ㉁㒊ࡢ㔜ᚰᗙᶆࢆ⤖ࡪ⥺ୖどᗋࡀᏑᅾࡍࡿ௬ᐃࡋࡓ㸬ࡑࡇ࡛㸪ࡇ ࡢ⪃ࢆࢩࢫࢸ࣒ࡍࡿࡓࡵ㸪ࡑࢀࡒࢀࡢᗙᶆ㛫ࡢ㊥㞳ࢆ⏝ࡋࡓ㸪どᗋ⨨
ࡢỴᐃᡭἲࢆ㛤Ⓨࡋࡓ㸬ࡋࡋ㸪ᅗ6.8㸦a㸧㸪㸦b㸧♧ࡋࡓࡼ࠺࡞⬻ᐇ㉁㒊ࡢᙧ≧
ࡀ༸ᙧ࡛㛗㍈᪉ྥࡀ㛗࠸࡛ࡣ㸪Ỵᐃࡋࡓどᗋ⨨ࡀどᗋ㡿ᇦᑐࡋ࡚๓㢌㒊
ഃኚືࡍࡿഴྥࡀぢࡽࢀࡓ㸬ࡑࡢࡓࡵ㸪⬻ᐇ㉁㒊㡿ᇦ࿘ᅖࡢ๓ᚋ୧ഃࡢ᭱እഃ
㒊㔜ᚰࡲ࡛ࡢ┤⥺㊥㞳㸪ୖグࡋࡓᕥྑ୧ഃࡢ᭱እഃ㒊㔜ᚰࡲ࡛ࡢ┤⥺㊥㞳
ࢆ⏝࠸࡚㸪どᗋ⨨ࢆ⿵ṇࡍࡿࡓࡵࡢ࣮ࣝࣝࢆタࡅ㸪⊂⮬సࡾฟࡋࡓᘧ㸦2㸧㸪
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
79
-㸦3㸧ࢆ⏝ࡋ࡚୧ഃࡢどᗋ⨨ࢆಟṇࡋࡓ㸬ᮏ◊✲࡛ࡣ㸪どᗋ⨨ࡢỴᐃ࠾࠸
࡚㸪࣮ࣝࣝࡸ⿵ṇᘧ࡞࡛⏝ࡋࡓࣃ࣓࣮ࣛࢱࢆࣄ࣮ࣗࣜࢫࢸࢵࢡ࣭ࣉ࣮ࣟ
ࢳࡼࡾᐃࡵࡓ㸬ࡑࢀᨾ㸪ᮍ▱࡞ᑐࡋ࡚㸪ࡇࡢࡼ࠺࡞ࣃ࣓࣮ࣛࢱ࡛ᵓ⠏ࡉ
ࢀࡓᮏࢩࢫࢸ࣒ࢆ㐺⏝ࡋࡓሙྜࡣ㸪どᗋ⨨ࡢỴᐃ⢭ᗘࡢపୗࡢࡳ࡞ࡽࡎ㸪 DWIࡢ⾲♧㍤ᗘࡢኚືࢆࡶࡶࡓࡽࡍྍ⬟ᛶࡀ࠶ࡿ᥎ ࡉࢀࡿ㸬ࡑࡢࡓࡵ㸪ࢃࢀ
ࢃࢀࡣ㸪ᐈほⓗホ౯ࡢಙ㢗ᛶࡀ㧗࠸ࡉࢀ࡚࠸ࡿ㸪ࢸࢫࢺ⏝ࢆ⏝ࡋࡓࣂࣜ
ࢹ࣮ࢩࣙࣥࢸࢫࢺࢆᐇࡋ㸪ᮏࢩࢫࢸ࣒ࡢ᭷ຠᛶࡘ࠸࡚ホ౯ࡋࡓ㸬
ࢸࢫࢺ⏝࠾࠸࡚㸪Ỵᐃࡉࢀࡓどᗋ⨨ࡣ㸪どᗋࡢ㍯㒌ෆࡍ࡚ྵࡲࢀ
࡚࠸ࡓ㸦ᅗ 6.8㸦c㸧㸪㸦d㸧㸧㸬ࡋࡓࡀࡗ࡚㸪ᮏどᗋ⨨Ỵᐃᡭἲࡣ㸪MRI ⨨㛫࠾
ࡼࡧ⿕᳨⪅㛫࡛౫Ꮡࡏࡎどᗋ⨨ࢆỴᐃ࡛ࡁ㸪DWIࡢ⾲♧᮲௳ࡢᶆ‽࠾࠸
࡚༑ศ⏝࡛ࡁࡿ⪃࠼ࡿ㸬
ಙྕᙉᗘࢆィ ࡍࡿどᗋࡣ㸪㝞ᪧᛶࡢฟ⾑ࡸࣛࢡࢼ᱾ሰࡀ⏕ࡌ࡚࠸ࡿࡇࡀ࠶
ࡿ㸬ࡑࡢࡓࡵ㸪どᗋ⨨࠾ࡅࡿᖹᆒ⏬⣲್㸪⬻ᐇ㉁㒊࠾ࡅࡿ⃰ᗘࣄࢫࢺࢢ
࣒ࣛࡢ᭱㢖ᗘ࡞ࡿ⏬⣲್ࢆ⏝ࡋ࡚㸪b0⏬ീࡢᕥྑ୧ഃࡢどᗋࡽṇᖖ࡞どᗋ
ഃࢆ㑅ᢥࡍࡿᡭἲࢆ⪃ࡋࡓ㸬ᮏどᗋ㑅ᢥᡭἲࡣ㸪ࡍ࡚ࡢᏛ⩦⏝࠾ࡼࡧࢸ ࢫࢺ⏝࠾࠸࡚㸪ṇᖖ࡞どᗋഃࢆ㑅ࡧฟࡍࡇࡀ࡛ࡁࡓ㸬
ᮏࢩࢫࢸ࣒࠾ࡅࡿ୍㐃ࡢฎ⌮㈝ࡸࡍ㛫ࢆィ ࡋࡓ⤖ᯝ㸪ᮏィ⟬ᶵࢫ࣌ࢵ
ࢡ㸦Pentium D, CPU 3.60 GHz㸧࠾࠸࡚㸪1࠶ࡓࡾ⣙0.5⛊࡞ࡗࡓ㸬ࡲࡓ㸪 ᮏࢩࢫࢸ࣒ࡢ≉ᛶホ౯࠾࠸࡚㸪ᮏᡭἲᡭື᪉ἲࡼࡾㄪ⠇ࡉࢀࡓ DWI ࡢ
⏬⣲ࡽồࡵࡓᖹᆒ⏬⣲್ࢆ⏝࠸࡚㸪᭷ពᕪ᳨ᐃࢆ⾜ࡗࡓ⤖ᯝ㸪Ꮫ⩦⏝࠾ࡼ
ࡧࢸࢫࢺ⏝ࡶ5 %ࡢ༴㝤⋡࡛᭷ពᕪ࡞ࡋࡢุᐃ࡞ࡗࡓ㸬ྠᵝ㸪೫ᕪ
⋡ࢆồࡵࡓ⤖ᯝ㸪Ꮫ⩦⏝㛫࡛᭱6.30 %࡞ࡾ㸪ࢸࢫࢺ⏝㛫࡛ࡣ㸪Ꮫ⩦
⏝ྠ➼࡞≉ᛶ⤖ᯝ࡞ࡗࡓ㸬ࢃࢀࢃࢀࡣ㸪ከࡃࡢ⏬ീࢆほᐹࡋ࡚ᚓࡽࢀࡓ
▱㆑ࡽ㸪೫ᕪ⋡ࡀ20 %ࢆ㉸࠼ࡿ⏬ീẚ㍑ⓗ᫂ࡽ࡞ኚࡀ⌧ࢀࡿࡶࡢㄆ
㆑ࡋ࡚࠸ࡿ㸬ࡉࡽ㸪ᮏᡭἲᡭື᪉ἲࡼࡾ⾲♧᮲௳ࡀỴᐃࡉࢀࡓ DWI ࢆほ ᐹࡋ㸪ほⓗ࡞㢮ఝᛶࡘ࠸࡚ẚ㍑ホ౯ࡋࡓ⤖ᯝ㸪⬻ᐇ㉁㒊ࡢಙྕᙉᗘࡸ⏬ീࢥ
ࣥࢺࣛࢫࢺ᫂ࡽ࡞ᕪ␗ࡀㄆࡵࡽࢀࡎ㸪‶㊊࡛ࡁࡿ⛬ᗘ୍⮴ࡋ࡚࠸ࡓ㸬௨ୖ
ࡢ⤖ᯝࡼࡾ㸪ᮏࢩࢫࢸ࣒ࡣ㸪࣮ࣝࣝࡸ⿵ṇᘧ࡞ࡢỴᐃ⏝ࡉࢀ࡚࠸࡞࠸ࢸࢫ ࢺ⏝ࢆ⏝ࡋ࡚ࡶ㸪Ꮫ⩦⏝ྠ➼ࡢᏳᐃࡋࡓ≉ᛶࢆᚓࡿࡇࡀ࡛ࡁ㸪ᡭ
ື᪉ἲኚࢃࡾ࡞࠸⏬ീࢆ㠀ᖖ▷㛫࡛ฟຊ࡛ࡁࡿࡇࡽ㸪᭷ຠᛶࡀ♧၀ࡉ
ࢀࡓ㸬
ᮏ◊✲࡛ࡣ㸪⬻ᐇ㉁㒊ࡢᅇ㌿ฎ⌮࠾ࡅࡿṇ୰▮≧⥺ࡢタᐃࢆᡭືࡼࡾỴᐃ ࡋ࡚࠸ࡿ㸬ᮏᡭἲࡣ㸪⬻ࡢᕥྑᑐ⛠ᛶᇶ࡙࠸࡚ᵓ⠏ࡉࢀ࡚࠸ࡿࡓࡵ㸪⮬ື
ࡢᡂྥࡅ࡚㸪ṇ୰▮≧⥺ࡢഴࡁࢆ㧗⢭ᗘ࡛⿵ṇ࡛ࡁࡿᢏ⾡ࡢ㛤Ⓨࡀ㔜せㄢ
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
㢟࡛࠶ࡾ㸪ᚋ㸪ᅇ㌿ฎ⌮㛵ࡍࡿᚑ᮶◊✲ࢆ㋃ࡲ࠼ࡓ᳨ウࡀᚲせ࡛࠶ࡿ㸬
ࡲࡓ㸪ᮏどᗋ⨨Ỵᐃᡭἲࡣ㸪ࣄ࣮ࣗࣜࢫࢸࢵࢡ࣭ࣉ࣮ࣟࢳࡼࡾタᐃࡋ ࡓ࣮ࣝࣝࡸ⿵ṇᘧࢆ⏝ࡋ࡚ᵓ⠏ࡉࢀ࡚࠾ࡾ㸪⌮ㄽⓗ࡞᭱㐺ࡣ⾜ࢃࢀ࡚࠸࡞࠸㸬 Ꮫ⩦⏝࠾࠸࡚㸪どᗋࡢ㍯㒌ࡣᨺᑕ⥺⛉་ࡢᡭືࡢୗ㸪࣐࣮࢟ࣥࢢࡉࢀ࡚࠾
ࡾ㸪Ỵᐃࡍࡁどᗋ⨨ࡣࡍุ࡛᫂ࡋ࡚࠸ࡿࡇࡽ㸪ᚋ㸪᭱㐺ࣝࢦࣜ
ࢬ࣒ࢆ㐺⏝ࡋ࡚㸪᭦㧗⢭ᗘ࡞ᡭἲࡢ㛤Ⓨດࡵ࡚࠸ࡁࡓ࠸⪃࠼࡚࠸ࡿ㸬 6.5 ⤖ㄒ
ᮏ◊✲࡛ࡣ㸪ASIST-Japanࡼࡾ⪃ࡉࢀࡓDWIࡢ⾲♧᮲௳ࢆỴᐃࡍࡿ᪉ἲࡢ ᐇ⏝ࢆ┠ⓗ㸪b0⏬ീࡢᕥྑ୧ഃࡢどᗋ⨨ࢆỴᐃࡋ㸪ṇᖖ࡞どᗋഃࢆ㑅ᢥࡍ
ࡿᡭἲࢆ㛤Ⓨࡋࡓ㸬ࡑࡋ࡚㸪㑅ᢥࡉࢀࡓどᗋ⨨ࡢಙྕᙉᗘࢆ⏝ࡋ࡚㸪DWIࡢ
⾲♧᮲௳ࢆㄪ⠇ࡍࡿࢩࢫࢸ࣒ࢆ㛤Ⓨࡋࡓ㸬ࡑࡢ⤖ᯝ㸪⪃ࡋࡓ᪉ἲࡣ㸪MRI⨨
࠾ࡼࡧ⿕᳨⪅౫Ꮡࡏࡎ㸪どᗋ⨨ࢆṇ☜Ỵᐃ࡛ࡁ㸪ᮏࢩࢫࢸ࣒ࡣ㸪㛤Ⓨ
⏝ࡋ࡚࠸࡞࠸ࢆ⏝࠸ࡓࣂࣜࢹ࣮ࢩࣙࣥࢸࢫࢺ࠾࠸࡚ࡶ㸪Ꮫ⩦⏝ྠ➼
ࡢ≉ᛶ࡞ࡗࡓ㸬 ཧ⪃ᩥ⊩
[1] Wiegell MR, Tuch DS, Larsson HB, Wedeen VJ, “Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging,” Neuroimage, vol.19, no.2, pp.391-401, 2003.
[2] Duan Y, Li X, Xi Y, “Thalamus segmentation from diffusion tensor magnetic resonance imaging,” Int J Biomed Imaging, vol.2007, pp.1-5, 2007.
[3] Boesen K, Rehm K, Schaper K, Stoltzner S, Woods R, Lüders E, Rottenberg D,
“Quantitative comparison of four brain extraction algorithms,” Neuroimage, vol.22, no.3, pp.1255-1261, 2004.
[4] Rex DE, Shattuck DW, Woods RP, Narr KL, Luders E, Rehm K, Stoltzner SE, Rottenberg DA, Toga AW, “A meta-algorithm for brain extraction in MRI,”
Neuroimage, vol.23, no.2, pp.625-637, 2004.
[5] Helms G, Kallenberg K, Dechent P, “Contrast-driven approach to intracranial segmentation using a combination of T2- and T1-weighted 3D MRI data sets,” J Magn Reson Imaging, vol.24, no.4, pp.790-795, 2006.
[6] 㯮ᕝᆂ㸪୕ᾆ ಙ㸪す⏣ ┾㸪ᬒᒣ㝧୍㸪ⱑᮧ⫱㑻㸪͆MRI⬻⏬ീ࠾ࡅࡿ
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
81
-㢌ෆ㡿ᇦࡢ⮬ືᢳฟἲ㸪͇㟁ẼᏛㄽᩥㄅ C㸪vol.124㸪no.9㸪pp.1780-1789㸪 2004㸬
[7] ᕝ㍤ᙪ㸪࿘ ྥᰤ㸪ཎ Ṋྐ㸪⸨⏣ᗈᚿ㸪ᶓᒣ㱟㑻㸪㏆⸨ᾈྐ㸪වᯇ㞞 அ㸪ᫍ ༤㸪͆యᖿ㒊㠀㐀ᙳX⥺CT⏬ീ࠾ࡅࡿ⫢⮚ࢺࣛࢫࡢᵓ⠏ࡑ ࡢ⫢⮚⮬ືᢳฟἲࡢᛂ⏝㸪͇㟁Ꮚሗ㏻ಙᏛㄽᩥㄅ㸪vol.J91-D㸪no.7㸪 pp.1837-1850㸪2008㸬
[8] బ⸨ṇஅ㸪͆⚄⤒ᦆയ㒊≧㸸どᗋࡢೃ㸪͇⥲ྜࣜࣁࣅࣜࢸ࣮ࢩࣙࣥ㸪 vol.34㸪no.10㸪pp.963-970㸪2006㸬
[9] ዟ⏣ెᘏ㸪͆⬻ฟ⾑ࡢணᚋࡢ᳨ウ㸪͇Neurosurg Emerg㸪vol.13㸪no.1㸪pp.63-71㸪 2008㸬
➨6❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦1㸧
➨
➨ 7 ❶
㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻ MR ⏬ീ࠾ࡅࡿ
⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦 2 㸧
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
➨
➨ 7 ❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻ MR ⏬ീ࠾ࡅࡿ
⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦 2 㸧
7.1 ⥴ゝ
➨6❶࡛ࡣ㸪ASIST-Japanࡼࡾ⪃ࡉࢀࡓDWIࡢ⾲♧᮲௳ㄪ⠇ἲࡢᐇ⏝ࢆ
┠ⓗ㸪b0⏬ീࡢᕥྑ୧ഃࡢどᗋ⨨ࢆỴᐃࡋ࡚ṇᖖ࡞どᗋഃࢆ㑅ᢥࡋ㸪ᚓࡽࢀ
ࡓどᗋࡢಙྕᙉᗘࢆ⏝ࡋ࡚ DWI ࡢ⾲♧᮲௳ࢆ⮬ືㄪ⠇ࡍࡿࢩࢫࢸ࣒ࢆ㛤Ⓨࡋ ࡓ㸬ࡑࡢ⤖ᯝ㸪MRI ⨨࠾ࡼࡧ⿕᳨⪅౫Ꮡࡏࡎどᗋ⨨ࢆṇ☜Ỵᐃ࡛ࡁ㸪 㛤Ⓨᮍ⏝ࡢࢸࢫࢺ⏝ࢆ⏝࠸ࡓ᳨ド࠾࠸࡚ࡶ㸪Ꮫ⩦⏝ྠ➼ࡢ≉ᛶ
ࢆᚓࡿࡇࡀ࡛ࡁࡓ㸬ࡋࡋ㸪ROIࢆタᐃࡍࡿどᗋࡀฟ⾑ࡸ↓≧ᛶࡢࣛࢡࢼ᱾
ሰࡢዲⓎ㒊࡛࠶ࡿ࠸࠺ၥ㢟Ⅼࡀᣲࡆࡽࢀ࡚࠸ࡓ㸬どᗋࡇࢀࡽᝈࡢ᪤ ࡀ
࠶ࡗࡓሙྜ㸪ಙྕᙉᗘࡀᖜపಙྕࡲࡓࡣ㧗ಙྕࢆ࿊ࡋ㸪⬻ᐇ㉁㒊㡿ᇦᑐࡋ
࡚3 %⛬ᗘࡢ㠃✚ࢆᣢࡘどᗋ㓄⨨ࡉࢀࡓROIࡼࡾ⏬⣲್ࢆィ ࡋ࡚DWIࡢ
⾲♧᮲௳ࡀㄪ⠇ࡉࢀࡿ㸪ಙྕᙉᗘࡸ⏬ീࢥࣥࢺࣛࢫࢺࡣⴭࡋࡃኚࡍࡿ㸬ᐇ㝿
ࡣ㸪┠ど࡚ኚ㒊ࢆእࡋࡓ⨨ROIࢆタᐃࡋᑐฎࡉࢀ࡚࠸ࡿࡶࡢᛮࢃࢀ
ࡿࡀ㸪ࡑࡢሙྜࡣROIࡢࡁࡉࡀᴟࡵ࡚ᑠࡉࡃ࡞ࡾ㸪ᖹᆒ⏬⣲್ࡢィ ࠾࠸
࡚⤫ィⓗኚືࢆక࠺ࡇࡘ࡞ࡀࡿ㸬ࡉࡽ㸪㉸㧗㏿ീࢩ࣮ࢣࣥࢫ࡛࠶ࡿEcho
Planar Imaging㸦EPI㸧ἲࢆ⏝࠸࡚ീࡉࢀ࡚࠸ࡿb0⏬ീࡣ㸪ീ㡿ᇦ࠾ࡅࡿ☢
⋡ࡢ㐪࠸ࡼࡿᙳ㡪ࢆཷࡅࡿࡓࡵ㸪どᗋ㡿ᇦෆฟ⾑࡞ࡢኚࡀᏑᅾࡋࡓሙ
ྜ㸪ࡑࡢ࿘㎶ࡢ⏬⣲್ࡣኚືࡍࡿ㸬ࡲࡓ㸪EPI ἲࡣ⏬ീ࠾ࡅࡿಙྕᑐ㞧㡢ẚ 㸦signal to noise ration㸸SNR㸧ࡢపୗࢆᣍࡃࡓࡵ㸪ṇᖖ࡞どᗋ㡿ᇦ࠾࠸࡚ࡶ⏬⣲
್ࡢኚືࢆక࠺㸬ࡋࡓࡀࡗ࡚㸪ASIST-Japan ࡢ⪃᪉ἲ࠾ࡼࡧࡑࡢ᪉ἲࢆ⮬ື
ࡉࡏࡓඛ⾜◊✲᪉ἲࡣ㸪ࡇࢀࡽࡢၥ㢟Ⅼࡼࡾ⾲♧᮲௳ࡢㄪ⠇ᙳ㡪ࢆཬࡰࡍࡶ
ࡢ᥎ ࡉࢀࡿ㸬
ࢃࢀࢃࢀࡣ㸪どᗋࡢಙྕᙉᗘࢆ⏝ࡍࡿ ASIST-Japan ࡢ⪃᪉ἲ␗࡞ࡿ㸪᪂ ࡋ࠸DWIࡢ⾲♧᮲௳ㄪ⠇ᡭἲࡋ࡚㸪b0⏬ീࡢ⬻ᐇ㉁㒊ෆࡢ⃰ᗘࣄࢫࢺࢢ࣒ࣛ
࠾ࡅࡿ᭱ࡶ㢖ᗘࡀ㧗ࡃ࡞ࡿ⏬⣲್ࢆ⏝ࡍࡿᡭἲࢆᥦࡍࡿ㸬ᮏ◊✲࡛ࡣ㸪㉸
ᛴᛶᮇ⬻᱾ሰࡢ DWI b0 ⏬ീ࡛ᵓ⠏ࡉࢀࡓ⏬ീࢹ࣮ࢱ࣮࣋ࢫࢆ⏝ࡋ࡚
ASIST-Japanࡢ⪃᪉ἲ࠾ࡼࡧᮏᡭἲࢆᐇࡋ㸪ㄪ⠇ࡉࢀࡓDWIࡽồࡵࡓ⏬ീ
ホ౯ᣦᶆほᐹ⪅ᐇ㦂ࡼࡿ⤖ᯝࢆ⏝࠸࡚ᮏᡭἲࡢ⢭ᗘ࠾ࡼࡧ≉ᛶࢆホ౯ࡋ࡚㸪
᭷ຠᛶࡘ࠸᳨࡚ウࡋࡓ㸬
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
84
-ᅗ7.1 ⬻DWI࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ᡭἲࡢᴫせ
7.2 ᪉ἲ
7.2.1 ⏬ീࢹ࣮ࢱ࣮࣋ࢫ
ᐇ㦂⏝ࡋࡓ⏬ീࢹ࣮ࢱ࣮࣋ࢫࡣ㸪2005ᖺ11᭶ࡽ 2007ᖺ11 ᭶ࡲ࡛⩌
㤿┴ෆ3タ࡛1.5 TࡢMRI⨨3ᶵ✀㸦GENESIS SIGNA㸪SIGNA EXCITE㸸GE
♫〇㸪Gyroscan Intera㸸PHILIPS♫〇㸧ࢆ⏝࠸࡚ീࡉࢀࡓ㸪㉸ᛴᛶᮇ⬻᱾ሰ
60ࡢ⬻DWI࠾ࡼࡧb0⏬ീ࡛ᵓ⠏ࡉࢀ࡚࠸ࡿ㸬ࡣ㸪⏨ᛶ40ྡ㸪ዪᛶ20ྡ㸪 ᖺ㱋22㹼90 ṓ㸦ᖹᆒ68.2±14.1㸧࡛࠶ࡿ㸬࡞࠾㸪60ࡢ b0 ⏬ീ࠾࠸࡚㸪ᕥ
ྑ୧ഃࡢどᗋᝈࡀ࠶ࡗࡓࡣ16㸦26.7 %㸧㸪∦ഃࡢどᗋᝈࡀ࠶ࡗࡓ
ࡣ16㸦26.7 %㸧㸪ィ48㡿ᇦࡢどᗋኚࡀᏑᅾࡍࡿ㸬
ᮏ◊✲࡛ࡣ㸪ྛࡽどᗋࡀᥥฟࡉࢀ࡚࠸ࡿᇶᗏ᰾ࣞ࣋ࣝࡢࢫࣛࢫീࢆ㑅 ᢥࡋ⏝ࡋࡓ㸬࡞࠾㸪⏬ീࢹ࣮ࢱࡢ⏝㝿ࡋ㸪ᮏタࡢ⌮ጤဨࡢᢎㄆࢆྲྀ
ᚓࡋ࡚࠸ࡿ㸬ീ᮲௳ࡣ㸪Spin-Echoἲࡢ EPI㸪b್㸸0㸪1000 s/mm2㸪TR㸸3472 㹼10000 ms㸪TE㸸86㹼102 ms㸪Flip Angle㸸90r㸪ࢫࣛࢫཌ㸸5 mm㸪ࢫࣛࢫ 㛫㝸㸸1㹼2 mm㸪MPG༳ຍ㍈㸸3᪉ྥ࡛࠶ࡿ㸬
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
ᅗ7.2 ⬻ᐇ㉁㒊ࡢᢳฟฎ⌮㸬㸦a㸧b0⏬ീ㸪㸦b㸧2್ฎ⌮ࢆࡋࡓ⏬ീ㸪㸦c㸧ࣛ
࣋ࣜࣥࢢฎ⌮✰ᇙࡵฎ⌮ࢆࡋࡓ2್⏬ീ㸪㸦d㸧ࣛ࣋ࣜࣥࢢฎ⌮✰ᇙࡵฎ
⌮ࢆࡋࡓཎ⏬ീ
7.2.2 ᥦᡭἲࡢᴫせ
DWI࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ᡭἲࡢᴫせࢆᅗ7.1♧ࡍ㸬ᮏᡭἲࡣ㸪๓ฎ⌮
ࡋ࡚㸪ධຊࡋࡓb0⏬ീࡢ㝵ㄪࢆṇつࡋ㸪2್ฎ⌮ࣛ࣋ࣜࣥࢢฎ⌮ࢆ⏝࠸
࡚⬻ᐇ㉁㒊ࢆᢳฟࡋࡓ㸬ḟ㸪⬻ᐇ㉁㒊ࢆᢳฟࡋࡓཎ⏬ീᑐࡋ࡚⃰ᗘࣄࢫࢺࢢ
࣒ࣛࢆィ⟬ࡋ㸪᭱ࡶ㢖ᗘࡀ㧗ࡃ࡞ࡿ⏬⣲್ࢆồࡵࡓ㸬᭱ᚋ㸪ồࡵࡓ⏬⣲್ࢆ
⏝ࡋ࡚㸪DWIࡢ⾲♧᮲௳ࢆㄪ⠇ࡋࡓ㸬
7.2.3 ⬻ᐇ㉁㒊ࡢᢳฟฎ⌮
ᢳฟฎ⌮ᡭἲࡣ㸪ࡲࡎ㸪ീࡉࢀࡓ⬻b0⏬ീ㸦matrix size: 256×256, gray scale: 12 bits, pixel size: 0.820㹼0.937 mm㸧ࢆࢥࣥࣆ࣮ࣗࢱㄞࡳ㎸ࡳ㸪๓ฎ⌮ࡋ࡚㸪b0
⏬ീࡢ㝵ㄪࢆṇつࡋࡓ㸬ලయⓗࡣ㸪b0⏬ീࡢ᭱ᑠ⏬⣲್࠾ࡼࡧ᭱⏬⣲್ࢆ
ồࡵ㸪⥺ᙧ㝵ㄪኚฎ⌮ࢆ⏝࠸࡚8 bits㸦0㹼255㸧㝵ㄪኚࡋࡓ㸬ḟ㸪㝵ㄪኚ
ࡋࡓ⏬ീᑐࡋ㸪2್ฎ⌮ࢆ⾜ࡗࡓ㸬࡞࠾㸪ࡋࡁ࠸್タᐃࡣ㸪60ࢆ⏝
ࡋ࡚ࡋࡁ࠸್ࢆ㡰ḟኚࡉࡏ㸪2ྡࡢᨺᑕ⥺⛉་㸦⮫ᗋ⤒㦂12ᖺ࠾ࡼࡧ29ᖺ㸪
ࡶᨺᑕ⥺⛉ᑓ㛛་ࡢ㈨᱁ࢆྲྀᚓ㸧ࡀྜ㆟ࡢࡶ࣐࣮࢟ࣥࢢࡋࡓ⬻ᐇ㉁㒊㡿
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
86
-ᇦෆྵࡲࢀࡿಙྕ⏬⣲ᩘࡢྜࢆ⟬ฟࡋ࡚㸪⬻ᐇ㉁㒊ࡀ࡛ࡁࡿ㝈ࡾṇ☜ᢳฟ
࡛ࡁ㸪ୟࡘࡍ࡚ࡢ୍࡛ᐃࡢྜ࡞ࡿ⏬⣲್ࢆᐃࡵࡓ㸬ᅗ 7.2㸦a㸧 b0
⏬ീ㸪㸦b㸧2್ฎ⌮ࢆࡋࡓ⏬ീࢆ♧ࡍ㸬ࡑࡢᚋ㸪᫂ࡽᑠࡉ࠸㝜ᙳࢆ㝖 ཤࡍࡿࡓࡵ㸪ࣛ࣋ࣜࣥࢢฎ⌮ࢆ⏝࠸࡚㝜ᙳࡢ㠃✚ࢆィ⟬ࡋ㸪㠃✚ࡀ⏬⣲ᩘࡢ 15 %௨ୗ࡛࠶ࡿࡶࡢࡣ㝖እࡋࡓ㸬ࡑࡋ࡚㸪ࣛ࣋ࣜࣥࢢฎ⌮ࢆ⏝࠸ࡓ✰ᇙࡵฎ⌮ࢆ
ࡋ࡚b0⏬ീࡢ⬻ᐇ㉁㒊ࢆᢳฟࡋࡓ㸬ᅗ 7.2㸦c㸧㸪㸦d㸧⬻ᐇ㉁㒊ࢆᢳฟࡋࡓ 2
್⏬ീ࠾ࡼࡧཎ⏬ീࢆࡑࢀࡒࢀ♧ࡍ㸬
7.2.4 ⃰ᗘࣄࢫࢺࢢ࣒ࣛゎᯒ
CT ⏬ീࡸ MR ⏬ീ࡞ࡢ་⏝⏬ീࡣ㸪㛫ࡸ⨨㛫࠾࠸࡚⏬⣲್ࡀኚື
ࡍࡿ㸬ࢃࢀࢃࢀࡢඛ⾜◊✲࠾࠸࡚㸪⬻ CT ⏬ീࡢ㝵ㄪࢆṇつࡍࡿࡓࡵ㸪ᢳฟ ࡋࡓ⬻ᐇ㉁㒊ࡽồࡵࡓ⃰ᗘࣄࢫࢺࢢ࣒ࣛࡢ᭱ࡶ㢖ᗘࡀ㧗ࡃ࡞ࡿ⏬⣲್ࢆ⏝ࡍ
ࡿᡭἲࢆྲྀࡾධࢀ㸪㧗⢭ᗘ࡞ฎ⌮ࢆᐇ⌧ࡋࡓ [1]㹼[3]㸬ࡲࡓ㸪ᮏ◊✲࠾࠸࡚㸪 どᗋࡢ⏬⣲್⬻ᐇ㉁㒊ࡢ⃰ᗘࣄࢫࢺࢢ࣒ࣛࡢ᭱㢖ᗘ࡞ࡿ⏬⣲್ࡀྠ⛬ᗘ࡛࠶
ࡿࡇࢆぢฟࡋࡓ㸬௨ୖࡢᡤぢࡽ㸪ᮏ◊✲࡛ࡣ㸪b0⏬ീࡽᢳฟࡋࡓ⬻ᐇ㉁㒊 ࡢཎ⏬ീᑐࡋ࡚⃰ᗘࣄࢫࢺࢢ࣒ࣛࢆィ⟬ࡋ㸪㢖ᗘࡀ᭱࡞ࡿ⏬⣲್ࢆỴᐃࡋ
࡚㸪DWIࡢ⾲♧᮲௳ࡢㄪ⠇௦⏝ࡋࡓ㸬
7.2.5 DWIࡢ⾲♧᮲௳ࡢㄪ⠇
ീࡉࢀࡓ⬻DWI㸦matrix size: 256×256, gray scale: 12 bits, pixel size: 0.820㹼
0.937 mm㸧ࢆࢥࣥࣆ࣮ࣗࢱㄞࡳ㎸ࡳ㸪⃰ᗘࣄࢫࢺࢢ࣒ࣛゎᯒࡼࡾồࡵࡓ᭱㢖
ᗘ࡞ࡿ⏬⣲್ࢆWWタᐃࡋ࡚㸪DWIࢆ8 bits㸦0㹼255㸧㝵ㄪኚࡋࡓ㸬
7.2.6 ࢩࢫࢸ࣒ࡢ≉ᛶホ౯
་⏝⏬ീ࠾ࡅࡿ⏬ീホ౯࡛ࡣ㸪㏻ᖖ㸪ࢥࣥࢺࣛࢫࢺẚࡸSNR࡞ࡢᣦᶆ [4]
㹼[8]ࡀ⏝ࡉࢀ࡚࠸ࡿ㸬ࡋࡋ㸪ᮏ◊✲࡛ࡇࢀࡽࡢᣦᶆࢆ㐺⏝ࡍࡿሙྜ㸪ࡈ
࡛⬻ⓑ㉁ࡸ⬻⅊ⓑ㉁࡞ࡢỴࡵࡽࢀࡓ ᐃ⨨ROIࢆタᐃࡍࡿࡇࡀᴟࡵ࡚
ᅔ㞴࡛࠶ࡿࡇ㸪ࡲࡓ㸪ROIࡀタᐃ࡛ࡁ࡚ࡶ ᐃ⢭ᗘࡢపୗࢆࡶࡶࡓࡽࡍྍ⬟ᛶ ࡀ㧗࠸ࡇ࡞ࡀ᥎ ࡉࢀࡓ㸬ࢃࢀࢃࢀࡣ㸪ಙྕᙉᗘࡸ⏬ീࢥࣥࢺࣛࢫࢺࢆᐃ㔞
ⓗホ౯ࡍࡿࡓࡵࡢ᪂ࡋ࠸⏬ീホ౯ᣦᶆࡋ࡚㸪ㄪ⠇ࡉࢀࡓ DWI ࡽồࡵࡓ⃰
ᗘࣄࢫࢺࢢ࣒ࣛᑐࡍࡿ᭱㢖ᗘ࡞ࡿ⏬⣲್㸦PVFRE㸧㸪᭱㢖ᗘᑐࡍࡿ್༙
࡞ࡿ⏬⣲್ࡢᖜ㸦PVFWHM㸧ࢆồࡵ㸪ᮏ◊✲⏝ࡋࡓ㸬ᅗ 7.3 㸪PVFRE PVFWHMࡢヲ⣽ࡘ࠸࡚♧ࡍ㸬
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
ᅗ7.3 ᮏ◊✲࡚⏝ࡋࡓ⏬ീホ౯ᣦᶆࡢヲ⣽
60ࡢ⬻DWIb0⏬ീࢆ⏝࠸࡚㸪ASIST-Japanࡼࡾ⪃ࡉࢀࡓ᪉ἲࢆ㸪
ୖグࡋࡓ2ྡࡢᨺᑕ⥺⛉་ࡢྜ㆟ࡢࡶᡭື࡛ᐇࡋ㸦ᡭື᪉ἲ㸧㸪ࡉࡽ㸪㛤Ⓨ
ࡋࡓᮏࢩࢫࢸ࣒ࢆᐇ⾜ࡉࡏ࡚㸦ᮏᡭἲ㸧㸪ࡑࢀࡒࢀࡢ᪉ἲࡼࡾᚓࡽࢀࡓ8 bits㝵 ㄪࡢDWIࡽ⃰ᗘࣄࢫࢺࢢ࣒ࣛࢆィ⟬ࡋ㸪PVFRE࠾ࡼࡧPVFWHMࢆồࡵࡓ㸬࡞࠾㸪 ᡭື᪉ἲ࠾ࡅࡿどᗋࡢROIࡢタᐃࡣ㸪ᨺᑕ⥺⛉་ࡢ┠ど࡚ࡈ㐺ࡋ ࡓᑍἲࡢᙧROIࡀ㓄⨨ࡉࢀࡓ㸬ࡑࡢ㝿㸪どᗋ㡿ᇦ᫂ࡽ࡞ኚࡀ࠶ࡿ࡛
ࡣ㸪ኚ㒊ࢆእࡋ࡚ROIࡀ㓄⨨ࡉࢀࡓ㸬ࡲࡓ㸪ᡭື᪉ἲ࠾ࡅࡿ DWIࡢ⾲♧᮲
௳ࡢㄪ⠇ࡣ㸪ᮏࢩࢫࢸ࣒ࡢ㛤Ⓨ⎔ቃྠ୍ࡢࡶࡢࢆ⏝࠸㸪ROIࡽồࡵࡓᖹᆒ⏬
⣲್ࢆ⏝ࡋ࡚8 bits㝵ㄪኚࡋࡓ㸬
ᮏ◊✲࡛ࡣ㸪ࡢ⏬ീホ౯ᣦᶆࡋ࡚㸪ྛᡭἲࡼࡾㄪ⠇ࡉࢀࡓ DWI ࡽồ
ࡵࡓ⃰ᗘࣄࢫࢺࢢ࣒ࣛࢆ⏝ࡋ㸪┦┦㛵್ࢆồࡵࡓ㸬┦┦㛵್ࡣ㸪୍⯡⏬
ീ࠾ࡅࡿࣃࢱ࣮ࣥㄆ㆑ࡸ⨨ྜࢃࡏࡢ㝿㐺⏝ࡉࢀࡿᣦᶆ࡛࠶ࡾ㸪⏬ീࡢᙧ≧
㢮ఝᗘࢆホ౯ࡍࡿࡓࡵࡢᑻᗘ࡛࠶ࡿ [9][10]㸬ࢃࢀࢃࢀࡣ㸪⃰ᗘࣄࢫࢺࢢ࣒ࣛࡢᙧ
≧╔┠ࡋ㸪┦┦㛵್ࢆ⏝࠸࡚㢮ఝᗘࢆホ౯ࡍࡿࡇ࡛㸪㛫࠾ࡅࡿಙྕ
ᙉᗘ࠾ࡼࡧ⏬ീࢥࣥࢺࣛࢫࢺࡢኚືࢆᢕᥱ࡛ࡁࡿࡶࡢ⪃࠼㸪ᮏ◊✲⏝ࡋࡓ㸬
࡞࠾㸪⃰ᗘࣄࢫࢺࢢ࣒ࣛᑐࡍࡿ┦┦㛵್ࡣ㸪⬻ᐇ㉁㒊ࡢࡁࡉࡣᙳ㡪ࡉࢀ
࡞࠸㸬⟬ฟ᪉ἲࡣ㸪ࡲࡎ㸪ྛᡭἲࡼࡾㄪ⠇ࡉࢀࡓ60ࡢDWIࡽᩘࢆ⏝
࠸࡚20ࢆ㑅ᢥࡋ㸪㛫࡛190ࡢ࣮࣌ࢆ⏝ពࡋࡓ㸬ࡑࡋ࡚㸪ྛ࣮࣌࠾
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
88
-ᅗ7.4 2-AFCἲ࠾ࡅࡿ⏬ീ⾲♧᪉ἲ
ࡅࡿ⬻ᐇ㉁㒊ࡢ⃰ᗘࣄࢫࢺࢢ࣒ࣛᑐࡍࡿ┦┦㛵್ࢆ௨ୗ♧ࡍᘧ㸦1㸧ࡼࡾ
ồࡵࡓ㸬
㸦1㸧
ࡇࡇ࡛㸪C㺃C ࡣ┦┦㛵್㸪B ࡣ㝵ㄪᩘ㸪 㸪 㸪࠾ࡼࡧ ࡣࡑࢀ
ࡒࢀ2ࡘࡢ⏬ീࡢ⬻ᐇ㉁㒊㡿ᇦࡢ⏬⣲࠾ࡅࡿ⏬⣲್ࡢ㢖ᗘ㸪ᖹᆒ್࠾ࡼࡧᶆ‽
೫ᕪ࡛࠶ࡿ㸬
ࡇࢀࡽࡢ3ࡘࡢ⏬ീホ౯ᣦᶆࢆ⏝࠸ࡓᡭἲ㛫ࡢẚ㍑ホ౯ࡣ㸪⤫ィ㔞ࢆ⏝࠸ࡿ
㸪⤫ィⓗ᭷ពᕪ᳨ᐃࡶేࡏ࡚⾜࠸㸪⢭ᗘ࠾ࡼࡧ≉ᛶࡘ࠸᳨࡚ウࡋࡓ㸬 ࡉࡽ㸪ࢃࢀࢃࢀࡣ㸪ୖグࡢ190ࡢ࣮࣌ࢆ⏝ࡋࡓ㸪ほᐹ⪅ࡼࡿ2⫥ᙉไ
㑅ᢥἲ [11][12]ࢆᐇࡋࡓ㸬ලయⓗࡣ㸪ࡲࡎ㸪ほᐹ⪅ᐇ㦂ࢆጞࡵࡿ๓㸪ほᐹ
⪅◊✲ࡢᴫせࢆᥦ౪ࡋ㸪ホ౯ࡢᡭ㡰ࢆㄝ᫂ࡋࡓ㸬ḟ㸪ᡭἲࡈࡢ190࣮࣌
ࡢ⏬ീࢆྠ୍ LCD ࣔࢽࢱୖ୪࡚⾲♧ࡋࡓ㸬࡞࠾㸪ᡭἲ㛫ࡢྛ࣮࣌ࡢᕥྑ
ࡢ㓄⨨ࡣࡈ࡛ኚࡉࡏࡓ㸬ᅗ7.4 㸪2-AFC ἲ࠾ࡅࡿ⏬ീ⾲♧᪉ἲࢆ♧
ࡍ㸬ほᐹ⪅ࡣ㸪DWI࠾ࡼࡧADC mapࡢ࣮࣌ᑐࡍࡿ⬻ᐇ㉁㒊ࡢಙྕᙉᗘ࠾
ࡼࡧ⏬ീࢥࣥࢺࣛࢫࢺࡢ㢮ఝᛶࢆ║ࡋ࡚㸪ࡼࡾ㢮ఝࡋ࡚࠸ࡿᡭἲࢆ㑅ᢥࡍࡿ
ࡼ࠺౫㢗ࡋࡓ㸬ᡭἲ㛫࠾ࡅࡿẚ㍑ホ౯ࡣ㸪ᡭἲࡈ࡛㑅ᢥࡉࢀࡓྜࢆᇶ⾜
g
f, f,g σf,σg
1 B
0
BB
1
g f
g g f C f
C㺃
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
⾲7.1 ᡭື࠾ࡼࡧᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓDWIࡢ⃰ᗘࣄࢫࢺࢢ࣒ࣛࡽồࡵࡓ PVFREࡢ㛫࠾ࡅࡿ⤫ィ㔞
࠸㸪⾲♧᮲௳ࡢᶆ‽ࡘ࠸᳨࡚ウࡋࡓ㸬࡞࠾㸪どぬホ౯ࡋࡓほᐹ⪅ࡣ㸪9㹼29
ᖺ㸦ᖹᆒ14.6s8.2㸧ࡢ⤒㦂ࢆᣢࡘ5ྡࡢᨺᑕ⥺⛉ᑓ㛛་࡛࠶ࡿ㸬ࡲࡓ㸪ほᐹ፹య
ࡣỗ⏝ࡢᾮᬗࣔࢽࢱࢆ⏝ࡋ㸪ほᐹ㊥㞳ࡣ⮬⏤ࡋ㸪ほᐹ㛫ࡣ20⛊௨ෆ࡛⾜࠺
ࡼ࠺ほᐹ⪅౫㢗ࡋࡓ㸬 7.3 ⤖ᯝ
⾲ 7.1 㸪60 ࠾࠸࡚ᡭື᪉ἲᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓ DWIࡢ⃰ᗘࣄ
ࢫࢺࢢ࣒ࣛࡽồࡵࡓ PVFREࢆ⏝࠸࡚㸪㛫࡛ᖹᆒ್㸪ᶆ‽೫ᕪ㸪࠾ࡼࡧኚື
ಀᩘࢆồࡵࡓ⤖ᯝࢆ♧ࡍ㸬ᡭື᪉ἲ࠾ࡅࡿPVFREࡢᖹᆒ್࠾ࡼࡧኚືಀᩘࡣ㸪 ࡑࢀࡒࢀ 109.6s14.2㸪s13.0 %࡞ࡗࡓࡢᑐࡋ㸪ᮏᡭἲ࡛ࡣ㸪ࡑࢀࡒࢀ113.6
s8.0㸪s7.0 %࡞ࡗࡓ㸬ࡲࡓ㸪ᡭື᪉ἲ࠾ࡼࡧᮏᡭἲ㛫࠾࠸࡚ᶆ‽೫ᕪࡢᕪ
ࢆ᳨ᐃࡋࡓ⤖ᯝ㸪༴㝤⋡ࡀ᭷ពỈ‽0.001ࡼࡾపࡃ࡞ࡗࡓ㸦df㸻59㸪P< .001㸧㸬
⾲ 7.2 㸪60 ࠾࠸࡚ᡭື᪉ἲᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓ DWIࡢ⃰ᗘࣄ
ࢫࢺࢢ࣒ࣛࡽồࡵࡓ PVFWHMࢆ⏝࠸࡚㸪㛫࡛⤫ィ㔞ࢆồࡵࡓ⤖ᯝࢆ♧ࡍ㸬 ᡭື᪉ἲ࠾ࡅࡿ PVFWHMࡢᖹᆒ್࠾ࡼࡧኚືಀᩘࡣ㸪ࡑࢀࡒࢀ 38.3s9.3㸪s
24.2 %࡞ࡗࡓࡢᑐࡋ㸪ᮏᡭἲ࡛ࡣ㸪ࡑࢀࡒࢀ39.7s6.6㸪s16.5 %࡞ࡗࡓ㸬
᭷ពᕪ᳨ᐃ⤖ᯝࡣ㸪༴㝤⋡ࡀ᭷ពỈ‽0.01ࡼࡾపࡃ࡞ࡗࡓ㸦df㸻59㸪P< .01㸧㸬
⾲7.3㸪ྛᡭἲࡼࡾㄪ⠇ࡉࢀࡓ20190࣮࣌ࡢDWIࢆ⏝࠸࡚㸪⬻ᐇ
㉁㒊ࡢ⃰ᗘࣄࢫࢺࢢ࣒ࣛᑐࡍࡿ┦┦㛵್ࢆồࡵ㸪㛫࡛⤫ィ㔞ࢆồࡵࡓ⤖
ᯝࢆ♧ࡍ㸬ᡭື᪉ἲ࠾ࡅࡿ┦┦㛵್ࡢᖹᆒ್࠾ࡼࡧኚືಀᩘࡣ㸪ࡑࢀࡒࢀ
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
90
-⾲7.2 ᡭື࠾ࡼࡧᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓDWIࡢ⃰ᗘࣄࢫࢺࢢ࣒ࣛࡽồࡵࡓ PVFWHMࡢ㛫࠾ࡅࡿ⤫ィ㔞
⾲7.3 ᡭື࠾ࡼࡧᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓDWIࡢ⃰ᗘࣄࢫࢺࢢ࣒ࣛࢆ⏝ࡋ࡚
ồࡵࡓ┦┦㛵್ࡢ㛫࠾ࡅࡿ⤫ィ㔞
0.598s0.300㸪s50.2 %࡞ࡗࡓࡢᑐࡋ㸪ᮏᡭἲ࡛ࡣ㸪ࡑࢀࡒࢀ 0.812s0.158㸪
s19.5 %࡞ࡗࡓ㸬ࡲࡓ㸪୧ᡭἲ㛫࠾࠸࡚ᖹᆒ್ࡢᕪࢆ᳨ᐃࡋࡓ⤖ᯝ㸪༴㝤⋡
ࡀ᭷ពỈ‽0.001ࡼࡾపࡃ࡞ࡗࡓ㸦df㸻189㸪P< .001㸧㸬
⾲7.4 㸪ྛᡭἲࡼࡾㄪ⠇ࡉࢀࡓ20190 ࣮࣌ࡢDWIࢆ⏝࠸࡚㸪5ྡ ࡢほᐹ⪅ࡼࡿ2⫥ᙉไ㑅ᢥἲࢆᐇࡋ㸪ᡭἲࡈࡢ㑅ᢥ⋡ࢆồࡵ㸪ほᐹ⪅㛫࡛
⤫ィ㔞ࢆồࡵࡓ⤖ᯝࢆ♧ࡍ㸬ᡭື᪉ἲ࠾ࡅࡿほᐹ⪅㛫࡛ࡢᖹᆒ㑅ᢥ⋡ࡣ㸪27.8 %
࡞ࡗࡓࡢᑐࡋ㸪ᮏᡭἲ࡛ࡣ㸪72.2 %࡞ࡗࡓ㸬
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
⾲7.4 ᡭື࠾ࡼࡧᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓDWIࢆ⏝ࡋࡓ2⫥ᙉไ㑅ᢥἲࡼ
ࡾᚓࡽࢀࡓ㑅ᢥ⋡ࡢほᐹ⪅㛫࠾ࡅࡿ⤫ィ㔞
ᅗ 7.5 㸪ᡭື᪉ἲᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓ㸪3ࡢ DWI ࢆ♧ࡍ㸬ᅗ 7.5 㸦a㸧㸪㸦b㸧࠾ࡼࡧ㸦c㸧ࡣᡭື᪉ἲ㸪ᅗ7.5㸦d㸧㸪㸦e㸧࠾ࡼࡧ㸦f㸧ࡣᮏᡭἲࡼࡾ
ㄪ⠇ࡉࢀࡓDWI࡛࠶ࡿ㸬ᮏࢩࢫࢸ࣒ࢆ⏝࠸࡚⾲♧᮲௳ࢆㄪ⠇ࡋࡓDWIࡣ㸪ᡭື
ࡼࡾㄪ⠇ࡉࢀࡓ DWI ẚ㸪ಙྕᙉᗘࡸ⏬ീࢥࣥࢺࣛࢫࢺࡀほⓗᴟࡵ࡚
㢮ఝࡋ࡚࠾ࡾ㸪ࡢ㛫࠾࠸࡚ࡶ᫂ࡽ࡞ᕪ␗ࡣㄆࡵࡽࢀ࡞ࡗࡓ㸬 7.4 ⪃ᐹ
ᮏ◊✲࡛ࡣ㸪ASIST-Japan ࡼࡾ⪃ࡉࢀࡓ᪉ἲࡢၥ㢟Ⅼࢆᨵၿࡋ㸪་ᖌࡀ⾑
ᰦ⁐ゎ⒪ἲࡢ㐺ᛂࢆྍ⬟࡞㝈ࡾ᪩ࡃṇ☜Ỵᐃ࡛ࡁࡿࡇࢆ┠ⓗ㸪⬻⾑⟶ᝈ ࡢዲⓎ㒊࡛࠶ࡿどᗋࡢಙྕᙉᗘࢆ⏝ࡏࡎ㸪⃰ᗘࣄࢫࢺࢢ࣒ࣛࡢ᭱㢖ᗘ࡞
ࡿ⏬⣲್ࢆ⏝࠸࡚㸪DWIࡢ⾲♧᮲௳ࢆㄪ⠇ࡍࡿᡭἲࢆ㛤Ⓨࡋࡓ㸬ᮏࢩࢫࢸ࣒ࡢ≉
ᛶホ౯࠾࠸࡚㸪ᮏᡭἲASIST-Japanࡢᡭື᪉ἲࡼࡾㄪ⠇ࡉࢀࡓDWIࡢ⃰ᗘ
ࣄࢫࢺࢢ࣒ࣛࡽồࡵࡓ PVFRE࠾ࡼࡧ PVFWHMࢆ⏝࠸࡚㸪㛫࡛⤫ィ㔞ࢆồࡵ
ࡓ⤖ᯝ㸪ᮏᡭἲ࠾ࡅࡿኚືಀᩘࡣ㸪ࡑࢀࡒࢀs7.0 %㸪s16.5 %࡞ࡾ㸪ᡭື᪉ ἲẚ㍑ࡋ࡚ⴭࡋࡃኚືࡀᢚไࡉࢀࡓ㸬ྠᵝ㸪PVFREPVFWHMࢆ⏝࠸࡚㸪ᮏᡭ ἲᡭື᪉ἲ㛫࠾ࡅࡿᶆ‽೫ᕪࡢᕪᑐࡍࡿ᭷ពᕪ᳨ᐃࢆ⾜ࡗࡓ⤖ᯝ㸪ࡑࢀࡒ
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
92
-ᅗ7.5 3ࡢDWIࡢẚ㍑㸬㸦a㸧࣭㸦b㸧࣭㸦c㸧ᡭື᪉ἲࡼࡾㄪ⠇ࡉࢀࡓDWI㸪 㸦d㸧࣭㸦e㸧࣭㸦f㸧ᮏᡭἲࡼࡾㄪ⠇ࡉࢀࡓDWI
ࢀ0.1 %࠾ࡼࡧ1 %ࡢ༴㝤⋡࡛᭷ពᕪ࠶ࡾࡢุᐃ࡞ࡗࡓ㸬ࡲࡓ㸪ྛᡭἲࡼࡾㄪ
⠇ࡉࢀࡓ DWI ࡢ⃰ᗘࣄࢫࢺࢢ࣒ࣛᑐࡍࡿᙧ≧㢮ఝᗘࢆ┦┦㛵್ࢆ⏝࠸࡚ホ ౯ࡋࡓ⤖ᯝ㸪ᡭື᪉ἲ࡛ࡣ㛫࡛ᖹᆒ 0.598 ࡞ࡗࡓࡢᑐࡋ㸪ᮏᡭἲ࡛ࡣ㸪
0.812࡞ࡾ㸪୧ᡭἲ㛫᭷ព࡞ᕪࡀㄆࡵࡽࢀࡓ㸬┦┦㛵್ࡣ㸪ᙧ≧ࡀ୍⮴ࡍࡿ
㧗್ࢆ♧ࡍᣦᶆ࡛࠶ࡿ㸬௨ୖࡢ⏬ീホ౯ᣦᶆࢆ⏝࠸ࡓ⤖ᯝࡼࡾ㸪ᮏᡭἲࢆ㐺
⏝ࡍࡿࡇ࡛㸪㛫࠾ࡅࡿ DWI ࡢ⾲♧㍤ᗘࡢኚືࢆᢚไ࡛ࡁࡿࡇࡀ♧၀ ࡉࢀࡓ㸬
ᮏ◊✲࡛ࡣ㸪ྛᡭἲࡼࡾㄪ⠇ࡉࢀࡓ 60 ࡢ DWIࡽ 20ࢆ㑅ᢥࡋ㸪 190ࡢ࣮࣌ࢆ⏝ពࡋ࡚㸪ほᐹ⪅ࡼࡿ 2⫥ᙉไ㑅ᢥἲࢆᐇࡋ㸪㛫࠾ࡅ
ࡿ⬻ᐇ㉁㒊ࡢ⾲♧㍤ᗘࡢ㢮ఝᗘࢆどぬⓗᐃ㔞ホ౯ࡋࡓ㸬ࡑࡢ⤖ᯝ㸪ࡍ࡚ࡢほ ᐹ⪅࠾࠸࡚㸪ᮏᡭἲࡢ㑅ᢥ⋡ࡀⴭࡋࡃ㧗࠸್࡞ࡾ㸪ほᐹ⪅㛫࡛ࡢᖹᆒ㑅ᢥ⋡
ࡀ 72.2 %࡞ࡗࡓ㸬ࡲࡓ㸪ᮏᡭἲᡭື᪉ἲࡼࡾ⾲♧᮲௳ࡀỴᐃࡉࢀࡓ DWI
ࢆどぬⓗẚ㍑ホ౯ࡋࡓ⤖ᯝ㸪ᮏࢩࢫࢸ࣒ࡼࡾㄪ⠇ࡉࢀࡓ DWI ࡣ㸪ᡭື࡛ㄪ
⠇ࡉࢀࡓ DWI ẚ㸪⬻ᐇ㉁㒊ࡢಙྕᙉᗘࡸ⏬ീࢥࣥࢺࣛࢫࢺ᫂ࡽ࡞ᕪ␗
ࡀㄆࡵࡽࢀࡎ㸪ᴟࡵ࡚㢮ఝࡋ࡚࠸ࡓ㸬ᮏࢩࢫࢸ࣒࠾ࡅࡿ୍㐃ࡢฎ⌮㈝ࡸࡍ
㛫ࡣ㸪ᮏィ⟬ᶵࢫ࣌ࢵࢡ㸦Pentium D, CPU 3.60 GHz㸧࠾࠸࡚㸪1࠶ࡓࡾ⣙
0.25⛊ィ ࡉࢀࡓ㸬
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
ᮏ◊✲࠾࠸࡚㸪ASIST-Japan ࡢ⪃᪉ἲࢆᐇࡍࡿ㝿㸪どᗋ㡿ᇦኚࡀ࠶
ࡿ࡛ࡣ㸪ኚࢆእࡋࡓ⨨ROIࡀタᐃࡉࢀࡓ㸬ࡑࡢࡓࡵ㸪ᖹᆒ⏬⣲್ࡢィ ࠾࠸࡚ኚືࢆక࠺ࡇࡀ᥎ ࡉࢀࡓ㸬ࢃࢀࢃࢀࡣ㸪どᗋኚࡀᏑᅾࡍࡿ32
ࡢb0⏬ീୖࡢ48㡿ᇦࡢどᗋᑐࡋ㸪ኚࢆྵࡵࡓROIタᐃኚࢆእࡋࡓ ROIタᐃࢆᐇࡋ㸪ᖹᆒ⏬⣲್ᑐࡍࡿኚືಀᩘࢆồࡵࡓ㸬ࡑࡢ⤖ᯝ㸪㛫࡛
ࡑࢀࡒࢀᖹᆒs17.7 %㸪s9.4 %ࡢኚື࡞ࡗࡓ㸬ࡉࡽ㸪ྛ✀ROIタᐃࡼࡾồ
ࡵࡓᖹᆒ⏬⣲್ࢆ⏝࠸࡚㸪ࡈ࡛┦ᑐ೫ᕪ⋡ࢆィ⟬ࡋࡓ⤖ᯝ㸪㛫࡛ᖹᆒ 12.5 %࡞ࡗࡓ㸬ࡇࢀࡽࡢ⤖ᯝࡼࡾ㸪どᗋ㡿ᇦኚࡀ࠶ࡿᑐࡋ࡚ኚࢆ
እࡋࡓROIタᐃ࡛ࡣ㸪10 %⛬ᗘࡢኚືࢆྵࡴࡇࡀࢃࡗࡓ㸬ᮏᡭἲࡣ㸪⃰ᗘࣄ
ࢫࢺࢢ࣒ࣛ࠾ࡅࡿṇᖖ⬻ᐇ㉁㒊ࡢ⏬⣲್ࡢ⠊ᅖᑐࡍࡿ㢖ᗘศᕸࡽ᭱㢖್
࡞ࡿ⏬⣲್ࢆồࡵ࡚࠸ࡿࡓࡵ㸪どᗋࢆྵࡵࡓ⬻ᐇ㉁㒊ෆಙྕᙉᗘࡀ␗࡞ࡿኚ ࡀᏑᅾࡋ࡚ࡶᙳ㡪ࢆཷࡅ࡞࠸ࡶࡢ⪃࠼ࡿ㸬ࡘࡲࡾ㸪DWIࡢ⾲♧᮲௳ࡢㄪ⠇
⏝ࡍࡿ⏬⣲್ࡢ ᐃ࠾ࡅࡿኚືࡀ㸪ᮏ◊✲࠾ࡅࡿࢩࢫࢸ࣒ࡢ≉ᛶホ౯⤖ᯝ
ᫎࡉࢀࡓゝ࠼ࡿ㸬ࡋࡓࡀࡗ࡚㸪どᗋࡢಙྕᙉᗘࢆ⏝ࡍࡿࡇ࡞ࡃ㸪Ᏻᐃ ࡋࡓ⏬ീ⾲♧ࢆ㠀ᖖ▷㛫࡛⾜࠼ࡿᮏࢩࢫࢸ࣒ࡣ㸪⏬ീデ᩿⾑ᰦ⁐ゎ⒪ἲࡢ
㎿㏿ୟࡘṇ☜࡞ุ᩿࠾࠸࡚᭷⏝࡛࠶ࡿ⪃࠼ࡿ㸬
ᮏࢩࢫࢸ࣒ࡢ≉ᛶホ౯࠾࠸࡚㸪ASIST-Japan ࡢ⪃᪉ἲࢆẚ㍑ᑐ㇟ࡋࡓࡓ
ࡵ㸪ྛࡽどᗋࡀᥥฟࡉࢀ࡚࠸ࡿࢫࣛࢫീࢆ㑅ᢥࡋ⏝ࡋࡓ㸬ࡑࡢࡓࡵ㸪 ᮏࢩࢫࢸ࣒ࢆᐇ⾜ࡉࡏࡿ๓ẁ㝵࠾࠸࡚ᡭື᧯సࡀᚲせ࡞ࡿ㸬⮬ືࡢ
ᡂྥࡅ࡚㸪ീࡉࢀࡓb0⏬ീࡢࢫࣛࢫീࢆ⏝࠸ࡿࡇ࡛ゎỴ࡛ࡁࡿ⪃࠼
ࡽࢀ㸪ᚋ㸪ࡇࢀࡽࢆ㋃ࡲ࠼ࡓ᳨ウࡀᚲせ࡛࠶ࡿ㸬 7.5 ⤖ㄒ
ᮏ◊✲࡛ࡣ㸪ASIST-Japanࡼࡾ⪃ࡉࢀࡓ㸪DWI ࡢ⾲♧᮲௳ࢆỴᐃࡍࡿ᪉ἲ
࠾ࡅࡿၥ㢟Ⅼࡢᨵၿᐇ⏝ࢆ┠ⓗ㸪b0⏬ീࡢ⬻ᐇ㉁㒊࠾ࡅࡿ⃰ᗘࣄࢫࢺ ࢢ࣒ࣛࢆ⏝ࡋࡓ㸪DWIࡢ⾲♧᮲௳ㄪ⠇ࢩࢫࢸ࣒ࢆ㛤Ⓨࡋࡓ㸬ࡑࡢ⤖ᯝ㸪ࢃࢀࢃ
ࢀࡢ⪃ᡭἲࡣ㸪b0⏬ീୖROIࢆタᐃࡍࡿࡇ࡞ࡃ㸪Ᏻᐃࡋࡓಙྕᙉᗘ࠾ࡼࡧ
⏬ീࢥࣥࢺࣛࢫࢺࡢDWIࢆ▷㛫ฟຊࡍࡿࡇࡀ࡛ࡁࡓ㸬
ཧ⪃ᩥ⊩
[1] Nagashima H, Harakawa T, “Computer-aided diagnostic scheme for detection of acute cerebral infarctions on brain CT images,” Journal of Signal Processing, vol.12, no.1,
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
94 -pp.73-80, 2008.
[2] 㛗ᓥᏹᖾ㸪ཎᕝဴ⨾㸪͆ࢥࣥࢺࣛࣛࢸࣛࣝᕪศᢏ⾡ࢆ⏝࠸ࡓࢥࣥࣆ࣮ࣗࢱᨭ
デ᩿ࢩࢫࢸ࣒㸫⬻ CT ⏬ീ࠾ࡅࡿᛴᛶᮇ⬻᱾ሰ᳨ฟࡢᛂ⏝㸫㸪͇㟁ẼᏛ
ㄽᩥㄅC㸪vol.128㸪no.11㸪pp.1687-1695㸪2008㸬
[3] 㛗ᓥᏹᖾ㸪ཎᕝဴ⨾㸪ⓑ▼㡰㸪ᅵ㑥㞝㸪ⓑ▼᫂ஂ㸪㡲Ọ┾୍㸪͆⬻CT⏬
ീ࠾ࡅࡿᛴᛶᮇ⬻᱾ሰࡢࢥࣥࣆ࣮ࣗࢱࡼࡿ᳨ฟ㸪͇Med Imag Tech㸪vol.27㸪 no.1㸪pp.30-38㸪2009㸬
[4] Heiland S, Dietrich O, Sartor K, “Diffusion-weighted imaging of the brain:
comparison of stimulated- and spin-echo echo-planar sequences,” Neuroradiology, vol.43, no.6, pp.442-447, 2001.
[5] 㛗ᓥᏹᖾ㸪ཎᕝဴ⨾㸪ᆏᮏ ⫕㸪బ㔝ⰾ▱㸪ⓑ▼᫂ஂ㸪༑ᔒᆒ㸪͆ప⃰ᗘ DSA ⏬ീ࠾ࡅࡿࣄࢫࢺࢢ࣒ࣛኚࢆ⏝࠸ࡓࢥࣥࢺࣛࢫࢺᨵၿࡢᇶ♏ⓗ᳨
ウ㸪͇ಙྕฎ⌮㸪vol.8㸪no.2㸪pp.147-156㸪2004㸬
[6] Nagashima H, Harakawa T, Sakamoto H, Sano Y, Shiraishi A, Hoshina M, Igarashi H,
“Improvement of signal-to-noise ratio using a genetic algorithm for low-density DSA images,” Journal of Signal Processing, vol.8, no.6, pp.495-500, 2004.
[7] ᕷᕝᘯ㸪ཎ Ꮥ๎㸪⩚ಙḟ㸪ᒣཱྀ ຌ㸪ᶫ୍ஓ㸪͆CT ࠾ࡅࡿಙྕ㞧 㡢ẚࡼࡿపࢥࣥࢺࣛࢫࢺศゎ⬟ࡢホ౯㸪͇་⏝⏬ീሗᏛ㞧ㄅ㸪vol.24㸪 no.3㸪pp.106-111㸪2007㸬
[8] Kitajima K, Kaji Y, Kuroda K, Sugimura K, “High b-value diffusion-weighted imaging in normal and malignant peripheral zone tissue of the prostate: effect of signal-to-noise ratio,” Magn Reson Med Sci, vol.7, no.2, pp.93-99, 2008.
[9] ᒣᮏࡵࡄࡳ㸪▼⏣㝯⾜㸪ᕝୗ㑳⏕㸪ᙳᮏṇஅ㸪⸨ᕝග୍㸪Ỉᡞᕝⰾᕭ㸪♽ẕ
ດ㸪▼᰿ṇ༤㸪ఀ⸨㝧㸪⛅ᒣᐿ㸪͆⬚㒊୕ḟඖ CT ⏬ീ࠾ࡅࡿ⤖⠇≧
㝜ᙳࡢ⮬ື᳨ฟἲࡢ㛤Ⓨ㸪͇᪥ᨺᢏᏛㄅ㸪vol.62㸪no.4㸪pp.555-564㸪2006㸬
[10] ᪂⏣ಟᖹ㸪ᮏ㇂⚽ሀ㸪῝ぢᛅ㸪ὸဴஓ㸪㉥ሯᏕ㞝㸪࿋ ວ㸪Ṋ⏣ ᚭ㸪
⧊ෆ ᪼㸪㐲⸨ၨ࿃㸪Ώ㑓㡰ஂ㸪͆⫵㡿ᇦࡢᝏᛶ⭘⒆ࡢ⒪ᚋࡢ⤒㐣ほᐹᨭ
ࡢࡓࡵࡢPET/CT⏬ീฎ⌮㸪͇㟁Ꮚሗ㏻ಙᏛᢏ⾡◊✲ሗ࿌㸪vol.107㸪no.461㸪 pp.319-324㸪2008㸬
[11] ሯ⩏㸪◁ᒇᩜᛅ㸪ᑠᑎྜྷ⾨㸪͆C-Dࢲࢢ࣒ࣛAFCἲ㸸ᐇ㦂㸸⏬ീ
ホ౯㸸ึᏛ⪅ࡢࡓࡵࡢᐇ㦂ධ㛛᭩㸪͇᪥ᮏࢡࢭ࣭ࣝࢩࣗࣉ࣮ࣜࣥ࢞ฟ∧㸪ᮾ
ி㸪pp.71-80㸪1994㸬
[12] Shiraishi J, Abe H, Ichikawa K, Schmidt RA, Doi K, “Observer study for evaluating potential utility of a super-high-resolution LCD in the detection of clustered
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
microcalcifications on digital mammograms,” J Digit Imaging, vol.23, no.2, pp.161-169, 2010.
➨7❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦2㸧
96
-➨
➨ 8 ❶
㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻ MR ⏬ീ࠾ࡅࡿ
⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦 3 㸧
➨8❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦3㸧
97
-➨
➨ 8 ❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻ MR ⏬ീ࠾ࡅࡿ
⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦 3 㸧
8.1 ⥴ゝ
➨6❶࡛ࡣ㸪ASIST-Japanࡼࡗ࡚⪃ࡉࢀࡓDWIࡢ⾲♧᮲௳ࢆㄪ⠇ࡍࡿ᪉ἲ ࡢ⮬ືࢩࢫࢸ࣒㛵ࡍࡿ◊✲ࡘ࠸࡚㏙㸪➨7❶࡛ࡣ㸪どᗋ⨨ࡢಙྕᙉᗘ
ࢆ⏝ࡏࡎ⾲♧᮲௳ࢆㄪ⠇ࡍࡿᡭἲࡋ࡚㸪b0⏬ീࡢ⬻ᐇ㉁㒊࠾ࡅࡿ⃰ᗘࣄ
ࢫࢺࢢ࣒ࣛゎᯒࢆ⏝ࡋࡓ DWI ࡢ⮬ືㄪ⠇ࢩࢫࢸ࣒㛵ࡍࡿ◊✲ࡘ࠸࡚㏙
ࡓ㸬
ඛ⾜◊✲࠾࠸࡚㸪ࢃࢀࢃࢀࡣᐇ㝿⮫ᗋ⌧ሙฟྥࡁ㸪⌧ᆅㄪᰝࢆᐇࡋ࡚
ࡁࡓࡀ㸪ࡑࡢ㝿㸪࠶ࡿタ࠾࠸࡚㸪ീࡉࢀࡓDWI࠾ࡼࡧb0⏬ീࢆ⏬ീࢧ࣮
ࣂࡸ⏬ീฎ⌮࣮࣡ࢡࢫࢸ࣮ࢩࣙࣥ㌿㏦ࡍࡿ㝿㸪b0⏬ീࡢ㝵ㄪࡢࡳࡀ㠀ྍ㏫ⓗ
ᅽ ⦰ฎ⌮ࡉ ࢀࡿ⏬ീಖ Ꮡ㏻ಙࢩ ࢫࢸ࣒㸦picture archiving and communication
systems㸸PACS㸧ࡢᏑᅾࢆ☜ㄆࡋࡓ㸬DWIb0⏬ീࡢ㝵ㄪࡀྠ୍࡛࡞࠸ሙྜ㸪b0
⏬ീࢆ⏝࠸࡚࠸ࡿ ASIST-Japan ࡢᡭື᪉ἲࡸඛ⾜◊✲᪉ἲࡣ⏝ࡍࡿࡇࡀ࡛ࡁ
࡞࠸㸬
ᮏ◊✲࡛ࡣ㸪ࡇࢀࡲ࡛ᥦࡉࢀࡓ DWI ࡢ⾲♧᮲௳ࡢㄪ⠇᪉ἲࢆᨵၿࡍࡿࡓ
ࡵ㸪b0 ⏬ീࢆ⏝ࡍࡿࡇ࡞ࡃDWI ࢆ㐺ṇ⾲♧ࡍࡿࡓࡵࡢ⮬ືㄪ⠇ࢩࢫࢸ࣒ࢆ
㛤Ⓨࡋࡓ㸬ࡉࡽ㸪DWIୖࡢ㧗ಙྕ㡿ᇦࡀT2 shine through⌧㇟ࡼࡿࡶࡢ࡞ࡢ
㸪ᣑᩓᢚไࡼࡿࡶࡢ࡞ࡢࢆ⡆᫆ⓗุ᩿࡛ࡁࡿADC mapࡀDWIࡶ㸪
㉸ᛴᛶᮇ⬻᱾ሰᑐࡍࡿ⏬ീデ᩿ࡸ⒪㐺ᛂࡢỴᐃ⏝ࡉࢀ࡚࠸ࡿ୰࡛㸪ADC mapࡢ⾲♧᮲௳ࡢᶆ‽㛵ࡍࡿ◊✲ࡣࡲ࡛ሗ࿌ࡉࢀ࡚࠸࡞࠸ࡇࡽ㸪ADC map࠾ࡅࡿ⾲♧᮲௳ࡢ⮬ືㄪ⠇ࢩࢫࢸ࣒ࡘ࠸࡚ࡶ㛤Ⓨࡋࡓ㸬
8.2 ᪉ἲ
8.2.1 ⏬ീࢹ࣮ࢱ࣮࣋ࢫ
ᮏ◊✲⏝ࡋࡓ⏬ീࢹ࣮ࢱ࣮࣋ࢫࡣ㸪⩌㤿┴ෆ2タ2⨨㸦GENESIS SIGNA㸪 SIGNA EXCITE㸸GEᶓἙ࣓ࢹ࢝ࣝ♫〇1.5 T㸧࡚2005ᖺ2᭶ࡽ2007ᖺ2
᭶ࡲ࡛ീࡉࢀࡓ㸪Ⓨᚋ6㛫௨ෆࡢ㉸ᛴᛶᮇ⬻᱾ሰ44ࡢDWIb0
⏬ീ࡛ᵓᡂࡉࢀ࡚࠸ࡿ㸬ࡑࡢෆヂࡣ㸪⏨ᛶ29ྡ㸪ዪᛶ15ྡ㸪ᖺ㱋22㹼89ṓ㸦ᖹ
ᆒ66.9s14.9ṓ㸧࡛࠶ࡿ㸬࡞࠾㸪ᮏ◊✲࠾ࡅࡿ⏬ീࢹ࣮ࢱ࣮࣋ࢫࡢ⏝㝿ࡋ㸪
➨8❶ ㉸ᛴᛶᮇ⬻᱾ሰࢆᑐ㇟ࡋࡓ⬻MR⏬ീ࠾ࡅࡿ⾲♧᮲௳⮬ືㄪ⠇ࢩࢫࢸ࣒㸦3㸧
ᅗ8.1 ⬻DWI࠾ࡼࡧADC map࠾ࡅࡿ⾲♧᮲௳ࡢ⮬ືㄪ⠇ἲࡢᴫせ
ᮏタࡢ⌮ጤဨࡢᑂᰝࢆཷࡅ㸪ᢎㄆࢆྲྀᚓࡋ࡚࠸ࡿ㸬ീ᮲௳ࡣ㸪ീࢩ࣮
ࢣࣥࢫ㸸SE-EPI㸪TR㸸5000㹼10000 ms㸪TE㸸86㹼102 ms㸪ࣇࣜࢵࣉゅ㸸90°㸪ࢫ
ࣛࢫཌ㸸5 mm㸪ࢫࣛࢫ㛫㝸㸸6㹼8 mm㸪MPG㸸3᪉ྥ࡛࠶ࡿ㸬
8.2.2 3ḟඖ⏬ീࡢసᡂ
ᮏ◊✲࡚㛤Ⓨࡋࡓ⬻DWI࠾ࡼࡧADC map࠾ࡅࡿ⾲♧᮲௳ࡢ⮬ືㄪ⠇ἲࡢ ᴫせࢆᅗ8.1♧ࡍ㸬ᮏᡭἲ࠾࠸࡚㸪ࡲࡎ㸪⬻DWIb0⏬ീ㸦matrix size: 256×256, gray scale: 12 bits, pixel size: 0.820㹼0.937 mm㸧ࢆࢥࣥࣆ࣮ࣗࢱෆධຊࡋ㸪ࢫࣛ
ࢫീࢆྜᡂࡋ࡚ྛ⏬ീࡢ3ḟඖ⏬ീࢆసᡂࡋࡓ㸬࡞࠾㸪DWIb0⏬ീࢆ㞟 ࡍࡿ㝿༳ຍࡍࡿMPGࡢᙉᗘࡣ㸪ࡑࢀࡒࢀ0 s/mm2࠾ࡼࡧ1000 s/mm2࡛࠶ࡿ㸬
8.2.3 ⬻ᐇ㉁㒊ࡢᢳฟADC mapࡢసᡂ
⬻ᐇ㉁㒊ࡢᢳฟ࠾࠸࡚㸪ࡲࡎ㸪ุูศᯒἲ [1]ࢆ⏝࠸ࡓࡋࡁ࠸್ฎ⌮ࡢ㐺⏝
ࡼࡾ㸪3ḟඖDWIࢆ2್ࡋࡓ㸬ࡑࡢᚋ㸪2್ࡉࢀࡓ3ḟඖ⏬ീෆࡢೃ⿵㡿 ᇦࢆᢳฟࡋ㸪⏬ീ࣎ࢡࢭࣝᩘᑐࡍࡿᢳฟೃ⿵㡿ᇦࡢ࣎ࢡࢭࣝᩘࡢྜࡋ࡚
1 %ࡢࡋࡁ࠸ࢆタᐃࡍࡿࡇࡼࡗ࡚㸪᫂ࡽ㞧㡢ᛮࢃࢀࡿೃ⿵ࢆ㝖ཤࡋࡓ㸬
᭱ᚋ㸪⃰ῐࡢ㌿ฎ⌮㸪ࣛ࣋ࣜࣥࢢ࠾ࡼࡧ⭾ᙇ࣭⦰ฎ⌮ࢆ⏝࠸࡚㸪⬻ᐇ㉁㒊
ࢆᢳฟࡋࡓ㸬ᅗ8.2ྛฎ⌮㐣⛬ࡢ⏬ീࢆ♧ࡍ㸬ᅗ8.2㸦a㸧ࡣDWI㸪㸦b㸧ࡣࡋࡁ