Domestic Societies

555.@–àŽR’¼lC‚ì’¼–çCÎì‰ë–çCŽ›“cN•F
‰·“x‰Â•ÏMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ð—p‚¢‚½‚ŽRA•¨‚Ì“€Œ‹‰ß’ö‚̉ðÍ
—ߘa4”N6ŒŽ26“úC‘æ67‰ñ’ቷ¶•¨HŠw‰ï‘å‰ïiƒIƒ“ƒ‰ƒCƒ“jCB11C

 

554.@Erika Takahashi, Tomoki Miyasaka, Satoshi Funayama, Daiki Tamada, Utaroh Motosugi, Hiroyuki Morisaka, Hiroshi Onishi, Yasuhiko Terada
Improved performance of deep-learning-based super-resolution of clinical brain images improved by decreasing reduction factor
Reduction factor‚̉ü‘P‚É‚æ‚é—Õ°”]‰æ‘œ‚̃fƒB[ƒvƒ‰[ƒjƒ“ƒO’´‰ð‘œ‚Ì«”\Œüã
—ߘa3”N9ŒŽ10“ú`9ŒŽ12“úC‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiƒnƒCƒuƒŠƒbƒhjCiP143-J) P265

 

553.@Tomoki Miyasaka, Erika Takahashi, Sayaka Tojima, Shigehito Yamada, Yasuhiko Terada
MR microimaging of marsupial embryo and neonate specimens using a 4.7T vertical superconducting magnet
4.7TcŒ^’´“d“±Ž¥Î‚ð—p‚¢‚½—L‘Ü—ÞãóEV¶Že•W–{‚ÌMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒO
—ߘa3”N9ŒŽ10“ú`9ŒŽ12“úC‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiƒnƒCƒuƒŠƒbƒhjCiP136-J) P262

 

552.@Tsuyoshi Ueyama, Keisuke Yoshida, Yuichi Suzuki, Hideyuki Iwanaga, Osamu Abe, Yasuhiko Terada
Distortion correction of diffusion-weighted image by FSL learning model using 3D U-net
3D U-net‚ð—p‚¢‚½FSL‚ÌŠwKƒ‚ƒfƒ‹‚É‚æ‚éŠgŽU‹­’²‰æ‘œ‚̘c‚Ý•â³
—ߘa3”N9ŒŽ10“ú`9ŒŽ12“úC‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiƒnƒCƒuƒŠƒbƒhjCiP071-J) P239
Šwp§—ãÜ

 

551.@Keisuke Yoshida, Yasuhiko Terada
Image restoration for spiral imaging using dAUTOMAP and GIRF
dAUTOMAP‚ÆGIRF‚ð—p‚¢‚½Spiral‰æ‘œ‚̃A[ƒ`ƒtƒ@ƒNƒg•â³‚ÌŒŸ“¢
—ߘa3”N9ŒŽ10“ú`9ŒŽ12“úC‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiƒnƒCƒuƒŠƒbƒhjCiP045-J) P228

 

550.@Katsumi Kose, Ryoichi Kose, Yasuhiko Terada
Bloch simulation of the 3-point Dixon method on biological systems
¶‘Ì‚ð‘ÎÛ‚Æ‚µ‚½3 point Dixon–@‚ÌBloch simulation
—ߘa3”N9ŒŽ10“ú`9ŒŽ12“úC‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiƒnƒCƒuƒŠƒbƒhjCiO3-002) P203

 

549.@Kazuyuki Makihara, Kazuya Sakaguchi, Masayuki Yamaguchi, Ken Ito, Yusaku Hori, Taro Semba, Yasuhiro Funahashi, Hirofumi Fujii, Yasuhiko Terada
Evaluation of drug activity of a novel anticancer drug E7130 in different human breast cancer models by DCE-MRI clustering analysis
DCE-MRIƒNƒ‰ƒXƒ^[‰ðÍ‚É‚æ‚éˆÙ‚È‚éƒqƒg“û‚ª‚ñƒ‚ƒfƒ‹‚ɑ΂·‚éV‹KR‚ª‚ñÜE7130‚ÌŠˆ«•]‰¿
—ߘa3”N9ŒŽ10“ú`9ŒŽ12“úC‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiƒnƒCƒuƒŠƒbƒhjCiO2-069) P197

 

548.@Kazuki Kunieda, Yuto Murakami, Yasuhiko Terada
Development of double helix dipole (DHD) coils for 7T MR microscopy
7T MRƒ}ƒCƒNƒƒXƒRƒs[—p‚Ìdouble helix dipole (DHD)ƒRƒCƒ‹‚ÌŠJ”­
—ߘa3”N9ŒŽ10“ú`9ŒŽ12“úC‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiƒnƒCƒuƒŠƒbƒhjCiO2-068) P197

 

547.@Tomoki Miyasaka, Satoshi Funayama, Daiki Tamada, Utaroh Motosugi, Hiroyuki Morisaka, Hiroshi Onishi, Yasuhiko Terada
Multi-coil CS reconstruction using deep learning under parallel imaging constraints
ƒpƒ‰ƒŒƒ‹ƒCƒ[ƒWƒ“ƒO§–ñ‰º‚É‚¨‚¯‚édeep learning‚ð—p‚¢‚½ƒ}ƒ‹ƒ`ƒRƒCƒ‹CSÄ\¬
—ߘa3”N9ŒŽ10“ú`9ŒŽ12“úC‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiƒnƒCƒuƒŠƒbƒhjCiO2-022) P182

 

546.@Mari Minami, Shigehito Yamada, Yasuhiko Terada
Initial study of DTI of a chemically fixed human fetus
‘Ù¶Šú‰Šú‚Ì‘ÙŽ™‰»ŠwŒÅ’è•W–{‚ÌDTI‚̉ŠúŒŸ“¢
—ߘa3”N9ŒŽ10“ú`9ŒŽ12“úC‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiƒnƒCƒuƒŠƒbƒhjCiO1-010) P164

 

545.@–qŒ´˜aKCŽ›“cN•F
9.4T cŒ^’´“d“±Ž¥Î‚ð—p‚¢‚½ƒqƒgãóŽq‰»ŠwŒÅ’è•W–{‚Ì‚•ª‰ð”\ƒCƒ[ƒWƒ“ƒO
—ߘa3”N8ŒŽ18“ú@‘æ25‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒIƒ“ƒ‰ƒCƒ“ŠJÃjC‚‚­‚ÎFŽY‘Œ¤CiO6jP29-32

 

544.@š Ž}˜a‹PC‘ºã—Y“lCŽ›“cN•F
MR ƒ}ƒCƒNƒƒXƒRƒs[—p double helix dipole Œ^ RF ƒRƒCƒ‹‚ÌŠJ”­
—ߘa3”N8ŒŽ18“ú@‘æ25‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒIƒ“ƒ‰ƒCƒ“ŠJÃjC‚‚­‚ÎFŽY‘Œ¤CiO5jP25-28

 

543.@‹g“cŒ\—CCãŽR‹B, Ž›“cN•F
‹³Žt‚È‚µ[‘wŠwK‚ð—p‚¢‚½ŠgŽU‹­’²‰æ‘œ‚É‚¨‚¯‚é 3 ŽŸŒ³˜c‚Ý•â³
—ߘa3”N8ŒŽ18“ú@‘æ25‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒIƒ“ƒ‰ƒCƒ“ŠJÃjC‚‚­‚ÎFŽY‘Œ¤CiO4jP21-24

 

542.@‹{â’mŽ÷CMŽRŒdC‹Ê“c‘å‹PC–{™‰F‘¾˜YCXã—T”VC‘å¼—mCŽ›“cN•F
Deep learning ‚ð—p‚¢‚½ƒ}ƒ‹ƒ`ƒRƒCƒ‹ compressed sensing Ä\¬
—ߘa3”N8ŒŽ18“ú@‘æ25‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒIƒ“ƒ‰ƒCƒ“ŠJÃjC‚‚­‚ÎFŽY‘Œ¤CiO3jP17-20

 

541.@‹£Ÿ”üC ‹£—ºˆêC Ž›“cN•F
QRAPMASTER –@‚ÌŽÀ‘•‚Æ Ž¥‰»ˆÚ“® Œø‰Ê‚̉e‹¿•]‰¿
—ߘa3”N8ŒŽ18“ú@‘æ25‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒIƒ“ƒ‰ƒCƒ“ŠJÃjC‚‚­‚ÎFŽY‘Œ¤CiO2jP13-16

 

540. ‹{â’mŽ÷, ûüì’¼–ç, Έ䊰’, Îì‰ë–ç, Ž›“cN•F
‰·“x‰Â•Ï MRI ‚É‚æ‚éA•¨‚¨‚æ‚ÑH•iƒTƒ“ƒvƒ‹‚̒ቷŽB‘œ
—ߘa3”N8ŒŽ18“ú@‘æ25‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒIƒ“ƒ‰ƒCƒ“ŠJÃjC‚‚­‚ÎFŽY‘Œ¤CiP1jP5-8

 

539. ‹{â’mŽ÷Cûüì’¼–çCÎì‰ë–çCŽ›“cN•F
‰Ô‰è“€Œ‹ŠÏŽ@—p‰·“x‰Â•ÏMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ÌŠJ”­
—ߘa3”N5ŒŽ29“ú`5ŒŽ30“úC‘æ66 ‰ñ’ቷ¶•¨HŠw‰ï‘å‰ïC“Œ‹žiƒIƒ“ƒ‰ƒCƒ“jCiB10jP27

 

538. Ž›“cN•F
[‘wŠwK‚ð—p‚¢‚½“ª•”MRIŒŸ¸‚Ì‚‘¬‰»‚ÌŽŽ‚Ý
—ߘa3”N1ŒŽ12“ú@JSMRMƒXƒ^ƒfƒBGu¶¬Œ^ŠwK“™‚ðŠˆ—p‚µ‚½’è—Ê“IMRƒCƒ[ƒWƒ“ƒOvƒjƒ…[ƒCƒ„[ƒZƒ~ƒi[i‚PjCL“‡iƒIƒ“ƒ‰ƒCƒ“j
µ‘Òu‰‰

 

537. Yasuhiko TeradaCTomoki MiyasakaCDaiki TamadaCSatoshi FunayamaCUtaroh MotosugiCHiroyuki MorisakaCHiroshi Onishi
Instability of deep learning in superresolution of clinical brain images
—Õ°”]‰æ‘œ‚Ì’´‰ð‘œ‚É‚¨‚¯‚édeep learning‚Ì•sˆÀ’è«
—ߘa2”N9ŒŽ11“ú`10ŒŽ4“úC‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiWebŠJÃjCiO-047)P120

 

536. Tomoki MiyasakaCSatoshi FunayamaCDaiki TamadaCUtaroh MotosugiCHiroyuki MorisakaCHiroshi OnishiCYasuhiko Terada
Multi contrast CS reconstruction using deep learning
Deep learning‚ð—p‚¢‚½ƒ}ƒ‹ƒ`ƒRƒ“ƒgƒ‰ƒXƒgCS Ä\¬
—ߘa2”N9ŒŽ11“ú`10ŒŽ4“úC‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiWebŠJÃjCiO-033)P115

 

535. Michiru KajiwaraCYasuhiko TeradaCRyohei KasedaCYusuke NakagawaCIchiei NaritaCSusumu SasakiCTomoyuki Haishi
Sodium imaging with a 1.5T-MRI by using a new cross-band repeater technique
ƒNƒƒXƒoƒ“ƒhƒŒƒs[ƒ^‚É‚æ‚Á‚Ä—Õ°—p1.5T Ž¥Î‚Å23Na-MRI‚ðŽÀŒ»‚·‚é
—ߘa2”N9ŒŽ11“ú`10ŒŽ4“úC‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiWebŠJÃjCiO-031)P115

 

534. Tomoki MiyasakaCMichiru KajiwaraCAkito KawasakiCYoshikazu OkamotoCYasuhiko Terada
Development and screening examination of a car-mounted portable MRI for wrist
ŽèŽñ—pŽÔÚƒ|[ƒ^ƒuƒ‹ MRI‚ÌŠJ”­‚ƃXƒNƒŠ[ƒjƒ“ƒOŽŽŒ±
—ߘa2”N9ŒŽ11“ú`10ŒŽ4“úC‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiWebŠJÃjCiO-025)P113

 

533. Kazuyuki MakiharaCKazuya SakaguchiCMasayuki YamaguchiCKen ItoCYusaku HoriCTaro SembaCYasuhiko FunabashiC
Hirofumi FujiiCYasuhiko Terada
Assessment of tumor blood perfusion fraction using k-means clustering of tumor Ktrans
values with E7130 in a breast cancer model
k- •½‹Ï–@‚É‚æ‚éDCE-MRI ‚Ì Ktrans ’lŽ©“®•ª—Þ‚ð—p‚¢‚½ƒqƒg“ûŠàƒ‚ƒfƒ‹‚ɑ΂·‚éV‹KR‚ª‚ñÜ E7130‚Ì–òŒø•]‰¿
—ߘa2”N9ŒŽ11“ú`10ŒŽ4“úC‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiWebŠJÃjCiP-097)P195

 

532. Tomoki MiyasakaCAi NakaoCDaiki TamadaCShintaro IchikawaCSatoshi FunayamaCUtaroh MotosugiCHiroyuki MorisakaC
Hiroshi OnishiCYasuhiko Terada
Initial clinical evaluation of deep-learning-based image synthesis and superresolution
using a clinical dataset of patients with brain lesions
”]•a•ÏŠ³ŽÒ‚Ì—Õ°ƒf[ƒ^ƒZƒbƒg‚É‚¨‚¯‚éƒfƒB[ƒvƒ‰[ƒjƒ“ƒO‚ð—p‚¢‚½‰æ‘œ‡¬‚Æ’´‰ð‘œ‚É‚æ‚鉊ú—Õ°•]‰¿
—ߘa2”N9ŒŽ11“ú`10ŒŽ4“úC‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiWebŠJÃjCiP-035)P174

 

531. Keisuke YoshidaCAi NakaoCYasuhiko Terada
Examination of an image restoration method for spiral scan using deep learning and GIRF
[‘wŠwK‚ÆGIRF ‚ð—p‚¢‚½ spiral ‰æ‘œ‚̃A[ƒ`ƒtƒ@ƒNƒg•â³–@‚ÌŒŸ“¢
—ߘa2”N9ŒŽ11“ú`10ŒŽ4“úC‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiWebŠJÃjCiP-014)P167

 

530. Yuto MurakamiCMasayuki YamaguchiCYasuhiko Terada
Large matrix imaging of the rat head using a 9.4T animal MRI
9.4T “®•¨—pMRI‚ð—p‚¢‚½ƒ‰ƒbƒg“ª•”‚ÌLarge Matrix ƒCƒ[ƒWƒ“ƒO
—ߘa2”N9ŒŽ11“ú`10ŒŽ4“úC‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïiWebŠJÃjCiP-006)P164

 

529. Ž›“c@N•F, r–Ø@—Í‘¾CZ‹g@WC–Ø@ˆÉ’m’j
BlochƒVƒ~ƒ…\ƒŒ[ƒVƒ‡ƒ“‚ÉŠî‚­³Šm«‚ÌŒüã‚ð–ÚŽw‚µ‚½QRAPMASTER‰ðÍ–@‚ÌŽÀ‘•
—ߘa2”N8ŒŽ28“úC‘æ24‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒIƒ“ƒ‰ƒCƒ“ŠJÃjC‚‚­‚ÎiP2)P9-12

 

528. –qŒ´@˜aK, âŒû@˜a–ç, ŽRŒû@‰ë”V, ˆÉ“¡@Œ›, –x@—Dì, å”g@‘¾˜Y, ‘D‹´@‘×”Ž,
“¡ˆä ”ŽŽj, Ž›“c N•F
DCE-MRIƒNƒ‰ƒXƒ^[‰ðÍ‚ð—p‚¢‚½ƒqƒg“ûŠàƒ‚ƒfƒ‹‚Ì–òŒø•]‰¿–@‚ÌŠJ”­
—ߘa2”N8ŒŽ28“úC‘æ24‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒIƒ“ƒ‰ƒCƒ“ŠJÃjC‚‚­‚ÎiP2)P13-16

 

527. ŠŒ´@¬¶CŽ›“c N•FCœ«“c —º•½C’†ì —S‰îC¬“c ˆê‰qC²X–Ø iC”qŽt ’q”V
ƒNƒƒXƒoƒ“ƒhƒŒƒs[ƒ^‚Ì‹Zp‚ð‰ž—p‚µ‚½NaƒCƒ[ƒWƒ“ƒO
—ߘa2”N8ŒŽ28“úC‘æ24‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒIƒ“ƒ‰ƒCƒ“ŠJÃjC‚‚­‚ÎiP2)P21-25

 

526. ‹{â@’mŽ÷CŠŒ´@¬¶Cìè@—ºlC‰ª–{@‰ÃˆêCŽ›“c@N•F
ƒ|[ƒ^ƒuƒ‹MRI‚ð—p‚¢‚½ŽèŽñf’f
—ߘa2”N8ŒŽ28“úC‘æ24‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒIƒ“ƒ‰ƒCƒ“ŠJÃjC‚‚­‚ÎiP2)P26-29

 

525. ‘ºã —Y“lCŽRŒû ‰ë”VCŽ›“c N•F
9.4T“®•¨—pMRI‚ð—p‚¢‚½ƒ‰ƒbƒg“ª•”‚ÌLarge MatrixƒCƒ[ƒWƒ“ƒO
—ߘa2”N8ŒŽ28“úC‘æ24‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒIƒ“ƒ‰ƒCƒ“ŠJÃjC‚‚­‚ÎiO6)P32-36

 

524. ‹g“c@Œ\—CCŽ›“c@N•F
dAUTOMAP‚ð—p‚¢‚½SpiralŽB‘œ‚É‚¨‚¯‚é‰æ‘œÄ\¬–@‚ÌŒŸ“¢
—ߘa2”N8ŒŽ28“úC‘æ24‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒIƒ“ƒ‰ƒCƒ“ŠJÃjC‚‚­‚ÎiP3)P37-40

 

523. Ž›“c@N•F
Å‹ß‚ÌŒ¤‹†Ð‰î `‚‘¬ƒCƒ[ƒWƒ“ƒO‚ƃ‚ƒoƒCƒ‹MRIŒŸ¸`
—ߘa‚P”N11ŒŽ20“úC‘æ4‰ñ MRIƒAƒ‰ƒCƒAƒ“ƒX‘ÛƒVƒ“ƒ|ƒWƒEƒ€2019Cç—t
µ‘Òu‰‰

 

522. Yasuhiko Terada, Ai Nakao, Daiki Tamada, Tomohiro Takamura, Utaroh Motosugi
Acceleration of clinical brain examination using deep learning (2): Clinical implementation and evaluation ƒfƒB[ƒvƒ‰[ƒjƒ“ƒO‚ð—p‚¢‚½—Õ°”]‰æ‘œŒŸ¸‚Ì‚‘¬‰»i‚QjF—Õ°ŒŸ¸‚Ö‚ÌŽÀ‘•‚Æ—Õ°•]‰¿
—ߘa1”N9ŒŽ20“úC‘æ47‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒF–{iO1-028jP178

 

521. Ai Nakao, Daiki Tamada, Tomohiro Takamura, Utaroh Motosugi, Yasuhiko Terada
Acceleration of clinical brain examination using deep learning (1): Neural network construction ƒfƒB[ƒvƒ‰[ƒjƒ“ƒO‚ð—p‚¢‚½—Õ°”]‰æ‘œŒŸ¸‚Ì‚‘¬‰»i‚PjFƒjƒ…[ƒ‰ƒ‹ƒlƒbƒgƒ[ƒN‚Ì\’z
—ߘa1”N9ŒŽ20“úC‘æ47‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒF–{iO1-027jP177

 

520. Michiru Kajiwara, Mayu Nakagomi, Yoshikazu Okamoto, Yasuhiko Terada
Field examination of baseball elbow using a car-mounted portable MRI –ì‹…•If’f—pŽÔÚƒ|[ƒ^ƒuƒ‹MRI‚ÌŽÀ’nŽŽŒ±
—ߘa1”N9ŒŽ21“úC‘æ47‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒF–{iO2-010jP204

 

519. Ryoichi Sasaki, Yasuhiko Terada
MRF-FISP without additional scans using deep neural network [‘wƒjƒ…[ƒ‰ƒ‹ƒlƒbƒgƒ[ƒN‚ðŽg‚Á‚½’ljÁƒXƒLƒƒƒ“‚ð•K—v‚Æ‚µ‚È‚¢MRF-FISP
—ߘa1”N9ŒŽ22“úC‘æ47‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒF–{iO3-026jP246

 

518. Yuto Murakami, Ryoichi Sasaki, Yasuhiko Terada
Acceleration of acquisition of relaxation time map for human embryo specimens ƒqƒgãóŽq•W–{‚̊ɘaŽžŠÔƒ}ƒbƒvŽæ“¾‚Ì‚‘¬‰»
—ߘa1”N9ŒŽ21“úC‘æ47‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒF–{iP2-A-40jP295

 

517. Kazuya Sakaguchi, Yasuhiko Terada
Performance Optimization of Arbitrary-Shape Actively Shielded Gradient Coils using Singular Value Decomposition and Arti?cial Bee Colony Algorithm ”\“®ŽÕ•ÁŒ^Œù”zƒRƒCƒ‹‚Ì”CˆÓ«”\Å“K‰»Žè–@‚ÌŠJ”­
—ߘa1”N9ŒŽ21“úC‘æ47‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒF–{iP2-A-42jP295

 

516. Naoya Takagawa, Yasuhiko Terada
Development of temperature-variable MR microimaging system (2) ‰·“x‰Â•ÏMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ÌŠJ”­i2j
—ߘa1”N9ŒŽ21“úC‘æ47‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒF–{iP2-A-44jP296

 

515. Kazuya Sakaguchi, Yasuhiko Terada, Masayuki Yamaguchi, Ken Ito, Yusaku Hori, Taro Semba, Yasuhiro Funahashi, Hirofumi Fujii
Automatic classification of experimental tumor ADC values using k-means clustering:Verification of E7130 drug efficacy for human breast cancer model k-•½‹Ï–@‚É‚æ‚éŽÀŒ±ŽîᇂÌADC’lŽ©“®•ª—ÞFƒqƒg“ûŠàƒ‚ƒfƒ‹‚ɑ΂·‚éV‹KR‚ª‚ñÜE7130–òŒø•]‰¿‚Ö‚Ì—˜ —p
—ߘa1”N9ŒŽ22“úC‘æ47‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒF–{iP3-A-01jP323

 

514. Katsumi Kose, Ryoichi Kose, Yasuhiko Terada, Daiki Tamada, Utaroh Motosugi
Simulation of living tissue using an MRI simulator MRI simulator‚É‚æ‚鶑̑gD‚̃Vƒ~ƒ…ƒŒ[ƒVƒ‡ƒ“
—ߘa1”N9ŒŽ21“úC‘æ47‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒF–{iP2-A-51jP298

 

513. ‹£ Ÿ”üC‹£ —ºˆêC Ž›“c N•F, ‹Ê“c ‘å‹P, –{™ ‰F‘¾˜Y
¶‘Ì‘gD‚Ì MRI simulation Žè–@‚ÌŠJ”­
—ߘa1”N8ŒŽ8“úC‘æ23‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰¡•liO8)P63-66

 

512. ‘ºã —Y“lC’‡‘º ûüŽu, Ž›“c N•F
11.7T ’´“`“±Ž¥Î‚ð—p‚¢‚½ƒqƒgãóŽq‚̃}ƒCƒNƒƒCƒ[ƒWƒ“ƒO
—ߘa1”N8ŒŽ8“úC‘æ23‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰¡•liP4)P37-40

 

511. ’†”ö ˆ¤CŽ›“c N•F
[‘wŠwK‚ð—p‚¢‚½‰i‹vŽ¥Î MRI ‚É‚¨‚¯‚é Spiral ‰æ‘œƒA[ƒ`ƒtƒ@ƒNƒg•â³–@‚ÌŠJ”­
—ߘa1”N8ŒŽ8“úC‘æ23‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰¡•liP3)P33-36

 

510. ²X–Ø –¸ˆêCŽ›“c N•F
[‘wŠwK‚ð—p‚¢‚½ MR fingerprinting ‚É‚¨‚¯‚鎞ŠÔ’Zk‚Æ„’踓xŒüã‚ÌŒŸ“¢
—ߘa1”N8ŒŽ8“úC‘æ23‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰¡•liP2)P29-32

 

509. ŠŒ´ ¬¶C‹{â ’mŽ÷C‰ª–{ ‰ÃˆêCŽ›“c N•F
ƒ|[ƒ^ƒuƒ‹ MRI ‚Ì–ì‹…ê‚Å‚ÌŽB‘œŽŽŒ±
—ߘa1”N8ŒŽ8“úC‘æ23‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰¡•liP1)P25-28

 

508.‘…@‹I‹vŽqCŽ›“c@N•FC•Ð‰ª@–MŒõCH–{@‡“ñC”qŽt@’q”V
PGSE-NMR‚ÅŠÏ‘ª‚·‚éƒK[ƒlƒbƒg“d‰ðŽ¿’†‚ÌLi+‚ÌÕ“ËE‰ñÜŒ»Û
—ߘa1”N8ŒŽ8“úC‘æ23‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰¡•liO4)P13-16

 

507.ûüì@’¼–çCŽ›“c@N•F
•X“_‰º‚Å‚ÌŽB‘œ‚ð–Ú“I‚Æ‚µ‚½4.7T‰·“x§ŒäMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ÌŠJ”­
—ߘa1”N8ŒŽ8“úC‘æ23‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰¡•liO3)P9-12

 

506@âŒû@˜a–çCŽ›“c@N•F
“ÁˆÙ’l•ª‰ð–@‚ÆABCƒAƒ‹ƒSƒŠƒYƒ€‚ð‘g‚݇‚킹‚½‰~“›Œ^‚̃V[ƒ‹ƒhŒù”zŽ¥êƒRƒCƒ‹‚ÌÅ“K‰»
—ߘa1”N8ŒŽ8“úC‘æ23‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰¡•liO2)P5-8

 

505 Ž›“c@N•FC’†”ö@ˆ¤C‹Ê“c@‘å‹PC–{™@‰F‘¾˜Y
[‘wŠwK‚ð—p‚¢‚½—Õ°”]‰æ‘œ‚ÌŽB‘œŽžŠÔ‚Ì’Zk‰»
—ߘa1”N8ŒŽ8“úC‘æ23‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰¡•liO1)P1-4

 

504. •½ì@‰ë•¶C¼‰º@”Í‹vC•Ÿ“c@Œ’“ñCŽ›“c@N•F
MRI‚ð—p‚¢‚½ƒGƒ“ƒ{ƒŠƒYƒ€‚Ì”­¶E‰ñ•œ‰ß’ö‚É‚¨‚¯‚é…•ª’Ê“±‚̉Ž‹‰»
•½¬31”N3ŒŽ20-23C‘æ130‰ñ“ú–{X—ÑŠw‰ï‘å‰ïCVŠƒiP1-087)
https://doi.org/10.11519/jfsc.130.0_296

 

503. Ž›“c@N•F
Fundamental of Extended Phase Graph
Šg’£ˆÊ‘ŠƒOƒ‰ƒtiEPGj‚ÌŠî‘b
•½¬30”N9ŒŽ7“úC‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹à‘ò@iE2-2) P136
‹³ˆçu‰‰

 

502.’†ž@^—DC“c•Ó@—ºŸC‹£@Ÿ”üC‰ª–{@‰ÃˆêC¯‡ ‘s‘åCŽ›“c@N•F
Development of portable MRI for detection of baseball elbow (3)
–ì‹…•I‰Šúf’f—pƒ|[ƒ^ƒuƒ‹MRI‚ÌŠJ”­(3)
•½¬30”N9ŒŽ7“úC‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹à‘ò@iO1-108) P184

 

501.¼ú±@~•½C”qŽt@’q”VCŽ›“c@N•F
A new method for fabricating gradient coils using printed circuit boards(2):Performance evaluation and application
ƒvƒŠƒ“ƒgŠî”‚ð—p‚¢‚½‰~“›Œ^Œù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­i2jF«”\•]‰¿‚Ɖž—p
•½¬30”N9ŒŽ7“úC‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹à‘ò@iO1-107) P184
—DG‰‰‘èÜ

 

‚T00.²X–Ø@–¸ˆêCŽ›“c@N•F
Acceleration of the Cartesian acquisition of MR fingerprinting Cartesianƒf[ƒ^ŽûW‚Å‚ÌMR fingerprinting‚É‚¨‚¯‚éŽB‘œ‚‘¬‰»
•½¬30”N9ŒŽ7“úC‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹à‘ò@iO1-046)P164

 

499.’†ž@^—DC“c•Ó@—ºŸC¯‡ ‘s‘åC‰ª–{@‰ÃˆêCŽ›“c@N•F
Deep convolutional neural network for denoising images of low-field scanners
’ᎥêMRI‚É‚¨‚¯‚édeep learning‚ð—p‚¢‚½ƒmƒCƒYœ‹Ž‚ÌŒŸ“¢
•½¬30”N9ŒŽ8“úC‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹à‘ò
iP2-A2-013)P252

 

498.âŒû@˜a–çC¼àV@WŽ÷CŽ›“c@N•F
Design of cylindrical gradient coils using singular value decomposition and genetic algorithm
“ÁˆÙ’l•ª‰ð”\‚ƈâ“`“IƒAƒ‹ƒSƒŠƒYƒ€‚ð‘g‚݇‚킹‚½‰~“›Œ^Œù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­
•½¬30”N9ŒŽ8“úC‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹à‘ò@
iP2-B2-007)P276

 

497.‚ì@’¼–çCŽ›“c@N•F
Development of temperature-variable MR microimaging system (I) ‰·“x‰Â•ÏMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ÌŠJ”­iIj
•½¬30”N9ŒŽ8“úC‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹à‘ò@
iP2-B2-006)P275

 

496.’†”ö@ˆ¤CŽ›“c@N•F
Non-Cartesian imaging for permanent magnet MRI systems
‰i‹vŽ¥ÎMRI‚É‚¨‚¯‚énon-Cartesian imaging
•½¬30”N9ŒŽ8“úC‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹à‘ò@
iP2-A8-057)P266

 

495.–xì@—F•ãCŽ›“c@N•F
Influence of temperature drift on flow measurements
•½¬30”N9ŒŽ8“úC‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹à‘ò@(PDF-118)P328

 

494.Z¼è@~•½C”qŽt@’q”VCŽ›“c@N•F
ƒvƒŠƒ“ƒgŠî”‚ð—p‚¢‚½Œù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­
•½¬‚R‚O”N‚WŒŽ‚Q‚P“úC‘æ22‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ–k‘åŠwC
iO10)P55-58

 

493.ZŽ›“c@N•FC’†ž@^—DC‰ª–{@‰Ãˆê
‰i‹vŽ¥Î MRI ‚É‚¨‚¯‚é[‘wŠwK‚ð—p‚¢‚½ƒmƒCƒYœ‹Ž‚ÌŒŸ“¢
•½¬‚R‚O”N‚WŒŽ‚Q‚O“úC‘æ22‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ–k‘åŠwC
iP8jP47-50

 

492.Z‚ì@’¼–çCŽ›“c@N•F
4.7T/89mm ŠJŒûcŒ^’´“`“±Ž¥Î‚ð—p‚¢‚½‰·“x‰Â•ÏMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ÌŠJ”­
•½¬‚R‚O”N‚WŒŽ‚Q‚O“úC‘æ22‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ–k‘åŠwC
iP4jP37-40

 

491.ZâŒû@˜a–çC¼àV@WŽ÷CŽ›“c@N•F
“ÁˆÙ’l•ª‰ð‚ƈâ“`“IƒAƒ‹ƒSƒŠƒYƒ€‚ð‘g‚݇‚킹‚½ ”CˆÓŒ`óŒù”zŽ¥êƒRƒCƒ‹‚ÌÝŒv‚¨‚æ‚ÑÅ“K‰»Žè–@‚ÌŠJ”­
•½¬‚R‚O”N‚WŒŽ‚Q‚O“úC‘æ22‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ–k‘åŠwC
iP3jP33-36

 

490.Z²X–Ø@–¸ˆêCŽ›“c@N•F
1.5TŽlŽˆ—p MRI ‚É‚¨‚¯‚é Cartesian MR fingerprinting ‚ÌŽB‘œ‚‘¬‰»
•½¬‚R‚O”N‚WŒŽ‚Q‚O“úC‘æ22‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ–k‘åŠwC
iP2jP29-32

 

489.›’†”ö ˆ¤CŽ›“c N•F
ŽlŽˆ—p MRI ‚É‚¨‚¯‚é non-Cartesian imaging –@‚ÌŠJ”­
•½¬‚R‚O”N‚WŒŽ‚Q‚O“úC‘æ22‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ–k‘åŠwC
iP1jP25-28

 

488.›–xì —F•ãC•Ÿ“c Œ’“ñCŽ›“c N•F
MRI ‚ð—p‚¢‚½‰®ŠOŽ÷–Ø‚ÌŽ÷‰t—¬ƒCƒ[ƒWƒ“ƒOFL—tŽ÷‚Æj—tŽ÷‚Ì”äŠr
•½¬‚R‚O”N‚WŒŽ‚Q‚O“úC‘æ22‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ–k‘åŠwC
iO2jP5-8

 

487.›’†ž ^—DC“c•Ó —ºŸC‰ª–{ ‰ÃˆêC¯‡ ‘s‘åCŽ›“c N•F
•’ÊŽÔ“‹ÚŒ^ƒ|[ƒ^ƒuƒ‹ MRI ‚ÌŠJ”­
•½¬‚R‚O”N‚WŒŽ‚Q‚O“úC‘æ22‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ–k‘åŠwC
iO1jP1-4

 

486.’·“cW‰À,›•Ÿ“cŒ’“ñ,Ž›“cN•F
‰®ŠO‚ÉAÍ‚³‚ꂽƒPƒ„ƒL‚Ì–Ø•”EŽt•”‚ÌŽ÷‰t—¬‘¬‚̉Ž‹‰»
•½¬‚Q‚X”N‚P‚PŒŽ‚P‚Q“úCŽ÷–؈ãŠw‰ï‘æ22‰ñ‘å‰ïC–@­‘åŠw¬‹àˆäƒLƒƒƒ“ƒpƒXC“Œ¬‹àˆä

 

485.¬—Ñ —D‘¾CŽ›“c@N•F
T 2 error originating from diffusion in MRF-FISP MRF-FISP‚É‚¨‚¯‚éŠgŽU‚É‹Nˆö‚·‚éT 2 „’è’l‚Ì’è—ÊŒë·
•½¬‚Q‚X”N‚XŒŽ‚P‚T“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{C
P2-A4-016(p306)

 

484.²X–Ø –¸ˆêC¬—Ñ@—D‘¾CŽ›“c@N•F
Musculoskeletal MR Fingerprinting using a 1.5T/280mm small-bore MRI 1.5T/280mmƒXƒ‚[ƒ‹ƒ{ƒAMRI‚ð—p‚¢‚½œ“î•”‚Ö‚ÌMR Fingerprinting‚̉ž—p
•½¬‚Q‚X”N‚XŒŽ‚P‚T“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{C
P2-B5-160(p378)

 

483.Ž›“c N•FC”qŽt@’q”V
Initial trials of relaxation time and ADC mapping of mice/rats MRF‚É‚æ‚é–ƒŒ‰ºƒ}ƒEƒX^ƒ‰ƒbƒg‚̊ɘaŽžŠÔ^ADCƒ}ƒbƒsƒ“ƒO‚̉ŠúŒŸ“¢
•½¬‚Q‚X”N‚XŒŽ‚P‚T“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{C
P2-B6-191(p394)

 

482.–xì —F•ãC‹£@Ÿ”üCŽ›“c@N•F
Error evaluation of QSI measurements for widely-distributed flow L‚¢‘¬“x•ª•z‚ð‚à‚—¬‚ê‚ÌQSIŒv‘ª‚̌뷕]‰¿
•½¬‚Q‚X”N‚XŒŽ‚P‚T“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{C
P2-B6-189 (p393)

 

481.âŒû ˜a–çC¼àV@WŽ÷C‹£@Ÿ”üCŽ›“c@N•F
New method for designing gradients using singular value decomposition and genetic algorithm “ÁˆÙ’l•ª‰ð–@‚ƈâ“`“IƒAƒ‹ƒSƒŠƒYƒ€‚ð‘g‚݇‚킹‚½Œù”zŽ¥êƒRƒCƒ‹«”\‚ÌÅ“K‰»Žè–@‚ÌŠJ”­
•½¬‚Q‚X”N‚XŒŽ‚P‚T“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{C
P2-B2-186(p391)

 

480.¼è ~•½C”qŽt@’q”VC‹£@Ÿ”üCŽ›“c@N•F
A new method for fabricating gradient coils using printed circuit boards ƒvƒŠƒ“ƒgŠî”‚ð—p‚¢‚½‰~“›Œ^Œù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­
•½¬‚Q‚X”N‚XŒŽ‚P‚T“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{C
P2-B2-185(p391)

 

479.’†ž ^—DC“c•Ó@—ºŸC‰ª–{ ‰ÃˆêC‹£@Ÿ”üCŽ›“c@N•F
Development of Sequence Generator for Portable MRI for Baseball Elbow Diagnosis –ì‹…•If’f—pƒ|[ƒ^ƒuƒ‹MRI—pƒV[ƒPƒ“ƒXƒWƒFƒlƒŒ[ƒ^[‚ÌŠJ”­
•½¬‚Q‚X”N‚XŒŽ‚P‚T“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{C
P2-B2-180(p388)

 

478. ‹Ê“c ‘å‹PC‹£@—ºˆêC–{™ ‰F‘¾˜YC‹£@Ÿ”ü
Development of mathematical phantoms for MRI simulators MRIƒVƒ~ƒ…ƒŒ[ƒ^‚ɑΉž‚µ‚½”—ƒtƒ@ƒ“ƒgƒ€‚̶¬Žè–@‚ÌŒŸ“¢
•½¬‚Q‚X”N‚XŒŽ‚P‚T“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{C
P2-B1-084(p340)

 

477. £ŒËˆä ˆ»ØC‹£@Ÿ”ü
Ultrashort echo-time imaging at 1.5 T using an insertable gradient coil and 3D cones trajectory ‘}“üŒ^Œù”zŽ¥êƒRƒCƒ‹‚Æ3D cones trajectory‚ð—p‚¢‚½’´’ZƒGƒR[ƒ^ƒCƒ€ƒCƒ[ƒWƒ“ƒO
•½¬‚Q‚X”N‚XŒŽ‚P‚U“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{CO3-31(p291)

 

476.‹£ —ºˆêC£ŒËˆä@ˆ»ØC‹£@Ÿ”ü
GPU-optimized 3D fast MRI simulator for non-Cartesian sampling GPU‚ð—p‚¢‚½”ñƒfƒJƒ‹ƒgÀ•WŒnƒTƒ“ƒvƒŠƒ“ƒO‘Ήž3D‚‘¬MRI simulator‚ÌŠJ”­
•½¬‚Q‚X”N‚XŒŽ‚P‚S“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{CO1-97(p217)

 

475. ¬—Ñ —D‘¾C‹£@Ÿ”üCŽ›“c@N•F
Development of a field camera system for a 1.5T/280mm superconducting magnet system and application to fast imaging method 1.5T/280mm@’´“`“±Ž¥Î‚É‚¨‚¯‚éfield cameraƒVƒXƒeƒ€‚ÌŠJ”­‚Æ‚‘¬ŽB‘œ–@‚ւ̉ž—p
•½¬‚Q‚X”N‚XŒŽ‚P‚S“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{CO1-91(p214)

 

474. “c•Ó —ºŸC‰ª–{ ‰ÃˆêC‹£@Ÿ”üCŽ›“c@N•F
Development of portable MRI for early detection of baseball elbow(2)@–ì‹…•I‰Šúf’f—pƒ|[ƒ^ƒuƒ‹MRI‚ÌŠJ”­i‚Qj
•½¬‚Q‚X”N‚XŒŽ‚P‚S“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{CO1-40(P188)

 

473.Ž›“c@N•F
non-Cartesian trajectory imaging
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µ‘Òu‰‰
472.‹£@Ÿ”ü, ‹£@—ºˆê
Can simulator create a paradigm shift in R & D of MRI? Simulator‚ÍMRI‚ÌŒ¤‹†ŠJ”­‚Ƀpƒ‰ƒ_ƒCƒ€ƒVƒtƒg‚ð‹N‚±‚¹‚é‚©H
•½¬‚Q‚X”N‚XŒŽ‚P‚S“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{CO1-96(p216)

 

471.Ž›“c@N•F
Fundamentals and recent developments in compressed sensing ˆ³kƒZƒ“ƒVƒ“ƒO‚ÌŠî‘b‚ÆÅV“®Œü
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µ‘Òu‰‰
470.‹£@Ÿ”ü
Historical evolution and future direction of MRI: What is the ultimate MRI? MRI‚Ì—ðŽj“Ii‰»‚Æ«—ˆ“W–] |‹†‹É‚ÌMRI‚ð‹‚ß‚Ä|
•½¬‚Q‚X”N‚XŒŽ‚P‚T“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{CPLiP36)
“Á•Êu‰‰

 

469.•½ì‰ë•¶CŽs‹´—²Ž©C•Ÿ“cŒ’“ñCŽ›“cN•F
uƒRƒ“ƒpƒNƒg‚l‚q‚h‚ð—p‚¢‚½Ž÷Š²‚ÌŽ÷‰t—¬‘¬•ª•z‚Ì“ú•Ï‰»‚Ì‘ª’èv
•½¬‚Q‚X”N‚XŒŽ‚P‚O“úC“ú–{A•¨Šw‰ï‘æ81‰ñ‘å‰ïC“Œ‹ž—‰È‘åŠw–ì“cƒLƒƒƒ“ƒpƒXCPL-020

 

468.‹£ Ÿ”üC‹£ —ºˆê
uMRI simulator ‚ð—p‚¢‚½ MP - RAGE ‚ÌÅ“K‰»‚ÌŽŽ‚Ý v
•½¬‚Q‚X”N‚WŒŽ‚V“úC‘æ‚Q‚P‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(O1)P1-4

 

467.¬—Ñ —D‘¾ C ‹£ Ÿ”ü C Ž›“c N•F
u1.5T/280mm ’´“`“±Ž¥Î‚É‚¨‚¯‚é field camera ƒVƒXƒeƒ€ŠJ”­ v
•½¬‚Q‚X”N‚WŒŽ‚V“úC‘æ‚Q‚P‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(O2)P5-8

 

466.“c•Ó —ºŸC ‰ª–{ ‰ÃˆêC‹£ Ÿ”üCŽ›“c N•F
u–ì‹…•If’f—pƒ|[ƒ^ƒuƒ‹ MRI ‚ÌŠJ”­ v
•½¬‚Q‚X”N‚WŒŽ‚V“úC‘æ‚Q‚P‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(O3)P9-12

 

465.‹Ê“c ‘å‹PC‹£ —ºˆêC‹£ Ÿ”üC–{™ ‰F‘¾˜Y
u”—ƒtƒ@ƒ“ƒgƒ€‚ÌŽÀŒ»‚ð–Ú“I‚Æ‚µ‚½”] MRI ‰æ‘œ‚Ì‘gDŽ©“®•ª—ÞŽè–@‚ÌŠJ”­ v
•½¬‚Q‚X”N‚WŒŽ‚V“úC‘æ‚Q‚P‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P5)P37-40

 

464.‹£ —ºˆêC£ŒËˆä ˆ»ØC‹£ Ÿ”ü
uGPU ‚ð—p‚¢‚½”ñƒfƒJƒ‹ƒgÀ•WŒnƒTƒ“ƒvƒŠƒ“ƒO‚ɑΉž‚µ‚½ 3D MRI simulator ‚ÌŠJ”­ v
•½¬‚Q‚X”N‚WŒŽ‚V“úC‘æ‚Q‚P‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P6)P41-44

 

463.£ŒËˆä ˆ»ØC ‹£ Ÿ”ü
u1.5T …•½ŠJŒû’´“`“±Ž¥Î‚É‚¨‚¯‚é 3D Cones –@‚ւ̉Q“d—¬‚̉e‹¿ v
•½¬‚Q‚X”N‚WŒŽ‚V“úC‘æ‚Q‚P‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P7)P45-48

 

462.¼è ~•½C”qŽt ’q”VC‹£ Ÿ”üCŽ›“c N•F
uƒvƒŠƒ“ƒgŠî”‚ð—p‚¢‚½‰~“›Œ^ƒV[ƒ‹ƒhƒOƒ‰ƒWƒGƒ“ƒgƒRƒCƒ‹‚ÌŠJ”­ v
•½¬‚Q‚X”N‚WŒŽ‚V“úC‘æ‚Q‚P‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P8)P49-52

 

461.–xì —F•ãC‹£ Ÿ”üCŽ›“c N•F
uq-space imaging ‚ð—p‚¢‚½‰®ŠOŽ÷–Ø‚ÌŽ÷‰t—¬Œv‘ª v
•½¬‚Q‚X”N‚WŒŽ‚V“úC‘æ‚Q‚P‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P10)P55-58

 

460.’†ž ^—DC“c•Ó —ºŸC‰ª–{ ‰ÃˆêC‹£ Ÿ”üCŽ›“c N•F
u–ì‹…•If’f—pƒ|[ƒ^ƒuƒ‹ MRI ‚É‚¨‚¯‚é ƒVƒ“ƒOƒ‹ƒIƒuƒŠ[ƒN‹@”\•t‚«ƒV[ƒPƒ“ƒXƒWƒFƒlƒŒ[ƒ^[‚ÌŠJ”­ v
•½¬‚Q‚X”N‚WŒŽ‚V“úC‘æ‚Q‚P‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P11)P59-62
459.Ž›“cN•F
uMRI‚ð—p‚¢‚½‰®ŠOŽ÷–Ø‚ÌŽ÷‰t—¬‚̉Ž‹‰»v
•½¬‚Q‚X”N‚RŒŽ‚Q‚P“úCA•¨ƒoƒCƒIŒ¤‹†‰ï ‘æ10‰ñ‰ï‡`A•¨‚Ö‚Ì•¨—HŠw‚̉ž—p`C(ˆêà)ƒoƒCƒIƒCƒ“ƒ_ƒXƒgƒŠ[‹¦‰ïC“Œ‹ž
µ‘Òu‰‰

 

458.Ž›“cN•F
uMR Fingerprinting“ü–åv
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457.ŽR“c—È‘¾CŽ›“cN•FC‹£Ÿ”ü
u‰~“›Œ^ƒ}ƒ‹ƒ`ƒT[ƒLƒ…ƒ‰[ƒVƒ€ƒRƒCƒ‹‚ð—p‚¢‚½ƒ_ƒCƒiƒ~ƒbƒNƒVƒ~ƒ“ƒOƒVƒXƒeƒ€‚ÌŠJ”­v
•½¬‚Q‚W”N‚P‚PŒŽ‚P‚W“úC‘æ‚T‚T‰ñ‚m‚l‚q“¢˜_‰ïCL“‡@P95ip324-325) ‚Œ

 

456.ŠFì@VCŽ›“cN•FC‹£Ÿ”ü
u‚l‚q‚h‚ð—p‚¢‚½¬Ž™œ”N—îŒv‘ª‚Ö‚ÌDeep Learning‚̉ž—pv
•½¬‚Q‚W”N‚P‚PŒŽ‚P‚W“úC‘æ‚T‚T‰ñ‚m‚l‚q“¢˜_‰ïCL“‡@P91ip316-317)

 

455.’·“cW‰ÀC•Ÿ“cŒ’“ñC‹£Ÿ”üCŽ›“cN•F
u0.2T‰i‹vŽ¥Î‚l‚q‚h‚ð—p‚¢‚½‰®ŠOŽ÷–Ø’†‚Ì…—A‘—Œv‘ª
•½¬‚Q‚W”N‚P‚PŒŽ‚P‚U“úC‘æ‚T‚T‰ñ‚m‚l‚q“¢˜_‰ïCL“‡@P82ip292-295)
ŽáŽèƒ|ƒXƒ^[Ü

 

454.¼àVWŽ÷Cˆ¢•”[ŽuC‹£Ÿ”üCŽ›“cN•F
u‘ȉ~“›Œ`Œù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­‚ƬŽ™œ”N—îŒv‘ª‚ւ̉ž—pv
•½¬‚Q‚W”N‚P‚PŒŽ‚P‚U“úC‘æ‚T‚T‰ñ‚m‚l‚q“¢˜_‰ïCL“‡@P80ip286-287)

 

453.–î–쇖çC¬—Ñ—D‘¾CŽ›“cN•FC‹£Ÿ”ü
u1.5T/280mm…•½ŠJŒûŒ^’´“`“±Ž¥Î‚Ì‚½‚߂̬“®•¨—pŒù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­‚Ɖž—pv
•½¬‚Q‚W”N‚P‚PŒŽ‚P‚W“úC‘æ‚T‚T‰ñ‚m‚l‚q“¢˜_‰ïCL“‡@P79ip284-285)

 

452.Ž›“cN•F
uIntroduction to MR Fingerprinting
MR fingerprinting“ü–åv
•½¬‚Q‚W”N‚XŒŽ‚P‚O“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@EL9-1 (p117)
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451.Ž›“cN•F
uMR microscopy accelerated by MR Fingerprinting
MRŽw–ä–@‚É‚æ‚éNMRƒ}ƒCƒNƒƒXƒRƒs[‚Ì‚‘¬‰»v
•½¬‚Q‚W”N‚XŒŽ‚P‚O“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@O-2-076 (p218)

 

450.¬—Ñ—D‘¾C‹£Ÿ”üCŽ›“cN•F
u Development of field monitoring system using field cameras for a 1.5 T superconducting magnet system
1.5T’´“`“±Ž¥Î‚É‚¨‚¯‚éField Camera‚ð—p‚¢‚½Ž¥êƒ‚ƒjƒ^ƒŠƒ“ƒOƒVƒXƒeƒ€‚ÌŠJ”­v
•½¬‚Q‚W”N‚XŒŽ‚P‚O“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@P-2-035 (p278)

 

449.–î–쇖çC¬—Ñ—D‘¾CŽ›“cN•FC‹£Ÿ”ü
u Development of an insertable gradient coil for a 1.5T/280mm horizontal bore superconducting magnet
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•½¬‚Q‚W”N‚XŒŽ‚P‚O“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@P-2-034 (p277)

 

448.‹£—ºˆêC‹£Ÿ”ü
uGPU optimized general purpose MRI simulator
GPU‚ÉÅ“K‰»‚µ‚½”Ä—pMRIƒVƒ~ƒ…ƒŒ[ƒ^‚ÌŠJ”­v
•½¬‚Q‚W”N‚XŒŽ‚P‚O“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@P-2-020 (p270)

 

447.“c•Ó—ºŸC‰ª@C•½C‰ª–{‰ÃˆêC‹£Ÿ”üCŽ›“cN•F
uDevelopment of portable MRI for early detection of baseball elbow –ì‹…•I‰Šúf’f—pƒ|[ƒ^ƒuƒ‹MRI‚ÌŠJ”­v
•½¬‚Q‚W”N‚XŒŽ‚P‚O“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@P-2-018 (p269)

 

446.£ŒËˆäˆ»ØC‹£Ÿ”ü
uDevelopment of spiral imaging for a 1.5T/280mm horizontal bore superconducting magnet
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•½¬‚Q‚W”N‚XŒŽ‚X“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@P-1-026 (p251)

 

445.£ŒËˆäˆ»ØCŽ™‹Ê“ÞC‹£Ÿ”ü
uSpiral imaging for a 9.4T/54mm vertical bore superconducting magnet
9.4T/54mmcŒ^ŠJŒûŒa’´“`“±Ž¥Î‚É‚¨‚¯‚éSpiral imaging‚ÌŠJ”­v
•½¬‚Q‚W”N‚XŒŽ‚X“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@P-1-025 (p251)

 

444.‹£Ÿ”üC‹£—ºˆê
uPotential and Problems of MRI simulators
MRI simulator‚̉”\«‚Æ–â‘è“_v
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443.ŽR“c—È‘¾C‹£Ÿ”üCŽ›“cN•F
u Development of a multi-circular shimming system for a 1.5 T/280 mm horizontal bore superconducting magnet
1.5T/280mm …•½ƒ{ƒA’´“`“±Ž¥Î‚Ì‚½‚߂̃}ƒ‹ƒ`ƒT[ƒLƒ…ƒ‰[ƒVƒ€ƒVƒXƒeƒ€‚ÌŠJ”­ v
•½¬‚Q‚W”N‚XŒŽ‚X“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@O-1-046 (p147)

 

442.‹£Ÿ”üC‹£—ºˆêC”qŽt’q”V
uDevelopment of the MRI software platform (II)
MRI software platform ‚ÌŠJ”­i2j v
•½¬‚Q‚W”N‚XŒŽ‚X“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@O-1-045 (p147)

 

441.¼àVWŽ÷Cˆ¢•”[ŽuC‹£Ÿ”üCŽ›“cN•F
u Design of oval gradient coils using current potential and singular value decomposition
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•½¬‚Q‚W”N‚XŒŽ‚X“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@O-1-044 (p146)

 

440.’·“cW‰ÀCŽ›“cN•FC‹£Ÿ”ü
u Error in QSI analysis for slow flow in a noisy environment
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•½¬‚Q‚W”N‚XŒŽ‚X“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@P-1-014 (p245)

 

439.Ž›“cN•F
u14.1T ‚É‚¨‚¯‚é MR fingerprintingv
•½¬‚Q‚W”N‚WŒŽ‚P‚O“úC‘æ‚Q‚O‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC”@OiP75-78j

 

438.‹£—ºˆêC‹£Ÿ”ü
uGPU ‚ð—p‚¢‚½’´‚‘¬ MRI ƒVƒ~ƒ…ƒŒ[ƒ^‚ÌŠJ”­v
•½¬‚Q‚W”N‚WŒŽ‚P‚O“úC‘æ‚Q‚O‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC”@OiP71-74j

 

437.¬—Ñ—D‘¾C‹£Ÿ”üCŽ›“cN•F
uField camera ‚ð—p‚¢‚½ k - space trajectory ‚ÌŒv‘ªv
•½¬‚Q‚W”N‚WŒŽ‚P‚O“úC‘æ‚Q‚O‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC”@OiP57-60j

 

436.¼àVWŽ÷Cˆ¢•”[ŽuC‹£Ÿ”üCŽ›“cN•F
u¬Ž™œ”N—îŒv‘ª—p‘ȉ~Œ`óŒù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­v
•½¬‚Q‚W”N‚WŒŽ‚P‚O“úC‘æ‚Q‚O‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC”@OiP53-56j

 

435.ŽR“c—È‘¾CŽ›“cN•FC‹£Ÿ”ü
u1.5T/280mm ’´“`“±Ž¥Î—p ƒ}ƒ‹ƒ`ƒT[ƒLƒ…ƒ‰[ƒVƒ€ ƒRƒCƒ‹ ƒVƒXƒeƒ€‚Ì ŠJ”­v
•½¬‚Q‚W”N‚WŒŽ‚P‚O“úC‘æ‚Q‚O‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC”@OiP49-52j

 

434.–î–쇖çC¬—Ñ—D‘¾CŽ›“cN•FC‹£Ÿ”ü
u1.5T/280mm …•½ŠJŒûŒ^’´“`“±Ž¥Î‚Ì‚½‚ß‚Ì‘}“üŒ^Œù”zŽ¥êƒRƒCƒ‹‚Ì»ì‚Æ•]‰¿v
•½¬‚Q‚W”N‚WŒŽ‚P‚O“úC‘æ‚Q‚O‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC”@OiP45-48j

 

433.ŠFì@VCŽ›“cN•FC‹£Ÿ”ü
uMRI‚ð—p‚¢‚½¬Ž™œ”N—’è‚Ö‚ÌDeep Learning‚̉ž—p‚ÌŒŸ“¢v
•½¬‚Q‚W”N‚WŒŽ‚P‚O“úC‘æ‚Q‚O‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC”@PiP35-38j

 

432.‰ª@C•½C‹£Ÿ”üCŽ›“cN•F
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427.‹£Ÿ”ü
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424.’·“cW‰ÀCŽ›“cN•FC‹£Ÿ”ü
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423.‹£—ºˆêC‹£Ÿ”ü
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422.’·“cW‰ÀC‘å’|—z‰îC‹£Ÿ”üCŽR“cdl
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420.¼àVWŽ÷CŽ›“cN•FC‹£Ÿ”ü
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419.–î–쇖çCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”V
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417.¼àVWŽ÷CŽ›“cN•FC‹£Ÿ”ü
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416.”qŽt’q”VC‹´–{ª‘¾˜YC–î–쇖çCŽ›“cN•FC‹£Ÿ”ü
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415.•Ÿ“‡³—TCŽ›“cN•FC‹£Ÿ”ü
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413.‹£Ÿ”üC‹£—ºˆêC”qŽt’q”V
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412.Ž™‹Ê“ÞC‹£Ÿ”ü
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411.ŽR“c—È‘¾CŽ›“cN•FC‹£Ÿ”ü
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410.ŠFì@VCŽ›“cN•FC‹£Ÿ”ü
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409.’·“cW‰ÀCŽ›“cN•FC‹£Ÿ”üC£ŒÃ‘ò—R•F
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408.£ŒËˆäˆ»ØC‹£Ÿ”ü
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407.–î–쇖çC‹£Ÿ”üCŽ›“cN•FC”qŽt’q”V
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405.•Ÿ“‡³—TCŽ›“cN•FC‹£Ÿ”ü
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404.‹£Ÿ”üC‹£—ºˆêC”qŽt’q”V
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403.Ž™‹Ê“ÞC‹£Ÿ”ü
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402.’·“cW‰ÀCŽ›“cN•FC‹£Ÿ”ü
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401.¼àVWŽ÷CŽ›“cN•FC‹£Ÿ”üCˆ¢•”[Žu
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400.Ž›“cN•F
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399.‹£Ÿ”ü
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398.‹£Ÿ”ü
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Development of a digital MRI system using general purpose digital units and board computers gArduino Dueh@
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396.Ž›“cN•F
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395.X˜e@‘C”–ˆäŽÀC‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”üC£ŒÃàV—R•F
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394. X˜e@‘CŽ›“cN•FC‹£Ÿ”üC”qŽt’q”VC£ŒÃàV—R•F
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393.‹£Ÿ”üC‘å’|—z‰îCŽR“c‘ñ”nC”qŽt’q”VCŽR“cdl
uƒqƒgãóŽq‰»ŠwŒÅ’è•W–{‚ÌNMRƒpƒ‰ƒƒ^‚É‚æ‚éÅ“KŽB‘œƒV[ƒPƒ“ƒX‚ÌŒŸ“¢v
•½¬‚Q‚U”N‚XŒŽ‚Q‚O“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siP-3-213jp419

 

392.“c@“ÖCŽ›“cN•FC‹£Ÿ”ü
u¶‘Ì“à‚Ì’x‚¢—¬“®‚Ì’è—ÊŒv‘ª‚Ì‚½‚߂̃tƒ@ƒ“ƒgƒ€ŽÀŒ±v
•½¬‚Q‚U”N‚XŒŽ‚Q‚O“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siP-3-210jp418

 

391.’Óc^lC‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
u”Ä—pƒfƒWƒ^ƒ‹‹@Ší‚ð—p‚¢‚½ƒfƒWƒ^ƒ‹MRIƒgƒ‰ƒ“ƒV[ƒo[‚ÌŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siP-2-141jp383

 

390.‹£Ÿ”üC”qŽt’q”V
u9.4T/54mmŠJŒûcŒ^’´“`“±Ž¥Î‚ð—p‚¢‚½MR microscope‚ÌŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siP-2-140jp382

 

389.㑺–í–çCŽ›“cN•FC‹£Ÿ”ü
u‚Ž¥ê‚É‚¨‚¯‚鎎—¿—U‹N•s‹ÏˆêŽ¥ê‚̃Vƒ~ƒ…ƒŒ[ƒVƒ‡ƒ“‚ÆŽÀŒ±‚É‚æ‚é•]‰¿v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siP-2-121jp373

 

388. ‹Ê“c‘å‹PC‹£Ÿ”ü
u k-space power distribution‚ð—p‚¢‚½Compressed SensingƒTƒ“ƒvƒŠƒ“ƒOÅ“K‰»Žè–@‚ÌŒŸ“¢v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siP-2-118jp371
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387.Ž™‹Ê“ÞCŽ›“cN•FC‹£Ÿ”ü
uMRIƒŠƒAƒ‹ƒ^ƒCƒ€ƒVƒ~ƒ…ƒŒ[ƒ^[‚ÌŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siP-2-105jp365

 

386.Ž›“cN•FCˆî‘º^–çC‹£Ÿ”üC‹{é@—ºC“¡‰iN¬C‹g‰ª@‘å
u¬Ž™œ”N—îŒv‘ª—p0.3Tƒ|[ƒ^ƒuƒ‹MRI‚ð—p‚¢‚½‘å‹K–͔팱ŽÒŒv‘ªv
•½¬‚Q‚U”N‚XŒŽ‚P‚W“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siP-1-053jp339

 

385.‘å’|—z‰îCŽR“c‘ñ”nC‹£Ÿ”üC”qŽt’q”VCŽR“cdl
u9.4T/54mmŠJŒûcŒ^’´“`“±Ž¥Î‚ð—p‚¢‚½ƒqƒgãóŽq•W–{Œü‚¯‚•ª‰ð”\MRI‚ÌŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siO-2-268jp281

 

384.ŽR“c‘ñ”nC‹£Ÿ”üCŽ›“cN•F
u¬Ž™œ”N—îŒv‘ª—pMRI‚Ì‚½‚ß‚ÌRFƒRƒCƒ‹‚ÌŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siO-2-267jp280

 

383.”qŽt’q”VCÎàVˆêŒ›C‹£Ÿ”ü
u¶‘̃}ƒEƒX• ‰çˆÊŽB‘œ‚Ì‚½‚߂̉±’ê‘ÎŒüŒ^Œù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siO-2-265jp279

 

382.ÎŒ\ˆê˜YCŽ›“cN•FC‹£Ÿ”ü
u¬Ž™œ”N—îŒv‘ª—pƒRƒ“ƒpƒNƒgMRI‚É‚¨‚¯‚éÃŽ¥êˆÀ’諂Ƌψꫂ̌üãv
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siO-2-260jp277

 

381.Ž›“cN•FC”qŽt’q”VC‹£Ÿ”ü
uDIXON–@‚Ì‚½‚߂̬Œ^‰i‹vŽ¥Î‚̉·“xˆÀ’è«‚ÌŒüãv
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siO-2-259jp276

 

380.ƒAƒNƒ‰ƒ€@ƒGƒ€ƒfƒB@ƒVƒƒƒnƒ_ƒg@ƒzƒTƒCƒ“CŽ›“cN•FC‹£Ÿ”ü
uCoupled circuit simulation of Z-and X-gradient eddy currents in a 9.4T narrow-bore MRI systemv
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siO-2-258jp276

 

379.ƒAƒNƒ‰ƒ€@ƒGƒ€ƒfƒB@ƒVƒƒƒnƒ_ƒg@ƒzƒTƒCƒ“CŽ›“cN•FC‹£Ÿ”ü
uTemporal-spatial responses of planar X-gradient eddy currents by solid angle coupled circuit methodv
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siO-2-257jp275

 

378.Ž›“cN•FC‹Ê“c‘å‹PCÎŒ\ˆê˜YC‹£Ÿ”üC–ìè‘åŠóC‹àŽqNmC‹g‰ª@‘å
uCS‚ð—p‚¢‚½¬Ž™œ”N—îŒv‘ª‚Ì‚‘¬‰»‚ÌŒŸ“¢v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siO-2-230jp262

 

377.‹Ê“c‘å‹PC‹£Ÿ”üCŽáŽR“N–çCXã—T”VCŽsìV‘¾˜YC²–쟜ACŽsì’qÍC–{™‰F‘¾˜Y
uCompressed Sensing‚ÆL1-SPIRiT‚ð—p‚¢‚½• •”3DGREƒCƒ[ƒWƒ“ƒOv
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siO-2-229jp261

 

376.‹Ê“c‘å‹PC‹£Ÿ”ü
u‚ŽŸ‚ÌÃŽ¥ê•s‹Ïˆê«‚ðl—¶‚µ‚½Self-Calibrated Compressed SensingƒAƒ‹ƒSƒŠ ƒYƒ€‚ÌŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siO-2-228jp261
375.•Ÿ“‡³—TCŽ›“cN•FC‹£Ÿ”ü
uæùœœ–§“xŒv‘ª—pƒRƒ“ƒpƒNƒgMRIƒVƒXƒeƒ€‚ÌŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siO-2-214jp254

 

374.”qŽt’q”VCÎàVˆêŒ›C‹£Ÿ”ü
uNMRŽ¥Î‚É‚æ‚鶑̃}ƒEƒX• ‰çˆÊŽB‘œ‚Ì‚½‚߂̉±’ê‘ÎŒüŒ^‚RŽ²Œù”zŽ¥êƒRƒCƒ‹v
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“úC‘æ‚P‚W‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‹à‘òiO10)p61-62

 

373.‹Ê“c‘å‹PC’‡‘º‚ŽuC‹£Ÿ”ü
u‚‰·’´“`“±ƒoƒ‹ƒNŽ¥Î‚É‚¨‚¯‚éƒ}ƒCƒXƒi[Œø‰Ê‚ðl—¶‚µ‚½Œù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­v
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“úC‘æ‚P‚W‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‹à‘òiO9)p57-60

 

372.‹£Ÿ”üC”qŽt’q”V
u9.4T/54mmƒ{ƒAcŒ^’´“`“±Ž¥Î‚ð—p‚¢‚½MR microscope‚Ì\’zv
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“úC‘æ‚P‚W‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‹à‘òiO8)p53-56

 

371.Ž›“cN•FCˆî‘º^–çCÎàVˆêŒ›CÎŒ\ˆê˜YC‹£Ÿ”ü
uƒ|[ƒ^ƒuƒ‹MRI‚ð—p‚¢‚½œ”N—îŒv‘ªv
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“úC‘æ‚P‚W‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‹à‘òiO7)p49-52

 

370.ŽR“c‘ñ”nC‹£Ÿ”üCŽ›“cN•F
u1.1T’´“`“±Ž¥Î‚ð—p‚¢‚½¬Ž™œ”N—îŒv‘ª‚Ì‚½‚ß‚ÌRFƒvƒ[ƒu‚ÌŠJ”­v
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“úC‘æ‚P‚W‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‹à‘òiP8)p41-44

 

369.ÎŒ\ˆê˜YCŽ›“cN•FC‹£Ÿ”ü
u‚‘¬ŽB‘œ‚ɑΉž‚µ‚½‰i‹vŽ¥ÎŽ¥‹C‰ñ˜H‚Ì’·ŽžŠÔ‰·“x§Œäv
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“úC‘æ‚P‚W‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‹à‘òiP7)p37-40

 

368.•Ÿ“‡³—TCŽ›“cN•FC‹£Ÿ”ü
u‚‘¬ƒXƒsƒ“ƒGƒR[–@‚É‚¨‚¯‚é‰Q“d—¬Œv‘ªv
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“úC‘æ‚P‚W‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‹à‘òiP6)p33-36

 

367.’Óc^lC‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
u32bitƒ{[ƒhƒRƒ“ƒsƒ…[ƒ^[(Arduino Due)‚ð—p‚¢‚½MRIƒpƒ‹ƒXƒvƒƒOƒ‰ƒ}‚ÌŠJ”­v
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“úC‘æ‚P‚W‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‹à‘òiP5)p29-32

 

366.“c@“ÖCŽ›“cN•FC‹£Ÿ”ü
uMRI‚ð—p‚¢‚½‰®ŠOƒPƒ„ƒLŽåŠ²‚Ì—¬‘¬•ª•zŒv‘ªv
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“úC‘æ‚P‚W‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‹à‘òiO2)p19-22

 

365.X˜e@‘CŽ›“cN•FC‹£Ÿ”üC£ŒÃ‘ò—R•F
uMR microimaging‚É‚æ‚é Ží‚È‚µƒJƒL‚ÌŽíŽq‘Þ‰»‰Û’ö‚ÌŠÏŽ@v
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“úC‘æ‚P‚W‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‹à‘òiO1)p15-18

 

364.Ž›“cN•FA‹g“c–¾ŠóŽqA‹£Ÿ”üAŒo’Ë~Žq
u’n‰ºŒsãü‰è‚̬’·‰ß’ö‚̃}ƒCƒNƒƒCƒ[ƒWƒ“ƒOv
•½¬‚Q‚U”N‚WŒŽ‚P‚P“úC‘æ‚P‚W‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‹à‘òiP4)p11-14

 

363.‹£Ÿ”ü
uCompact/Mobile MRI‚̉ߋŽEŒ»ÝE–¢—ˆv
•½¬‚Q‚U”N‚WŒŽ‚P‚P“úC‘æ‚P‚W‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‹à‘òiSL1)p1-4
“Á•Êu‰‰

 

362.X˜e@‘AŽ›“cN•FA‹£Ÿ”üA”qŽt’q”VA£ŒÃàV—R•F
uMR microimaging‚É‚æ‚é‰ÊŽÀˆÛŠÇ‘©\‘¢‚̉Ž‹‰»‚Æ’è—Ê“I•]‰¿v
•½¬‚Q‚U”N‚RŒŽ‚Q‚X“úC‰€Œ|Šw‰ï•½¬‚Q‚U”N“xt‹G‘å‰ïŒ¤‹†”­•\C’}”g‘åŠw‘æ‚QƒGƒŠƒAE‘æ‚RƒGƒŠƒA ‘æ13Šª•Êû1 P96

 

361.‹£Ÿ”ü
uƒqƒgãóŽq‰»ŠwŒÅ’è•W–{‚ÌMR microscopy‚É‚æ‚éŽOŽŸŒ³Œ`‘ÔŒv‘ªv
•½¬‚Q‚U”N‚PŒŽ‚R‚O“úCƒqƒg”]‚ÌŒ`‘ÔŒ`¬‚ÉŠÖ‚·‚éƒuƒŒ[ƒ“ƒXƒg[ƒ~ƒ“ƒOC“Œ‹ž‘åŠw‹³ˆçŠw•”‘æˆê‰ï‹cŽº
µ‘Òu‰‰
360.‹£Ÿ”ü
uMRI‘•’u‚ÌŽd‘g‚Ý‚ÆŒv‘ª‚ÌŠT—vv
•½¬‚Q‚T”N‚P‚PŒŽ‚P‚T“úCUltra@High Field-MRI@ƒ[ƒNƒVƒ‡ƒbƒvEƒgƒ‰ƒCƒAƒ‹ƒ†[ƒXC“Œ‘å”
µ‘Òu‰‰
359.‹£Ÿ”ü
uMRI‚ð—p‚¢‚½¶‘ÌŽŽ—¿‚È‚Ç‚Ì‚•ª‰ð”\ƒCƒ[ƒWƒ“ƒO‚ÌŒ»óv
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µ‘Òu‰‰

 

358.‹Ê“c‘å‹PC‹£Ÿ”üC–ö@—z‰îCˆÉ“¡‰ÀFC’‡‘º‚Žu
u‚‰·’´“`“±ƒoƒ‹ƒNŽ¥Î‚ð—p‚¢‚½‚•ª‰ð”\MRI @High-Resolution Magnetic Resonance Imaging Using a High Tc Bulk Superconducting Magnetv
•½¬25”N11ŒŽ12“úC‘æ52‰ñNMR“¢˜_‰ïD‹à‘òiP62j
Å—DGŽáŽèƒ|ƒXƒ^[Ü

 

357.ˆ¢•”‹ÓŽjA”qŽt’q”VA•yŠ~””nA‹£Ÿ”üA‹vP’C”Ž
u’´‚Ž¥ê(14.1T)-MRI‘•’u‚ð—p‚¢‚½ƒ}ƒEƒX”]‹@”\ƒCƒ[ƒWƒ“ƒOv
•½¬‚Q‚T”N‚XŒŽC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(P-3-247)

 

356.”qŽt’q”VAˆ¢•”‹ÓŽjA•yŠ~””nA‹£Ÿ”üA‹vP’C”Ž
uŠùÝ‚Ì14.1T-NMR‘•’u‚ðŠˆ—p‚µ‚½MRMICS‚É‚æ‚éƒ}ƒEƒX”]ƒCƒ[ƒWƒOv
•½¬‚Q‚T”N‚XŒŽC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(P-3-246)
355.‹£Ÿ”ü
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•½¬‚Q‚T”N‚XŒŽC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(EL8-1)
‹³ˆçu‰‰

 

354.ƒGƒ€ƒfƒBEƒVƒƒƒnƒ_ƒgEƒzƒTƒCƒ“EƒAƒNƒ‰ƒ€AŽ›“cN•FAÎŒ\ˆê˜YA‹£Ÿ”ü
uEddy Current Analysis of 0.3 T Permanent Magnet MRI Systems with Planar Gradient Coilv
•½¬‚Q‚T”N‚XŒŽC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(O-01-120)

 

353.ÎàVˆêŒ›A‹£Ÿ”üAŽ›“cN•F
uMRI—p•½”ÂŒ^Œù”zŽ¥êƒRƒCƒ‹‚ÌÅ“K‰»Žè–@‚ÌŒ¤‹†v
•½¬‚Q‚T”N‚XŒŽC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(O-01-116)

 

352.“àŠC’m”üA‹Ê“c‘å‹PAŽ›“cN•FA‹£Ÿ”üA‹{é@—ºAŽR•”‰psA“¡‰iN¬A
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u¬Ž™œ”N—îŒv‘ª—p‚l‚q‚h‚Ì‚½‚߂̃Rƒ“ƒsƒ…[ƒ^Žx‰‡f’fƒVƒXƒeƒ€‚ÌŠJ”­v
•½¬‚Q‚T”N‚XŒŽC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(O-01-112)

 

351.ˆî‘º^–çAŽ›“cN•FA‹£Ÿ”ü
u¬Ž™œ”N—îŒv‘ª—pRFƒvƒ[ƒu‚ÌŠJ”­iIIjv
•½¬‚Q‚T”N‚XŒŽC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(O-01-111)

 

350.Ž›“cN•FAÎàVˆêŒ›Aˆî‘º^–çAÎŒ\ˆê˜YA‹£Ÿ”ü
u¬Ž™œ”N—îŒv‘ª—p0.3Tƒ|[ƒ^ƒuƒ‹MRI‚ÌŠJ”­v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“úC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(O-01-110)

 

349.‹Ê“c‘å‹PA‹£Ÿ”ü
uCross Sampling‚ð—p‚¢‚½ÃŽ¥ê•s‹Ïˆê‰º‚É‚¨‚¯‚鈳kƒZƒ“ƒVƒ“ƒOƒAƒ‹ƒSƒŠƒYƒ€‚ÌŠJ”­v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“úC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(O-01-047)

 

348.‘å’|—z‰îAŽR“c‘ñ”nA‹£Ÿ”üAŽR“cdl
uƒqƒgãóŽq•W–{‚̃‰[ƒWƒ}ƒgƒŠƒNƒXŽB‘œŽè–@‚ÌŒŸ“¢v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“úC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(P-03-244)

 

347.X˜e@‘A‹£Ÿ”üA”qŽt’q”VA£ŒÃàV—R•F
uMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒO‚É‚æ‚鶑̎Ž—¿‚Ì”÷×\‘¢‚̉Ž‹‰»v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“úC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(P-03-243)

 

346.Ž›“cN•FA‰Í–ìÊ‹LA“àŠC’m”üAˆî‘º^–çA‹£Ÿ”üA‹{é@—ºAŽR•”‰psA“¡‰i@N¬A‹g‰ª@‘å
u¬Ž™œ”N—îŒv‘ª—pƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚Ì—L—p«v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“úC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(P-02-184)

 

345.‹Ê“c‘å‹PA‹£Ÿ”ü
uCompressed SensingƒAƒ‹ƒSƒŠƒYƒ€‚ð—p‚¢‚½ŽOŽŸŒ³MR Microscopyv
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“úC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(P-02-162)

 

344.‹´–{ª‘¾˜Y A‹£Ÿ”üA”qŽt’q”V
u ‚Ž¥ê‚•ª‰ð”\‚l‚q‚h‚É‚¨‚¯‚éƒfƒWƒ^ƒ‹ƒgƒ‰ƒ“ƒV[ƒo[‚Ì•]‰¿v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“úC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(P-02-125)

 

343.“c@“ÖAŽ›“cN•FA‹£Ÿ”ü
uƒI[ƒvƒ“Œ^RFƒvƒ[ƒu‚ÌŠJ”­‚Æin situŽB‘œ‚ւ̉ž—pv
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“úC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(P-02-124)
342.ŽR“c‘ñ”nA‹£Ÿ”ü
u‰~“›Œ^Œù”zŽ¥êƒRƒCƒ‹‚ÌÅ“K‰»Žè–@‚ÌŒ¤‹†v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“úC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(P-02-123)
341.ÎŒ\ˆê˜YAŽ›“cN•FA‹£Ÿ”ü
u¬Ž™œ”N—îŒv‘ª—p‚l‚q‚h‚Ì‚½‚߂̃Vƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ÌŠJ”­v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“úC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(P-02-122)

 

340.“àŠC’m”üC‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”üC‹{é—ºCŽR•”‰psC“¡‰iN¬C‹g‰ª‘å
uƒI[ƒvƒ“ƒ\[ƒX‚ð—p‚¢‚½¬Ž™œ”N—îŒv‘ª—pMRI‚Ì‚½‚߂̃Rƒ“ƒsƒ…[ƒ^Žx‰‡f’fƒVƒXƒeƒ€‚ÌŠJ”­v
•½¬‚Q‚T”N‚WŒŽ‚Q“úC‘æ‚P‚V‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P13)p63-p64

 

339.ˆî‘º^–çCŽ›“cN•FC‹£Ÿ”ü
u¬Ž™œ”N—îŒv‘ª—pRFƒvƒ[ƒu‚ÌÅ“K‰»iII jv
•½¬‚Q‚T”N‚WŒŽ‚Q“úC‘æ‚P‚V‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P4)p43-p44

 

338.ÎàVˆêŒ› CŽ›“cN•F C‹£Ÿ”ü
u“±‘Ì•‚ðl—¶‚µ‚½•½”ÂŒ^ƒOƒ‰ƒWƒGƒ“ƒgƒRƒCƒ‹‚Ì“d—ÍÅ“K‰»ÝŒvv
•½¬‚Q‚T”N‚WŒŽ‚Q“úC‘æ‚P‚V‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P3)p39-p42

 

337.ŽR“c‘ñ”n C‹£Ÿ”ü
uƒiƒ[ƒ{ƒAcŒ^’´“`“±Ž¥Î‚Ì‚½‚ß‚ÌŒù”zŽ¥êƒRƒCƒ‹‚ÌÝŒvv
•½¬‚Q‚T”N‚WŒŽ‚Q“úC‘æ‚P‚V‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P2)p35-p38

 

336.ÎŒ\ˆê˜Y CŽ›“cN•F C‹£Ÿ”ü
u0.3T ƒ|[ƒ^ƒuƒ‹ MRI—pƒVƒ“ƒOƒ‹ƒ`ƒƒƒlƒ€ƒRƒC‚ÌŠJ”­v
•½¬‚Q‚T”N‚WŒŽ‚Q“úC‘æ‚P‚V‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P1)p31-p34

 

335.”qŽt’q”VC‹£Ÿ”üCˆ¢•”‹ÓŽjC•yŠ~””nC‹vP’C”Ž
uŠùÝ‚ÌNMR‘•’u‚ðŠˆ—p‚µ‚½MRMICS‚É‚æ‚é600MHz‚ł̶‘̃}ƒEƒXŽB‘œ‚Ì
‰ŠúŒŸ“¢v
•½¬‚Q‚T”N‚WŒŽ‚Q“úC‘æ‚P‚V‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(O6)p21-p24

 

334.X˜e‘CŽ›“cN•FC‹£Ÿ”üC”qŽt’q”VC£ŒÃàV—R•F
uMR microimaging‚É‚æ‚é‰ÊŽÀˆÛŠÇ‘©\‘¢‚̉Ž‹‰»v
•½¬‚Q‚T”N‚WŒŽ‚Q“úC‘æ‚P‚V‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(O4)p13-16

 

333.‹Ê“c‘å‹PC‹£Ÿ”üC–ö—z‰îCˆÉ“¡‰ÀFC’‡‘º‚Žu
uV‹KÝŒv‚É‚æ‚邉·’´“`“±ƒoƒ‹ƒNŽ¥Î‚ð—p‚¢‚½MRI‚ÌŠJ”­v
•½¬‚Q‚T”N‚WŒŽ‚Q“úC‘æ‚P‚V‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(O3)p9-12

 

332.“c“ÖCŽ›“cN•FC‹£Ÿ”ü
u0.2T‰i‹vŽ¥ÎMRI‚ð—p‚¢‚½‰®ŠOŽ÷–؎劲‚Ì’·ŠúŠÔŒv‘ªv
•½¬‚Q‚T”N‚WŒŽ‚Q“úC‘æ‚P‚V‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(O2)p5-p8

 

331.Ž›“cN•FC‹£Ÿ”üC”qŽt’q”V
u‰i‹vŽ¥ÎŽ¥‹C‰ñ˜H‚ÌŽ¥êˆÀ’è«‚ÌŒüã‚ÆDixon–@‚ւ̉ž—pv
•½¬‚Q‚T”N‚WŒŽ‚Q“úC‘æ‚P‚V‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(O1)P1-P4

 

330.‹£Ÿ”ü
u‚l‚q‚h‚ÌŠî‘bv
•½¬24”N11ŒŽ7“úC‘æ‚T‚P‰ñ‚m‚l‚q“¢˜_‰ïC–¼ŒÃ‰®
‹³ˆçu‰‰
329.‹Ê“c‘å‹P
uˆ³kƒZƒ“ƒVƒ“ƒOi‚b‚rj‚Ì‚l‚q‚h‚Ö‚ÌŽÀ‘•‚ÌŽŽ‚Ý[—˜_‚ÆŽÀÛ[v
•½¬‚Q‚S”N‚P‚PŒŽ‚Q“úC‚‚­‚΂l‚q§˜b‰ïC‚‚­‚Î

 

328.‹´–{ª‘¾˜YC‹£Ÿ”üC”qŽt’q”V
uƒfƒBƒWƒ^ƒ‹rf‚ð—p‚¢‚½’ᎥêƒRƒ“ƒpƒNƒg‚l‚q‚hƒVƒXƒeƒ€‚É‚¨‚¯‚郉[ƒWƒ}ƒgƒŠƒNƒXŽB‘œv
•½¬24”N9ŒŽ7“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(p-2-106)

 

327.‹£Ÿ”ü
u‚l‚q‚h‚É‚¨‚¯‚é‹óŠÔ•ª‰ð”\‚Ö‚Ì’§í[—ðŽj“IƒŒƒrƒ…[‚Æ«—ˆ“W–][v
•½¬24”N9ŒŽ6“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(SL-3)
“Á•Êu‰‰
326.‰Í–ìÊ‹LC“àŠC’m”üC‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”üC‹{é@—ºCŽR•”‰psC
‹g‰ª@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚ð—p‚¢‚½¬Ž™œ”N—î‚Ì”»’è‚ÆÄŒ»«•]‰¿v
•½¬24”N9ŒŽ6“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(o-1-128)

 

325.‹ß“¡‘å‹MCŽ›“cN•FC‹£Ÿ”ü
uæùœœ–§“xŒv‘ª—pCompact MRI‚É‚¨‚¯‚éŒv‘ªÄŒ»«‚ÌŒüãv
•½¬24”N9ŒŽ6“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(o-1-129)

 

324.–x‰ê‰ëŽjCÎàVˆêŒ›C”¼“cW–çC‹£Ÿ”ü
ucŒ^’´“`“±Ž¥Î‚Ì‚½‚ß‚Ì‘åŒûŒaƒ\ƒŒƒmƒCƒh‚q‚eƒRƒCƒ‹Œù”zŽ¥êƒvƒ[ƒu‚ÌŠJ”­v
•½¬24”N9ŒŽ7“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(p-2-100)

 

323.ÎàVˆêŒ›CŽ›“cN•FC‹£Ÿ”ü
u‘½“_’TõŒ^Å“K‰»Žè–@‚É‚æ‚éŒù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­v
•½¬24”N9ŒŽ7“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(p-2-101)

 

322.ˆî‘º^–çCŽ›“cN•FC‹£Ÿ”ü
u¬Ž™œ”N—îŒv‘ª—p‚q‚eƒvƒ[ƒu‚ÌŠJ”­v
•½¬24”N9ŒŽ7“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(p-2-102)

 

321.–Ø‘º•ŽjC‰º‰Æ—SlC“¡è_FCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”VCŒ·ŠÔ@—mC
£ŒÃàV—R•F
u‘¾—z“d’r‹ì“®‚l‚q‚h‚ÌŠJ”­v
•½¬24”N9ŒŽ7“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(p-2-107)

 

320.‹Ê“c‘å‹PC‹£Ÿ”ü
uCartesianƒTƒ“ƒvƒ‹ƒŠƒ“ƒO–@‚É‚æ‚éCompressed Sensing‚ð—p‚¢‚½‚l‚qƒ}ƒCƒNƒƒXƒRƒs[‚ÌŒŸ“¢v
•½¬24”N9ŒŽ7“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(o-2-200)

 

319.Ž›“cN•FC‰Í–ìÊ‹LC“àŠC’m”üC‹£Ÿ”üC‹{é@—ºCŽR•”‰psC‹g‰ª@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚ð—p‚¢‚Ä”»’肵‚½¬Ž™œ”N—î‚Æœ‘ÌÏ‚Æ‚ÌŠÖŒWv
•½¬24”N9ŒŽ7“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(p-2-144)

 

318.“àŠC’m”üC‰Í–ìÊ‹LCŽ›“cN•FC‹£Ÿ”üC‹{é@—ºCŽR•”‰psC‹g‰ª@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚ð—p‚¢‚½¬Ž™œ”N—î‚Ì•]‰¿‚É‚¨‚¯‚éŽB‘œŽžŠÔ’Zk‚ÌŒŸ“¢v
•½¬24”N9ŒŽ7“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(p-2-145)

 

317.Ž›“cN•FCÎàVˆêŒ›Cˆî‘º^–çC‰Í–ìÊ‹LC“àŠC’m”üC‹Ê“c‘å‹PC‹£Ÿ”ü
u¬Ž™œ”N—îŒv‘ª—pƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚ÌŠJ”­v
•½¬24”N9ŒŽ7“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(o-2-210)

 

316.‹Ê“c‘å‹PC‹£Ÿ”üC”qŽt’q”V
uƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ð—p‚¢‚½‚P‚s‰i‹vŽ¥Î‚ÌÃŽ¥êƒVƒ~ƒ“ƒOv
•½¬24”N9ŒŽ7“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(o-2-211)

 

315.‹£Ÿ”üC–x‰ê‰ëŽjC‹Ê“c‘å‹PC‰º‰Æ—SlCŽ›“cN•FC‹´–{ª‘¾˜YC”qŽt’q”V
uNMR Microscopy‚É‚æ‚鶑̑gD‚Ìmicrostructure‚Ì’Šov
•½¬24”N9ŒŽ8“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(p-3-237)

 

314.‰º‰Æ—SlC–x‰ê‰ëŽjCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”VCŒ·ŠÔ@—mC£ŒÃ‘ò—R•F.
u¶‘̃Tƒ“ƒvƒ‹‚̬’·‚É”º‚¤‚m‚l‚qƒpƒ‰ƒƒ^•ª•zŒv‘ª‚ÆMicrostructure‚̉Ž‹‰»v
•½¬24”N9ŒŽ8“úC‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“s(p-3-238)

 

313.‰º‰Æ—SlC–x‰ê‰ëŽjCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”VCŒ·ŠÔ@—mC£ŒÃ‘ò—R•F
uMR microimaging ‚ð—p‚¢‚½‰ÊŽÀ‚Ì”÷×\‘¢‚Æ‚m‚l‚qƒpƒ‰ƒƒ^•ª•z‚ÌŒv‘ªv
•½¬‚Q‚S”N‚WŒŽ‚R“úC‘æ‚P‚U‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‘å’Ã(o9)

 

312.‹£Ÿ”üC“c@“ÖCX˜e@‘C‰º‰Æ—SlCŽ›“cN•FC”qŽt’q”VC•yŠ~””n
uŽ÷–ØŒv‘ª—pƒ‚ƒoƒCƒ‹‚l‚q‚h‚ÌŠJ”­v
•½¬‚Q‚S”N‚WŒŽ‚R“úC‘æ‚P‚U‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‘å’Ã(o8)

 

311.‹Ê“c‘å‹PC‹£Ÿ”ü
u‚l‚qƒ}ƒCƒNƒƒXƒRƒs[‚Ì‚½‚ß‚Ì‚f‚o‚f‚o‚t—p‚¢‚½Compressed Sensing ƒAƒ‹ƒSƒŠƒYƒ€‚ÌŠJ”­v
•½¬‚Q‚S”N‚WŒŽ‚R“úC‘æ‚P‚U‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‘å’Ã(o7)

 

310.ÎàVˆêŒ›CŽ›“cN•FC‹£Ÿ”ü
uŒù”zŽ¥êƒRƒCƒ‹ÝŒv‚É‚¨‚¯‚éˆâ“`“IƒAƒ‹ƒSƒŠƒYƒ€‚Æ—±ŽqŒQÅ“K‰»–@‚Ì”äŠrv
•½¬‚Q‚S”N‚WŒŽ‚R“úC‘æ‚P‚U‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‘å’Ã(p12)

 

309. ‰Í–ìÊ‹LC“àŠC’m”üCŽ›“cN•FC‹£Ÿ”üC‹{é@—ºCŽR•”‰psC‹g‰ª@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚ð—p‚¢‚½¬Ž™œ”N—î”»’è–@‚ÌŒŸ“¢v
•½¬‚Q‚S”N‚WŒŽ‚R“úC‘æ‚P‚U‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‘å’Ã(p9)

 

308.ˆÀ’B@¹C‹Ê“c‘å‹PC‹£Ÿ”ü
u‚l‚q‚h‚Ìá•XŒ¤‹†‚ւ̉ž—p@|Ïá‚Ì…•ª“Á«‹Èü‚ÌŒv‘ª[v
•½¬‚Q‚S”N‚WŒŽ‚R“úC‘æ‚P‚U‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‘å’Ã(p8)

 

307.”qŽt’q”V
u‹ó‹Cˆ³ƒZƒ“ƒT[‚ð—p‚¢‚½ƒ}ƒEƒXS”ŒÄ‹zŒŸoŠí‚ÌŠJ”­v
•½¬‚Q‚S”N‚WŒŽ‚R“úC‘æ‚P‚U‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‘å’Ã(p4)

 

306.‹ß“¡‘å‹MCŽ›“cN•FC‹£Ÿ”ü
uœ–§“xŒv‘ª—pƒRƒ“ƒpƒNƒg‚l‚q‚h‚É‚¨‚¯‚éŒv‘ªÄŒ»«Œüã‚ð–ÚŽw‚µ‚½‚q‚eƒRƒCƒ‹‚ÌÝŒvv
•½¬‚Q‚S”N‚WŒŽ‚R“úC‘æ‚P‚U‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‘å’Ã(p3)

 

305.“àŠC’m”üC‰Í–ìÊ‹LCŽ›“cN•FC‹£Ÿ”üC‹{é@—ºCŽR•”‰psC‹g‰ª@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚É‚¨‚¯‚éŽB‘œ‚Ì‚‘¬‰»‚ƬŽ™‚Ìœ”N—î•]‰¿‚ւ̉ž—pv
•½¬‚Q‚S”N‚WŒŽ‚R“úC‘æ‚P‚U‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‘å’Ã(p2)

 

304.ˆî‘º^–çCŽ›“cN•FC‹£Ÿ”ü
u¬Ž™œ”N—îŒv‘ª—p‚q‚eƒvƒ[ƒu‚ÌÅ“K‰»v
•½¬‚Q‚S”N‚WŒŽ‚R“úC‘æ‚P‚U‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‘å’Ã(p1)

 

303.–x‰ê‰ëŽjCÎàVˆêŒ›C‹£Ÿ”ü
ucŒ^’´“`“±Ž¥Î—p30‚‚Œaƒ\ƒŒƒmƒCƒhƒRƒCƒ‹‚ð—p‚¢‚½Œù”zŽ¥êƒvƒ[ƒu‚ÌŠJ”­v
•½¬‚Q‚S”N‚WŒŽ‚R“úC‘æ‚P‚U‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‘å’Ã(o4)

 

302.Ž›“cN•FC‰Í–ìÊ‹LCÎàVˆêŒ›Cˆî‘º^–çC“àŠC’m”üC‹Ê“c‘å‹PC‹£Ÿ”ü
u‰i‹vŽ¥Î•Ð‚ƃVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ð‘g‚݇‚킹‚½ÃŽ¥êƒVƒ~ƒ“ƒOv
•½¬‚Q‚S”N‚WŒŽ‚R“úC‘æ‚P‚U‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‘å’Ã(o1)

 

301.Ž›“cN•FC‰Í–ìÊ‹LC“àŠC’m”üCˆî‘º^–çC‹Ê“c‘å‹PC‹£Ÿ”üC‹g‰ª@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒgMRI‚É‚æ‚鬎™œ”N—îŒv‘ª‚̉”\«v
•½¬23”N11ŒŽ26“úC‘æ‚Q‚Q‰ñ“ú–{¬’·Šw‰ïC˜a‰ÌŽR

 

300.Ž›“cN•FC‹Ê“c‘å‹PC‹£Ÿ”ü
u‰·“x‰Â•ÏMRI‚ÌŠJ”­‚ƶ‘̃Tƒ“ƒvƒ‹‚̊ɘaŽžŠÔ‚¨‚æ‚ÑADCŒv‘ª‚ւ̉ž—pv
•½¬23”N10ŒŽ1“úC‘æ39‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC¬‘q(p-3-220)

 

299.‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
uƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ð—p‚¢‚½ƒoƒ‹ƒN’´“`“±Ž¥Î‚ÌÃŽ¥ê‹Ïˆê«‚̉ü‘Pv
•½¬23”N10ŒŽ1“úC‘æ39‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC¬‘q(p-3-219)

 

298.‹´–{ª‘¾˜YC‹£Ÿ”üC”qŽt’q”V
u’ᎥêƒRƒ“ƒpƒNƒgMRIƒVƒXƒeƒ€‚Ì‚’‚†ƒfƒBƒWƒ^ƒ‹‰»‚É‚æ‚é‰æŽ¿‚̉ü‘Pv
•½¬23”N10ŒŽ1“úC‘æ39‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC¬‘q(p-3-218)

 

297.”qŽt’q”V
uƒRƒ“ƒpƒNƒgMRI—p‰i‹vŽ¥Î‚Ì‚½‚߂̸–§‰·“x’²®Žè–@‚ÌŠJ”­v
•½¬23”N10ŒŽ1“úC‘æ39‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC¬‘q

 

296.–Ø‘º•ŽjC‹£Ÿ”üC‰º‰Æ—SlCŽ›“cN•FC”qŽt’q”VCŒ·ŠÔ@—mC£ŒÃàV—R•F
uƒRƒ“ƒpƒNƒgMRI—pŠJ•ÂŒ^ƒ\ƒŒƒmƒCƒhRFƒRƒCƒ‹‚ÌŠJ”­v
•½¬23”N10ŒŽ1“úC‘æ39‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC¬‘q(p-3-216)

 

295.‰º‰Æ—SlC–Ø‘º•ŽjC“¡è_FCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”VCŒ·ŠÔ@—mC£ŒÃàV—R•F
u¶‘̃Tƒ“ƒvƒ‹‚̬’·‚É”º‚¤ŠÉ˜aŽžŠÔ‚ÆADC‚ÌŒv‘ªv
•½¬23”N9ŒŽ30“úC‘æ39‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC¬‘q(p-2-148)

 

294.ŠÛŽRŒ\‰îC‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
u‰~“›Œ^ƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ÌŠJ”­i‚QjFŽÀ‘•‚Æ•]‰¿v
•½¬23”N9ŒŽ29“úC‘æ39‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC¬‘q(o-1-135)

 

293.‹Ê“c‘å‹PCŠÛŽRŒ\‰îC‹£Ÿ”ü
u‰~“›Œ^ƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ÌŠJ”­i‚PjFÝŒv‚Æ•]‰¿v
•½¬23”N9ŒŽ29“úC‘æ39‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC¬‘q(o-1-134)

 

292.‹´–{ª‘¾˜YC‹£Ÿ”üC”qŽt’q”V
uWindows-PC‚ð—p‚¢‚½MRIƒpƒ‹ƒX”­¶‘•’u‚ÌŠJ”­v
•½¬23”N9ŒŽ29“úC‘æ39‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC¬‘q(o-1-132)

 

291.‹ß“¡‘å‹MC‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
uæùœœ–§“xŒv‘ª—pƒRƒ“ƒpƒNƒgMRI‚É‚¨‚¯‚é‚RDFSE‚É‚æ‚éŒv‘ªÄŒ»«‚ÌŒüãv
•½¬23”N9ŒŽ29“úC‘æ39‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC¬‘q(o-1-86)

 

290.‰Í–ìÊ‹LC“àŠC’m”üCŽ›“cN•FC‹£Ÿ”üC‹g‰ª@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒgMRI‚ð—p‚¢‚½¬Ž™œ”N—î‚Ì•]‰¿v
•½¬23”N9ŒŽ29“úC‘æ39‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC¬‘q(o-1-85)

 

289.”qŽt’q”V
uÃŽ¥ê‹­“x0.3TˆÈ‰º‚Ìl‘Ì—pƒRƒ“ƒpƒNƒgMRI‚ð—p‚¢‚½¬“®•¨MRI‚ÌŒŸ“¢v
•½¬23”N9ŒŽ29“úC‘æ39‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC¬‘q

 

288.’‡‘º‚ŽuC¬ì‹±•½CŽ›“cN•FC‹£Ÿ”üC”qŽt’q”V
u‚‰·’´“`“±ƒoƒ‹ƒNŽ¥Î‚Å‚ÌMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOv
•½¬‚Q‚R”N‚WŒŽ‚T“úC‘æ‚P‚T‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰ªè

 

287.ˆÀ’B¹CŽRŒûŒåC¬ŠÖr_C‹£Ÿ”ü
uMRI‚Ìá•XŒ¤‹†‚ւ̉ž—p@\Ïá‚Ì…•ª•ÛŽ‹Èü‚ÌŒv‘ª\v
•½¬‚Q‚R”N‚WŒŽ‚T“úC‘æ‚P‚T‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰ªè

 

286.‰Í–ìÊ‹LC“àŠC’m”üCˆî‘º^–çCŽ›“cN•FC‹£Ÿ”üC‹g‰ª‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒgMRI‚ð—p‚¢‚½¬Ž™œ”N—î‚Ì•]‰¿v
•½¬‚Q‚R”N‚WŒŽ‚T“úC‘æ‚P‚T‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰ªè

 

285.‹ß“¡‘å‹MC‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
uæùœœ–§“xŒv‘ª—pƒRƒ“ƒpƒNƒgMRI‚É‚¨‚¯‚é‚RDFSE‚É‚æ‚éŒv‘ªÄŒ»«‚ÌŒüãv
•½¬‚Q‚R”N‚WŒŽ‚T“úC‘æ‚P‚T‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰ªè

 

284.–Ø‘º•ŽjC‰º‰Æ—SlC‹£Ÿ”üCŽ›“cN•FC”qŽt’q”VC•yŠ~””nCŒ·ŠÔ—mC£ŒÃàV—R•F
u‘¾—z“d’r‹ì“®‚ð‚ß‚´‚µ‚½’~“d’r‹ì“®Œ^ƒ‚ƒoƒCƒ‹MRI‚ÌŠJ”­v
•½¬‚Q‚R”N‚WŒŽ‚T“úC‘æ‚P‚T‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰ªè

 

283.‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
u—¬‚êŠÖ”–@‚ð—p‚¢‚½‰~“›Œ^ƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ÌÝŒvv
•½¬‚Q‚R”N‚WŒŽ‚T“úC‘æ‚P‚T‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰ªè

 

282.Ž›“cN•FC‹Ê“c‘å‹PC‹£Ÿ”ü
u‰·“x‰Â•ÏMRI‚ÌŠJ”­‚ƉʎÀ‚ÌNMRƒpƒ‰ƒƒ^Œv‘ª‚ւ̉ž—pv
•½¬‚Q‚R”N‚WŒŽ‚T“úC‘æ‚P‚T‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰ªè

 

281.‰º‰Æ—SlC–Ø‘º•ŽjC“¡è_FCŽ›“cN•FC‹£Ÿ”üCŒ·ŠÔ—mC£ŒÃàV—R•FC
u—œ‰ÊŽÀ‚̬’·‚É”º‚¤ŠÉ˜aŽžŠÔ‚ÆADC‚ÌŒv‘ªv
•½¬‚Q‚R”N‚WŒŽ‚T“úC‘æ‚P‚T‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰ªè

 

280.ŠÛŽRŒ\‰îC‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
ucŒ^’´“`“±Ž¥Î—p‰~“›Œ^ƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ÌŠJ”­v
•½¬‚Q‚R”N‚WŒŽ‚T“úC‘æ‚P‚T‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰ªè

 

279.”qŽt’q”V
uNdFebŒn¬Œ^MRIŽ¥‹C‰ñ˜H‚̉·“xƒhƒŠƒtƒg‚ðŽ•ž‚·‚邸“x‰·“xŠÇ—Žè–@‚ÌŠJ”­v
•½¬‚Q‚R”N‚WŒŽ‚T“úC‘æ‚P‚T‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‰ªè

 

278.–Ø‘º•ŽjC‰º‰Æ—SlC‹£Ÿ”üCŽ›“cN•FC”qŽt’q”VC•xŠ~””nCŒ·ŠÔ—mC£ŒÃàV—R•F
uŽ÷–Ø—pŽ©‘–Ž®ƒ|[ƒ^ƒuƒ‹‚l‚q‚h‚ÌŠJ”­v
•½¬‚Q‚Q”N‚WŒŽ‚U“úC‘æ‚P‚S‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ‹ž

 

277.’†ŽR’‰–¾C‹ß“¡‘å‹MCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”V
uæùœœ–§“xŒv‘ª—pƒRƒ“ƒpƒNƒg‚l‚q‚h‚É‚¨‚¯‚éŒv‘ªÄŒ»«‚ÌŒüãv
•½¬‚Q‚Q”N‚WŒŽ‚U“úC‘æ‚P‚S‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ‹ž

 

276.ˆÀ’B¹C”öŠÖr_C‹£Ÿ”ü
u‚l‚q‚h‚Ìá•XŒ¤‹†‚ւ̉ž—p\‚Ê‚êá’†‚Ì…‚Ì•ª•zŽB‘œ‚ÌŽŽ‚Ý\‘´‚Ì‚Qv
•½¬‚Q‚Q”N‚WŒŽ‚U“úC‘æ‚P‚S‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ‹ž

 

275.‹Ê“c‘å‹PC‹£Ÿ”ü
u‚e‚o‚f‚`‚ð—p‚¢‚½‚t‚r‚a’ÊM‚l‚q‚hƒVƒXƒeƒ€‚ÌŠJ”­v
•½¬‚Q‚Q”N‚WŒŽ‚U“úC‘æ‚P‚S‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ‹ž

 

274.ŠÛŽRŒ\‰îC‹Ê“c‘å‹PC¬ì‹±•½C‹£Ÿ”ü
ucŒ^’´“d“±Ž¥Î—pŒù”zŽ¥êƒRƒCƒ‹‚̈â“`“IƒAƒ‹ƒSƒŠƒYƒ€‚É‚æ‚éÅ“K‰»ÝŒvv
•½¬‚Q‚Q”N‚WŒŽ‚U“úC‘æ‚P‚S‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ‹ž

 

273.¬ì‹±•½C”¼“cW–çC‹£Ÿ”ü
uˆâ“`“IƒAƒ‹ƒSƒŠƒYƒ€‚ð—p‚¢‚½•½–ÊŒ^Œù”zŽ¥êƒRƒCƒ‹‚ÌÅ“K‰»ÝŒvv
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272.‹Ê“c‘å‹PC‹£Ÿ”ü
u‚f‚o‚t‚ð—p‚¢‚½‚R‚c‰æ‘œƒŠƒAƒ‹ƒ^ƒCƒ€‚l‚h‚oˆ—ƒVƒXƒeƒ€‚ÌŠJ”­v
•½¬‚Q‚Q”N‚WŒŽ‚U“úC‘æ‚P‚S‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“Œ‹ž

 

271.¬ì‹±•½C‹£Ÿ”ü
uˆâ“`“IƒAƒ‹ƒSƒŠƒYƒ€‚ð—p‚¢‚½•½–ÊŒ^Œù”zŽ¥êƒRƒCƒ‹‚ÌÅ“K‰»ÝŒvv
•½¬‚Q‚Q”N‚XŒŽ‚R‚O“úC‘æ‚R‚W‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‚‚­‚Î

 

270.ŠÛŽRŒ\‰îC¬ì‹±•½C‹Ê“c‘å‹PC‹£Ÿ”ü
ucŒ^’´“`“±Ž¥Î—pŒù”zŽ¥êƒRƒCƒ‹‚̈â“`“IƒAƒ‹ƒSƒŠƒYƒ€‚É‚æ‚éÅ“K‰»ÝŒvv
•½¬‚Q‚Q”N‚XŒŽ‚R‚O“úC‘æ‚R‚W‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‚‚­‚Î

 

269.‹Ê“c‘å‹PC‹£Ÿ”ü
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267.”qŽt’q”VC‹£Ÿ”ü
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