‘“ąŠw‰ļ

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‚Ģ‰ü‘P‚É‚ę‚é—Տ°”]‰ę‘œ‚ĢƒfƒB[ƒvƒ‰[ƒjƒ“ƒO’“‰š‘œ‚Ģ«”\Œüć
—ߘa3”N9ŒŽ10“ś`9ŒŽ12“śC‘ę49‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiƒnƒCƒuƒŠƒbƒhjCiP143-J)

 

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
4.7TcŒ^’““d“±Ž„Ī‚š—p‚¢‚½—L‘Ü—ŽćóEV¶Že•W–{‚ĢMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒO
—ߘa3”N9ŒŽ10“ś`9ŒŽ12“śC‘ę49‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiƒnƒCƒuƒŠƒbƒhjCiP136-J)

 

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‚š—p‚¢‚½FSL‚ĢŠwKƒ‚ƒfƒ‹‚É‚ę‚éŠgŽU‹­’²‰ę‘œ‚Ģ˜c‚Ż•ā³
—ߘa3”N9ŒŽ10“ś`9ŒŽ12“śC‘ę49‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiƒnƒCƒuƒŠƒbƒhjCiP071-J)
Šwp§—ćÜ

 

551.@Keisuke Yoshida, Yasuhiko Terada
Image restoration for spiral imaging using dAUTOMAP and GIRF
dAUTOMAP‚ĘGIRF‚š—p‚¢‚½Spiral‰ę‘œ‚ĢƒA[ƒ`ƒtƒ@ƒNƒg•ā³‚ĢŒŸ“¢
—ߘa3”N9ŒŽ10“ś`9ŒŽ12“śC‘ę49‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiƒnƒCƒuƒŠƒbƒhjCiP045-J)

 

550.@Katsumi Kose, Ryoichi Kose, Yasuhiko Terada
Bloch simulation of the 3-point Dixon method on biological systems
¶‘Ģ‚š‘ĪŪ‚Ę‚µ‚½3 point Dixon–@‚ĢBloch simulation
—ߘa3”N9ŒŽ10“ś`9ŒŽ12“śC‘ę49‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiƒnƒCƒuƒŠƒbƒhjCiO3-002)

 

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^[‰šĶ‚É‚ę‚éˆŁ‚Č‚éƒqƒg“ū‚Ŗ‚ńƒ‚ƒfƒ‹‚É‘Ī‚·‚éV‹KR‚Ŗ‚ńÜE7130‚ĢŠˆ«•]‰æ
—ߘa3”N9ŒŽ10“ś`9ŒŽ12“śC‘ę49‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiƒnƒCƒuƒŠƒbƒhjCiO2-069)

 

548.@Kazuki Kunieda, Yuto Murakami, Yasuhiko Terada
Development of double helix dipole (DHD) coils for 7T MR microscopy
7T MRƒ}ƒCƒNƒƒXƒRƒs[—p‚Ģdouble helix dipole (DHD)ƒRƒCƒ‹‚ĢŠJ”­
—ߘa3”N9ŒŽ10“ś`9ŒŽ12“śC‘ę49‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiƒnƒCƒuƒŠƒbƒhjCiO2-068)

 

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§–ń‰ŗ‚É‚Ø‚Æ‚édeep learning‚š—p‚¢‚½ƒ}ƒ‹ƒ`ƒRƒCƒ‹CSÄ\¬
—ߘa3”N9ŒŽ10“ś`9ŒŽ12“śC‘ę49‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiƒnƒCƒuƒŠƒbƒhjCiO2-022)

 

546.@Mari Minami, Shigehito Yamada, Yasuhiko Terada
Initial study of DTI of a chemically fixed human fetus
‘Ł¶Šś‰Šś‚Ģ‘ŁŽ™‰»ŠwŒÅ’č•W–{‚ĢDTI‚Ģ‰ŠśŒŸ“¢
—ߘa3”N9ŒŽ10“ś`9ŒŽ12“śC‘ę49‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiƒnƒCƒuƒŠƒbƒhjCiO1-010)

 

545.@–qŒ“˜aKCŽ›“cN•F
9.4T cŒ^’““d“±Ž„Ī‚š—p‚¢‚½ƒqƒgćóŽq‰»ŠwŒÅ’č•W–{‚Ģ‚•Ŗ‰š”\ƒCƒ[ƒWƒ“ƒO
—ߘa3”N8ŒŽ18“ś@‘ę25‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒIƒ“ƒ‰ƒCƒ“ŠJĆjC‚Ā‚­‚ĪFŽY‘Œ¤CiO6jP29-32

 

544.@š Ž}˜a‹PC‘ŗć—Y“lCŽ›“cN•F
MR ƒ}ƒCƒNƒƒXƒRƒs[—p double helix dipole Œ^ RF ƒRƒCƒ‹‚ĢŠJ”­
—ߘa3”N8ŒŽ18“ś@‘ę25‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒIƒ“ƒ‰ƒCƒ“ŠJĆjC‚Ā‚­‚ĪFŽY‘Œ¤CiO5jP25-28

 

543.@‹g“cŒ\—CCćŽR‹B, Ž›“cN•F
‹³Žt‚Č‚µ[‘wŠwK‚š—p‚¢‚½ŠgŽU‹­’²‰ę‘œ‚É‚Ø‚Æ‚é 3 ŽŸŒ³˜c‚Ż•ā³
—ߘa3”N8ŒŽ18“ś@‘ę25‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒIƒ“ƒ‰ƒCƒ“ŠJĆjC‚Ā‚­‚ĪFŽY‘Œ¤CiO4jP21-24

 

542.@‹{ā’mŽ÷CMŽRŒdC‹Ź“c‘å‹PC–{™‰F‘¾˜YCXć—T”VC‘吼—mCŽ›“cN•F
Deep learning ‚š—p‚¢‚½ƒ}ƒ‹ƒ`ƒRƒCƒ‹ compressed sensing Ä\¬
—ߘa3”N8ŒŽ18“ś@‘ę25‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒIƒ“ƒ‰ƒCƒ“ŠJĆjC‚Ā‚­‚ĪFŽY‘Œ¤CiO3jP17-20

 

541.@‹£Ÿ”üC ‹£—ŗˆźC Ž›“cN•F
QRAPMASTER –@‚ĢŽĄ‘•‚Ę Ž„‰»ˆŚ“® Œų‰Ź‚Ģ‰e‹æ•]‰æ
—ߘa3”N8ŒŽ18“ś@‘ę25‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒIƒ“ƒ‰ƒCƒ“ŠJĆjC‚Ā‚­‚ĪFŽY‘Œ¤CiO2jP13-16

 

540. ‹{ā’mŽ÷, ūüģ’¼–ē, ĪˆäŠ°’, Īģ‰ė–ē, Ž›“cN•F
‰·“x‰Ā•Ļ MRI ‚É‚ę‚éA•Ø‚Ø‚ę‚ѐH•iƒTƒ“ƒvƒ‹‚Ģ’į‰·ŽB‘œ
—ߘa3”N8ŒŽ18“ś@‘ę25‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒIƒ“ƒ‰ƒCƒ“ŠJĆjC‚Ā‚­‚ĪFŽY‘Œ¤CiP1jP5-8

 

539. ‹{ā’mŽ÷Cūüģ’¼–ēCĪģ‰ė–ēCŽ›“cN•F
‰Ō‰č“€Œ‹ŠĻŽ@—p‰·“x‰Ā•ĻMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ĢŠJ”­
—ߘa3”N5ŒŽ29“ś`5ŒŽ30“śC‘ę66 ‰ń’į‰·¶•ØHŠw‰ļ‘å‰ļC“Œ‹žiƒIƒ“ƒ‰ƒCƒ“jCiB10jP27

 

538. Ž›“cN•F
[‘wŠwK‚š—p‚¢‚½“Ŗ•”MRIŒŸø‚Ģ‚‘¬‰»‚ĢŽŽ‚Ż
—ߘa3”N1ŒŽ12“ś@JSMRMƒXƒ^ƒfƒBGu¶¬Œ^ŠwK“™‚šŠˆ—p‚µ‚½’č—Ź“IMRƒCƒ[ƒWƒ“ƒOvƒjƒ…[ƒCƒ„[ƒZƒ~ƒi[i‚PjCL“‡iƒIƒ“ƒ‰ƒCƒ“j
µ‘Ņu‰‰

 

537. Yasuhiko TeradaCTomoki MiyasakaCDaiki TamadaCSatoshi FunayamaCUtaroh MotosugiCHiroyuki MorisakaCHiroshi Onishi
Instability of deep learning in superresolution of clinical brain images
—Տ°”]‰ę‘œ‚Ģ’“‰š‘œ‚É‚Ø‚Æ‚édeep learning‚Ģ•sˆĄ’萫
—ߘa2”N9ŒŽ11“ś`10ŒŽ4“śC‘ę48‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiWebŠJĆjCiO-047)P120

 

536. Tomoki MiyasakaCSatoshi FunayamaCDaiki TamadaCUtaroh MotosugiCHiroyuki MorisakaCHiroshi OnishiCYasuhiko Terada
Multi contrast CS reconstruction using deep learning
Deep learning‚š—p‚¢‚½ƒ}ƒ‹ƒ`ƒRƒ“ƒgƒ‰ƒXƒgCS Ä\¬
—ߘa2”N9ŒŽ11“ś`10ŒŽ4“śC‘ę48‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiWebŠJĆjCiO-033)P115

 

535. Michiru KajiwaraCYasuhiko TeradaCRyohei KasedaCYusuke NakagawaCIchiei NaritaCSusumu SasakiCTomoyuki Haishi
Sodium imaging with a 1.5T-MRI by using a new cross-band repeater technique
ƒNƒƒXƒoƒ“ƒhƒŒƒs[ƒ^‚É‚ę‚Į‚ėՏ°—p1.5T Ž„Ī‚Å23Na-MRI‚šŽĄŒ»‚·‚é
—ߘa2”N9ŒŽ11“ś`10ŒŽ4“śC‘ę48‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiWebŠJĆjCiO-031)P115

 

534. Tomoki MiyasakaCMichiru KajiwaraCAkito KawasakiCYoshikazu OkamotoCYasuhiko Terada
Development and screening examination of a car-mounted portable MRI for wrist
ŽčŽń—pŽŌŚƒ|[ƒ^ƒuƒ‹ MRI‚ĢŠJ”­‚ĘƒXƒNƒŠ[ƒjƒ“ƒOŽŽŒ±
—ߘa2”N9ŒŽ11“ś`10ŒŽ4“śC‘ę48‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiWebŠJĆjCiO-025)P113

 

533. Kazuyuki MakiharaCKazuya SakaguchiCMasayuki YamaguchiCKen ItoCYusaku HoriCTaro SembaCYasuhiko FunabashiC
Hirofumi FujiiCYasuhiko Terada
Assessment of tumor blood perfusion fraction using k-means clustering of tumor Ktrans
values with E7130 in a breast cancer model
k- •½‹Ļ–@‚É‚ę‚éDCE-MRI ‚Ģ Ktrans ’lŽ©“®•Ŗ—Ž‚š—p‚¢‚½ƒqƒg“ūŠąƒ‚ƒfƒ‹‚É‘Ī‚·‚éV‹KR‚Ŗ‚ńÜ E7130‚Ģ–ņŒų•]‰æ
—ߘa2”N9ŒŽ11“ś`10ŒŽ4“śC‘ę48‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiWebŠJĆjCiP-097)P195

 

532. Tomoki MiyasakaCAi NakaoCDaiki TamadaCShintaro IchikawaCSatoshi FunayamaCUtaroh MotosugiCHiroyuki MorisakaC
Hiroshi OnishiCYasuhiko Terada
Initial clinical evaluation of deep-learning-based image synthesis and superresolution
using a clinical dataset of patients with brain lesions
”]•a•ĻŠ³ŽŅ‚Ģ—Տ°ƒf[ƒ^ƒZƒbƒg‚É‚Ø‚Æ‚éƒfƒB[ƒvƒ‰[ƒjƒ“ƒO‚š—p‚¢‚½‰ę‘œ‡¬‚Ę’“‰š‘œ‚É‚ę‚鏉Šś—Տ°•]‰æ
—ߘa2”N9ŒŽ11“ś`10ŒŽ4“śC‘ę48‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiWebŠJĆjCiP-035)P174

 

531. Keisuke YoshidaCAi NakaoCYasuhiko Terada
Examination of an image restoration method for spiral scan using deep learning and GIRF
[‘wŠwK‚ĘGIRF ‚š—p‚¢‚½ spiral ‰ę‘œ‚ĢƒA[ƒ`ƒtƒ@ƒNƒg•ā³–@‚ĢŒŸ“¢
—ߘa2”N9ŒŽ11“ś`10ŒŽ4“śC‘ę48‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiWebŠJĆjCiP-014)P167

 

530. Yuto MurakamiCMasayuki YamaguchiCYasuhiko Terada
Large matrix imaging of the rat head using a 9.4T animal MRI
9.4T “®•Ø—pMRI‚š—p‚¢‚½ƒ‰ƒbƒg“Ŗ•”‚ĢLarge Matrix ƒCƒ[ƒWƒ“ƒO
—ߘa2”N9ŒŽ11“ś`10ŒŽ4“śC‘ę48‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļiWebŠJĆjCiP-006)P164

 

529. Ž›“c@N•F, r–Ų@—Ķ‘¾CZ‹g@WCĀ–Ų@ˆÉ’m’j
BlochƒVƒ~ƒ…\ƒŒ[ƒVƒ‡ƒ“‚ÉŠī‚Ć‚­³Šm«‚ĢŒüć‚š–ŚŽw‚µ‚½QRAPMASTER‰šĶ–@‚ĢŽĄ‘•
—ߘa2”N8ŒŽ28“śC‘ę24‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒIƒ“ƒ‰ƒCƒ“ŠJĆjC‚Ā‚­‚ĪiP2)P9-12

 

528. –qŒ“@˜aK, āŒū@˜a–ē, ŽRŒū@‰ė”V, ˆÉ“”@Œ›, –x@—Dģ, å”g@‘¾˜Y, ‘D‹“@‘×”Ž,
“”ˆä ”ŽŽj, Ž›“c N•F
DCE-MRIƒNƒ‰ƒXƒ^[‰šĶ‚š—p‚¢‚½ƒqƒg“ūŠąƒ‚ƒfƒ‹‚Ģ–ņŒų•]‰æ–@‚ĢŠJ”­
—ߘa2”N8ŒŽ28“śC‘ę24‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒIƒ“ƒ‰ƒCƒ“ŠJĆjC‚Ā‚­‚ĪiP2)P13-16

 

527. ŠŒ“@¬¶CŽ›“c N•FCœ«“c —ŗ•½C’†ģ —S‰īC¬“c ˆź‰qC²X–Ų iC”qŽt ’q”V
ƒNƒƒXƒoƒ“ƒhƒŒƒs[ƒ^‚Ģ‹Zp‚š‰ž—p‚µ‚½NaƒCƒ[ƒWƒ“ƒO
—ߘa2”N8ŒŽ28“śC‘ę24‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒIƒ“ƒ‰ƒCƒ“ŠJĆjC‚Ā‚­‚ĪiP2)P21-25

 

526. ‹{ā@’mŽ÷CŠŒ“@¬¶Cģč@—ŗlC‰Ŗ–{@‰ĆˆźCŽ›“c@N•F
ƒ|[ƒ^ƒuƒ‹MRI‚š—p‚¢‚½ŽčŽńf’f
—ߘa2”N8ŒŽ28“śC‘ę24‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒIƒ“ƒ‰ƒCƒ“ŠJĆjC‚Ā‚­‚ĪiP2)P26-29

 

525. ‘ŗć —Y“lCŽRŒū ‰ė”VCŽ›“c N•F
9.4T“®•Ø—pMRI‚š—p‚¢‚½ƒ‰ƒbƒg“Ŗ•”‚ĢLarge MatrixƒCƒ[ƒWƒ“ƒO
—ߘa2”N8ŒŽ28“śC‘ę24‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒIƒ“ƒ‰ƒCƒ“ŠJĆjC‚Ā‚­‚ĪiO6)P32-36

 

524. ‹g“c@Œ\—CCŽ›“c@N•F
dAUTOMAP‚š—p‚¢‚½SpiralŽB‘œ‚É‚Ø‚Æ‚é‰ę‘œÄ\¬–@‚ĢŒŸ“¢
—ߘa2”N8ŒŽ28“śC‘ę24‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒIƒ“ƒ‰ƒCƒ“ŠJĆjC‚Ā‚­‚ĪiP3)P37-40

 

523. Ž›“c@N•F
Å‹ß‚ĢŒ¤‹†Š‰ī `‚‘¬ƒCƒ[ƒWƒ“ƒO‚Ęƒ‚ƒoƒCƒ‹MRIŒŸø`
—ߘa‚P”N11ŒŽ20“śC‘ę4‰ń MRIƒAƒ‰ƒCƒAƒ“ƒX‘ŪƒVƒ“ƒ|ƒWƒEƒ€2019Cē—t
µ‘Ņu‰‰

 

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‚š—p‚¢‚½—Տ°”]‰ę‘œŒŸø‚Ģ‚‘¬‰»i‚QjF—Տ°ŒŸø‚Ö‚ĢŽĄ‘•‚Ę—Õ°•]‰æ
—ߘa1”N9ŒŽ20“śC‘ę47‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒF–{iO1-028jP178

 

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‚š—p‚¢‚½—Տ°”]‰ę‘œŒŸø‚Ģ‚‘¬‰»i‚PjFƒjƒ…[ƒ‰ƒ‹ƒlƒbƒgƒ[ƒN‚Ģ\’z
—ߘa1”N9ŒŽ20“śC‘ę47‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒF–{iO1-027jP177

 

520. Michiru Kajiwara, Mayu Nakagomi, Yoshikazu Okamoto, Yasuhiko Terada
Field examination of baseball elbow using a car-mounted portable MRI –ģ‹…•If’f—pŽŌŚƒ|[ƒ^ƒuƒ‹MRI‚ĢŽĄ’nŽŽŒ±
—ߘa1”N9ŒŽ21“śC‘ę47‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒF–{iO2-010jP204

 

519. Ryoichi Sasaki, Yasuhiko Terada
MRF-FISP without additional scans using deep neural network [‘wƒjƒ…[ƒ‰ƒ‹ƒlƒbƒgƒ[ƒN‚šŽg‚Į‚½’Ē‰ĮƒXƒLƒƒƒ“‚š•K—v‚Ę‚µ‚Č‚¢MRF-FISP
—ߘa1”N9ŒŽ22“śC‘ę47‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒF–{iO3-026jP246

 

518. Yuto Murakami, Ryoichi Sasaki, Yasuhiko Terada
Acceleration of acquisition of relaxation time map for human embryo specimens ƒqƒgćóŽq•W–{‚ĢŠÉ˜aŽžŠŌƒ}ƒbƒvŽę“¾‚Ģ‚‘¬‰»
—ߘa1”N9ŒŽ21“śC‘ę47‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒF–{iP2-A-40jP295

 

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•ĮŒ^Œł”zƒRƒCƒ‹‚Ģ”CˆÓ«”\Å“K‰»Žč–@‚ĢŠJ”­
—ߘa1”N9ŒŽ21“śC‘ę47‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒF–{iP2-A-42jP295

 

516. Naoya Takagawa, Yasuhiko Terada
Development of temperature-variable MR microimaging system (2) ‰·“x‰Ā•ĻMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ĢŠJ”­i2j
—ߘa1”N9ŒŽ21“śC‘ę47‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒF–{iP2-A-44jP296

 

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‹Ļ–@‚É‚ę‚鎥Œ±Žīį‡‚ĢADC’lŽ©“®•Ŗ—ŽFƒqƒg“ūŠąƒ‚ƒfƒ‹‚É‘Ī‚·‚éV‹KR‚Ŗ‚ńÜE7130–ņŒų•]‰æ‚Ö‚Ģ—˜ —p
—ߘa1”N9ŒŽ22“śC‘ę47‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒF–{iP3-A-01jP323

 

514. Katsumi Kose, Ryoichi Kose, Yasuhiko Terada, Daiki Tamada, Utaroh Motosugi
Simulation of living tissue using an MRI simulator MRI simulator‚É‚ę‚鐶‘Ģ‘gD‚ĢƒVƒ~ƒ…ƒŒ[ƒVƒ‡ƒ“
—ߘa1”N9ŒŽ21“śC‘ę47‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒF–{iP2-A-51jP298

 

513. ‹£ Ÿ”üC‹£ —ŗˆźC Ž›“c N•F, ‹Ź“c ‘å‹P, –{™ ‰F‘¾˜Y
¶‘Ģ‘gD‚Ģ MRI simulation Žč–@‚ĢŠJ”­
—ߘa1”N8ŒŽ8“śC‘ę23‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰”•liO8)P63-66

 

512. ‘ŗć —Y“lC’‡‘ŗ ūüŽu, Ž›“c N•F
11.7T ’““`“±Ž„Ī‚š—p‚¢‚½ƒqƒgćóŽq‚Ģƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒO
—ߘa1”N8ŒŽ8“śC‘ę23‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰”•liP4)P37-40

 

511. ’†”ö ˆ¤CŽ›“c N•F
[‘wŠwK‚š—p‚¢‚½‰i‹vŽ„Ī MRI ‚É‚Ø‚Æ‚é Spiral ‰ę‘œƒA[ƒ`ƒtƒ@ƒNƒg•ā³–@‚ĢŠJ”­
—ߘa1”N8ŒŽ8“śC‘ę23‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰”•liP3)P33-36

 

510. ²X–Ų –øˆźCŽ›“c N•F
[‘wŠwK‚š—p‚¢‚½ MR fingerprinting ‚ɂ؂Ƃ鎞ŠŌ’Zk‚ʐ„’čø“xŒüć‚ĢŒŸ“¢
—ߘa1”N8ŒŽ8“śC‘ę23‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰”•liP2)P29-32

 

509. ŠŒ“ ¬¶C‹{ā ’mŽ÷C‰Ŗ–{ ‰ĆˆźCŽ›“c N•F
ƒ|[ƒ^ƒuƒ‹ MRI ‚Ģ–ģ‹…ź‚Å‚ĢŽB‘œŽŽŒ±
—ߘa1”N8ŒŽ8“śC‘ę23‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰”•liP1)P25-28

 

508.‘…@‹I‹vŽqCŽ›“c@N•FC•Š‰Ŗ@–MŒõCH–{@‡“ńC”qŽt@’q”V
PGSE-NMR‚ÅŠĻ‘Ŗ‚·‚éƒK[ƒlƒbƒg“d‰šŽæ’†‚ĢLi+‚ĢÕ“ĖE‰ńÜŒ»Ū
—ߘa1”N8ŒŽ8“śC‘ę23‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰”•liO4)P13-16

 

507.ūüģ@’¼–ēCŽ›“c@N•F
•X“_‰ŗ‚Å‚ĢŽB‘œ‚š–Ś“I‚Ę‚µ‚½4.7T‰·“x§ŒäMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ĢŠJ”­
—ߘa1”N8ŒŽ8“śC‘ę23‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰”•liO3)P9-12

 

506@āŒū@˜a–ēCŽ›“c@N•F
“ĮˆŁ’l•Ŗ‰š–@‚ĘABCƒAƒ‹ƒSƒŠƒYƒ€‚š‘g‚Ż‡‚ķ‚¹‚½‰~“›Œ^‚ĢƒV[ƒ‹ƒhŒł”zŽ„źƒRƒCƒ‹‚ĢÅ“K‰»
—ߘa1”N8ŒŽ8“śC‘ę23‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰”•liO2)P5-8

 

505 Ž›“c@N•FC’†”ö@ˆ¤C‹Ź“c@‘å‹PC–{™@‰F‘¾˜Y
[‘wŠwK‚š—p‚¢‚½—Տ°”]‰ę‘œ‚ĢŽB‘œŽžŠŌ‚Ģ’Zk‰»
—ߘa1”N8ŒŽ8“śC‘ę23‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰”•liO1)P1-4

 

504. •½ģ@‰ė•¶C¼‰ŗ@”Ķ‹vC•Ÿ“c@Œ’“ńCŽ›“c@N•F
MRI‚š—p‚¢‚½ƒGƒ“ƒ{ƒŠƒYƒ€‚Ģ”­¶E‰ń•œ‰ß’ö‚ɂ؂Ƃ鐅•Ŗ’Ź“±‚Ģ‰ĀŽ‹‰»
•½¬31”N3ŒŽ20-23C‘ę130‰ń“ś–{X—ŃŠw‰ļ‘å‰ļCVŠƒiP1-087)
https://doi.org/10.11519/jfsc.130.0_296

 

503. Ž›“c@N•F
Fundamental of Extended Phase Graph
Šg’£ˆŹ‘ŠƒOƒ‰ƒtiEPGj‚ĢŠī‘b
•½¬30”N9ŒŽ7“śC‘ę46‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ą‘ņ@iE2-2) P136
‹³ˆēu‰‰

 

502.’†ž@^—DC“c•Ó@—ŗŸC‹£@Ÿ”üC‰Ŗ–{@‰ĆˆźCÆ‡ ‘s‘åCŽ›“c@N•F
Development of portable MRI for detection of baseball elbow (3)
–ģ‹…•I‰Šśf’f—pƒ|[ƒ^ƒuƒ‹MRI‚ĢŠJ”­(3)
•½¬30”N9ŒŽ7“śC‘ę46‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ą‘ņ@iO1-108) P184

 

501.¼ś±@~•½C”qŽt@’q”VCŽ›“c@N•F
A new method for fabricating gradient coils using printed circuit boards(2):Performance evaluation and application
ƒvƒŠƒ“ƒgŠī”Ā‚š—p‚¢‚½‰~“›Œ^Œł”zŽ„źƒRƒCƒ‹‚ĢŠJ”­i2jF«”\•]‰æ‚ʉž—p
•½¬30”N9ŒŽ7“śC‘ę46‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ą‘ņ@iO1-107) P184
—DG‰‰‘čÜ

 

‚T00.²X–Ų@–øˆźCŽ›“c@N•F
Acceleration of the Cartesian acquisition of MR fingerprinting Cartesianƒf[ƒ^ŽūW‚Å‚ĢMR fingerprinting‚É‚Ø‚Æ‚éŽB‘œ‚‘¬‰»
•½¬30”N9ŒŽ7“śC‘ę46‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ą‘ņ@iO1-046)P164

 

499.’†ž@^—DC“c•Ó@—ŗŸCÆ‡ ‘s‘åC‰Ŗ–{@‰ĆˆźCŽ›“c@N•F
Deep convolutional neural network for denoising images of low-field scanners
’įŽ„źMRI‚É‚Ø‚Æ‚édeep learning‚š—p‚¢‚½ƒmƒCƒYœ‹Ž‚ĢŒŸ“¢
•½¬30”N9ŒŽ8“śC‘ę46‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ą‘ņ
iP2-A2-013)P252

 

498.āŒū@˜a–ēC¼ąV@WŽ÷CŽ›“c@N•F
Design of cylindrical gradient coils using singular value decomposition and genetic algorithm
“ĮˆŁ’l•Ŗ‰š”\‚Ęˆā“`“IƒAƒ‹ƒSƒŠƒYƒ€‚š‘g‚Ż‡‚ķ‚¹‚½‰~“›Œ^Œł”zŽ„źƒRƒCƒ‹‚ĢŠJ”­
•½¬30”N9ŒŽ8“śC‘ę46‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ą‘ņ@
iP2-B2-007)P276

 

497.‚ģ@’¼–ēCŽ›“c@N•F
Development of temperature-variable MR microimaging system (I) ‰·“x‰Ā•ĻMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ĢŠJ”­iIj
•½¬30”N9ŒŽ8“śC‘ę46‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ą‘ņ@
iP2-B2-006)P275

 

496.’†”ö@ˆ¤CŽ›“c@N•F
Non-Cartesian imaging for permanent magnet MRI systems
‰i‹vŽ„ĪMRI‚É‚Ø‚Æ‚énon-Cartesian imaging
•½¬30”N9ŒŽ8“śC‘ę46‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ą‘ņ@
iP2-A8-057)P266

 

495.–xģ@—F•ćCŽ›“c@N•F
Influence of temperature drift on flow measurements
•½¬30”N9ŒŽ8“śC‘ę46‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ą‘ņ@(PDF-118)P328

 

494.Z¼č@~•½C”qŽt@’q”VCŽ›“c@N•F
ƒvƒŠƒ“ƒgŠī”Ā‚š—p‚¢‚½Œł”zŽ„źƒRƒCƒ‹‚ĢŠJ”­
•½¬‚R‚O”N‚WŒŽ‚Q‚P“śC‘ę22‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ–k‘åŠwC
iO10)P55-58

 

493.ZŽ›“c@N•FC’†ž@^—DC‰Ŗ–{@‰Ćˆź
‰i‹vŽ„Ī MRI ‚É‚Ø‚Æ‚é[‘wŠwK‚š—p‚¢‚½ƒmƒCƒYœ‹Ž‚ĢŒŸ“¢
•½¬‚R‚O”N‚WŒŽ‚Q‚O“śC‘ę22‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ–k‘åŠwC
iP8jP47-50

 

492.Z‚ģ@’¼–ēCŽ›“c@N•F
4.7T/89mm ŠJŒūcŒ^’““`“±Ž„Ī‚š—p‚¢‚½‰·“x‰Ā•ĻMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ĢŠJ”­
•½¬‚R‚O”N‚WŒŽ‚Q‚O“śC‘ę22‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ–k‘åŠwC
iP4jP37-40

 

491.ZāŒū@˜a–ēC¼ąV@WŽ÷CŽ›“c@N•F
“ĮˆŁ’l•Ŗ‰š‚Ęˆā“`“IƒAƒ‹ƒSƒŠƒYƒ€‚š‘g‚Ż‡‚ķ‚¹‚½ ”CˆÓŒ`óŒł”zŽ„źƒRƒCƒ‹‚ĢŻŒv‚Ø‚ę‚ŃÅ“K‰»Žč–@‚ĢŠJ”­
•½¬‚R‚O”N‚WŒŽ‚Q‚O“śC‘ę22‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ–k‘åŠwC
iP3jP33-36

 

490.Z²X–Ų@–øˆźCŽ›“c@N•F
1.5TŽlŽˆ—p MRI ‚É‚Ø‚Æ‚é Cartesian MR fingerprinting ‚ĢŽB‘œ‚‘¬‰»
•½¬‚R‚O”N‚WŒŽ‚Q‚O“śC‘ę22‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ–k‘åŠwC
iP2jP29-32

 

489.›’†”ö ˆ¤CŽ›“c N•F
ŽlŽˆ—p MRI ‚É‚Ø‚Æ‚é non-Cartesian imaging –@‚ĢŠJ”­
•½¬‚R‚O”N‚WŒŽ‚Q‚O“śC‘ę22‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ–k‘åŠwC
iP1jP25-28

 

488.›–xģ —F•ćC•Ÿ“c Œ’“ńCŽ›“c N•F
MRI ‚š—p‚¢‚½‰®ŠOŽ÷–Ų‚ĢŽ÷‰t—¬ƒCƒ[ƒWƒ“ƒOFL—tŽ÷‚ʐj—tŽ÷‚Ģ”äŠr
•½¬‚R‚O”N‚WŒŽ‚Q‚O“śC‘ę22‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ–k‘åŠwC
iO2jP5-8

 

487.›’†ž ^—DC“c•Ó —ŗŸC‰Ŗ–{ ‰ĆˆźCÆ‡ ‘s‘åCŽ›“c N•F
•’ŹŽŌ“‹ŚŒ^ƒ|[ƒ^ƒuƒ‹ MRI ‚ĢŠJ”­
•½¬‚R‚O”N‚WŒŽ‚Q‚O“śC‘ę22‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ–k‘åŠwC
iO1jP1-4

 

486.’·“cW‰Ą,›•Ÿ“cŒ’“ń,Ž›“cN•F
‰®ŠO‚ɐAĶ‚³‚ź‚½ƒPƒ„ƒL‚Ģ–Ų•”EŽt•”‚ĢŽ÷‰t—¬‘¬‚Ģ‰ĀŽ‹‰»
•½¬‚Q‚X”N‚P‚PŒŽ‚P‚Q“śCŽ÷–ŲˆćŠw‰ļ‘ę22‰ń‘å‰ļC–@­‘åŠw¬‹ąˆäƒLƒƒƒ“ƒpƒXC“Œ¬‹ąˆä

 

485.¬—Ń —D‘¾CŽ›“c@N•F
T 2 error originating from diffusion in MRF-FISP MRF-FISP‚É‚Ø‚Æ‚éŠgŽU‚É‹Nˆö‚·‚éT 2 „’č’l‚Ģ’č—ŹŒė·
•½¬‚Q‚X”N‚XŒŽ‚P‚T“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{C
P2-A4-016(p306)

 

484.²X–Ų –øˆźC¬—с@—D‘¾CŽ›“c@N•F
Musculoskeletal MR Fingerprinting using a 1.5T/280mm small-bore MRI 1.5T/280mmƒXƒ‚[ƒ‹ƒ{ƒAMRI‚š—p‚¢‚½œ“ī•”‚Ö‚ĢMR Fingerprinting‚Ģ‰ž—p
•½¬‚Q‚X”N‚XŒŽ‚P‚T“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{C
P2-B5-160(p378)

 

483.Ž›“c N•FC”qŽt@’q”V
Initial trials of relaxation time and ADC mapping of mice/rats MRF‚É‚ę‚é–ƒŒ‰ŗƒ}ƒEƒX^ƒ‰ƒbƒg‚ĢŠÉ˜aŽžŠŌ^ADCƒ}ƒbƒsƒ“ƒO‚Ģ‰ŠśŒŸ“¢
•½¬‚Q‚X”N‚XŒŽ‚P‚T“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{C
P2-B6-191(p394)

 

482.–xģ —F•ćC‹£@Ÿ”üCŽ›“c@N•F
Error evaluation of QSI measurements for widely-distributed flow L‚¢‘¬“x•Ŗ•z‚š‚ą‚Ā—¬‚ź‚ĢQSIŒv‘Ŗ‚ĢŒė·•]‰æ
•½¬‚Q‚X”N‚XŒŽ‚P‚T“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{C
P2-B6-189 (p393)

 

481.āŒū ˜a–ēC¼ąV@WŽ÷C‹£@Ÿ”üCŽ›“c@N•F
New method for designing gradients using singular value decomposition and genetic algorithm “ĮˆŁ’l•Ŗ‰š–@‚Ęˆā“`“IƒAƒ‹ƒSƒŠƒYƒ€‚š‘g‚Ż‡‚ķ‚¹‚½Œł”zŽ„źƒRƒCƒ‹«”\‚ĢÅ“K‰»Žč–@‚ĢŠJ”­
•½¬‚Q‚X”N‚XŒŽ‚P‚T“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{C
P2-B2-186(p391)

 

480.¼č ~•½C”qŽt@’q”VC‹£@Ÿ”üCŽ›“c@N•F
A new method for fabricating gradient coils using printed circuit boards ƒvƒŠƒ“ƒgŠī”Ā‚š—p‚¢‚½‰~“›Œ^Œł”zŽ„źƒRƒCƒ‹‚ĢŠJ”­
•½¬‚Q‚X”N‚XŒŽ‚P‚T“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{C
P2-B2-185(p391)

 

479.’†ž ^—DC“c•Ó@—ŗŸC‰Ŗ–{ ‰ĆˆźC‹£@Ÿ”üCŽ›“c@N•F
Development of Sequence Generator for Portable MRI for Baseball Elbow Diagnosis –ģ‹…•If’f—pƒ|[ƒ^ƒuƒ‹MRI—pƒV[ƒPƒ“ƒXƒWƒFƒlƒŒ[ƒ^[‚ĢŠJ”­
•½¬‚Q‚X”N‚XŒŽ‚P‚T“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{C
P2-B2-180(p388)

 

478. ‹Ź“c ‘å‹PC‹£@—ŗˆźC–{™ ‰F‘¾˜YC‹£@Ÿ”ü
Development of mathematical phantoms for MRI simulators MRIƒVƒ~ƒ…ƒŒ[ƒ^‚É‘Ī‰ž‚µ‚½”—ƒtƒ@ƒ“ƒgƒ€‚Ģ¶¬Žč–@‚ĢŒŸ“¢
•½¬‚Q‚X”N‚XŒŽ‚P‚T“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{C
P2-B1-084(p340)

 

477. £ŒĖˆä ˆ»ŲC‹£@Ÿ”ü
Ultrashort echo-time imaging at 1.5 T using an insertable gradient coil and 3D cones trajectory ‘}“üŒ^Œł”zŽ„źƒRƒCƒ‹‚Ę3D cones trajectory‚š—p‚¢‚½’“’ZƒGƒR[ƒ^ƒCƒ€ƒCƒ[ƒWƒ“ƒO
•½¬‚Q‚X”N‚XŒŽ‚P‚U“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{CO3-31(p291)

 

476.‹£ —ŗˆźC£ŒĖˆä@ˆ»ŲC‹£@Ÿ”ü
GPU-optimized 3D fast MRI simulator for non-Cartesian sampling GPU‚š—p‚¢‚½”ńƒfƒJƒ‹ƒgĄ•WŒnƒTƒ“ƒvƒŠƒ“ƒO‘Ī‰ž3D‚‘¬MRI simulator‚ĢŠJ”­
•½¬‚Q‚X”N‚XŒŽ‚P‚S“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{CO1-97(p217)

 

475. ¬—Ń —D‘¾C‹£@Ÿ”üCŽ›“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„Ī‚É‚Ø‚Æ‚éfield cameraƒVƒXƒeƒ€‚ĢŠJ”­‚ʍ‚‘¬ŽB‘œ–@‚Ö‚Ģ‰ž—p
•½¬‚Q‚X”N‚XŒŽ‚P‚S“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{CO1-91(p214)

 

474. “c•Ó —ŗŸC‰Ŗ–{ ‰ĆˆźC‹£@Ÿ”üCŽ›“c@N•F
Development of portable MRI for early detection of baseball elbow(2)@–ģ‹…•I‰Šśf’f—pƒ|[ƒ^ƒuƒ‹MRI‚ĢŠJ”­i‚Qj
•½¬‚Q‚X”N‚XŒŽ‚P‚S“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{CO1-40(P188)

 

473.Ž›“c@N•F
non-Cartesian trajectory imaging
•½¬‚Q‚X”N‚XŒŽ‚P‚S“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{CKJS1-1(p50)
µ‘Ņu‰‰
472.‹£@Ÿ”ü, ‹£@—ŗˆź
Can simulator create a paradigm shift in R & D of MRI? Simulator‚ĶMRI‚ĢŒ¤‹†ŠJ”­‚Ƀpƒ‰ƒ_ƒCƒ€ƒVƒtƒg‚š‹N‚±‚¹‚é‚©H
•½¬‚Q‚X”N‚XŒŽ‚P‚S“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{CO1-96(p216)

 

471.Ž›“c@N•F
Fundamentals and recent developments in compressed sensing ˆ³kƒZƒ“ƒVƒ“ƒO‚ĢŠī‘b‚ĘÅV“®Œü
•½¬‚Q‚X”N‚XŒŽ‚P‚T“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{CS2-1(p43)
µ‘Ņu‰‰
470.‹£@Ÿ”ü
Historical evolution and future direction of MRI: What is the ultimate MRI? MRI‚Ģ—šŽj“Ii‰»‚ʏ«—ˆ“W–] |‹†‹É‚ĢMRI‚š‹‚߂ā|
•½¬‚Q‚X”N‚XŒŽ‚P‚T“śC‘ę‚S‚T‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‰F“s‹{CPLiP36)
“Į•Źu‰‰

 

469.•½ģ‰ė•¶CŽs‹“—²Ž©C•Ÿ“cŒ’“ńCŽ›“cN•F
uƒRƒ“ƒpƒNƒg‚l‚q‚h‚š—p‚¢‚½Ž÷Š²‚ĢŽ÷‰t—¬‘¬•Ŗ•z‚Ģ“ś•Ļ‰»‚Ģ‘Ŗ’čv
•½¬‚Q‚X”N‚XŒŽ‚P‚O“śC“ś–{A•ØŠw‰ļ‘ę81‰ń‘å‰ļC“Œ‹ž—‰Č‘åŠw–ģ“cƒLƒƒƒ“ƒpƒXCPL-020

 

468.‹£ Ÿ”üC‹£ —ŗˆź
uMRI simulator ‚š—p‚¢‚½ MP - RAGE ‚ĢÅ“K‰»‚ĢŽŽ‚Ż v
•½¬‚Q‚X”N‚WŒŽ‚V“śC‘ę‚Q‚P‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(O1)P1-4

 

467.¬—Ń —D‘¾ C ‹£ Ÿ”ü C Ž›“c N•F
u1.5T/280mm ’““`“±Ž„Ī‚É‚Ø‚Æ‚é field camera ƒVƒXƒeƒ€ŠJ”­ v
•½¬‚Q‚X”N‚WŒŽ‚V“śC‘ę‚Q‚P‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(O2)P5-8

 

466.“c•Ó —ŗŸC ‰Ŗ–{ ‰ĆˆźC‹£ Ÿ”üCŽ›“c N•F
u–ģ‹…•If’f—pƒ|[ƒ^ƒuƒ‹ MRI ‚ĢŠJ”­ v
•½¬‚Q‚X”N‚WŒŽ‚V“śC‘ę‚Q‚P‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(O3)P9-12

 

465.‹Ź“c ‘å‹PC‹£ —ŗˆźC‹£ Ÿ”üC–{™ ‰F‘¾˜Y
u”—ƒtƒ@ƒ“ƒgƒ€‚ĢŽĄŒ»‚š–Ś“I‚Ę‚µ‚½”] MRI ‰ę‘œ‚Ģ‘gDŽ©“®•Ŗ—ŽŽč–@‚ĢŠJ”­ v
•½¬‚Q‚X”N‚WŒŽ‚V“śC‘ę‚Q‚P‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(P5)P37-40

 

464.‹£ —ŗˆźC£ŒĖˆä ˆ»ŲC‹£ Ÿ”ü
uGPU ‚š—p‚¢‚½”ńƒfƒJƒ‹ƒgĄ•WŒnƒTƒ“ƒvƒŠƒ“ƒO‚É‘Ī‰ž‚µ‚½ 3D MRI simulator ‚ĢŠJ”­ v
•½¬‚Q‚X”N‚WŒŽ‚V“śC‘ę‚Q‚P‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(P6)P41-44

 

463.£ŒĖˆä ˆ»ŲC ‹£ Ÿ”ü
u1.5T …•½ŠJŒū’““`“±Ž„Ī‚É‚Ø‚Æ‚é 3D Cones –@‚Ö‚Ģ‰Q“d—¬‚Ģ‰e‹æ v
•½¬‚Q‚X”N‚WŒŽ‚V“śC‘ę‚Q‚P‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(P7)P45-48

 

462.¼č ~•½C”qŽt ’q”VC‹£ Ÿ”üCŽ›“c N•F
uƒvƒŠƒ“ƒgŠī”Ā‚š—p‚¢‚½‰~“›Œ^ƒV[ƒ‹ƒhƒOƒ‰ƒWƒGƒ“ƒgƒRƒCƒ‹‚ĢŠJ”­ v
•½¬‚Q‚X”N‚WŒŽ‚V“śC‘ę‚Q‚P‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(P8)P49-52

 

461.–xģ —F•ćC‹£ Ÿ”üCŽ›“c N•F
uq-space imaging ‚š—p‚¢‚½‰®ŠOŽ÷–Ų‚ĢŽ÷‰t—¬Œv‘Ŗ v
•½¬‚Q‚X”N‚WŒŽ‚V“śC‘ę‚Q‚P‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(P10)P55-58

 

460.’†ž ^—DC“c•Ó —ŗŸC‰Ŗ–{ ‰ĆˆźC‹£ Ÿ”üCŽ›“c N•F
u–ģ‹…•If’f—pƒ|[ƒ^ƒuƒ‹ MRI ‚É‚Ø‚Æ‚é ƒVƒ“ƒOƒ‹ƒIƒuƒŠ[ƒN‹@”\•t‚«ƒV[ƒPƒ“ƒXƒWƒFƒlƒŒ[ƒ^[‚ĢŠJ”­ v
•½¬‚Q‚X”N‚WŒŽ‚V“śC‘ę‚Q‚P‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(P11)P59-62
459.Ž›“cN•F
uMRI‚š—p‚¢‚½‰®ŠOŽ÷–Ų‚ĢŽ÷‰t—¬‚Ģ‰ĀŽ‹‰»v
•½¬‚Q‚X”N‚RŒŽ‚Q‚P“śCA•ØƒoƒCƒIŒ¤‹†‰ļ ‘ę10‰ń‰ļ‡`A•Ø‚Ö‚Ģ•Ø—HŠw‚Ģ‰ž—p`C(ˆźą)ƒoƒCƒIƒCƒ“ƒ_ƒXƒgƒŠ[‹¦‰ļC“Œ‹ž
µ‘Ņu‰‰

 

458.Ž›“cN•F
uMR Fingerprinting“ü–åv
•½¬‚Q‚X”N‚QŒŽ‚Q‚S“śC‘ę‚Q‚Q‰ń‚Ā‚­‚Ī‚l‚q§˜b‰ļC‚Ā‚­‚Ī‘åŠwˆćŠwƒGƒŠƒAŒ’Nˆć‰ČŠwƒCƒmƒx[ƒVƒ‡ƒ““‚WŠK‘åu‹`ŽŗC‚Ā‚­‚Ī
“Į•Źu‰‰

 

457.ŽR“c—Č‘¾CŽ›“cN•FC‹£Ÿ”ü
u‰~“›Œ^ƒ}ƒ‹ƒ`ƒT[ƒLƒ…ƒ‰[ƒVƒ€ƒRƒCƒ‹‚š—p‚¢‚½ƒ_ƒCƒiƒ~ƒbƒNƒVƒ~ƒ“ƒOƒVƒXƒeƒ€‚ĢŠJ”­v
•½¬‚Q‚W”N‚P‚PŒŽ‚P‚W“śC‘ę‚T‚T‰ń‚m‚l‚q“¢˜_‰ļCL“‡@P95ip324-325) ‚Œ

 

456.ŠFģ@VCŽ›“cN•FC‹£Ÿ”ü
u‚l‚q‚h‚š—p‚¢‚½¬Ž™œ”N—īŒv‘Ŗ‚Ö‚ĢDeep Learning‚Ģ‰ž—pv
•½¬‚Q‚W”N‚P‚PŒŽ‚P‚W“śC‘ę‚T‚T‰ń‚m‚l‚q“¢˜_‰ļCL“‡@P91ip316-317)

 

455.’·“cW‰ĄC•Ÿ“cŒ’“ńC‹£Ÿ”üCŽ›“cN•F
u0.2T‰i‹vŽ„Ī‚l‚q‚h‚š—p‚¢‚½‰®ŠOŽ÷–Ų’†‚Ģ…—A‘—Œv‘Ŗ
•½¬‚Q‚W”N‚P‚PŒŽ‚P‚U“śC‘ę‚T‚T‰ń‚m‚l‚q“¢˜_‰ļCL“‡@P82ip292-295)
ŽįŽčƒ|ƒXƒ^[Ü

 

454.¼ąVWŽ÷Cˆ¢•”[ŽuC‹£Ÿ”üCŽ›“cN•F
u‘ȉ~“›Œ`Œł”zŽ„źƒRƒCƒ‹‚ĢŠJ”­‚ʏ¬Ž™œ”N—īŒv‘Ŗ‚Ö‚Ģ‰ž—pv
•½¬‚Q‚W”N‚P‚PŒŽ‚P‚U“śC‘ę‚T‚T‰ń‚m‚l‚q“¢˜_‰ļCL“‡@P80ip286-287)

 

453.–ī–ģ‡–ēC¬—Ń—D‘¾CŽ›“cN•FC‹£Ÿ”ü
u1.5T/280mm…•½ŠJŒūŒ^’““`“±Ž„Ī‚Ģ‚½‚ß‚Ģ¬“®•Ø—pŒł”zŽ„źƒRƒCƒ‹‚ĢŠJ”­‚ʉž—pv
•½¬‚Q‚W”N‚P‚PŒŽ‚P‚W“śC‘ę‚T‚T‰ń‚m‚l‚q“¢˜_‰ļCL“‡@P79ip284-285)

 

452.Ž›“cN•F
uIntroduction to MR Fingerprinting
MR fingerprinting“ü–åv
•½¬‚Q‚W”N‚XŒŽ‚P‚O“śC‘ę‚S‚S‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‘å‹{@EL9-1 (p117)
‹³ˆēu‰‰
451.Ž›“cN•F
uMR microscopy accelerated by MR Fingerprinting
MRŽw–ä–@‚É‚ę‚éNMRƒ}ƒCƒNƒƒXƒRƒs[‚Ģ‚‘¬‰»v
•½¬‚Q‚W”N‚XŒŽ‚P‚O“śC‘ę‚S‚S‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‘å‹{@O-2-076 (p218)

 

450.¬—Ń—D‘¾C‹£Ÿ”üCŽ›“cN•F
u Development of field monitoring system using field cameras for a 1.5 T superconducting magnet system
1.5T’““`“±Ž„Ī‚É‚Ø‚Æ‚éField Camera‚š—p‚¢‚½Ž„źƒ‚ƒjƒ^ƒŠƒ“ƒOƒVƒXƒeƒ€‚ĢŠJ”­v
•½¬‚Q‚W”N‚XŒŽ‚P‚O“śC‘ę‚S‚S‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‘å‹{@P-2-035 (p278)

 

449.–ī–ģ‡–ēC¬—Ń—D‘¾CŽ›“cN•FC‹£Ÿ”ü
u Development of an insertable gradient coil for a 1.5T/280mm horizontal bore superconducting magnet
1.5T/280mm…•½ŠJŒūŒ^’““`“±Ž„Ī‚Ģ‚½‚ß‚Ģ‘}“üŒ^Œł”zŽ„źƒRƒCƒ‹‚ĢŠJ”­v
•½¬‚Q‚W”N‚XŒŽ‚P‚O“śC‘ę‚S‚S‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‘å‹{@P-2-034 (p277)

 

448.‹£—ŗˆźC‹£Ÿ”ü
uGPU optimized general purpose MRI simulator
GPU‚ɍœK‰»‚µ‚½”Ä—pMRIƒVƒ~ƒ…ƒŒ[ƒ^‚ĢŠJ”­v
•½¬‚Q‚W”N‚XŒŽ‚P‚O“śC‘ę‚S‚S‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‘å‹{@P-2-020 (p270)

 

447.“c•Ó—ŗŸC‰Ŗ@C•½C‰Ŗ–{‰ĆˆźC‹£Ÿ”üCŽ›“cN•F
uDevelopment of portable MRI for early detection of baseball elbow –ģ‹…•I‰Šśf’f—pƒ|[ƒ^ƒuƒ‹MRI‚ĢŠJ”­v
•½¬‚Q‚W”N‚XŒŽ‚P‚O“śC‘ę‚S‚S‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‘å‹{@P-2-018 (p269)

 

446.£ŒĖˆäˆ»ŲC‹£Ÿ”ü
uDevelopment of spiral imaging for a 1.5T/280mm horizontal bore superconducting magnet
1.5T/280mm…•½ŠJŒū’““`“±Ž„Ī‚É‚Ø‚Æ‚éƒXƒpƒCƒ‰ƒ‹ƒCƒ[ƒWƒ“ƒO‚ĢŠJ”­v
•½¬‚Q‚W”N‚XŒŽ‚X“śC‘ę‚S‚S‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‘å‹{@P-1-026 (p251)

 

445.£ŒĖˆäˆ»ŲCŽ™‹Ź“ŽC‹£Ÿ”ü
uSpiral imaging for a 9.4T/54mm vertical bore superconducting magnet
9.4T/54mmcŒ^ŠJŒūŒa’““`“±Ž„Ī‚É‚Ø‚Æ‚éSpiral imaging‚ĢŠJ”­v
•½¬‚Q‚W”N‚XŒŽ‚X“śC‘ę‚S‚S‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‘å‹{@P-1-025 (p251)

 

444.‹£Ÿ”üC‹£—ŗˆź
uPotential and Problems of MRI simulators
MRI simulator‚Ģ‰Ā”\«‚Ę–ā‘č“_v
•½¬‚Q‚W”N‚XŒŽ‚P‚O“śC‘ę‚S‚S‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‘å‹{@P-2-019 (p270)

 

443.ŽR“c—Č‘¾C‹£Ÿ”üCŽ›“cN•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„Ī‚Ģ‚½‚ß‚Ģƒ}ƒ‹ƒ`ƒT[ƒLƒ…ƒ‰[ƒVƒ€ƒVƒXƒeƒ€‚ĢŠJ”­ v
•½¬‚Q‚W”N‚XŒŽ‚X“śC‘ę‚S‚S‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‘å‹{@O-1-046 (p147)

 

442.‹£Ÿ”üC‹£—ŗˆźC”qŽt’q”V
uDevelopment of the MRI software platform (II)
MRI software platform ‚ĢŠJ”­i2j v
•½¬‚Q‚W”N‚XŒŽ‚X“śC‘ę‚S‚S‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‘å‹{@O-1-045 (p147)

 

441.¼ąVWŽ÷Cˆ¢•”[ŽuC‹£Ÿ”üCŽ›“cN•F
u Design of oval gradient coils using current potential and singular value decomposition
“ĮˆŁ’l•Ŗ‰š–@‚š—p‚¢‚½‘ȉ~“›Œ^Œł”zŽ„źƒRƒCƒ‹‚ĢŻŒvv
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440.’·“cW‰ĄCŽ›“cN•FC‹£Ÿ”ü
u Error in QSI analysis for slow flow in a noisy environment
ƒmƒCƒY‚Ģ‘½‚¢ŠĀ‹«’†‚É‚Ø‚Æ‚é’x‚¢—¬‚ź‚Ģ QSI ‰šĶ‚É‘Ī‚·‚éŒė·•]‰æv
•½¬‚Q‚W”N‚XŒŽ‚X“śC‘ę‚S‚S‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‘å‹{@P-1-014 (p245)

 

439.Ž›“cN•F
u14.1T ‚É‚Ø‚Æ‚é MR fingerprintingv
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438.‹£—ŗˆźC‹£Ÿ”ü
uGPU ‚š—p‚¢‚½’“‚‘¬ MRI ƒVƒ~ƒ…ƒŒ[ƒ^‚ĢŠJ”­v
•½¬‚Q‚W”N‚WŒŽ‚P‚O“śC‘ę‚Q‚O‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC”@OiP71-74j

 

437.¬—Ń—D‘¾C‹£Ÿ”üCŽ›“cN•F
uField camera ‚š—p‚¢‚½ k - space trajectory ‚ĢŒv‘Ŗv
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436.¼ąVWŽ÷Cˆ¢•”[ŽuC‹£Ÿ”üCŽ›“cN•F
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•½¬‚Q‚W”N‚WŒŽ‚P‚O“śC‘ę‚Q‚O‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC”@OiP53-56j

 

435.ŽR“c—Č‘¾CŽ›“cN•FC‹£Ÿ”ü
u1.5T/280mm ’““`“±Ž„Ī—p ƒ}ƒ‹ƒ`ƒT[ƒLƒ…ƒ‰[ƒVƒ€ ƒRƒCƒ‹ ƒVƒXƒeƒ€‚Ģ ŠJ”­v
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434.–ī–ģ‡–ēC¬—Ń—D‘¾CŽ›“cN•FC‹£Ÿ”ü
u1.5T/280mm …•½ŠJŒūŒ^’““`“±Ž„Ī‚Ģ‚½‚ß‚Ģ‘}“üŒ^Œł”zŽ„źƒRƒCƒ‹‚Ģ»ģ‚Ę•]‰æv
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433.ŠFģ@VCŽ›“cN•FC‹£Ÿ”ü
uMRI‚š—p‚¢‚½¬Ž™œ”N—ī‘Ŗ’č‚Ö‚ĢDeep Learning‚Ģ‰ž—p‚ĢŒŸ“¢v
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432.‰Ŗ@C•½C‹£Ÿ”üCŽ›“cN•F
uƒfƒB[ƒvƒ‰[ƒjƒ“ƒO‚š—p‚¢‚½MR‰ę‘œ‚ĢƒZƒOƒƒ“ƒe[ƒVƒ‡ƒ“‚Ģ‰ŠśŒŸ“¢v
•½¬‚Q‚W”N‚WŒŽ‚P‚O“śC‘ę‚Q‚O‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC”@PiP31-34j

 

431.“c•Ó—ŗŸC‰Ŗ@C•½C‹£Ÿ”üCŽ›“cN•F
u–ģ‹…•I‰Šśf’f—pƒ|[ƒ^ƒuƒ‹MRI‚É‚Ø‚Æ‚é“ńŽŸƒVƒ€«”\•]‰æv
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430.£ŒĖˆäˆ»ŲC‹£Ÿ”ü
u1.5T/280mm…•½ŠJŒū’““`“±Ž„Ī‚É‚Ø‚Æ‚éƒXƒpƒCƒ‰ƒ‹ƒXƒLƒƒƒ“‚ĢŽĄ‘•v
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429.‘…‹I‹vŽqC‹£Ÿ”üCŠÖ@Žu˜N
u‚•ŖŽq“d‰šŽæ‚ĢƒŠƒ`ƒEƒ€ƒCƒIƒ“i 7 Li j‚ĘƒAƒjƒIƒ“ ( 19 F) ‚ĢŠgŽU‚ĘˆźŽŸŒ³ profile ‚ĢŠĻ‘Ŗv
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428.’·“cW‰ĄC•Ÿ“cŒ’“ńC‹£Ÿ”üCŽ›“cN•F
u0.2T ‰i‹vŽ„Ī MRI ‚š—p‚¢‚½‰®ŠOŽ÷–Ų “ą Ž÷‰t‚Ģ—¬‘¬‘Ŗ’č ( ‚h‚h )v
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427.‹£Ÿ”ü
uMR microscopy‚š—p‚¢‚½‰ę‘œƒf[ƒ^ƒx[ƒX\’z‚Ģ‚½‚ß‚ĢƒqƒgćóŽqŽB‘œv
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426.’Ć“c^lC‹£Ÿ”ü
u”Ä—pƒfƒWƒ^ƒ‹‹@Šķ‚š—p‚¢‚½ƒX[ƒp[ƒiƒCƒLƒXƒgMRIƒgƒ‰ƒ“ƒV[ƒo[‚ĢŠJ”­v
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425.ŠFģ@VCŽ›“cN•FC‹£Ÿ”üCŽR“cdl
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424.’·“cW‰ĄCŽ›“cN•FC‹£Ÿ”ü
u¶‘Ģ“ą‚É‚Ø‚Æ‚é’x‚¢—¬‘¬‚Ģ‘Ŗ’č–@‚ĢŒŸ“¢v
•½¬‚Q‚V”N‚XŒŽ‚P‚P“śC‘ę‚S‚R‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“Œ‹ž@iP-2-054jp379
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423.‹£—ŗˆźC‹£Ÿ”ü
uGPGPU‚š—p‚¢‚½‚‘¬MRI simulator‚ĢŠJ”­v
•½¬‚Q‚V”N‚XŒŽ‚P‚P“śC‘ę‚S‚R‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“Œ‹ž@iP-2-053jp379

 

422.’·“cW‰ĄC‘å’|—z‰īC‹£Ÿ”üCŽR“cdl
uƒqƒgćóŽq‰»ŠwŒÅ’č•W–{‚ĢŠgŽUƒeƒ“ƒ\ƒ‹ƒCƒ[ƒWƒ“ƒOv
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421.Ž›“cN•FC‹Ź“c‘å‹PCĪ@Œ\ˆź˜YC‹£Ÿ”üC–ģč‘¾ŠóC‹ąŽqNmC‹{é@—ŗC‹g‰Ŗ@‘å
u¬Ž™œ”N—īŒv‘Ŗ‚Ģ‚½‚ß‚ĢCSƒTƒ“ƒvƒŠƒ“ƒOƒpƒ^[ƒ“‚ĢÅ“K‰»v
•½¬‚Q‚V”N‚XŒŽ‚P‚P“śC‘ę‚S‚R‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“Œ‹ž@iP-2-038jp371

 

420.¼ąVWŽ÷CŽ›“cN•FC‹£Ÿ”ü
uDUCAS(Design tools Using Current potential And SVD)‚š —p‚¢‚½ƒOƒ‰ƒWƒGƒ“ƒgƒRƒCƒ‹—p GUIŻŒvƒ\ƒtƒgƒEƒFƒA‚ĢŠJ”­v
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419.–ī–ģ‡–ēCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”V
uEcho Planar Imaging‚š—p‚¢‚½1.0T‰i‹vŽ„ĪMRIƒVƒXƒeƒ€‚Ģ•]‰æv
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418.ŽR“c—Č‘¾CŽ›“cN•FC‹£Ÿ”ü
u•½–ŹŒ^ƒ}ƒ‹ƒ`ƒT[ƒLƒ…ƒ‰[ƒVƒ€ƒRƒCƒ‹ƒVƒXƒeƒ€‚ĢŠJ”­v
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417.¼ąVWŽ÷CŽ›“cN•FC‹£Ÿ”ü
uPC-MRIƒRƒ“ƒgƒ[ƒ‰[‚š—p‚¢‚½‰Q“d—¬§ŒäŽč–@‚ĢŠJ”­v
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416.”qŽt’q”VC‹“–{Ŗ‘¾˜YC–ī–ģ‡–ēCŽ›“cN•FC‹£Ÿ”ü
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415.•Ÿ“‡³—TCŽ›“cN•FC‹£Ÿ”ü
uƒRƒ“ƒpƒNƒgMRI‚š—p‚¢‚½ęłœœ–§“x‚Ģ’č—Ź“IŒv‘ŖŽč–@‚ĢŒŸ“¢v
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414.Ž›“cN•FCĪąVˆźŒ›C‹£Ÿ”ü
uArtificial bee colonyƒAƒ‹ƒSƒŠƒYƒ€‚š—p‚¢‚½Œł”zŽ„źƒRƒCƒ‹‚ĢŻŒvv
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413.‹£Ÿ”üC‹£—ŗˆźC”qŽt’q”V
uMRI software platform‚ĢŠJ”­v
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412.Ž™‹Ź“ŽC‹£Ÿ”ü
u9.4TcŒ^’““`“±Ž„Ī‚É‚Ø‚Æ‚éEcho Planar Imaging ‚ĢŠJ”­v
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411.ŽR“c—Č‘¾CŽ›“cN•FC‹£Ÿ”ü
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410.ŠFģ@VCŽ›“cN•FC‹£Ÿ”ü
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409.’·“cW‰ĄCŽ›“cN•FC‹£Ÿ”üC£ŒĆ‘ņ—R•F
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•½¬‚Q‚V”N‚WŒŽ‚P‚Q“śC‘ę‚P‚X‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļCŒcœä‹`m‘åŠw“ś‹gƒLƒƒƒ“ƒpƒX—ˆ‰ŽÉ‚QŠK@‘å‰ļ‹cŽŗ+’†‰ļ‹cŽŗ@iP9)p53-56

 

408.£ŒĖˆäˆ»ŲC‹£Ÿ”ü
u9.4TcŒ^’““`“±Ž„ĪMRI‚š—p‚¢‚½ZTEƒCƒ[ƒWƒ“ƒO‚ĢŽŽ‚Ż
•½¬‚Q‚V”N‚WŒŽ‚P‚Q“śC‘ę‚P‚X‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļCŒcœä‹`m‘åŠw“ś‹gƒLƒƒƒ“ƒpƒX—ˆ‰ŽÉ‚QŠK@‘å‰ļ‹cŽŗ+’†‰ļ‹cŽŗ@iP6)p45-48

 

407.–ī–ģ‡–ēC‹£Ÿ”üCŽ›“cN•FC”qŽt’q”V
u1T90mm‚É‚Ø‚Æ‚éIREPI‚š—p‚¢‚½‚‘¬T1ƒ}ƒbƒsƒ“ƒO‚ĢŽĄ‘•v
•½¬‚Q‚V”N‚WŒŽ‚P‚Q“śC‘ę‚P‚X‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļCŒcœä‹`m‘åŠw“ś‹gƒLƒƒƒ“ƒpƒX—ˆ‰ŽÉ‚QŠK@‘å‰ļ‹cŽŗ+’†‰ļ‹cŽŗ@iP3)p33-36

 

406.’Ć“c^lCŽ›“cN•FC‹£Ÿ”ü
u”Ä—pƒfƒWƒ^ƒ‹‹@Šķ‚š—p‚¢‚½ƒX[ƒp[ƒiƒCƒLƒXƒgMRIƒgƒ‰ƒ“ƒV[ƒo[‚ĢŠJ”­v
•½¬‚Q‚V”N‚WŒŽ‚P‚Q“śC‘ę‚P‚X‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļCŒcœä‹`m‘åŠw“ś‹gƒLƒƒƒ“ƒpƒX—ˆ‰ŽÉ‚QŠK@‘å‰ļ‹cŽŗ+’†‰ļ‹cŽŗ@iP2)p29-32

 

405.•Ÿ“‡³—TCŽ›“cN•FC‹£Ÿ”ü
u곍œŽB‘œ—pRFƒvƒ[ƒu‚ĢŠJ”­v
•½¬‚Q‚V”N‚WŒŽ‚P‚Q“śC‘ę‚P‚X‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļCŒcœä‹`m‘åŠw“ś‹gƒLƒƒƒ“ƒpƒX—ˆ‰ŽÉ‚QŠK@‘å‰ļ‹cŽŗ+’†‰ļ‹cŽŗ@iP1)p25-28

 

404.‹£Ÿ”üC‹£—ŗˆźC”qŽt’q”V
uMRI software platformiMRI“‡ŠJ”­ŠĀ‹«j‚ĢŠJ”­
•½¬‚Q‚V”N‚WŒŽ‚P‚Q“śC‘ę‚P‚X‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļCŒcœä‹`m‘åŠw“ś‹gƒLƒƒƒ“ƒpƒX—ˆ‰ŽÉ‚QŠK@‘å‰ļ‹cŽŗ+’†‰ļ‹cŽŗ@iOP7)21-24

 

403.Ž™‹Ź“ŽC‹£Ÿ”ü
u9.4TcŒ^’““`“±Ž„ĪMRI‚É‚Ø‚Æ‚é’“‚‘¬ƒCƒ[ƒWƒ“ƒO‚ĢŒŸ“¢[EPI‚ĘSpiral‚Ģ”äŠr[
•½¬‚Q‚V”N‚WŒŽ‚P‚Q“śC‘ę‚P‚X‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļCŒcœä‹`m‘åŠw“ś‹gƒLƒƒƒ“ƒpƒX—ˆ‰ŽÉ‚QŠK@‘å‰ļ‹cŽŗ+’†‰ļ‹cŽŗ@iOP5)p15-18

 

402.’·“cW‰ĄCŽ›“cN•FC‹£Ÿ”ü
u0.2T‰i‹vŽ„ĪMRI‚š—p‚¢‚½‰®ŠOŽ÷–Ų“ąŽ÷‰t‚Ģ—¬‘¬‘Ŗ’čv
•½¬‚Q‚V”N‚WŒŽ‚P‚Q“śC‘ę‚P‚X‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļCŒcœä‹`m‘åŠw“ś‹gƒLƒƒƒ“ƒpƒX—ˆ‰ŽÉ‚QŠK@‘å‰ļ‹cŽŗ+’†‰ļ‹cŽŗ@iOP4)p11-14

 

401.¼ąVWŽ÷CŽ›“cN•FC‹£Ÿ”üCˆ¢•”[Žu
u•½”ĀŒ^Œł”zŽ„źƒRƒCƒ‹ŻŒv‚É‚Ø‚Æ‚éDUCAS‚Ģ«”\•]‰æv
•½¬‚Q‚V”N‚WŒŽ‚P‚Q“śC‘ę‚P‚X‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļCŒcœä‹`m‘åŠw“ś‹gƒLƒƒƒ“ƒpƒX—ˆ‰ŽÉ‚QŠK@‘å‰ļ‹cŽŗ+’†‰ļ‹cŽŗ@iOP2)p5-8

 

400.Ž›“cN•F
uMR Fingerprinting ‚ĢŽŽ‚Żv
•½¬‚Q‚V”N‚WŒŽ‚P‚Q“śC‘ę‚P‚X‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļCŒcœä‹`m‘åŠw“ś‹gƒLƒƒƒ“ƒpƒX—ˆ‰ŽÉ‚QŠK@‘å‰ļ‹cŽŗ+’†‰ļ‹cŽŗ iOP1jp1-4

 

399.‹£Ÿ”ü
uMRIŒ¤‹†‚ĢŒ»ó‚Ę“W–]v
•½¬‚Q‚V”N‚UŒŽ‚Q‚R“śC‚ꂱ‚Ķ‚ÜNMRŒ¤‹†‰ļ@‚Q‚OŽü”N‹L”OŒöŠJƒVƒ“ƒ|ƒWƒEƒ€C—‰»ŠwŒ¤‹†Š‰”•lŽ–‹ĘŠ@Œš—¬“ƒz[ƒ‹
µ‘Ņu‰‰

 

398.‹£Ÿ”ü
uHigh-Field MR Microscopy of Chemically Fixed Human Embryosv
•½¬‚Q‚V”N‚TŒŽ‚V“śC“ś–{¶•Ø•Ø—Šw‰ļ‹ćBŽx•”EŒF–{‘åŠwƒCƒ[ƒWƒ“ƒOƒZƒ~ƒi[uNMR‚ĘMRI‚Ģē®ēvCŒF–{‘åŠw–ņŠw•”‹{–{‹L”OŠŁ
µ‘Ņu‰‰

 

397.’Ć“c^lC‹Ź“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
u”Ä—pƒfƒWƒ^ƒ‹‹@Šķ‚ĘArduino Due‚š—p‚¢‚½ƒfƒWƒ^ƒ‹MRIƒVƒXƒeƒ€‚ĢŠJ”­v
Development of a digital MRI system using general purpose digital units and board computers gArduino Dueh@
•½¬26”N11ŒŽ4“śC‘ę53‰ńNMR“¢˜_‰ļC‘åć‘åŠwƒRƒ“ƒxƒ“ƒVƒ‡ƒ“ƒZƒ“ƒ^[iP-92jp43

 

396.Ž›“cN•F
uMR microscopy‚É‚ę‚éA•Ø‚Ģin situŽlŽŸŒ³ŠĻŽ@v
•½¬26”N11ŒŽ4“śCƒVƒ“ƒ|ƒWƒEƒ€@A•Ø‚Ģ”ɐBķ—Ŗ‚šl‚¦‚é`11–¼‚ĢŒ¤‹†ŽŅ‚Ŗ’ń‹Ÿ‚·‚éA•Ø”ɐB‚ĢÅV˜b‘č`C–¼ŒĆ‰®‘åŠwE–ģˆĖŠwpŒš—¬‰ļŠŁ‚PŠK

 

395.X˜e@‘C”–ˆäŽĄC‹Ź“c‘å‹PCŽ›“cN•FC‹£Ÿ”üC£ŒĆąV—R•F
uMRI‚š—p‚¢‚½ƒjƒzƒ“ƒiƒV‰ŹŽĄˆŪŠĒ‘©\‘¢‚Ģ‰šĶv
•½¬‚Q‚U”N‚XŒŽ‚Q‚V“śC‰€Œ|Šw‰ļ•½¬‚Q‚U”N“xH‹G‘å‰ļC²‰ź‘åŠw–{ÆƒLƒƒƒ“ƒpƒXi‰Ź003EŒū“Ŗjp90

 

394. X˜e@‘CŽ›“cN•FC‹£Ÿ”üC”qŽt’q”VC£ŒĆąV—R•F
uƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒO‚É‚Ø‚Æ‚éƒZƒOƒƒ“ƒe[ƒVƒ‡ƒ“Œė·‚ĢŒŸ“¢v
•½¬‚Q‚U”N‚XŒŽ‚Q‚O“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siP-3-214jp420

 

393.‹£Ÿ”üC‘å’|—z‰īCŽR“c‘ń”nC”qŽt’q”VCŽR“cdl
uƒqƒgćóŽq‰»ŠwŒÅ’č•W–{‚ĢNMRƒpƒ‰ƒƒ^‚É‚ę‚éÅ“KŽB‘œƒV[ƒPƒ“ƒX‚ĢŒŸ“¢v
•½¬‚Q‚U”N‚XŒŽ‚Q‚O“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siP-3-213jp419

 

392.“c@“ցCŽ›“cN•FC‹£Ÿ”ü
u¶‘Ģ“ą‚Ģ’x‚¢—¬“®‚Ģ’č—ŹŒv‘Ŗ‚Ģ‚½‚ß‚Ģƒtƒ@ƒ“ƒgƒ€ŽĄŒ±v
•½¬‚Q‚U”N‚XŒŽ‚Q‚O“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siP-3-210jp418

 

391.’Ć“c^lC‹Ź“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
u”Ä—pƒfƒWƒ^ƒ‹‹@Šķ‚š—p‚¢‚½ƒfƒWƒ^ƒ‹MRIƒgƒ‰ƒ“ƒV[ƒo[‚ĢŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siP-2-141jp383

 

390.‹£Ÿ”üC”qŽt’q”V
u9.4T/54mmŠJŒūcŒ^’““`“±Ž„Ī‚š—p‚¢‚½MR microscope‚ĢŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siP-2-140jp382

 

389.ć‘ŗ–ķ–ēCŽ›“cN•FC‹£Ÿ”ü
u‚Ž„ź‚ɂ؂Ƃ鎎—æ—U‹N•s‹ĻˆźŽ„ź‚ĢƒVƒ~ƒ…ƒŒ[ƒVƒ‡ƒ“‚ĘŽĄŒ±‚É‚ę‚é•]‰æv
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siP-2-121jp373

 

388. ‹Ź“c‘å‹PC‹£Ÿ”ü
u k-space power distribution‚š—p‚¢‚½Compressed SensingƒTƒ“ƒvƒŠƒ“ƒOÅ“K‰»Žč–@‚ĢŒŸ“¢v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siP-2-118jp371
Šwp§—ćÜ
387.Ž™‹Ź“ŽCŽ›“cN•FC‹£Ÿ”ü
uMRIƒŠƒAƒ‹ƒ^ƒCƒ€ƒVƒ~ƒ…ƒŒ[ƒ^[‚ĢŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siP-2-105jp365

 

386.Ž›“cN•FCˆī‘ŗ^–ēC‹£Ÿ”üC‹{é@—ŗC“”‰iN¬C‹g‰Ŗ@‘å
u¬Ž™œ”N—īŒv‘Ŗ—p0.3Tƒ|[ƒ^ƒuƒ‹MRI‚š—p‚¢‚½‘å‹K–Ķ”ķŒ±ŽŅŒv‘Ŗv
•½¬‚Q‚U”N‚XŒŽ‚P‚W“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siP-1-053jp339

 

385.‘å’|—z‰īCŽR“c‘ń”nC‹£Ÿ”üC”qŽt’q”VCŽR“cdl
u9.4T/54mmŠJŒūcŒ^’““`“±Ž„Ī‚š—p‚¢‚½ƒqƒgćóŽq•W–{Œü‚ƍ‚•Ŗ‰š”\MRI‚ĢŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siO-2-268jp281

 

384.ŽR“c‘ń”nC‹£Ÿ”üCŽ›“cN•F
u¬Ž™œ”N—īŒv‘Ŗ—pMRI‚Ģ‚½‚ß‚ĢRFƒRƒCƒ‹‚ĢŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siO-2-267jp280

 

383.”qŽt’q”VCĪąVˆźŒ›C‹£Ÿ”ü
u¶‘Ģƒ}ƒEƒX• ‰ēˆŹŽB‘œ‚Ģ‚½‚ß‚Ģ‰±’ź‘ĪŒüŒ^Œł”zŽ„źƒRƒCƒ‹‚ĢŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siO-2-265jp279

 

382.ĪŒ\ˆź˜YCŽ›“cN•FC‹£Ÿ”ü
u¬Ž™œ”N—īŒv‘Ŗ—pƒRƒ“ƒpƒNƒgMRI‚É‚Ø‚Æ‚éĆŽ„źˆĄ’萫‚Ę‹Ļˆź«‚ĢŒüćv
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siO-2-260jp277

 

381.Ž›“cN•FC”qŽt’q”VC‹£Ÿ”ü
uDIXON–@‚Ģ‚½‚ß‚Ģ¬Œ^‰i‹vŽ„Ī‚Ģ‰·“xˆĄ’萫‚ĢŒüćv
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siO-2-259jp276

 

380.ƒAƒNƒ‰ƒ€@ƒGƒ€ƒfƒB@ƒVƒƒƒnƒ_ƒg@ƒzƒTƒCƒ“CŽ›“cN•FC‹£Ÿ”ü
uCoupled circuit simulation of Z-and X-gradient eddy currents in a 9.4T narrow-bore MRI systemv
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siO-2-258jp276

 

379.ƒAƒNƒ‰ƒ€@ƒGƒ€ƒfƒB@ƒVƒƒƒnƒ_ƒg@ƒzƒTƒCƒ“CŽ›“cN•FC‹£Ÿ”ü
uTemporal-spatial responses of planar X-gradient eddy currents by solid angle coupled circuit methodv
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siO-2-257jp275

 

378.Ž›“cN•FC‹Ź“c‘å‹PCĪŒ\ˆź˜YC‹£Ÿ”üC–ģč‘åŠóC‹ąŽqNmC‹g‰Ŗ@‘å
uCS‚š—p‚¢‚½¬Ž™œ”N—īŒv‘Ŗ‚Ģ‚‘¬‰»‚ĢŒŸ“¢v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siO-2-230jp262

 

377.‹Ź“c‘å‹PC‹£Ÿ”üCŽįŽR“N–ēCXć—T”VCŽsģV‘¾˜YC²–ģŸœACŽsģ’qĶC–{™‰F‘¾˜Y
uCompressed Sensing‚ĘL1-SPIRiT‚š—p‚¢‚½• •”3DGREƒCƒ[ƒWƒ“ƒOv
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siO-2-229jp261

 

376.‹Ź“c‘å‹PC‹£Ÿ”ü
u‚ŽŸ‚ĢĆŽ„ź•s‹Ļˆź«‚šl—¶‚µ‚½Self-Calibrated Compressed SensingƒAƒ‹ƒSƒŠ ƒYƒ€‚ĢŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siO-2-228jp261
375.•Ÿ“‡³—TCŽ›“cN•FC‹£Ÿ”ü
u곍œœ–§“xŒv‘Ŗ—pƒRƒ“ƒpƒNƒgMRIƒVƒXƒeƒ€‚ĢŠJ”­v
•½¬‚Q‚U”N‚XŒŽ‚P‚X“śC‘ę‚S‚Q‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“siO-2-214jp254

 

374.”qŽt’q”VCĪąVˆźŒ›C‹£Ÿ”ü
uNMRŽ„Ī‚É‚ę‚鐶‘Ģƒ}ƒEƒX• ‰ēˆŹŽB‘œ‚Ģ‚½‚ß‚Ģ‰±’ź‘ĪŒüŒ^‚RŽ²Œł”zŽ„źƒRƒCƒ‹v
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“śC‘ę‚P‚W‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‹ą‘ņiO10)p61-62

 

373.‹Ź“c‘å‹PC’‡‘ŗ‚ŽuC‹£Ÿ”ü
u‚‰·’““`“±ƒoƒ‹ƒNŽ„Ī‚É‚Ø‚Æ‚éƒ}ƒCƒXƒi[Œų‰Ź‚šl—¶‚µ‚½Œł”zŽ„źƒRƒCƒ‹‚ĢŠJ”­v
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“śC‘ę‚P‚W‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‹ą‘ņiO9)p57-60

 

372.‹£Ÿ”üC”qŽt’q”V
u9.4T/54mmƒ{ƒAcŒ^’““`“±Ž„Ī‚š—p‚¢‚½MR microscope‚Ģ\’zv
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“śC‘ę‚P‚W‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‹ą‘ņiO8)p53-56

 

371.Ž›“cN•FCˆī‘ŗ^–ēCĪąVˆźŒ›CĪŒ\ˆź˜YC‹£Ÿ”ü
uƒ|[ƒ^ƒuƒ‹MRI‚š—p‚¢‚½œ”N—īŒv‘Ŗv
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“śC‘ę‚P‚W‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‹ą‘ņiO7)p49-52

 

370.ŽR“c‘ń”nC‹£Ÿ”üCŽ›“cN•F
u1.1T’““`“±Ž„Ī‚š—p‚¢‚½¬Ž™œ”N—īŒv‘Ŗ‚Ģ‚½‚ß‚ĢRFƒvƒ[ƒu‚ĢŠJ”­v
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“śC‘ę‚P‚W‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‹ą‘ņiP8)p41-44

 

369.ĪŒ\ˆź˜YCŽ›“cN•FC‹£Ÿ”ü
u‚‘¬ŽB‘œ‚É‘Ī‰ž‚µ‚½‰i‹vŽ„ĪŽ„‹C‰ń˜H‚Ģ’·ŽžŠŌ‰·“x§Œäv
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“śC‘ę‚P‚W‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‹ą‘ņiP7)p37-40

 

368.•Ÿ“‡³—TCŽ›“cN•FC‹£Ÿ”ü
u‚‘¬ƒXƒsƒ“ƒGƒR[–@‚É‚Ø‚Æ‚é‰Q“d—¬Œv‘Ŗv
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“śC‘ę‚P‚W‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‹ą‘ņiP6)p33-36

 

367.’Ć“c^lC‹Ź“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
u32bitƒ{[ƒhƒRƒ“ƒsƒ…[ƒ^[(Arduino Due)‚š—p‚¢‚½MRIƒpƒ‹ƒXƒvƒƒOƒ‰ƒ}‚ĢŠJ”­v
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“śC‘ę‚P‚W‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‹ą‘ņiP5)p29-32

 

366.“c@“ցCŽ›“cN•FC‹£Ÿ”ü
uMRI‚š—p‚¢‚½‰®ŠOƒPƒ„ƒLŽåŠ²‚Ģ—¬‘¬•Ŗ•zŒv‘Ŗv
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“śC‘ę‚P‚W‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‹ą‘ņiO2)p19-22

 

365.X˜e@‘CŽ›“cN•FC‹£Ÿ”üC£ŒĆ‘ņ—R•F
uMR microimaging‚É‚ę‚é Žķ‚Č‚µƒJƒL‚ĢŽķŽq‘Ž‰»‰Ū’ö‚ĢŠĻŽ@v
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“śC‘ę‚P‚W‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‹ą‘ņiO1)p15-18

 

364.Ž›“cN•FA‹g“c–¾ŠóŽqA‹£Ÿ”üAŒo’Ė~Žq
u’n‰ŗŒsćü‰č‚Ģ¬’·‰ß’ö‚Ģƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOv
•½¬‚Q‚U”N‚WŒŽ‚P‚P“śC‘ę‚P‚W‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‹ą‘ņiP4)p11-14

 

363.‹£Ÿ”ü
uCompact/Mobile MRI‚Ģ‰ß‹ŽEŒ»ŻE–¢—ˆv
•½¬‚Q‚U”N‚WŒŽ‚P‚P“śC‘ę‚P‚W‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‹ą‘ņiSL1)p1-4
“Į•Źu‰‰

 

362.X˜e@‘AŽ›“cN•FA‹£Ÿ”üA”qŽt’q”VA£ŒĆąV—R•F
uMR microimaging‚É‚ę‚鉏ŽĄˆŪŠĒ‘©\‘¢‚Ģ‰ĀŽ‹‰»‚Ę’č—Ź“I•]‰æv
•½¬‚Q‚U”N‚RŒŽ‚Q‚X“śC‰€Œ|Šw‰ļ•½¬‚Q‚U”N“xt‹G‘å‰ļŒ¤‹†”­•\C’}”g‘åŠw‘ę‚QƒGƒŠƒAE‘ę‚RƒGƒŠƒA ‘ę13ŠŖ•Źū1 P96

 

361.‹£Ÿ”ü
uƒqƒgćóŽq‰»ŠwŒÅ’č•W–{‚ĢMR microscopy‚É‚ę‚éŽOŽŸŒ³Œ`‘ŌŒv‘Ŗv
•½¬‚Q‚U”N‚PŒŽ‚R‚O“śCƒqƒg”]‚ĢŒ`‘ŌŒ`¬‚ÉŠÖ‚·‚éƒuƒŒ[ƒ“ƒXƒg[ƒ~ƒ“ƒOC“Œ‹ž‘åŠw‹³ˆēŠw•”‘ęˆź‰ļ‹cŽŗ
µ‘Ņu‰‰
360.‹£Ÿ”ü
uMRI‘•’u‚ĢŽd‘g‚Ż‚ĘŒv‘Ŗ‚ĢŠT—vv
•½¬‚Q‚T”N‚P‚PŒŽ‚P‚T“śCUltra@High Field-MRI@ƒ[ƒNƒVƒ‡ƒbƒvEƒgƒ‰ƒCƒAƒ‹ƒ†[ƒXC“Œ‘唐
µ‘Ņu‰‰
359.‹£Ÿ”ü
uMRI‚š—p‚¢‚½¶‘ĢŽŽ—æ‚Č‚Ē‚Ģ‚•Ŗ‰š”\ƒCƒ[ƒWƒ“ƒO‚ĢŒ»óv
•½¬‚Q‚T”N‚P‚OŒŽ‚Q‚W“śC“ś–{•ŖŒõ‰ļƒeƒ‰ƒwƒ‹ƒc•ŖŒõ•”‰ļ@ƒVƒ“ƒ|ƒWƒEƒ€C‹ž“siI-2)
µ‘Ņu‰‰

 

358.‹Ź“c‘å‹PC‹£Ÿ”üC–ö@—z‰īCˆÉ“”‰ĄFC’‡‘ŗ‚Žu
u‚‰·’““`“±ƒoƒ‹ƒNŽ„Ī‚š—p‚¢‚½‚•Ŗ‰š”\MRI @High-Resolution Magnetic Resonance Imaging Using a High Tc Bulk Superconducting Magnetv
•½¬25”N11ŒŽ12“śC‘ę52‰ńNMR“¢˜_‰ļD‹ą‘ņiP62j
Å—DGŽįŽčƒ|ƒXƒ^[Ü

 

357.ˆ¢•”‹ÓŽjA”qŽt’q”VA•yŠ~””nA‹£Ÿ”üA‹vP’C”Ž
u’“‚Ž„ź(14.1T)-MRI‘•’u‚š—p‚¢‚½ƒ}ƒEƒX”]‹@”\ƒCƒ[ƒWƒ“ƒOv
•½¬‚Q‚T”N‚XŒŽC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(P-3-247)

 

356.”qŽt’q”VAˆ¢•”‹ÓŽjA•yŠ~””nA‹£Ÿ”üA‹vP’C”Ž
uŠłŻ‚Ģ14.1T-NMR‘•’u‚šŠˆ—p‚µ‚½MRMICS‚É‚ę‚éƒ}ƒEƒX”]ƒCƒ[ƒWƒOv
•½¬‚Q‚T”N‚XŒŽC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(P-3-246)
355.‹£Ÿ”ü
u‚l‚q‚h‚Ģƒn[ƒhƒEƒFƒAFƒfƒWƒ^ƒ‹ƒgƒ‰ƒ“ƒV[ƒo[‚ĢŽd‘g‚Ż‚ĘŽĄŪv
•½¬‚Q‚T”N‚XŒŽC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(EL8-1)
‹³ˆēu‰‰

 

354.ƒGƒ€ƒfƒBEƒVƒƒƒnƒ_ƒgEƒzƒTƒCƒ“EƒAƒNƒ‰ƒ€AŽ›“cN•FAĪŒ\ˆź˜YA‹£Ÿ”ü
uEddy Current Analysis of 0.3 T Permanent Magnet MRI Systems with Planar Gradient Coilv
•½¬‚Q‚T”N‚XŒŽC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(O-01-120)

 

353.ĪąVˆźŒ›A‹£Ÿ”üAŽ›“cN•F
uMRI—p•½”ĀŒ^Œł”zŽ„źƒRƒCƒ‹‚ĢÅ“K‰»Žč–@‚ĢŒ¤‹†v
•½¬‚Q‚T”N‚XŒŽC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(O-01-116)

 

352.“ąŠC’m”üA‹Ź“c‘å‹PAŽ›“cN•FA‹£Ÿ”üA‹{é@—ŗAŽR•”‰psA“”‰iN¬A
‹g‰Ŗ@‘å
u¬Ž™œ”N—īŒv‘Ŗ—p‚l‚q‚h‚Ģ‚½‚ß‚ĢƒRƒ“ƒsƒ…[ƒ^Žx‰‡f’fƒVƒXƒeƒ€‚ĢŠJ”­v
•½¬‚Q‚T”N‚XŒŽC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(O-01-112)

 

351.ˆī‘ŗ^–ēAŽ›“cN•FA‹£Ÿ”ü
u¬Ž™œ”N—īŒv‘Ŗ—pRFƒvƒ[ƒu‚ĢŠJ”­iIIjv
•½¬‚Q‚T”N‚XŒŽC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(O-01-111)

 

350.Ž›“cN•FAĪąVˆźŒ›Aˆī‘ŗ^–ēAĪŒ\ˆź˜YA‹£Ÿ”ü
u¬Ž™œ”N—īŒv‘Ŗ—p0.3Tƒ|[ƒ^ƒuƒ‹MRI‚ĢŠJ”­v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“śC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(O-01-110)

 

349.‹Ź“c‘å‹PA‹£Ÿ”ü
uCross Sampling‚š—p‚¢‚½ĆŽ„ź•s‹Ļˆź‰ŗ‚ɂ؂Ƃ鈳kƒZƒ“ƒVƒ“ƒOƒAƒ‹ƒSƒŠƒYƒ€‚ĢŠJ”­v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“śC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(O-01-047)

 

348.‘å’|—z‰īAŽR“c‘ń”nA‹£Ÿ”üAŽR“cdl
uƒqƒgćóŽq•W–{‚Ģƒ‰[ƒWƒ}ƒgƒŠƒNƒXŽB‘œŽč–@‚ĢŒŸ“¢v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“śC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(P-03-244)

 

347.X˜e@‘A‹£Ÿ”üA”qŽt’q”VA£ŒĆąV—R•F
uMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒO‚É‚ę‚鐶‘ĢŽŽ—æ‚Ģ”÷×\‘¢‚Ģ‰ĀŽ‹‰»v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“śC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(P-03-243)

 

346.Ž›“cN•FA‰Ķ–ģŹ‹LA“ąŠC’m”üAˆī‘ŗ^–ēA‹£Ÿ”üA‹{é@—ŗAŽR•”‰psA“”‰i@N¬A‹g‰Ŗ@‘å
u¬Ž™œ”N—īŒv‘Ŗ—pƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚Ģ—L—p«v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“śC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(P-02-184)

 

345.‹Ź“c‘å‹PA‹£Ÿ”ü
uCompressed SensingƒAƒ‹ƒSƒŠƒYƒ€‚š—p‚¢‚½ŽOŽŸŒ³MR Microscopyv
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“śC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(P-02-162)

 

344.‹“–{Ŗ‘¾˜Y A‹£Ÿ”üA”qŽt’q”V
u ‚Ž„ź‚•Ŗ‰š”\‚l‚q‚h‚É‚Ø‚Æ‚éƒfƒWƒ^ƒ‹ƒgƒ‰ƒ“ƒV[ƒo[‚Ģ•]‰æv
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“śC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(P-02-125)

 

343.“c@“ցAŽ›“cN•FA‹£Ÿ”ü
uƒI[ƒvƒ“Œ^RFƒvƒ[ƒu‚ĢŠJ”­‚Ęin situŽB‘œ‚Ö‚Ģ‰ž—pv
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“śC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(P-02-124)
342.ŽR“c‘ń”nA‹£Ÿ”ü
u‰~“›Œ^Œł”zŽ„źƒRƒCƒ‹‚ĢÅ“K‰»Žč–@‚ĢŒ¤‹†v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“śC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(P-02-123)
341.ĪŒ\ˆź˜YAŽ›“cN•FA‹£Ÿ”ü
u¬Ž™œ”N—īŒv‘Ŗ—p‚l‚q‚h‚Ģ‚½‚ß‚ĢƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ĢŠJ”­v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“śC‘ę‚S‚P‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC“擇(P-02-122)

 

340.“ąŠC’m”üC‹Ź“c‘å‹PCŽ›“cN•FC‹£Ÿ”üC‹{é—ŗCŽR•”‰psC“”‰iN¬C‹g‰Ŗ‘å
uƒI[ƒvƒ“ƒ\[ƒX‚š—p‚¢‚½¬Ž™œ”N—īŒv‘Ŗ—pMRI‚Ģ‚½‚ß‚ĢƒRƒ“ƒsƒ…[ƒ^Žx‰‡f’fƒVƒXƒeƒ€‚ĢŠJ”­v
•½¬‚Q‚T”N‚WŒŽ‚Q“śC‘ę‚P‚V‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(P13)p63-p64

 

339.ˆī‘ŗ^–ēCŽ›“cN•FC‹£Ÿ”ü
u¬Ž™œ”N—īŒv‘Ŗ—pRFƒvƒ[ƒu‚ĢÅ“K‰»iII jv
•½¬‚Q‚T”N‚WŒŽ‚Q“śC‘ę‚P‚V‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(P4)p43-p44

 

338.ĪąVˆźŒ› CŽ›“cN•F C‹£Ÿ”ü
u“±‘Ģ•‚šl—¶‚µ‚½•½”ĀŒ^ƒOƒ‰ƒWƒGƒ“ƒgƒRƒCƒ‹‚Ģ“d—ĶÅ“K‰»ŻŒvv
•½¬‚Q‚T”N‚WŒŽ‚Q“śC‘ę‚P‚V‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(P3)p39-p42

 

337.ŽR“c‘ń”n C‹£Ÿ”ü
uƒiƒ[ƒ{ƒAcŒ^’““`“±Ž„Ī‚Ģ‚½‚ß‚ĢŒł”zŽ„źƒRƒCƒ‹‚ĢŻŒvv
•½¬‚Q‚T”N‚WŒŽ‚Q“śC‘ę‚P‚V‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(P2)p35-p38

 

336.ĪŒ\ˆź˜Y CŽ›“cN•F C‹£Ÿ”ü
u0.3T ƒ|[ƒ^ƒuƒ‹ MRI—pƒVƒ“ƒOƒ‹ƒ`ƒƒƒlƒ€ƒRƒC‚ĢŠJ”­v
•½¬‚Q‚T”N‚WŒŽ‚Q“śC‘ę‚P‚V‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(P1)p31-p34

 

335.”qŽt’q”VC‹£Ÿ”üCˆ¢•”‹ÓŽjC•yŠ~””nC‹vP’C”Ž
uŠłŻ‚ĢNMR‘•’u‚šŠˆ—p‚µ‚½MRMICS‚É‚ę‚é600MHz‚Å‚Ģ¶‘Ģƒ}ƒEƒXŽB‘œ‚Ģ
‰ŠśŒŸ“¢v
•½¬‚Q‚T”N‚WŒŽ‚Q“śC‘ę‚P‚V‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(O6)p21-p24

 

334.X˜e‘CŽ›“cN•FC‹£Ÿ”üC”qŽt’q”VC£ŒĆąV—R•F
uMR microimaging‚É‚ę‚鉏ŽĄˆŪŠĒ‘©\‘¢‚Ģ‰ĀŽ‹‰»v
•½¬‚Q‚T”N‚WŒŽ‚Q“śC‘ę‚P‚V‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(O4)p13-16

 

333.‹Ź“c‘å‹PC‹£Ÿ”üC–ö—z‰īCˆÉ“”‰ĄFC’‡‘ŗ‚Žu
uV‹KŻŒv‚É‚ę‚鍂‰·’““`“±ƒoƒ‹ƒNŽ„Ī‚š—p‚¢‚½MRI‚ĢŠJ”­v
•½¬‚Q‚T”N‚WŒŽ‚Q“śC‘ę‚P‚V‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(O3)p9-12

 

332.“c“ցCŽ›“cN•FC‹£Ÿ”ü
u0.2T‰i‹vŽ„ĪMRI‚š—p‚¢‚½‰®ŠOŽ÷–ŲŽåŠ²‚Ģ’·ŠśŠŌŒv‘Ŗv
•½¬‚Q‚T”N‚WŒŽ‚Q“śC‘ę‚P‚V‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(O2)p5-p8

 

331.Ž›“cN•FC‹£Ÿ”üC”qŽt’q”V
u‰i‹vŽ„ĪŽ„‹C‰ń˜H‚ĢŽ„źˆĄ’萫‚ĢŒüć‚ĘDixon–@‚Ö‚Ģ‰ž—pv
•½¬‚Q‚T”N‚WŒŽ‚Q“śC‘ę‚P‚V‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“c’¬(O1)P1-P4

 

330.‹£Ÿ”ü
u‚l‚q‚h‚ĢŠī‘bv
•½¬24”N11ŒŽ7“śC‘ę‚T‚P‰ń‚m‚l‚q“¢˜_‰ļC–¼ŒĆ‰®
‹³ˆēu‰‰
329.‹Ź“c‘å‹P
uˆ³kƒZƒ“ƒVƒ“ƒOi‚b‚rj‚Ģ‚l‚q‚h‚Ö‚ĢŽĄ‘•‚ĢŽŽ‚Ż[—˜_‚ĘŽĄŪ[v
•½¬‚Q‚S”N‚P‚PŒŽ‚Q“śC‚Ā‚­‚Ī‚l‚q§˜b‰ļC‚Ā‚­‚Ī

 

328.‹“–{Ŗ‘¾˜YC‹£Ÿ”üC”qŽt’q”V
uƒfƒBƒWƒ^ƒ‹rf‚š—p‚¢‚½’įŽ„źƒRƒ“ƒpƒNƒg‚l‚q‚hƒVƒXƒeƒ€‚ɂ؂Ƃ郉[ƒWƒ}ƒgƒŠƒNƒXŽB‘œv
•½¬24”N9ŒŽ7“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(p-2-106)

 

327.‹£Ÿ”ü
u‚l‚q‚h‚É‚Ø‚Æ‚é‹óŠŌ•Ŗ‰š”\‚Ö‚Ģ’§ķ[—šŽj“IƒŒƒrƒ…[‚ʏ«—ˆ“W–][v
•½¬24”N9ŒŽ6“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(SL-3)
“Į•Źu‰‰
326.‰Ķ–ģŹ‹LC“ąŠC’m”üC‹Ź“c‘å‹PCŽ›“cN•FC‹£Ÿ”üC‹{é@—ŗCŽR•”‰psC
‹g‰Ŗ@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚š—p‚¢‚½¬Ž™œ”N—ī‚Ģ”»’č‚ĘÄŒ»«•]‰æv
•½¬24”N9ŒŽ6“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(o-1-128)

 

325.‹ß“”‘å‹MCŽ›“cN•FC‹£Ÿ”ü
u곍œœ–§“xŒv‘Ŗ—pCompact MRI‚É‚Ø‚Æ‚éŒv‘ŖÄŒ»«‚ĢŒüćv
•½¬24”N9ŒŽ6“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(o-1-129)

 

324.–x‰ź‰ėŽjCĪąVˆźŒ›C”¼“cW–ēC‹£Ÿ”ü
ucŒ^’““`“±Ž„Ī‚Ģ‚½‚ß‚Ģ‘åŒūŒaƒ\ƒŒƒmƒCƒh‚q‚eƒRƒCƒ‹Œł”zŽ„źƒvƒ[ƒu‚ĢŠJ”­v
•½¬24”N9ŒŽ7“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(p-2-100)

 

323.ĪąVˆźŒ›CŽ›“cN•FC‹£Ÿ”ü
u‘½“_’TõŒ^Å“K‰»Žč–@‚É‚ę‚éŒł”zŽ„źƒRƒCƒ‹‚ĢŠJ”­v
•½¬24”N9ŒŽ7“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(p-2-101)

 

322.ˆī‘ŗ^–ēCŽ›“cN•FC‹£Ÿ”ü
u¬Ž™œ”N—īŒv‘Ŗ—p‚q‚eƒvƒ[ƒu‚ĢŠJ”­v
•½¬24”N9ŒŽ7“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(p-2-102)

 

321.–Ų‘ŗ•ŽjC‰ŗ‰Ę—SlC“”č_FCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”VCŒ·ŠŌ@—mC
£ŒĆąV—R•F
u‘¾—z“d’r‹ģ“®‚l‚q‚h‚ĢŠJ”­v
•½¬24”N9ŒŽ7“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(p-2-107)

 

320.‹Ź“c‘å‹PC‹£Ÿ”ü
uCartesianƒTƒ“ƒvƒ‹ƒŠƒ“ƒO–@‚É‚ę‚éCompressed Sensing‚š—p‚¢‚½‚l‚qƒ}ƒCƒNƒƒXƒRƒs[‚ĢŒŸ“¢v
•½¬24”N9ŒŽ7“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(o-2-200)

 

319.Ž›“cN•FC‰Ķ–ģŹ‹LC“ąŠC’m”üC‹£Ÿ”üC‹{é@—ŗCŽR•”‰psC‹g‰Ŗ@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚š—p‚¢‚Ä”»’肵‚½¬Ž™œ”N—ī‚ʍœ‘ĢĻ‚Ę‚ĢŠÖŒWv
•½¬24”N9ŒŽ7“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(p-2-144)

 

318.“ąŠC’m”üC‰Ķ–ģŹ‹LCŽ›“cN•FC‹£Ÿ”üC‹{é@—ŗCŽR•”‰psC‹g‰Ŗ@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚š—p‚¢‚½¬Ž™œ”N—ī‚Ģ•]‰æ‚É‚Ø‚Æ‚éŽB‘œŽžŠŌ’Zk‚ĢŒŸ“¢v
•½¬24”N9ŒŽ7“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(p-2-145)

 

317.Ž›“cN•FCĪąVˆźŒ›Cˆī‘ŗ^–ēC‰Ķ–ģŹ‹LC“ąŠC’m”üC‹Ź“c‘å‹PC‹£Ÿ”ü
u¬Ž™œ”N—īŒv‘Ŗ—pƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚ĢŠJ”­v
•½¬24”N9ŒŽ7“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(o-2-210)

 

316.‹Ź“c‘å‹PC‹£Ÿ”üC”qŽt’q”V
uƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚š—p‚¢‚½‚P‚s‰i‹vŽ„Ī‚ĢĆŽ„źƒVƒ~ƒ“ƒOv
•½¬24”N9ŒŽ7“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(o-2-211)

 

315.‹£Ÿ”üC–x‰ź‰ėŽjC‹Ź“c‘å‹PC‰ŗ‰Ę—SlCŽ›“cN•FC‹“–{Ŗ‘¾˜YC”qŽt’q”V
uNMR Microscopy‚É‚ę‚鐶‘Ģ‘gD‚Ģmicrostructure‚Ģ’Šov
•½¬24”N9ŒŽ8“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(p-3-237)

 

314.‰ŗ‰Ę—SlC–x‰ź‰ėŽjCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”VCŒ·ŠŌ@—mC£ŒĆ‘ņ—R•F.
u¶‘ĢƒTƒ“ƒvƒ‹‚Ģ¬’·‚É”ŗ‚¤‚m‚l‚qƒpƒ‰ƒƒ^•Ŗ•zŒv‘Ŗ‚ĘMicrostructure‚Ģ‰ĀŽ‹‰»v
•½¬24”N9ŒŽ8“śC‘ę40‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‹ž“s(p-3-238)

 

313.‰ŗ‰Ę—SlC–x‰ź‰ėŽjCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”VCŒ·ŠŌ@—mC£ŒĆ‘ņ—R•F
uMR microimaging ‚š—p‚¢‚½‰ŹŽĄ‚Ģ”÷×\‘¢‚Ę‚m‚l‚qƒpƒ‰ƒƒ^•Ŗ•z‚ĢŒv‘Ŗv
•½¬‚Q‚S”N‚WŒŽ‚R“śC‘ę‚P‚U‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‘å’Ć(o9)

 

312.‹£Ÿ”üC“c@“ցCX˜e@‘C‰ŗ‰Ę—SlCŽ›“cN•FC”qŽt’q”VC•yŠ~””n
uŽ÷–ŲŒv‘Ŗ—pƒ‚ƒoƒCƒ‹‚l‚q‚h‚ĢŠJ”­v
•½¬‚Q‚S”N‚WŒŽ‚R“śC‘ę‚P‚U‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‘å’Ć(o8)

 

311.‹Ź“c‘å‹PC‹£Ÿ”ü
u‚l‚qƒ}ƒCƒNƒƒXƒRƒs[‚Ģ‚½‚ß‚Ģ‚f‚o‚f‚o‚t—p‚¢‚½Compressed Sensing ƒAƒ‹ƒSƒŠƒYƒ€‚ĢŠJ”­v
•½¬‚Q‚S”N‚WŒŽ‚R“śC‘ę‚P‚U‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‘å’Ć(o7)

 

310.ĪąVˆźŒ›CŽ›“cN•FC‹£Ÿ”ü
uŒł”zŽ„źƒRƒCƒ‹ŻŒv‚É‚Ø‚Æ‚éˆā“`“IƒAƒ‹ƒSƒŠƒYƒ€‚Ę—±ŽqŒQÅ“K‰»–@‚Ģ”äŠrv
•½¬‚Q‚S”N‚WŒŽ‚R“śC‘ę‚P‚U‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‘å’Ć(p12)

 

309. ‰Ķ–ģŹ‹LC“ąŠC’m”üCŽ›“cN•FC‹£Ÿ”üC‹{é@—ŗCŽR•”‰psC‹g‰Ŗ@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚š—p‚¢‚½¬Ž™œ”N—ī”»’č–@‚ĢŒŸ“¢v
•½¬‚Q‚S”N‚WŒŽ‚R“śC‘ę‚P‚U‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‘å’Ć(p9)

 

308.ˆĄ’B@¹C‹Ź“c‘å‹PC‹£Ÿ”ü
u‚l‚q‚h‚Ģį•XŒ¤‹†‚Ö‚Ģ‰ž—p@|Ļį‚Ģ…•Ŗ“Į«‹Čü‚ĢŒv‘Ŗ[v
•½¬‚Q‚S”N‚WŒŽ‚R“śC‘ę‚P‚U‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‘å’Ć(p8)

 

307.”qŽt’q”V
u‹ó‹Cˆ³ƒZƒ“ƒT[‚š—p‚¢‚½ƒ}ƒEƒXS”ŒÄ‹zŒŸoŠķ‚ĢŠJ”­v
•½¬‚Q‚S”N‚WŒŽ‚R“śC‘ę‚P‚U‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‘å’Ć(p4)

 

306.‹ß“”‘å‹MCŽ›“cN•FC‹£Ÿ”ü
uœ–§“xŒv‘Ŗ—pƒRƒ“ƒpƒNƒg‚l‚q‚h‚É‚Ø‚Æ‚éŒv‘ŖÄŒ»«Œüć‚š–ŚŽw‚µ‚½‚q‚eƒRƒCƒ‹‚ĢŻŒvv
•½¬‚Q‚S”N‚WŒŽ‚R“śC‘ę‚P‚U‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‘å’Ć(p3)

 

305.“ąŠC’m”üC‰Ķ–ģŹ‹LCŽ›“cN•FC‹£Ÿ”üC‹{é@—ŗCŽR•”‰psC‹g‰Ŗ@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚É‚Ø‚Æ‚éŽB‘œ‚Ģ‚‘¬‰»‚ʏ¬Ž™‚Ģœ”N—ī•]‰æ‚Ö‚Ģ‰ž—pv
•½¬‚Q‚S”N‚WŒŽ‚R“śC‘ę‚P‚U‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‘å’Ć(p2)

 

304.ˆī‘ŗ^–ēCŽ›“cN•FC‹£Ÿ”ü
u¬Ž™œ”N—īŒv‘Ŗ—p‚q‚eƒvƒ[ƒu‚ĢÅ“K‰»v
•½¬‚Q‚S”N‚WŒŽ‚R“śC‘ę‚P‚U‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‘å’Ć(p1)

 

303.–x‰ź‰ėŽjCĪąVˆźŒ›C‹£Ÿ”ü
ucŒ^’““`“±Ž„Ī—p30‚‚Œaƒ\ƒŒƒmƒCƒhƒRƒCƒ‹‚š—p‚¢‚½Œł”zŽ„źƒvƒ[ƒu‚ĢŠJ”­v
•½¬‚Q‚S”N‚WŒŽ‚R“śC‘ę‚P‚U‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‘å’Ć(o4)

 

302.Ž›“cN•FC‰Ķ–ģŹ‹LCĪąVˆźŒ›Cˆī‘ŗ^–ēC“ąŠC’m”üC‹Ź“c‘å‹PC‹£Ÿ”ü
u‰i‹vŽ„Ī•Š‚ĘƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚š‘g‚Ż‡‚ķ‚¹‚½ĆŽ„źƒVƒ~ƒ“ƒOv
•½¬‚Q‚S”N‚WŒŽ‚R“śC‘ę‚P‚U‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‘å’Ć(o1)

 

301.Ž›“cN•FC‰Ķ–ģŹ‹LC“ąŠC’m”üCˆī‘ŗ^–ēC‹Ź“c‘å‹PC‹£Ÿ”üC‹g‰Ŗ@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒgMRI‚É‚ę‚鏬Ž™œ”N—īŒv‘Ŗ‚Ģ‰Ā”\«v
•½¬23”N11ŒŽ26“śC‘ę‚Q‚Q‰ń“ś–{¬’·Šw‰ļC˜a‰ĢŽR

 

300.Ž›“cN•FC‹Ź“c‘å‹PC‹£Ÿ”ü
u‰·“x‰Ā•ĻMRI‚ĢŠJ”­‚ʐ¶‘ĢƒTƒ“ƒvƒ‹‚ĢŠÉ˜aŽžŠŌ‚Ø‚ę‚ŃADCŒv‘Ŗ‚Ö‚Ģ‰ž—pv
•½¬23”N10ŒŽ1“śC‘ę39‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC¬‘q(p-3-220)

 

299.‹Ź“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
uƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚š—p‚¢‚½ƒoƒ‹ƒN’““`“±Ž„Ī‚ĢĆŽ„ź‹Ļˆź«‚Ģ‰ü‘Pv
•½¬23”N10ŒŽ1“śC‘ę39‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC¬‘q(p-3-219)

 

298.‹“–{Ŗ‘¾˜YC‹£Ÿ”üC”qŽt’q”V
u’įŽ„źƒRƒ“ƒpƒNƒgMRIƒVƒXƒeƒ€‚Ģ‚’‚†ƒfƒBƒWƒ^ƒ‹‰»‚É‚ę‚é‰ęŽæ‚Ģ‰ü‘Pv
•½¬23”N10ŒŽ1“śC‘ę39‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC¬‘q(p-3-218)

 

297.”qŽt’q”V
uƒRƒ“ƒpƒNƒgMRI—p‰i‹vŽ„Ī‚Ģ‚½‚ß‚Ģø–§‰·“x’²®Žč–@‚ĢŠJ”­v
•½¬23”N10ŒŽ1“śC‘ę39‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC¬‘q

 

296.–Ų‘ŗ•ŽjC‹£Ÿ”üC‰ŗ‰Ę—SlCŽ›“cN•FC”qŽt’q”VCŒ·ŠŌ@—mC£ŒĆąV—R•F
uƒRƒ“ƒpƒNƒgMRI—pŠJ•ĀŒ^ƒ\ƒŒƒmƒCƒhRFƒRƒCƒ‹‚ĢŠJ”­v
•½¬23”N10ŒŽ1“śC‘ę39‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC¬‘q(p-3-216)

 

295.‰ŗ‰Ę—SlC–Ų‘ŗ•ŽjC“”č_FCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”VCŒ·ŠŌ@—mC£ŒĆąV—R•F
u¶‘ĢƒTƒ“ƒvƒ‹‚Ģ¬’·‚É”ŗ‚¤ŠÉ˜aŽžŠŌ‚ĘADC‚ĢŒv‘Ŗv
•½¬23”N9ŒŽ30“śC‘ę39‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC¬‘q(p-2-148)

 

294.ŠŪŽRŒ\‰īC‹Ź“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
u‰~“›Œ^ƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ĢŠJ”­i‚QjFŽĄ‘•‚Ę•]‰æv
•½¬23”N9ŒŽ29“śC‘ę39‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC¬‘q(o-1-135)

 

293.‹Ź“c‘å‹PCŠŪŽRŒ\‰īC‹£Ÿ”ü
u‰~“›Œ^ƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ĢŠJ”­i‚PjFŻŒv‚Ę•]‰æv
•½¬23”N9ŒŽ29“śC‘ę39‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC¬‘q(o-1-134)

 

292.‹“–{Ŗ‘¾˜YC‹£Ÿ”üC”qŽt’q”V
uWindows-PC‚š—p‚¢‚½MRIƒpƒ‹ƒX”­¶‘•’u‚ĢŠJ”­v
•½¬23”N9ŒŽ29“śC‘ę39‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC¬‘q(o-1-132)

 

291.‹ß“”‘å‹MC‹Ź“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
u곍œœ–§“xŒv‘Ŗ—pƒRƒ“ƒpƒNƒgMRI‚É‚Ø‚Æ‚é‚RDFSE‚É‚ę‚éŒv‘ŖÄŒ»«‚ĢŒüćv
•½¬23”N9ŒŽ29“śC‘ę39‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC¬‘q(o-1-86)

 

290.‰Ķ–ģŹ‹LC“ąŠC’m”üCŽ›“cN•FC‹£Ÿ”üC‹g‰Ŗ@‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒgMRI‚š—p‚¢‚½¬Ž™œ”N—ī‚Ģ•]‰æv
•½¬23”N9ŒŽ29“śC‘ę39‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC¬‘q(o-1-85)

 

289.”qŽt’q”V
uĆŽ„ź‹­“x0.3TˆČ‰ŗ‚Ģl‘Ģ—pƒRƒ“ƒpƒNƒgMRI‚š—p‚¢‚½¬“®•ØMRI‚ĢŒŸ“¢v
•½¬23”N9ŒŽ29“śC‘ę39‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC¬‘q

 

288.’‡‘ŗ‚ŽuC¬ģ‹±•½CŽ›“cN•FC‹£Ÿ”üC”qŽt’q”V
u‚‰·’““`“±ƒoƒ‹ƒNŽ„Ī‚Å‚ĢMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOv
•½¬‚Q‚R”N‚WŒŽ‚T“śC‘ę‚P‚T‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰Ŗč

 

287.ˆĄ’B¹CŽRŒūŒåC¬ŠÖr_C‹£Ÿ”ü
uMRI‚Ģį•XŒ¤‹†‚Ö‚Ģ‰ž—p@\Ļį‚Ģ…•Ŗ•ŪŽ‹Čü‚ĢŒv‘Ŗ\v
•½¬‚Q‚R”N‚WŒŽ‚T“śC‘ę‚P‚T‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰Ŗč

 

286.‰Ķ–ģŹ‹LC“ąŠC’m”üCˆī‘ŗ^–ēCŽ›“cN•FC‹£Ÿ”üC‹g‰Ŗ‘å
uƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒgMRI‚š—p‚¢‚½¬Ž™œ”N—ī‚Ģ•]‰æv
•½¬‚Q‚R”N‚WŒŽ‚T“śC‘ę‚P‚T‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰Ŗč

 

285.‹ß“”‘å‹MC‹Ź“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
u곍œœ–§“xŒv‘Ŗ—pƒRƒ“ƒpƒNƒgMRI‚É‚Ø‚Æ‚é‚RDFSE‚É‚ę‚éŒv‘ŖÄŒ»«‚ĢŒüćv
•½¬‚Q‚R”N‚WŒŽ‚T“śC‘ę‚P‚T‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰Ŗč

 

284.–Ų‘ŗ•ŽjC‰ŗ‰Ę—SlC‹£Ÿ”üCŽ›“cN•FC”qŽt’q”VC•yŠ~””nCŒ·ŠŌ—mC£ŒĆąV—R•F
u‘¾—z“d’r‹ģ“®‚š‚ß‚“‚µ‚½’~“d’r‹ģ“®Œ^ƒ‚ƒoƒCƒ‹MRI‚ĢŠJ”­v
•½¬‚Q‚R”N‚WŒŽ‚T“śC‘ę‚P‚T‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰Ŗč

 

283.‹Ź“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
u—¬‚źŠÖ”–@‚š—p‚¢‚½‰~“›Œ^ƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ĢŻŒvv
•½¬‚Q‚R”N‚WŒŽ‚T“śC‘ę‚P‚T‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰Ŗč

 

282.Ž›“cN•FC‹Ź“c‘å‹PC‹£Ÿ”ü
u‰·“x‰Ā•ĻMRI‚ĢŠJ”­‚ʉŹŽĄ‚ĢNMRƒpƒ‰ƒƒ^Œv‘Ŗ‚Ö‚Ģ‰ž—pv
•½¬‚Q‚R”N‚WŒŽ‚T“śC‘ę‚P‚T‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰Ŗč

 

281.‰ŗ‰Ę—SlC–Ų‘ŗ•ŽjC“”č_FCŽ›“cN•FC‹£Ÿ”üCŒ·ŠŌ—mC£ŒĆąV—R•FC
u—œ‰ŹŽĄ‚Ģ¬’·‚É”ŗ‚¤ŠÉ˜aŽžŠŌ‚ĘADC‚ĢŒv‘Ŗv
•½¬‚Q‚R”N‚WŒŽ‚T“śC‘ę‚P‚T‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰Ŗč

 

280.ŠŪŽRŒ\‰īC‹Ź“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
ucŒ^’““`“±Ž„Ī—p‰~“›Œ^ƒVƒ“ƒOƒ‹ƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ĢŠJ”­v
•½¬‚Q‚R”N‚WŒŽ‚T“śC‘ę‚P‚T‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰Ŗč

 

279.”qŽt’q”V
uNdFebŒn¬Œ^MRIŽ„‹C‰ń˜H‚Ģ‰·“xƒhƒŠƒtƒg‚šŽ•ž‚·‚鍂ø“x‰·“xŠĒ—Žč–@‚ĢŠJ”­v
•½¬‚Q‚R”N‚WŒŽ‚T“śC‘ę‚P‚T‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC‰Ŗč

 

278.–Ų‘ŗ•ŽjC‰ŗ‰Ę—SlC‹£Ÿ”üCŽ›“cN•FC”qŽt’q”VC•xŠ~””nCŒ·ŠŌ—mC£ŒĆąV—R•F
uŽ÷–Ų—pŽ©‘–Ž®ƒ|[ƒ^ƒuƒ‹‚l‚q‚h‚ĢŠJ”­v
•½¬‚Q‚Q”N‚WŒŽ‚U“śC‘ę‚P‚S‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ‹ž

 

277.’†ŽR’‰–¾C‹ß“”‘å‹MCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”V
u곍œœ–§“xŒv‘Ŗ—pƒRƒ“ƒpƒNƒg‚l‚q‚h‚É‚Ø‚Æ‚éŒv‘ŖÄŒ»«‚ĢŒüćv
•½¬‚Q‚Q”N‚WŒŽ‚U“śC‘ę‚P‚S‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ‹ž

 

276.ˆĄ’B¹C”öŠÖr_C‹£Ÿ”ü
u‚l‚q‚h‚Ģį•XŒ¤‹†‚Ö‚Ģ‰ž—p\‚Ź‚źį’†‚Ģ…‚Ģ•Ŗ•zŽB‘œ‚ĢŽŽ‚Ż\‘“‚Ģ‚Qv
•½¬‚Q‚Q”N‚WŒŽ‚U“śC‘ę‚P‚S‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ‹ž

 

275.‹Ź“c‘å‹PC‹£Ÿ”ü
u‚e‚o‚f‚`‚š—p‚¢‚½‚t‚r‚a’ŹM‚l‚q‚hƒVƒXƒeƒ€‚ĢŠJ”­v
•½¬‚Q‚Q”N‚WŒŽ‚U“śC‘ę‚P‚S‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ‹ž

 

274.ŠŪŽRŒ\‰īC‹Ź“c‘å‹PC¬ģ‹±•½C‹£Ÿ”ü
ucŒ^’““d“±Ž„Ī—pŒł”zŽ„źƒRƒCƒ‹‚Ģˆā“`“IƒAƒ‹ƒSƒŠƒYƒ€‚É‚ę‚éÅ“K‰»ŻŒvv
•½¬‚Q‚Q”N‚WŒŽ‚U“śC‘ę‚P‚S‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ‹ž

 

273.¬ģ‹±•½C”¼“cW–ēC‹£Ÿ”ü
uˆā“`“IƒAƒ‹ƒSƒŠƒYƒ€‚š—p‚¢‚½•½–ŹŒ^Œł”zŽ„źƒRƒCƒ‹‚ĢÅ“K‰»ŻŒvv
•½¬‚Q‚Q”N‚WŒŽ‚U“śC‘ę‚P‚S‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ‹ž

 

272.‹Ź“c‘å‹PC‹£Ÿ”ü
u‚f‚o‚t‚š—p‚¢‚½‚R‚c‰ę‘œƒŠƒAƒ‹ƒ^ƒCƒ€‚l‚h‚oˆ—ƒVƒXƒeƒ€‚ĢŠJ”­v
•½¬‚Q‚Q”N‚WŒŽ‚U“śC‘ę‚P‚S‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC“Œ‹ž

 

271.¬ģ‹±•½C‹£Ÿ”ü
uˆā“`“IƒAƒ‹ƒSƒŠƒYƒ€‚š—p‚¢‚½•½–ŹŒ^Œł”zŽ„źƒRƒCƒ‹‚ĢÅ“K‰»ŻŒvv
•½¬‚Q‚Q”N‚XŒŽ‚R‚O“śC‘ę‚R‚W‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‚Ā‚­‚Ī

 

270.ŠŪŽRŒ\‰īC¬ģ‹±•½C‹Ź“c‘å‹PC‹£Ÿ”ü
ucŒ^’““`“±Ž„Ī—pŒł”zŽ„źƒRƒCƒ‹‚Ģˆā“`“IƒAƒ‹ƒSƒŠƒYƒ€‚É‚ę‚éÅ“K‰»ŻŒvv
•½¬‚Q‚Q”N‚XŒŽ‚R‚O“śC‘ę‚R‚W‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‚Ā‚­‚Ī

 

269.‹Ź“c‘å‹PC‹£Ÿ”ü
u‚e‚o‚f‚`‚š—p‚¢‚½‚k‚‰‚Ž‚•‚˜ƒx[ƒX‚l‚q‚hƒVƒXƒeƒ€‚ĢŠJ”­v
•½¬‚Q‚Q”N‚XŒŽ‚R‚O“śC‘ę‚R‚W‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‚Ā‚­‚Ī

 

268.’†ŽR’‰–¾C‹ß“”‘å‹MCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”V
u곍œœ–§“xŒv‘Ŗ—pƒRƒ“ƒpƒNƒg‚l‚q‚h‚É‚Ø‚Æ‚é‚s‚a‚u‚eÄŒ»«‚ĢŒüćv
•½¬‚Q‚Q”N‚XŒŽ‚R‚O“śC‘ę‚R‚W‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‚Ā‚­‚Ī

 

267.”qŽt’q”VC‹£Ÿ”ü
u¬“®•Ø—pƒxƒ“ƒ`ƒgƒbƒv‚l‚q‚h‚ĢŠJ”­v
•½¬‚Q‚Q”N‚XŒŽ‚R‚O“śC‘ę‚R‚W‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‚Ā‚­‚Ī

 

266.‹£Ÿ”üC”É–Ų—Ē‰ī
u’Pˆźƒ`ƒƒƒ“ƒlƒ‹ƒVƒ€ƒRƒCƒ‹‚ĢŠJ”­v
•½¬‚Q‚Q”N‚XŒŽ‚R‚O“śC‘ę‚R‚W‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‚Ā‚­‚Ī

 

265.–Ų‘ŗ•ŽjC‹£Ÿ”üC‰ŗ‰Ę—SlCŽ›“cN•FC”qŽt’q”VCŒ·ŠŌ@—mC£ŒĆąV—R•F
u‰i‹vŽ„Ī‚š—p‚¢‚½Ž©‘–Ž®ƒ‚ƒoƒCƒ‹‚l‚q‚h‚ĢŠJ”­v
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