‘“ąŠw‰ļ

578. Ž›“c@N•F
Image quality evaluation for MR images processed by AI
AI‚ÉŠÖ‚·‚éMRI‰ę‘œ‚Ģ•]‰æ
—ߘa5”N9ŒŽ22“ś`9ŒŽ24“śC‘ę51‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒyˆä‘ņCEL08-2
‹³ˆēu‰‰

 

577. Naoto Momiyama, Tomoyuki Haishi, Yasuhiko Terada
Development of single-port, inductively coupled 1H/23Na dual-tuned RF coils for small animals for 9.4 T vertical-bore superconducting MRI
¬“®•Ø‚š‘ĪŪ‚Ę‚µ‚½9.4TcŒ^ƒƒCƒhƒ{ƒAMRI—pƒVƒ“ƒOƒ‹ƒ|[ƒg1H/23Na Dual-Tuned RFƒRƒCƒ‹‚ĢŠJ”­
—ߘa5”N9ŒŽ22“ś`9ŒŽ24“śC‘ę51‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒyˆä‘ņCOS05-2
‘å‰ļ’·ÜŠī‘b•”–å—DGÜ

 

576. Kazuki Kunieda, Kazuyuki Makihara, Shigehito Yamada, Yasuhiko Terada
Brain finer structures of a human embryo depicted by MR microscopy with different contrasts
ˆŁ‚Č‚éƒRƒ“ƒgƒ‰ƒXƒg‚ĢMR microscopy‚É‚ę‚éƒqƒgćóŽq”]‚Ģ”÷×\‘¢‚Ģ•`o
—ߘa5”N9ŒŽ22“ś`9ŒŽ24“śC‘ę51‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒyˆä‘ņCOS09-5

 

575. Naoto Fujita, Suguru Yokosawa, Toru Shirai, Yasuhiko Terada
Model-based deep learning reconstruction for accelerating T2 mapping
ƒ‚ƒfƒ‹ƒx[ƒXDL‰ę‘œÄ\¬‚ĢŠg’£‚É‚ę‚éT2ƒ}ƒbƒsƒ“ƒO‚‘¬‰»‚ĢŒŸ“¢
—ߘa5”N9ŒŽ22“ś`9ŒŽ24“śC‘ę51‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒyˆä‘ņCOS23-1

 

574. Tsuyoshi Ueyama, Erika Takahashi, Naoto Fujita, Yuichi Suzuki, Hideyuki Iwanaga, Osamu Abe, Yasuhiko Terada
Distortion correction of diffusion weighted images by deep learning applying transformer
Transformer‚š‰ž—p‚µ‚½[‘wŠwK‚É‚ę‚éŠgŽU‹­’²‰ę‘œ‚Ģ˜c‚Ż•ā³‚ĢŠJ”­
—ߘa5”N9ŒŽ22“ś`9ŒŽ24“śC‘ę51‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒyˆä‘ņCOS24-3

 

573. Yuta Sugimoto, Naoto Fujita, Daiki Tamada, Satoshi Funayama, Shintaro Ichikawa, Satoshi Goshima, Yasuhiko Terada
Weakly supervised deep learning segmentation of the stomach and duodenum for the creation of 3D MRCP MIP images
3D MRCP MIP‰ę‘œ‚Ģģ¬‚š‘z’肵‚½Žć‹³Žt‚ ‚č[‘wŠwK‚É‚ę‚éˆŻ‚Ø‚ę‚я\“ńŽw’°‚Ģ’Šo
—ߘa5”N9ŒŽ22“ś`9ŒŽ24“śC‘ę51‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒyˆä‘ņCOS23-6

 

572. Hiroya Nakamura, Masayuki Yamaguchi, Yasuhiko Terada
Quantitative susceptibility mapping of Super Paramagnetic Iron Oxide (SPIO) at low and high fields
’įŽ„ź‚ʍ‚Ž„ź‰ŗ‚Å‚Ģ’“ķŽ„«Ž_‰»“S(SPIO)‚Ģ’č—Ź“IŽ„‰»—¦ƒ}ƒbƒsƒ“ƒO
—ߘa5”N9ŒŽ22“ś`9ŒŽ24“śC‘ę51‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒyˆä‘ņCPS10-3

 

571. Ziyu Fu, Naoto Fujita, Yasuhiko Terada
Comparing the robustness of a physics-guided MRI reconstruction neural network under self-supervised and fully-supervised learning paradigms
Ž©ŒČ‹³Žt‚ ‚čŠwK‚Ę‹³Žt‚ ‚čŠwK‰ŗ‚É‚Ø‚Æ‚éMRI‰ę‘œÄ\¬ƒjƒ…[ƒ‰ƒ‹ƒlƒbƒgƒ[ƒN‚ĢƒƒoƒXƒg«‚Ģ”äŠr
—ߘa5”N9ŒŽ22“ś`9ŒŽ24“śC‘ę51‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļCŒyˆä‘ņCPS11-3

 

570. “”“c’¼lC‰”‘ņ@rC”’’–@‹œCŽ›“cN•F
ƒ‚ƒfƒ‹ƒx[ƒXŒ^[‘wŠwK‰ę‘œÄ\¬‚É‚ę‚éMR’č—Ź’lƒ}ƒbƒsƒ“ƒO‚Ģ‚‘¬‰»‚ĢŒŸ“¢
—ߘa5”N7ŒŽ27“ś`7ŒŽ29“śC‘ę42‰ń“ś–{ˆć—p‰ę‘œHŠw‰ļ‘å‰ļC‘åćCOP9-4
‘å‰ļ§—ćÜ

 

569. –ąŽR’¼lCĪģ‰ė–ēC‚ģ’¼–ēCĪˆäŠ°’CŽ›“cN•F
A•Ø“€Œ‹—lŽ®‚Č‚Ē‚Ģ‰šĶ‚Ģ‚½‚ß‚Ģ40‚‚ƒ\ƒŒƒmƒCƒhƒRƒCƒ‹‚š—p‚¢‚½‰·“x‰Ā•ĻMRI‚ĢŠJ”­
—ߘa5”N6ŒŽ17“ś`6ŒŽ18“śC‘ę‚U‚W‰ń’į‰·¶•ØHŠw‰ļ‘å‰ļC‰Y˜aiƒIƒ“ƒ‰ƒCƒ“jCB10

 

568. Ž›“c@N•F
’įŽ„źMRI‚Ģ‹ß‹µ
—ߘa5”N3ŒŽ29“śCiŒöŽŠj“ś–{Ž„‹CŠw‰ļ@‘ę241‰ńŒ¤‹†‰ļ@‘ę8‰ńƒoƒCƒIƒ}ƒOƒlƒeƒBƒbƒNƒXź–匤‹†‰ļCƒIƒ“ƒ‰ƒCƒ“ŠJĆCP19-25

 

567.@ZNaoto Momiyama, Yoshikazu Okamoto, Yukiyo Shimizu, Yasuhiko Terada
Application of Mobile MRI to Upper Extremity Examination for Wheelchair Users
ƒ‚ƒoƒCƒ‹MRI‚ĢŽŌˆÖŽqƒ†[ƒU[Œü‚ÆćŽˆ‰^“®ŠķŒŸø‚Ö‚Ģ‰ž—p
—ߘa4”N9ŒŽ9“ś`9ŒŽ11“śC‘ę50‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC–¼ŒĆ‰®CDP09-3

 

566. ZNaoto Momiyama, Tomoyuki Haishi, Yasuhiko Terada
Development of 1H-23Na Dual-Tuned gradient Probe for 9.4T Vertical WideBore Superconducting MRI for Rat's body
9.4TcŒ^ƒƒCƒhƒ{ƒA¬‘Ģƒ‰ƒbƒg• •”—p1H-23NaDual-Tuned gradientƒvƒ[ƒu‚ĢŠJ”­
—ߘa4”N9ŒŽ9“ś`9ŒŽ11“śC‘ę50‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC–¼ŒĆ‰®CPP12-2

 

565. ZNaoto Fujita, Suguru Yokosawa, Toru Shirai, Yasuhiko Terada
Performance and generalizability of public deep learning models for multicoil image reconstruction.
ƒ}ƒ‹ƒ`ƒRƒCƒ‹‰ę‘œÄ\¬—pŒöŠJƒfƒB[ƒvƒ‰[ƒjƒ“ƒOƒ‚ƒfƒ‹ŠŌ‚Ģ«”\‚Ø‚ę‚єĉ»«”\‚Ģ•]‰æ
—ߘa4”N9ŒŽ9“ś`9ŒŽ11“śC‘ę50‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC–¼ŒĆ‰®COS02-2

 

564. ZErika Takahashi, Naoto Fujita, Keisuke Yoshida, Yasuhiko Terada
Distortion Correction Methods for Diffusion-Weighted Images Using Deep Learning
Deep Learning‚š—p‚¢‚½ŠgŽU‹­’²‰ę‘œ‚Ģ˜c‚Ż•ā³–@‚ĢŒŸ“¢
—ߘa4”N9ŒŽ9“ś`9ŒŽ11“śC‘ę50‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC–¼ŒĆ‰®COS10-3

 

563. ZMari Minami, Yasuhiko Terada
Development of planer gradients with cylindrical shielded gradients for vertical wide-bore superconducting magnets
cŒ^ƒƒCƒhƒ{ƒA’““d“±Ž„Ī—p‚Ģ‰~“›Œ^ƒV[ƒ‹ƒhƒRƒCƒ‹‚š“‹Ś‚µ‚½•½s•½”ĀŒ^Œł”zŽ„źƒRƒCƒ‹‚ĢŠJ”­
—ߘa4”N9ŒŽ9“ś`9ŒŽ11“śC‘ę50‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC–¼ŒĆ‰®CPP12-4

 

562. ZKazuki Kunieda, Kazuyuki Makihara, Shigehito Yamada, Yasuhiko Terada
High resolution MR microscopy of human embryo at 9.4 T
9.4 T‰ŗ‚É‚Ø‚Æ‚éƒqƒgćóŽq‚Ģ‚•Ŗ‰šMRƒ}ƒCƒNƒƒXƒRƒs[
—ߘa4”N9ŒŽ9“ś`9ŒŽ11“śC‘ę50‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC–¼ŒĆ‰®CPP13-3

 

561. Yasuhiko Terada
Advances in Magnetic Resonance Imaging: From Low-Field to High-Field and Back to Low-Field MRI
—ߘa4”N9ŒŽ6“ś`9ŒŽ8“śC‘ę46‰ń“ś–{Ž„‹CŠw‰ļŠwpu‰‰‰ļC’·–ģiMB‘åŠwjC07aA-1
µ‘Ņu‰‰

 

560. “”“c’¼lC‰”‘ņrC”’’–‹œCŽ›“cN•F
uMR ‰ę‘œÄ\¬—pƒfƒB[ƒvƒ‰[ƒjƒ“ƒOƒ‚ƒfƒ‹‚ĢƒƒoƒXƒg«‚Ģ•]‰æ
—ߘa4”N8ŒŽ8“śC’†‰›‘åŠwŒćŠy‰€ƒLƒƒƒ“ƒpƒXC‘ę26 ‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒnƒCƒuƒŠƒbƒhjCP4CP35-38

 

559. “ģ䝗¢CŽ›“cN•F
cŒ^ƒƒCƒhƒ{ƒA’““d“±Ž„Ī—p‚Ģ‰~“›Œ^ƒV[ƒ‹ƒhƒRƒCƒ‹‚š“‹Ś‚µ‚½•½s•½”ĀŒ^Œł”zŽ„źƒRƒCƒ‹‚ĢŠJ”­
—ߘa4”N8ŒŽ8“śC’†‰›‘åŠwŒćŠy‰€ƒLƒƒƒ“ƒpƒXC‘ę26 ‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒnƒCƒuƒŠƒbƒhjCO6CP25-28

 

558. š Ž}˜a‹PC–qŒ“˜aKC,ŽR“cdlC,’‡‘ŗ‚ŽuCŽ›“cN•F
ƒqƒgćóŽq•W–{ 3 ŽŸŒ³ƒAƒgƒ‰ƒX\’z‚ÉŒü‚Æ‚½ MR ƒ}ƒCƒNƒƒXƒRƒs[
—ߘa4”N8ŒŽ8“śC’†‰›‘åŠwŒćŠy‰€ƒLƒƒƒ“ƒpƒXC‘ę26 ‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒnƒCƒuƒŠƒbƒhjCOY2CP21-24

 

557. –ąŽR ’¼lCĪģ‰ė–ēCŽ›“cN•F
‰·“x‰Ā•ĻMR ƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚š—p‚¢‚½ ‚ŽRA•Ø‚Ģ“€Œ‹‰ß’ö‚Ģ‰šĶ
—ߘa4”N8ŒŽ8“śC’†‰›‘åŠwŒćŠy‰€ƒLƒƒƒ“ƒpƒXC‘ę26 ‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒnƒCƒuƒŠƒbƒhjCOY1CP11-14

 

556. Ž›“cN•FC“ģ䝗¢C–ąŽR’¼lCŽR“cdl
ƒ‚ƒfƒ‹ƒx[ƒXÄ\¬‚š—p‚¢‚½ŠgŽUƒeƒ“ƒ\ƒ‹ƒCƒ[ƒWƒ“ƒO‚É‚Ø‚Æ‚é‰ę‘œ˜c‚Ż•ā³
—ߘa4”N8ŒŽ8“śC’†‰›‘åŠwŒćŠy‰€ƒLƒƒƒ“ƒpƒXC‘ę26 ‰ń‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļiƒnƒCƒuƒŠƒbƒhjCO3CP7-10

 

555. –ąŽR’¼lC‚ģ’¼–ēCĪģ‰ė–ēCŽ›“cN•F
‰·“x‰Ā•ĻMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚š—p‚¢‚½‚ŽRA•Ø‚Ģ“€Œ‹‰ß’ö‚Ģ‰šĶ
—ߘa‚S”N6ŒŽ25“ś`6ŒŽ26“śC‘ę67‰ń’į‰·¶•ØHŠw‰ļCiB11jCP32

 

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) 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
4.7T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) 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‚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) P239
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) P228

 

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) 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^[‰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) P197

 

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) 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§–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) P182

 

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) P164

 

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
•½¬‚Q‚W”N‚XŒŽ‚X“śC‘ę‚S‚S‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‘å‹{@O-1-044 (p146)

 

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
•½¬‚Q‚W”N‚WŒŽ‚P‚O“śC‘ę‚Q‚O‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC”@OiP75-78j

 

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
•½¬‚Q‚W”N‚WŒŽ‚P‚O“śC‘ę‚Q‚O‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC”@OiP57-60j

 

436.¼ąVWŽ÷Cˆ¢•”[ŽuC‹£Ÿ”üCŽ›“cN•F
u¬Ž™œ”N—īŒv‘Ŗ—p‘ȉ~Œ`óŒł”zŽ„źƒRƒCƒ‹‚ĢŠJ”­v
•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
•½¬‚Q‚W”N‚WŒŽ‚P‚O“śC‘ę‚Q‚O‰ńNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ļC”@OiP49-52j

 

434.–ī–ģ‡–ēC¬—Ń—D‘¾CŽ›“cN•FC‹£Ÿ”ü
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433.ŠFģ@VCŽ›“cN•FC‹£Ÿ”ü
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432.‰Ŗ@C•½C‹£Ÿ”üCŽ›“cN•F
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429.‘…‹I‹vŽqC‹£Ÿ”üCŠÖ@Žu˜N
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428.’·“cW‰ĄC•Ÿ“cŒ’“ńC‹£Ÿ”üCŽ›“cN•F
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427.‹£Ÿ”ü
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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
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423.‹£—ŗˆźC‹£Ÿ”ü
uGPGPU‚š—p‚¢‚½‚‘¬MRI simulator‚ĢŠJ”­v
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422.’·“cW‰ĄC‘å’|—z‰īC‹£Ÿ”üCŽR“cdl
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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
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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
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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
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414.Ž›“cN•FCĪąVˆźŒ›C‹£Ÿ”ü
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413.‹£Ÿ”üC‹£—ŗˆźC”qŽt’q”V
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412.Ž™‹Ź“ŽC‹£Ÿ”ü
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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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408.£ŒĖˆäˆ»ŲC‹£Ÿ”ü
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407.–ī–ģ‡–ēC‹£Ÿ”üCŽ›“cN•FC”qŽt’q”V
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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
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405.•Ÿ“‡³—TCŽ›“cN•FC‹£Ÿ”ü
u곍œŽB‘œ—pRFƒvƒ[ƒu‚ĢŠJ”­v
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404.‹£Ÿ”üC‹£—ŗˆźC”qŽt’q”V
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403.Ž™‹Ź“ŽC‹£Ÿ”ü
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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
•½¬‚Q‚Q”N‚XŒŽ‚R‚O“śC‘ę‚R‚W‰ń“ś–{Ž„‹C‹¤–ĀˆćŠw‰ļ‘å‰ļC‚Ā‚­‚Ī

 

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