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578. Ž›“c@N•F
Image quality evaluation for MR images processed by AI
AI‚ÉŠÖ‚·‚éMRI‰æ‘œ‚Ì•]‰¿
—ߘa5”N9ŒŽ22“ú`9ŒŽ24“úC‘æ51‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒyˆä‘òCEL08-2
‹³ˆçu‰‰

 

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
¬“®•¨‚ð‘ÎÛ‚Æ‚µ‚½9.4TcŒ^ƒƒCƒhƒ{ƒAMRI—pƒVƒ“ƒOƒ‹ƒ|[ƒg1H/23Na Dual-Tuned RFƒRƒCƒ‹‚ÌŠJ”­
—ߘa5”N9ŒŽ22“ú`9ŒŽ24“úC‘æ51‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒyˆä‘òCOS05-2
‘å‰ï’·ÜŠî‘b•”–å—DGÜ

 

576. Kazuki Kunieda, Kazuyuki Makihara, Shigehito Yamada, Yasuhiko Terada
Brain finer structures of a human embryo depicted by MR microscopy with different contrasts
ˆÙ‚È‚éƒRƒ“ƒgƒ‰ƒXƒg‚ÌMR microscopy‚É‚æ‚éƒqƒgãóŽq”]‚Ì”÷×\‘¢‚Ì•`o
—ߘa5”N9ŒŽ22“ú`9ŒŽ24“úC‘æ51‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒyˆä‘òCOS09-5

 

575. Naoto Fujita, Suguru Yokosawa, Toru Shirai, Yasuhiko Terada
Model-based deep learning reconstruction for accelerating T2 mapping
ƒ‚ƒfƒ‹ƒx[ƒXDL‰æ‘œÄ\¬‚ÌŠg’£‚É‚æ‚éT2ƒ}ƒbƒsƒ“ƒO‚‘¬‰»‚ÌŒŸ“¢
—ߘa5”N9ŒŽ22“ú`9ŒŽ24“úC‘æ51‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒyˆä‘òCOS23-1

 

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‚ð‰ž—p‚µ‚½[‘wŠwK‚É‚æ‚éŠgŽU‹­’²‰æ‘œ‚̘c‚ݕⳂ̊J”­
—ߘa5”N9ŒŽ22“ú`9ŒŽ24“úC‘æ51‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒyˆä‘òCOS24-3

 

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‰æ‘œ‚Ì쬂ð‘z’肵‚½Žã‹³Žt‚ ‚è[‘wŠwK‚É‚æ‚éˆÝ‚¨‚æ‚Ñ\“ñŽw’°‚Ì’Šo
—ߘa5”N9ŒŽ22“ú`9ŒŽ24“úC‘æ51‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒyˆä‘òCOS23-6

 

572. Hiroya Nakamura, Masayuki Yamaguchi, Yasuhiko Terada
Quantitative susceptibility mapping of Super Paramagnetic Iron Oxide (SPIO) at low and high fields
’Ꭵê‚Æ‚Ž¥ê‰º‚Å‚Ì’´íŽ¥«Ž_‰»“S(SPIO)‚Ì’è—Ê“IŽ¥‰»—¦ƒ}ƒbƒsƒ“ƒO
—ߘa5”N9ŒŽ22“ú`9ŒŽ24“úC‘æ51‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒyˆä‘òCPS10-3

 

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©ŒÈ‹³Žt‚ ‚èŠwK‚Æ‹³Žt‚ ‚èŠwK‰º‚É‚¨‚¯‚éMRI‰æ‘œÄ\¬ƒjƒ…[ƒ‰ƒ‹ƒlƒbƒgƒ[ƒN‚̃ƒoƒXƒg«‚Ì”äŠr
—ߘa5”N9ŒŽ22“ú`9ŒŽ24“úC‘æ51‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïCŒyˆä‘òCPS11-3

 

570. “¡“c’¼lC‰¡‘ò@rC”’’–@‹œCŽ›“cN•F
ƒ‚ƒfƒ‹ƒx[ƒXŒ^[‘wŠwK‰æ‘œÄ\¬‚É‚æ‚éMR’è—Ê’lƒ}ƒbƒsƒ“ƒO‚Ì‚‘¬‰»‚ÌŒŸ“¢
—ߘa5”N7ŒŽ27“ú`7ŒŽ29“úC‘æ42‰ñ“ú–{ˆã—p‰æ‘œHŠw‰ï‘å‰ïC‘åãCOP9-4
‘å‰ï§—ãÜ

 

569. –àŽR’¼lCÎì‰ë–çC‚ì’¼–çCΈ䊰’CŽ›“cN•F
A•¨“€Œ‹—lŽ®‚Ȃǂ̉ðÍ‚Ì‚½‚ß‚Ì40‚‚ƒ\ƒŒƒmƒCƒhƒRƒCƒ‹‚ð—p‚¢‚½‰·“x‰Â•ÏMRI‚ÌŠJ”­
—ߘa5”N6ŒŽ17“ú`6ŒŽ18“úC‘æ‚U‚W‰ñ’ቷ¶•¨HŠw‰ï‘å‰ïC‰Y˜aiƒIƒ“ƒ‰ƒCƒ“jCB10

 

568. Ž›“c@N•F
’ᎥêMRI‚Ì‹ß‹µ
—ߘa5”N3ŒŽ29“úCiŒöŽÐj“ú–{Ž¥‹CŠw‰ï@‘æ241‰ñŒ¤‹†‰ï@‘æ8‰ñƒoƒCƒIƒ}ƒOƒlƒeƒBƒbƒNƒXê–匤‹†‰ïCƒIƒ“ƒ‰ƒCƒ“ŠJÃCP19-25

 

567.@ZNaoto Momiyama, Yoshikazu Okamoto, Yukiyo Shimizu, Yasuhiko Terada
Application of Mobile MRI to Upper Extremity Examination for Wheelchair Users
ƒ‚ƒoƒCƒ‹MRI‚ÌŽÔˆÖŽqƒ†[ƒU[Œü‚¯ãŽˆ‰^“®ŠíŒŸ¸‚ւ̉ž—p
—ߘa4”N9ŒŽ9“ú`9ŒŽ11“úC‘æ50‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC–¼ŒÃ‰®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
9.4TcŒ^ƒƒCƒhƒ{ƒA¬‘̃‰ƒbƒg• •”—p1H-23NaDual-Tuned gradientƒvƒ[ƒu‚ÌŠJ”­
—ߘa4”N9ŒŽ9“ú`9ŒŽ11“úC‘æ50‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC–¼ŒÃ‰®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‹‰æ‘œÄ\¬—pŒöŠJƒfƒB[ƒvƒ‰[ƒjƒ“ƒOƒ‚ƒfƒ‹ŠÔ‚Ì«”\‚¨‚æ‚єĉ»«”\‚Ì•]‰¿
—ߘa4”N9ŒŽ9“ú`9ŒŽ11“úC‘æ50‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC–¼ŒÃ‰®COS02-2

 

564. ZErika Takahashi, Naoto Fujita, Keisuke Yoshida, Yasuhiko Terada
Distortion Correction Methods for Diffusion-Weighted Images Using Deep Learning
Deep Learning‚ð—p‚¢‚½ŠgŽU‹­’²‰æ‘œ‚̘c‚Ý•â³–@‚ÌŒŸ“¢
—ߘa4”N9ŒŽ9“ú`9ŒŽ11“úC‘æ50‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC–¼ŒÃ‰®COS10-3

 

563. ZMari Minami, Yasuhiko Terada
Development of planer gradients with cylindrical shielded gradients for vertical wide-bore superconducting magnets
cŒ^ƒƒCƒhƒ{ƒA’´“d“±Ž¥Î—p‚̉~“›Œ^ƒV[ƒ‹ƒhƒRƒCƒ‹‚ð“‹Ú‚µ‚½•½s•½”ÂŒ^Œù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­
—ߘa4”N9ŒŽ9“ú`9ŒŽ11“úC‘æ50‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC–¼ŒÃ‰®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‰º‚É‚¨‚¯‚éƒqƒgãóŽq‚Ì‚•ª‰ðMRƒ}ƒCƒNƒƒXƒRƒs[
—ߘa4”N9ŒŽ9“ú`9ŒŽ11“úC‘æ50‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC–¼ŒÃ‰®CPP13-3

 

561. Yasuhiko Terada
Advances in Magnetic Resonance Imaging: From Low-Field to High-Field and Back to Low-Field MRI
—ߘa4”N9ŒŽ6“ú`9ŒŽ8“úC‘æ46‰ñ“ú–{Ž¥‹CŠw‰ïŠwpu‰‰‰ïC’·–ìiMB‘åŠwjC07aA-1
µ‘Òu‰‰

 

560. “¡“c’¼lC‰¡‘òrC”’’–‹œCŽ›“cN•F
uMR ‰æ‘œÄ\¬—pƒfƒB[ƒvƒ‰[ƒjƒ“ƒOƒ‚ƒfƒ‹‚̃ƒoƒXƒg«‚Ì•]‰¿
—ߘa4”N8ŒŽ8“úC’†‰›‘åŠwŒãŠy‰€ƒLƒƒƒ“ƒpƒXC‘æ26 ‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒnƒCƒuƒŠƒbƒhjCP4CP35-38

 

559. “ìä—¢CŽ›“cN•F
cŒ^ƒƒCƒhƒ{ƒA’´“d“±Ž¥Î—p‚̉~“›Œ^ƒV[ƒ‹ƒhƒRƒCƒ‹‚ð“‹Ú‚µ‚½•½s•½”ÂŒ^Œù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­
—ߘa4”N8ŒŽ8“úC’†‰›‘åŠwŒãŠy‰€ƒLƒƒƒ“ƒpƒXC‘æ26 ‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒnƒCƒuƒŠƒbƒhjCO6CP25-28

 

558. š Ž}˜a‹PC–qŒ´˜aKC,ŽR“cdlC,’‡‘º‚ŽuCŽ›“cN•F
ƒqƒgãóŽq•W–{ 3 ŽŸŒ³ƒAƒgƒ‰ƒX\’z‚ÉŒü‚¯‚½ MR ƒ}ƒCƒNƒƒXƒRƒs[
—ߘa4”N8ŒŽ8“úC’†‰›‘åŠwŒãŠy‰€ƒLƒƒƒ“ƒpƒXC‘æ26 ‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒnƒCƒuƒŠƒbƒhjCOY2CP21-24

 

557. –àŽR ’¼lCÎì‰ë–çCŽ›“cN•F
‰·“x‰Â•ÏMR ƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ð—p‚¢‚½ ‚ŽRA•¨‚Ì“€Œ‹‰ß’ö‚̉ðÍ
—ߘa4”N8ŒŽ8“úC’†‰›‘åŠwŒãŠy‰€ƒLƒƒƒ“ƒpƒXC‘æ26 ‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒnƒCƒuƒŠƒbƒhjCOY1CP11-14

 

556. Ž›“cN•FC“ìä—¢C–àŽR’¼lCŽR“cdl
ƒ‚ƒfƒ‹ƒx[ƒXÄ\¬‚ð—p‚¢‚½ŠgŽUƒeƒ“ƒ\ƒ‹ƒCƒ[ƒWƒ“ƒO‚É‚¨‚¯‚é‰æ‘œ˜c‚Ý•â³
—ߘa4”N8ŒŽ8“úC’†‰›‘åŠwŒãŠy‰€ƒLƒƒƒ“ƒpƒXC‘æ26 ‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïiƒnƒCƒuƒŠƒbƒhjCO3CP7-10

 

555. –àŽR’¼lC‚ì’¼–çCÎì‰ë–çCŽ›“cN•F
‰·“x‰Â•ÏMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ð—p‚¢‚½‚ŽRA•¨‚Ì“€Œ‹‰ß’ö‚̉ðÍ
—ߘa‚S”N6ŒŽ25“ú`6ŒŽ26“úC‘æ67‰ñ’ቷ¶•¨HŠw‰ïCiB11jCP32

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

494.Z¼è@~•½C”qŽt@’q”VCŽ›“c@N•F
ƒvƒŠƒ“ƒgŠî”‚ð—p‚¢‚½Œù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­
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iO10)P55-58

 

493.ZŽ›“c@N•FC’†ž@^—DC‰ª–{@‰Ãˆê
‰i‹vŽ¥Î MRI ‚É‚¨‚¯‚é[‘wŠwK‚ð—p‚¢‚½ƒmƒCƒYœ‹Ž‚ÌŒŸ“¢
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iP8jP47-50

 

492.Z‚ì@’¼–çCŽ›“c@N•F
4.7T/89mm ŠJŒûcŒ^’´“`“±Ž¥Î‚ð—p‚¢‚½‰·“x‰Â•ÏMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOƒVƒXƒeƒ€‚ÌŠJ”­
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iP4jP37-40

 

491.ZâŒû@˜a–çC¼àV@WŽ÷CŽ›“c@N•F
“ÁˆÙ’l•ª‰ð‚ƈâ“`“IƒAƒ‹ƒSƒŠƒYƒ€‚ð‘g‚݇‚킹‚½ ”CˆÓŒ`óŒù”zŽ¥êƒRƒCƒ‹‚ÌÝŒv‚¨‚æ‚ÑÅ“K‰»Žè–@‚ÌŠJ”­
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iP3jP33-36

 

490.Z²X–Ø@–¸ˆêCŽ›“c@N•F
1.5TŽlŽˆ—p MRI ‚É‚¨‚¯‚é Cartesian MR fingerprinting ‚ÌŽB‘œ‚‘¬‰»
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iP2jP29-32

 

489.›’†”ö ˆ¤CŽ›“c N•F
ŽlŽˆ—p MRI ‚É‚¨‚¯‚é non-Cartesian imaging –@‚ÌŠJ”­
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iP1jP25-28

 

488.›–xì —F•ãC•Ÿ“c Œ’“ñCŽ›“c N•F
MRI ‚ð—p‚¢‚½‰®ŠOŽ÷–Ø‚ÌŽ÷‰t—¬ƒCƒ[ƒWƒ“ƒOFL—tŽ÷‚Æj—tŽ÷‚Ì”äŠr
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iO2jP5-8

 

487.›’†ž ^—DC“c•Ó —ºŸC‰ª–{ ‰ÃˆêC¯‡ ‘s‘åCŽ›“c N•F
•’ÊŽÔ“‹ÚŒ^ƒ|[ƒ^ƒuƒ‹ MRI ‚ÌŠJ”­
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iO1jP1-4

 

486.’·“cW‰À,›•Ÿ“cŒ’“ñ,Ž›“cN•F
‰®ŠO‚ÉAÍ‚³‚ꂽƒPƒ„ƒL‚Ì–Ø•”EŽt•”‚ÌŽ÷‰t—¬‘¬‚̉Ž‹‰»
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485.¬—Ñ —D‘¾CŽ›“c@N•F
T 2 error originating from diffusion in MRF-FISP MRF-FISP‚É‚¨‚¯‚éŠgŽU‚É‹Nˆö‚·‚éT 2 „’è’l‚Ì’è—ÊŒë·
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P2-A4-016(p306)

 

484.²X–Ø –¸ˆêC¬—Ñ@—D‘¾CŽ›“c@N•F
Musculoskeletal MR Fingerprinting using a 1.5T/280mm small-bore MRI 1.5T/280mmƒXƒ‚[ƒ‹ƒ{ƒAMRI‚ð—p‚¢‚½œ“î•”‚Ö‚ÌMR Fingerprinting‚̉ž—p
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P2-B5-160(p378)

 

483.Ž›“c N•FC”qŽt@’q”V
Initial trials of relaxation time and ADC mapping of mice/rats MRF‚É‚æ‚é–ƒŒ‰ºƒ}ƒEƒX^ƒ‰ƒbƒg‚̊ɘaŽžŠÔ^ADCƒ}ƒbƒsƒ“ƒO‚̉ŠúŒŸ“¢
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P2-B6-191(p394)

 

482.–xì —F•ãC‹£@Ÿ”üCŽ›“c@N•F
Error evaluation of QSI measurements for widely-distributed flow L‚¢‘¬“x•ª•z‚ð‚à‚—¬‚ê‚ÌQSIŒv‘ª‚̌뷕]‰¿
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P2-B6-189 (p393)

 

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

 

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

 

479.’†ž ^—DC“c•Ó@—ºŸC‰ª–{ ‰ÃˆêC‹£@Ÿ”üCŽ›“c@N•F
Development of Sequence Generator for Portable MRI for Baseball Elbow Diagnosis –ì‹…•If’f—pƒ|[ƒ^ƒuƒ‹MRI—pƒV[ƒPƒ“ƒXƒWƒFƒlƒŒ[ƒ^[‚ÌŠJ”­
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P2-B2-180(p388)

 

478. ‹Ê“c ‘å‹PC‹£@—ºˆêC–{™ ‰F‘¾˜YC‹£@Ÿ”ü
Development of mathematical phantoms for MRI simulators MRIƒVƒ~ƒ…ƒŒ[ƒ^‚ɑΉž‚µ‚½”—ƒtƒ@ƒ“ƒgƒ€‚̶¬Žè–@‚ÌŒŸ“¢
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P2-B1-084(p340)

 

477. £ŒËˆä ˆ»ØC‹£@Ÿ”ü
Ultrashort echo-time imaging at 1.5 T using an insertable gradient coil and 3D cones trajectory ‘}“üŒ^Œù”zŽ¥êƒRƒCƒ‹‚Æ3D cones trajectory‚ð—p‚¢‚½’´’ZƒGƒR[ƒ^ƒCƒ€ƒCƒ[ƒWƒ“ƒO
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476.‹£ —ºˆêC£ŒËˆä@ˆ»ØC‹£@Ÿ”ü
GPU-optimized 3D fast MRI simulator for non-Cartesian sampling GPU‚ð—p‚¢‚½”ñƒfƒJƒ‹ƒgÀ•WŒnƒTƒ“ƒvƒŠƒ“ƒO‘Ήž3D‚‘¬MRI simulator‚ÌŠJ”­
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475. ¬—Ñ —D‘¾C‹£@Ÿ”üCŽ›“c@N•F
Development of a field camera system for a 1.5T/280mm superconducting magnet system and application to fast imaging method 1.5T/280mm@’´“`“±Ž¥Î‚É‚¨‚¯‚éfield cameraƒVƒXƒeƒ€‚ÌŠJ”­‚Æ‚‘¬ŽB‘œ–@‚ւ̉ž—p
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474. “c•Ó —ºŸC‰ª–{ ‰ÃˆêC‹£@Ÿ”üCŽ›“c@N•F
Development of portable MRI for early detection of baseball elbow(2)@–ì‹…•I‰Šúf’f—pƒ|[ƒ^ƒuƒ‹MRI‚ÌŠJ”­i‚Qj
•½¬‚Q‚X”N‚XŒŽ‚P‚S“úC‘æ‚S‚T‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‰F“s‹{CO1-40(P188)

 

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

 

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

 

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

 

468.‹£ Ÿ”üC‹£ —ºˆê
uMRI simulator ‚ð—p‚¢‚½ MP - RAGE ‚ÌÅ“K‰»‚ÌŽŽ‚Ý v
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467.¬—Ñ —D‘¾ C ‹£ Ÿ”ü C Ž›“c N•F
u1.5T/280mm ’´“`“±Ž¥Î‚É‚¨‚¯‚é field camera ƒVƒXƒeƒ€ŠJ”­ v
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466.“c•Ó —ºŸC ‰ª–{ ‰ÃˆêC‹£ Ÿ”üCŽ›“c N•F
u–ì‹…•If’f—pƒ|[ƒ^ƒuƒ‹ MRI ‚ÌŠJ”­ v
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465.‹Ê“c ‘å‹PC‹£ —ºˆêC‹£ Ÿ”üC–{™ ‰F‘¾˜Y
u”—ƒtƒ@ƒ“ƒgƒ€‚ÌŽÀŒ»‚ð–Ú“I‚Æ‚µ‚½”] MRI ‰æ‘œ‚Ì‘gDŽ©“®•ª—ÞŽè–@‚ÌŠJ”­ v
•½¬‚Q‚X”N‚WŒŽ‚V“úC‘æ‚Q‚P‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P5)P37-40

 

464.‹£ —ºˆêC£ŒËˆä ˆ»ØC‹£ Ÿ”ü
uGPU ‚ð—p‚¢‚½”ñƒfƒJƒ‹ƒgÀ•WŒnƒTƒ“ƒvƒŠƒ“ƒO‚ɑΉž‚µ‚½ 3D MRI simulator ‚ÌŠJ”­ v
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463.£ŒËˆä ˆ»ØC ‹£ Ÿ”ü
u1.5T …•½ŠJŒû’´“`“±Ž¥Î‚É‚¨‚¯‚é 3D Cones –@‚ւ̉Q“d—¬‚̉e‹¿ v
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462.¼è ~•½C”qŽt ’q”VC‹£ Ÿ”üCŽ›“c N•F
uƒvƒŠƒ“ƒgŠî”‚ð—p‚¢‚½‰~“›Œ^ƒV[ƒ‹ƒhƒOƒ‰ƒWƒGƒ“ƒgƒRƒCƒ‹‚ÌŠJ”­ v
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461.–xì —F•ãC‹£ Ÿ”üCŽ›“c N•F
uq-space imaging ‚ð—p‚¢‚½‰®ŠOŽ÷–Ø‚ÌŽ÷‰t—¬Œv‘ª v
•½¬‚Q‚X”N‚WŒŽ‚V“úC‘æ‚Q‚P‰ñ‚m‚l‚qƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P10)P55-58

 

460.’†ž ^—DC“c•Ó —ºŸC‰ª–{ ‰ÃˆêC‹£ Ÿ”üCŽ›“c N•F
u–ì‹…•If’f—pƒ|[ƒ^ƒuƒ‹ MRI ‚É‚¨‚¯‚é ƒVƒ“ƒOƒ‹ƒIƒuƒŠ[ƒN‹@”\•t‚«ƒV[ƒPƒ“ƒXƒWƒFƒlƒŒ[ƒ^[‚ÌŠJ”­ v
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459.Ž›“cN•F
uMRI‚ð—p‚¢‚½‰®ŠOŽ÷–Ø‚ÌŽ÷‰t—¬‚̉Ž‹‰»v
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458.Ž›“cN•F
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456.ŠFì@VCŽ›“cN•FC‹£Ÿ”ü
u‚l‚q‚h‚ð—p‚¢‚½¬Ž™œ”N—îŒv‘ª‚Ö‚ÌDeep Learning‚̉ž—pv
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455.’·“cW‰ÀC•Ÿ“cŒ’“ñC‹£Ÿ”üCŽ›“cN•F
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•½¬‚Q‚W”N‚P‚PŒŽ‚P‚U“úC‘æ‚T‚T‰ñ‚m‚l‚q“¢˜_‰ïCL“‡@P82ip292-295)
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u‘ȉ~“›Œ`Œù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­‚ƬŽ™œ”N—îŒv‘ª‚ւ̉ž—pv
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453.–î–쇖çC¬—Ñ—D‘¾CŽ›“cN•FC‹£Ÿ”ü
u1.5T/280mm…•½ŠJŒûŒ^’´“`“±Ž¥Î‚Ì‚½‚߂̬“®•¨—pŒù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­‚Ɖž—pv
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452.Ž›“cN•F
uIntroduction to MR Fingerprinting
MR fingerprinting“ü–åv
•½¬‚Q‚W”N‚XŒŽ‚P‚O“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@EL9-1 (p117)
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451.Ž›“cN•F
uMR microscopy accelerated by MR Fingerprinting
MRŽw–ä–@‚É‚æ‚éNMRƒ}ƒCƒNƒƒXƒRƒs[‚Ì‚‘¬‰»v
•½¬‚Q‚W”N‚XŒŽ‚P‚O“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@O-2-076 (p218)

 

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

 

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

 

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

 

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

 

446.£ŒËˆäˆ»ØC‹£Ÿ”ü
uDevelopment of spiral imaging for a 1.5T/280mm horizontal bore superconducting magnet
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445.£ŒËˆäˆ»ØCŽ™‹Ê“ÞC‹£Ÿ”ü
uSpiral imaging for a 9.4T/54mm vertical bore superconducting magnet
9.4T/54mmcŒ^ŠJŒûŒa’´“`“±Ž¥Î‚É‚¨‚¯‚éSpiral imaging‚ÌŠJ”­v
•½¬‚Q‚W”N‚XŒŽ‚X“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@P-1-025 (p251)

 

444.‹£Ÿ”üC‹£—ºˆê
uPotential and Problems of MRI simulators
MRI simulator‚̉”\«‚Æ–â‘è“_v
•½¬‚Q‚W”N‚XŒŽ‚P‚O“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@P-2-019 (p270)

 

443.ŽR“c—È‘¾C‹£Ÿ”üCŽ›“cN•F
u Development of a multi-circular shimming system for a 1.5 T/280 mm horizontal bore superconducting magnet
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442.‹£Ÿ”üC‹£—ºˆêC”qŽt’q”V
uDevelopment of the MRI software platform (II)
MRI software platform ‚ÌŠJ”­i2j v
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441.¼àVWŽ÷Cˆ¢•”[ŽuC‹£Ÿ”üCŽ›“cN•F
u Design of oval gradient coils using current potential and singular value decomposition
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440.’·“cW‰ÀCŽ›“cN•FC‹£Ÿ”ü
u Error in QSI analysis for slow flow in a noisy environment
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•½¬‚Q‚W”N‚XŒŽ‚X“úC‘æ‚S‚S‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‘å‹{@P-1-014 (p245)

 

439.Ž›“cN•F
u14.1T ‚É‚¨‚¯‚é MR fingerprintingv
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438.‹£—ºˆêC‹£Ÿ”ü
uGPU ‚ð—p‚¢‚½’´‚‘¬ MRI ƒVƒ~ƒ…ƒŒ[ƒ^‚ÌŠJ”­v
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437.¬—Ñ—D‘¾C‹£Ÿ”üCŽ›“cN•F
uField camera ‚ð—p‚¢‚½ k - space trajectory ‚ÌŒv‘ªv
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436.¼àVWŽ÷Cˆ¢•”[ŽuC‹£Ÿ”üCŽ›“cN•F
u¬Ž™œ”N—îŒv‘ª—p‘ȉ~Œ`óŒù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­v
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435.ŽR“c—È‘¾CŽ›“cN•FC‹£Ÿ”ü
u1.5T/280mm ’´“`“±Ž¥Î—p ƒ}ƒ‹ƒ`ƒT[ƒLƒ…ƒ‰[ƒVƒ€ ƒRƒCƒ‹ ƒVƒXƒeƒ€‚Ì ŠJ”­v
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434.–î–쇖çC¬—Ñ—D‘¾CŽ›“cN•FC‹£Ÿ”ü
u1.5T/280mm …•½ŠJŒûŒ^’´“`“±Ž¥Î‚Ì‚½‚ß‚Ì‘}“üŒ^Œù”zŽ¥êƒRƒCƒ‹‚Ì»ì‚Æ•]‰¿v
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433.ŠFì@VCŽ›“cN•FC‹£Ÿ”ü
uMRI‚ð—p‚¢‚½¬Ž™œ”N—’è‚Ö‚ÌDeep Learning‚̉ž—p‚ÌŒŸ“¢v
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432.‰ª@C•½C‹£Ÿ”üCŽ›“cN•F
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431.“c•Ó—ºŸC‰ª@C•½C‹£Ÿ”üCŽ›“cN•F
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430.£ŒËˆäˆ»ØC‹£Ÿ”ü
u1.5T/280mm…•½ŠJŒû’´“`“±Ž¥Î‚É‚¨‚¯‚éƒXƒpƒCƒ‰ƒ‹ƒXƒLƒƒƒ“‚ÌŽÀ‘•v
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429.‘…‹I‹vŽqC‹£Ÿ”üCŠÖ@Žu˜N
u‚•ªŽq“d‰ðŽ¿‚̃Šƒ`ƒEƒ€ƒCƒIƒ“i 7 Li j‚ƃAƒjƒIƒ“ ( 19 F) ‚ÌŠgŽU‚ƈꎟŒ³ profile ‚ÌŠÏ‘ªv
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428.’·“cW‰ÀC•Ÿ“cŒ’“ñC‹£Ÿ”üCŽ›“cN•F
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427.‹£Ÿ”ü
uMR microscopy‚ð—p‚¢‚½‰æ‘œƒf[ƒ^ƒx[ƒX\’z‚Ì‚½‚߂̃qƒgãóŽqŽB‘œv
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426.’Óc^lC‹£Ÿ”ü
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425.ŠFì@VCŽ›“cN•FC‹£Ÿ”üCŽR“cdl
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424.’·“cW‰ÀCŽ›“cN•FC‹£Ÿ”ü
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423.‹£—ºˆêC‹£Ÿ”ü
uGPGPU‚ð—p‚¢‚½‚‘¬MRI simulator‚ÌŠJ”­v
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422.’·“cW‰ÀC‘å’|—z‰îC‹£Ÿ”üCŽR“cdl
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421.Ž›“cN•FC‹Ê“c‘å‹PCÎ@Œ\ˆê˜YC‹£Ÿ”üC–ì葾ŠóC‹àŽqNmC‹{é@—ºC‹g‰ª@‘å
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420.¼àVWŽ÷CŽ›“cN•FC‹£Ÿ”ü
uDUCAS(Design tools Using Current potential And SVD)‚ð —p‚¢‚½ƒOƒ‰ƒWƒGƒ“ƒgƒRƒCƒ‹—p GUIÝŒvƒ\ƒtƒgƒEƒFƒA‚ÌŠJ”­v
•½¬‚Q‚V”N‚XŒŽ‚P‚P“úC‘æ‚S‚R‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“Œ‹ž@iP-2-011jp358

 

419.–î–쇖çCŽ›“cN•FC‹£Ÿ”üC”qŽt’q”V
uEcho Planar Imaging‚ð—p‚¢‚½1.0T‰i‹vŽ¥ÎMRIƒVƒXƒeƒ€‚Ì•]‰¿v
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418.ŽR“c—È‘¾CŽ›“cN•FC‹£Ÿ”ü
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417.¼àVWŽ÷CŽ›“cN•FC‹£Ÿ”ü
uPC-MRIƒRƒ“ƒgƒ[ƒ‰[‚ð—p‚¢‚½‰Q“d—¬§ŒäŽè–@‚ÌŠJ”­v
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416.”qŽt’q”VC‹´–{ª‘¾˜YC–î–쇖çCŽ›“cN•FC‹£Ÿ”ü
u1.0T‰i‹vŽ¥Î‚É‚æ‚éEcho Planar Imaging‚ð—p‚¢‚½¶‘̃}ƒEƒX‚ÌŽB‘œv
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415.•Ÿ“‡³—TCŽ›“cN•FC‹£Ÿ”ü
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414.Ž›“cN•FCÎàVˆêŒ›C‹£Ÿ”ü
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413.‹£Ÿ”üC‹£—ºˆêC”qŽt’q”V
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412.Ž™‹Ê“ÞC‹£Ÿ”ü
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411.ŽR“c—È‘¾CŽ›“cN•FC‹£Ÿ”ü
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410.ŠFì@VCŽ›“cN•FC‹£Ÿ”ü
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409.’·“cW‰ÀCŽ›“cN•FC‹£Ÿ”üC£ŒÃ‘ò—R•F
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408.£ŒËˆäˆ»ØC‹£Ÿ”ü
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407.–î–쇖çC‹£Ÿ”üCŽ›“cN•FC”qŽt’q”V
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406.’Óc^lCŽ›“cN•FC‹£Ÿ”ü
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405.•Ÿ“‡³—TCŽ›“cN•FC‹£Ÿ”ü
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404.‹£Ÿ”üC‹£—ºˆêC”qŽt’q”V
uMRI software platformiMRI“‡ŠJ”­ŠÂ‹«j‚ÌŠJ”­
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403.Ž™‹Ê“ÞC‹£Ÿ”ü
u9.4TcŒ^’´“`“±Ž¥ÎMRI‚É‚¨‚¯‚é’´‚‘¬ƒCƒ[ƒWƒ“ƒO‚ÌŒŸ“¢[EPI‚ÆSpiral‚Ì”äŠr[
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402.’·“cW‰ÀCŽ›“cN•FC‹£Ÿ”ü
u0.2T‰i‹vŽ¥ÎMRI‚ð—p‚¢‚½‰®ŠOŽ÷–Ø“àŽ÷‰t‚Ì—¬‘¬‘ª’èv
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401.¼àVWŽ÷CŽ›“cN•FC‹£Ÿ”üCˆ¢•”[Žu
u•½”ÂŒ^Œù”zŽ¥êƒRƒCƒ‹ÝŒv‚É‚¨‚¯‚éDUCAS‚Ì«”\•]‰¿v
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400.Ž›“cN•F
uMR Fingerprinting ‚ÌŽŽ‚Ýv
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399.‹£Ÿ”ü
uMRIŒ¤‹†‚ÌŒ»ó‚Æ“W–]v
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µ‘Òu‰‰

 

398.‹£Ÿ”ü
uHigh-Field MR Microscopy of Chemically Fixed Human Embryosv
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µ‘Òu‰‰

 

397.’Óc^lC‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
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Development of a digital MRI system using general purpose digital units and board computers gArduino Dueh@
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396.Ž›“cN•F
uMR microscopy‚É‚æ‚éA•¨‚Ìin situŽlŽŸŒ³ŠÏŽ@v
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395.X˜e@‘C”–ˆäŽÀC‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”üC£ŒÃàV—R•F
uMRI‚ð—p‚¢‚½ƒjƒzƒ“ƒiƒV‰ÊŽÀˆÛŠÇ‘©\‘¢‚̉ðÍv
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394. X˜e@‘CŽ›“cN•FC‹£Ÿ”üC”qŽt’q”VC£ŒÃàV—R•F
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393.‹£Ÿ”üC‘å’|—z‰îCŽR“c‘ñ”nC”qŽt’q”VCŽR“cdl
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392.“c@“ÖCŽ›“cN•FC‹£Ÿ”ü
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391.’Óc^lC‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”ü
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390.‹£Ÿ”üC”qŽt’q”V
u9.4T/54mmŠJŒûcŒ^’´“`“±Ž¥Î‚ð—p‚¢‚½MR microscope‚ÌŠJ”­v
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389.㑺–í–çCŽ›“cN•FC‹£Ÿ”ü
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388. ‹Ê“c‘å‹PC‹£Ÿ”ü
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387.Ž™‹Ê“ÞCŽ›“cN•FC‹£Ÿ”ü
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386.Ž›“cN•FCˆî‘º^–çC‹£Ÿ”üC‹{é@—ºC“¡‰iN¬C‹g‰ª@‘å
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385.‘å’|—z‰îCŽR“c‘ñ”nC‹£Ÿ”üC”qŽt’q”VCŽR“cdl
u9.4T/54mmŠJŒûcŒ^’´“`“±Ž¥Î‚ð—p‚¢‚½ƒqƒgãóŽq•W–{Œü‚¯‚•ª‰ð”\MRI‚ÌŠJ”­v
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384.ŽR“c‘ñ”nC‹£Ÿ”üCŽ›“cN•F
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383.”qŽt’q”VCÎàVˆêŒ›C‹£Ÿ”ü
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382.ÎŒ\ˆê˜YCŽ›“cN•FC‹£Ÿ”ü
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381.Ž›“cN•FC”qŽt’q”VC‹£Ÿ”ü
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380.ƒAƒNƒ‰ƒ€@ƒGƒ€ƒfƒB@ƒVƒƒƒnƒ_ƒg@ƒzƒTƒCƒ“CŽ›“cN•FC‹£Ÿ”ü
uCoupled circuit simulation of Z-and X-gradient eddy currents in a 9.4T narrow-bore MRI systemv
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379.ƒAƒNƒ‰ƒ€@ƒGƒ€ƒfƒB@ƒVƒƒƒnƒ_ƒg@ƒzƒTƒCƒ“CŽ›“cN•FC‹£Ÿ”ü
uTemporal-spatial responses of planar X-gradient eddy currents by solid angle coupled circuit methodv
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siO-2-257jp275

 

378.Ž›“cN•FC‹Ê“c‘å‹PCÎŒ\ˆê˜YC‹£Ÿ”üC–ìè‘åŠóC‹àŽqNmC‹g‰ª@‘å
uCS‚ð—p‚¢‚½¬Ž™œ”N—îŒv‘ª‚Ì‚‘¬‰»‚ÌŒŸ“¢v
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377.‹Ê“c‘å‹PC‹£Ÿ”üCŽáŽR“N–çCXã—T”VCŽsìV‘¾˜YC²–쟜ACŽsì’qÍC–{™‰F‘¾˜Y
uCompressed Sensing‚ÆL1-SPIRiT‚ð—p‚¢‚½• •”3DGREƒCƒ[ƒWƒ“ƒOv
•½¬‚Q‚U”N‚XŒŽ‚P‚X“úC‘æ‚S‚Q‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC‹ž“siO-2-229jp261

 

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

 

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

 

373.‹Ê“c‘å‹PC’‡‘º‚ŽuC‹£Ÿ”ü
u‚‰·’´“`“±ƒoƒ‹ƒNŽ¥Î‚É‚¨‚¯‚éƒ}ƒCƒXƒi[Œø‰Ê‚ðl—¶‚µ‚½Œù”zŽ¥êƒRƒCƒ‹‚ÌŠJ”­v
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372.‹£Ÿ”üC”qŽt’q”V
u9.4T/54mmƒ{ƒAcŒ^’´“`“±Ž¥Î‚ð—p‚¢‚½MR microscope‚Ì\’zv
•½¬‚Q‚U”N‚WŒŽ‚P‚Q“úC‘æ‚P‚W‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC‹à‘òiO8)p53-56

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

356.”qŽt’q”VAˆ¢•”‹ÓŽjA•yŠ~””nA‹£Ÿ”üA‹vP’C”Ž
uŠùÝ‚Ì14.1T-NMR‘•’u‚ðŠˆ—p‚µ‚½MRMICS‚É‚æ‚éƒ}ƒEƒX”]ƒCƒ[ƒWƒOv
•½¬‚Q‚T”N‚XŒŽC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(P-3-246)
355.‹£Ÿ”ü
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•½¬‚Q‚T”N‚XŒŽC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(EL8-1)
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354.ƒGƒ€ƒfƒBEƒVƒƒƒnƒ_ƒgEƒzƒTƒCƒ“EƒAƒNƒ‰ƒ€AŽ›“cN•FAÎŒ\ˆê˜YA‹£Ÿ”ü
uEddy Current Analysis of 0.3 T Permanent Magnet MRI Systems with Planar Gradient Coilv
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353.ÎàVˆêŒ›A‹£Ÿ”üAŽ›“cN•F
uMRI—p•½”ÂŒ^Œù”zŽ¥êƒRƒCƒ‹‚ÌÅ“K‰»Žè–@‚ÌŒ¤‹†v
•½¬‚Q‚T”N‚XŒŽC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(O-01-116)

 

352.“àŠC’m”üA‹Ê“c‘å‹PAŽ›“cN•FA‹£Ÿ”üA‹{é@—ºAŽR•”‰psA“¡‰iN¬A
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351.ˆî‘º^–çAŽ›“cN•FA‹£Ÿ”ü
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350.Ž›“cN•FAÎàVˆêŒ›Aˆî‘º^–çAÎŒ\ˆê˜YA‹£Ÿ”ü
u¬Ž™œ”N—îŒv‘ª—p0.3Tƒ|[ƒ^ƒuƒ‹MRI‚ÌŠJ”­v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“úC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(O-01-110)

 

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

 

347.X˜e@‘A‹£Ÿ”üA”qŽt’q”VA£ŒÃàV—R•F
uMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒO‚É‚æ‚鶑̎Ž—¿‚Ì”÷×\‘¢‚̉Ž‹‰»v
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346.Ž›“cN•FA‰Í–ìÊ‹LA“àŠC’m”üAˆî‘º^–çA‹£Ÿ”üA‹{é@—ºAŽR•”‰psA“¡‰i@N¬A‹g‰ª@‘å
u¬Ž™œ”N—îŒv‘ª—pƒI[ƒvƒ“Œ^ƒRƒ“ƒpƒNƒg‚l‚q‚h‚Ì—L—p«v
•½¬‚Q‚T”N‚XŒŽ‚Q‚O“úC‘æ‚S‚P‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ïC“¿“‡(P-02-184)

 

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

 

344.‹´–{ª‘¾˜Y A‹£Ÿ”üA”qŽt’q”V
u ‚Ž¥ê‚•ª‰ð”\‚l‚q‚h‚É‚¨‚¯‚éƒfƒWƒ^ƒ‹ƒgƒ‰ƒ“ƒV[ƒo[‚Ì•]‰¿v
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343.“c@“ÖAŽ›“cN•FA‹£Ÿ”ü
uƒI[ƒvƒ“Œ^RFƒvƒ[ƒu‚ÌŠJ”­‚Æin situŽB‘œ‚ւ̉ž—pv
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342.ŽR“c‘ñ”nA‹£Ÿ”ü
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341.ÎŒ\ˆê˜YAŽ›“cN•FA‹£Ÿ”ü
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340.“àŠC’m”üC‹Ê“c‘å‹PCŽ›“cN•FC‹£Ÿ”üC‹{é—ºCŽR•”‰psC“¡‰iN¬C‹g‰ª‘å
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•½¬‚Q‚T”N‚WŒŽ‚Q“úC‘æ‚P‚V‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(P13)p63-p64

 

339.ˆî‘º^–çCŽ›“cN•FC‹£Ÿ”ü
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338.ÎàVˆêŒ› CŽ›“cN•F C‹£Ÿ”ü
u“±‘Ì•‚ðl—¶‚µ‚½•½”ÂŒ^ƒOƒ‰ƒWƒGƒ“ƒgƒRƒCƒ‹‚Ì“d—ÍÅ“K‰»ÝŒvv
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337.ŽR“c‘ñ”n C‹£Ÿ”ü
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336.ÎŒ\ˆê˜Y CŽ›“cN•F C‹£Ÿ”ü
u0.3T ƒ|[ƒ^ƒuƒ‹ MRI—pƒVƒ“ƒOƒ‹ƒ`ƒƒƒlƒ€ƒRƒC‚ÌŠJ”­v
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335.”qŽt’q”VC‹£Ÿ”üCˆ¢•”‹ÓŽjC•yŠ~””nC‹vP’C”Ž
uŠùÝ‚ÌNMR‘•’u‚ðŠˆ—p‚µ‚½MRMICS‚É‚æ‚é600MHz‚ł̶‘̃}ƒEƒXŽB‘œ‚Ì
‰ŠúŒŸ“¢v
•½¬‚Q‚T”N‚WŒŽ‚Q“úC‘æ‚P‚V‰ñNMRƒ}ƒCƒNƒƒCƒ[ƒWƒ“ƒOŒ¤‹†‰ïC“c’¬(O6)p21-p24

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

293.‹Ê“c‘å‹PCŠÛŽRŒ\‰îC‹£Ÿ”ü
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289.”qŽt’q”V
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279.”qŽt’q”V
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275.‹Ê“c‘å‹PC‹£Ÿ”ü
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