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578. Ž›“c@N•F
Image quality evaluation for MR images processed by AI
AI‚ÉŠÖ‚·‚éMRI‰æ‘œ‚Ì•]‰¿
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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”
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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
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575. Naoto Fujita, Suguru Yokosawa, Toru Shirai, Yasuhiko Terada
Model-based deep learning reconstruction for accelerating T2 mapping
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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”
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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
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572. Hiroya Nakamura, Masayuki Yamaguchi, Yasuhiko Terada
Quantitative susceptibility mapping of Super Paramagnetic Iron Oxide (SPIO) at low and high fields
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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
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570. “¡“c’¼lC‰¡‘ò@rC”’’–@‹œCŽ›“cN•F
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A•¨“€Œ‹—lŽ®‚Ȃǂ̉ðÍ‚Ì‚½‚ß‚Ì40‚‚ƒ\ƒŒƒmƒCƒhƒRƒCƒ‹‚ð—p‚¢‚½‰·“x‰Â•ÏMRI‚ÌŠJ”
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568. Ž›“c@N•F
’ᎥêMRI‚Ì‹ß‹µ
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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
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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”
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565. ZNaoto Fujita, Suguru Yokosawa, Toru Shirai, Yasuhiko Terada
Performance and generalizability of public deep learning models for multicoil image reconstruction.
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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“¢
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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”
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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[
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561. Yasuhiko Terada
Advances in Magnetic Resonance Imaging: From Low-Field to High-Field and Back to Low-Field MRI
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560. “¡“c’¼lC‰¡‘òrC”’’–‹œCŽ›“cN•F
uMR ‰æ‘œÄ\¬—pƒfƒB[ƒvƒ‰[ƒjƒ“ƒOƒ‚ƒfƒ‹‚̃ƒoƒXƒg«‚Ì•]‰¿
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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”
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558. š Ž}˜a‹PC–qŒ´˜aKC,ŽR“cdlC,’‡‘º‚ŽuCŽ›“cN•F
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557. –àŽR ’¼lCÎì‰ë–çCŽ›“cN•F
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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
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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
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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
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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
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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
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544.@š Ž}˜a‹PC‘ºã—Y“lCŽ›“cN•F
MR ƒ}ƒCƒNƒƒXƒRƒs[—p double helix dipole Œ^ RF ƒRƒCƒ‹‚ÌŠJ”
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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
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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\¬
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541.@‹£Ÿ”üC ‹£—ºˆêC Ž›“cN•F
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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
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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“‚ɊŠm«‚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”
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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”
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510. ²X–Ø –¸ˆêCŽ›“c N•F
[‘wŠwK‚ð—p‚¢‚½ MR fingerprinting ‚É‚¨‚¯‚鎞ŠÔ’Zk‚Æ„’踓xŒüã‚ÌŒŸ“¢
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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”
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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
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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
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497.‚ì@’¼–çCŽ›“c@N•F
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iP2-B2-006)P275
496.’†”ö@ˆ¤CŽ›“c@N•F
Non-Cartesian imaging for permanent magnet MRI systems
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iP2-A8-057)P266
495.–xì@—F•ãCŽ›“c@N•F
Influence of temperature drift on flow measurements
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494.Z¼è@~•½C”qŽt@’q”VCŽ›“c@N•F
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490.Z²X–Ø@–¸ˆêCŽ›“c@N•F
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484.²X–Ø –¸ˆêC¬—Ñ@—D‘¾CŽ›“c@N•F
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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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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”
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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”
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479.’†ž ^—DC“c•Ó@—ºŸC‰ª–{ ‰ÃˆêC‹£@Ÿ”üCŽ›“c@N•F
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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
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473.Ž›“c@N•F
non-Cartesian trajectory imaging
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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
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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|
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468.‹£ Ÿ”üC‹£ —ºˆê
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467.¬—Ñ —D‘¾ C ‹£ Ÿ”ü C Ž›“c N•F
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464.‹£ —ºˆêC£ŒËˆä ˆ»ØC‹£ Ÿ”ü
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459.Ž›“cN•F
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458.Ž›“cN•F
uMR Fingerprinting“ü–åv
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378.Ž›“cN•FC‹Ê“c‘å‹PCÎŒ\ˆê˜YC‹£Ÿ”üC–ìè‘åŠóC‹àŽqNmC‹g‰ª@‘å
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289.”qŽt’q”V
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