Domestic Societies

555.�@–àŽR’¼�l�C�‚�ì’¼–ç�C�Î�ì‰ë–ç�CŽ›“c�N•F
‰·“x‰Â•ÏMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOƒVƒXƒeƒ€‚ð—p‚¢‚½�‚ŽR�A•¨‚Ì“€Œ‹‰ß’ö‚̉ð�Í
—ߘa4”N6ŒŽ26“ú�C‘æ67‰ñ’ቷ�¶•¨�HŠw‰ï‘å‰ï�iƒIƒ“ƒ‰ƒCƒ“�j�CB11�C

 

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�«”\Œü�ã
—ߘa3”N9ŒŽ10“ú�`9ŒŽ12“ú�C‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�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¥�΂ð—p‚¢‚½—L‘Ü—Þãó�E�V�¶Že•W–{‚ÌMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒO
—ߘa3”N9ŒŽ10“ú�`9ŒŽ12“ú�C‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�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‚ð—p‚¢‚½FSL‚ÌŠw�Kƒ‚ƒfƒ‹‚É‚æ‚éŠgŽU‹­’²‰æ‘œ‚̘c‚Ý•â�³
—ߘa3”N9ŒŽ10“ú�`9ŒŽ12“ú�C‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�iƒnƒCƒuƒŠƒbƒh�j�C�iP071-J) P239
Šw�p�§—ã�Ü

 

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•â�³‚ÌŒŸ“¢
—ߘa3”N9ŒŽ10“ú�`9ŒŽ12“ú�C‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�iƒnƒCƒuƒŠƒbƒh�j�C�iP045-J) P228

 

550.�@Katsumi Kose, Ryoichi Kose, Yasuhiko Terada
Bloch simulation of the 3-point Dixon method on biological systems
�¶‘Ì‚ð‘Î�Û‚Æ‚µ‚½3 point Dixon–@‚ÌBloch simulation
—ߘa3”N9ŒŽ10“ú�`9ŒŽ12“ú�C‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�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^�[‰ð�͂ɂæ‚éˆÙ‚È‚éƒqƒg“û‚ª‚ñƒ‚ƒfƒ‹‚ɑ΂·‚é�V‹K�R‚ª‚ñ�ÜE7130‚ÌŠˆ�«•]‰¿
—ߘa3”N9ŒŽ10“ú�`9ŒŽ12“ú�C‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�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‚Ìdouble helix dipole (DHD)ƒRƒCƒ‹‚ÌŠJ”­
—ߘa3”N9ŒŽ10“ú�`9ŒŽ12“ú�C‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�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�§–ñ‰º‚É‚¨‚¯‚édeep learning‚ð—p‚¢‚½ƒ}ƒ‹ƒ`ƒRƒCƒ‹CS�Ä�\�¬
—ߘa3”N9ŒŽ10“ú�`9ŒŽ12“ú�C‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�iƒnƒCƒuƒŠƒbƒh�j�C�iO2-022) P182

 

546.�@Mari Minami, Shigehito Yamada, Yasuhiko Terada
Initial study of DTI of a chemically fixed human fetus
‘Ù�¶Šú�‰Šú‚َ̑™‰»ŠwŒÅ’è•W–{‚ÌDTI‚Ì�‰ŠúŒŸ“¢
—ߘa3”N9ŒŽ10“ú�`9ŒŽ12“ú�C‘æ49‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�iƒnƒCƒuƒŠƒbƒh�j�C�iO1-010) P164

 

545.�@–qŒ´˜a�K�CŽ›“c�N•F
9.4T �cŒ^’´“d“±Ž¥�΂ð—p‚¢‚½ƒqƒgãóŽq‰»ŠwŒÅ’è•W–{‚Ì�‚•ª‰ð”\ƒCƒ��[ƒWƒ“ƒO
—ߘa3”N8ŒŽ18“ú�@‘æ25‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�iƒIƒ“ƒ‰ƒCƒ“ŠJ�Ã�j�C‚‚­‚Î�FŽY‘�Œ¤�C�iO6�jP29-32

 

544.�@š Ž}˜a‹P�C‘º�ã—Y“l�CŽ›“c�N•F
MR ƒ}ƒCƒNƒ�ƒXƒRƒs�[—p double helix dipole Œ^ RF ƒRƒCƒ‹‚ÌŠJ”­
—ߘa3”N8ŒŽ18“ú�@‘æ25‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�iƒIƒ“ƒ‰ƒCƒ“ŠJ�Ã�j�C‚‚­‚Î�FŽY‘�Œ¤�C�iO5�jP25-28

 

543.�@‹g“cŒ\—C�C�ãŽR‹B, Ž›“c�N•F
‹³Žt‚È‚µ�[‘wŠw�K‚ð—p‚¢‚½ŠgŽU‹­’²‰æ‘œ‚É‚¨‚¯‚é 3 ŽŸŒ³˜c‚Ý•â�³
—ߘa3”N8ŒŽ18“ú�@‘æ25‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�iƒIƒ“ƒ‰ƒCƒ“ŠJ�Ã�j�C‚‚­‚Î�FŽY‘�Œ¤�C�iO4�jP21-24

 

542.�@‹{�â’mŽ÷�C�MŽRŒd�C‹Ê“c‘å‹P�C–{�™‰F‘¾˜Y�C�X�ã—T”V�C‘å�¼—m�CŽ›“c�N•F
Deep learning ‚ð—p‚¢‚½ƒ}ƒ‹ƒ`ƒRƒCƒ‹ compressed sensing �Ä�\�¬
—ߘa3”N8ŒŽ18“ú�@‘æ25‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�iƒIƒ“ƒ‰ƒCƒ“ŠJ�Ã�j�C‚‚­‚Î�FŽY‘�Œ¤�C�iO3�jP17-20

 

541.�@‹��£�Ÿ”ü�C ‹��£—ºˆê�C Ž›“c�N•F
QRAPMASTER –@‚ÌŽÀ‘•‚Æ Ž¥‰»ˆÚ“® Œø‰Ê‚̉e‹¿•]‰¿
—ߘa3”N8ŒŽ18“ú�@‘æ25‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�iƒIƒ“ƒ‰ƒCƒ“ŠJ�Ã�j�C‚‚­‚Î�FŽY‘�Œ¤�C�iO2�jP13-16

 

540. ‹{�â’mŽ÷, ûü�ì’¼–ç, �Έ䊰�’, �Î�ì‰ë–ç, Ž›“c�N•F
‰·“x‰Â•Ï MRI ‚É‚æ‚é�A•¨‚¨‚æ‚Ñ�H•iƒTƒ“ƒvƒ‹‚̒ቷŽB‘œ
—ߘa3”N8ŒŽ18“ú�@‘æ25‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�iƒIƒ“ƒ‰ƒCƒ“ŠJ�Ã�j�C‚‚­‚Î�FŽY‘�Œ¤�C�iP1�jP5-8

 

539. ‹{�â’mŽ÷�Cûü�ì’¼–ç�C�Î�ì‰ë–ç�CŽ›“c�N•F
‰Ô‰è“€Œ‹ŠÏŽ@—p‰·“x‰Â•ÏMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOƒVƒXƒeƒ€‚ÌŠJ”­
—ߘa3”N5ŒŽ29“ú�`5ŒŽ30“ú�C‘æ66 ‰ñ’ቷ�¶•¨�HŠw‰ï‘å‰ï�C“Œ‹ž�iƒIƒ“ƒ‰ƒCƒ“�j�C�iB10�jP27

 

538. Ž›“c�N•F
�[‘wŠw�K‚ð—p‚¢‚½“ª•”MRIŒŸ�¸‚Ì�‚‘¬‰»‚ÌŽŽ‚Ý
—ߘa3”N1ŒŽ12“ú�@JSMRMƒXƒ^ƒfƒBG�u�¶�¬Œ^Šw�K“™‚ðŠˆ—p‚µ‚½’è—Ê“IMRƒCƒ��[ƒWƒ“ƒO�vƒjƒ…�[ƒCƒ„�[ƒZƒ~ƒi�[�i‚P�j�C�L“‡�iƒIƒ“ƒ‰ƒCƒ“�j
�µ‘Ò�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
—Õ�°”]‰æ‘œ‚Ì’´‰ð‘œ‚É‚¨‚¯‚édeep learning‚Ì•sˆÀ’è�«
—ߘa2”N9ŒŽ11“ú�`10ŒŽ4“ú�C‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�iWebŠJ�Ã�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‚ð—p‚¢‚½ƒ}ƒ‹ƒ`ƒRƒ“ƒgƒ‰ƒXƒgCS �Ä�\�¬
—ߘa2”N9ŒŽ11“ú�`10ŒŽ4“ú�C‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�iWebŠJ�Ã�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‚æ‚Á‚Ä—Õ�°—p1.5T Ž¥�΂Å23Na-MRI‚ðŽÀŒ»‚·‚é
—ߘa2”N9ŒŽ11“ú�`10ŒŽ4“ú�C‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�iWebŠJ�Ã�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
ŽèŽñ—pŽÔ�Úƒ|�[ƒ^ƒuƒ‹ MRI‚ÌŠJ”­‚ƃXƒNƒŠ�[ƒjƒ“ƒOŽŽŒ±
—ߘa2”N9ŒŽ11“ú�`10ŒŽ4“ú�C‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�iWebŠJ�Ã�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‹Ï–@‚É‚æ‚éDCE-MRI ‚Ì Ktrans ’lŽ©“®•ª—Þ‚ð—p‚¢‚½ƒqƒg“ûŠàƒ‚ƒfƒ‹‚ɑ΂·‚é�V‹K�R‚ª‚ñ�Ü E7130‚Ì–òŒø•]‰¿
—ߘa2”N9ŒŽ11“ú�`10ŒŽ4“ú�C‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�iWebŠJ�Ã�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•ÏŠ³ŽÒ‚Ì—Õ�°ƒf�[ƒ^ƒZƒbƒg‚É‚¨‚¯‚éƒfƒB�[ƒvƒ‰�[ƒjƒ“ƒO‚ð—p‚¢‚½‰æ‘œ�‡�¬‚Æ’´‰ð‘œ‚É‚æ‚é�‰Šú—Õ�°•]‰¿
—ߘa2”N9ŒŽ11“ú�`10ŒŽ4“ú�C‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�iWebŠJ�Ã�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‚ÆGIRF ‚ð—p‚¢‚½ spiral ‰æ‘œ‚̃A�[ƒ`ƒtƒ@ƒNƒg•â�³–@‚ÌŒŸ“¢
—ߘa2”N9ŒŽ11“ú�`10ŒŽ4“ú�C‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�iWebŠJ�Ã�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 “®•¨—pMRI‚ð—p‚¢‚½ƒ‰ƒbƒg“ª•”‚ÌLarge Matrix ƒCƒ��[ƒWƒ“ƒO
—ߘa2”N9ŒŽ11“ú�`10ŒŽ4“ú�C‘æ48‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�iWebŠJ�Ã�j�C�iP-006)P164

 

529. Ž›“c�@�N•F, �r–Ø�@—Í‘¾�C�Z‹g�@�W�C�–Ø�@ˆÉ’m’j
BlochƒVƒ~ƒ…�\ƒŒ�[ƒVƒ‡ƒ“‚ÉŠî‚­�³Šm�«‚ÌŒü�ã‚ð–ÚŽw‚µ‚½QRAPMASTER‰ð�Í–@‚ÌŽÀ‘•
—ߘa2”N8ŒŽ28“ú�C‘æ24‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�iƒIƒ“ƒ‰ƒCƒ“ŠJ�Ã�j�C‚‚­‚Î�iP2)P9-12

 

528. –qŒ´�@˜a�K, �âŒû�@˜a–ç, ŽRŒû�@‰ë”V, ˆÉ“¡�@Œ›, –x�@—D�ì, �å”g�@‘¾˜Y, ‘D‹´�@‘×”Ž,
“¡ˆä ”ŽŽj, Ž›“c �N•F
DCE-MRIƒNƒ‰ƒXƒ^�[‰ð�Í‚ð—p‚¢‚½ƒqƒg“ûŠàƒ‚ƒfƒ‹‚Ì–òŒø•]‰¿–@‚ÌŠJ”­
—ߘa2”N8ŒŽ28“ú�C‘æ24‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�iƒIƒ“ƒ‰ƒCƒ“ŠJ�Ã�j�C‚‚­‚Î�iP2)P13-16

 

527. Š�Œ´�@�¬�¶�CŽ›“c �N•F�Cœ«“c —º•½�C’†�ì —S‰î�C�¬“c ˆê‰q�C�²�X–Ø �i�C”qŽt ’q”V
ƒNƒ�ƒXƒoƒ“ƒhƒŒƒs�[ƒ^‚Ì‹Z�p‚ð‰ž—p‚µ‚½NaƒCƒ��[ƒWƒ“ƒO
—ߘa2”N8ŒŽ28“ú�C‘æ24‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�iƒIƒ“ƒ‰ƒCƒ“ŠJ�Ã�j�C‚‚­‚Î�iP2)P21-25

 

526. ‹{�â�@’mŽ÷�CŠ�Œ´�@�¬�¶�C�ì�è�@—º�l�C‰ª–{�@‰Ãˆê�CŽ›“c�@�N•F
ƒ|�[ƒ^ƒuƒ‹MRI‚ð—p‚¢‚½ŽèŽñ�f’f
—ߘa2”N8ŒŽ28“ú�C‘æ24‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�iƒIƒ“ƒ‰ƒCƒ“ŠJ�Ã�j�C‚‚­‚Î�iP2)P26-29

 

525. ‘º�ã —Y“l�CŽRŒû ‰ë”V�CŽ›“c �N•F
9.4T“®•¨—pMRI‚ð—p‚¢‚½ƒ‰ƒbƒg“ª•”‚ÌLarge MatrixƒCƒ��[ƒWƒ“ƒO
—ߘa2”N8ŒŽ28“ú�C‘æ24‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�iƒIƒ“ƒ‰ƒCƒ“ŠJ�Ã�j�C‚‚­‚Î�iO6)P32-36

 

524. ‹g“c�@Œ\—C�CŽ›“c�@�N•F
dAUTOMAP‚ð—p‚¢‚½SpiralŽB‘œ‚É‚¨‚¯‚鉿‘œ�Ä�\�¬–@‚ÌŒŸ“¢
—ߘa2”N8ŒŽ28“ú�C‘æ24‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�iƒIƒ“ƒ‰ƒCƒ“ŠJ�Ã�j�C‚‚­‚Î�iP3)P37-40

 

523. Ž›“c�@�N•F
�ŋ߂̌¤‹†�Љî �`�‚‘¬ƒCƒ��[ƒWƒ“ƒO‚ƃ‚ƒoƒCƒ‹MRIŒŸ�¸�`
—ߘa‚P”N11ŒŽ20“ú�C‘æ4‰ñ MRIƒAƒ‰ƒCƒAƒ“ƒX�‘�ÛƒVƒ“ƒ|ƒWƒEƒ€2019�C�ç—t
�µ‘Ò�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‚ð—p‚¢‚½—Õ�°”]‰æ‘œŒŸ�¸‚Ì�‚‘¬‰»�i‚Q�j�F—Õ�°ŒŸ�¸‚Ö‚ÌŽÀ‘•‚Æ—Õ�°•]‰¿
—ߘa1”N9ŒŽ20“ú�C‘æ47‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�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‚ð—p‚¢‚½—Õ�°”]‰æ‘œŒŸ�¸‚Ì�‚‘¬‰»�i‚P�j�Fƒjƒ…�[ƒ‰ƒ‹ƒlƒbƒgƒ��[ƒN‚Ì�\’z
—ߘa1”N9ŒŽ20“ú�C‘æ47‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�CŒF–{�iO1-027�jP177

 

520. Michiru Kajiwara, Mayu Nakagomi, Yoshikazu Okamoto, Yasuhiko Terada
Field examination of baseball elbow using a car-mounted portable MRI –ì‹…•I�f’f—pŽÔ�Úƒ|�[ƒ^ƒuƒ‹MRI‚ÌŽÀ’nŽŽŒ±
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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
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518. Yuto Murakami, Ryoichi Sasaki, Yasuhiko Terada
Acceleration of acquisition of relaxation time map for human embryo specimens ƒqƒgãóŽq•W–{‚̊ɘaŽžŠÔƒ}ƒbƒvŽæ“¾‚Ì�‚‘¬‰»
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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”­
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516. Naoya Takagawa, Yasuhiko Terada
Development of temperature-variable MR microimaging system (2) ‰·“x‰Â•ÏMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOƒVƒXƒeƒ€‚ÌŠJ”­�i2�j
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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—Þ�Fƒqƒg“ûŠàƒ‚ƒfƒ‹‚ɑ΂·‚é�V‹K�R‚ª‚ñ�ÜE7130–òŒø•]‰¿‚Ö‚Ì—˜ —p
—ߘa1”N9ŒŽ22“ú�C‘æ47‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�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‚æ‚é�¶‘Ì‘g�D‚̃Vƒ~ƒ…ƒŒ�[ƒVƒ‡ƒ“
—ߘa1”N9ŒŽ21“ú�C‘æ47‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�CŒF–{�iP2-A-51�jP298

 

513. ‹��£ �Ÿ”ü�C‹��£ —ºˆê�C Ž›“c �N•F, ‹Ê“c ‘å‹P, –{�™ ‰F‘¾˜Y
�¶‘Ì‘g�D‚Ì MRI simulation Žè–@‚ÌŠJ”­
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512. ‘º�ã —Y“l�C’‡‘º ûüŽu, Ž›“c �N•F
11.7T ’´“`“±Ž¥�΂ð—p‚¢‚½ƒqƒgãóŽq‚̃}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒO
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511. ’†”ö ˆ¤�CŽ›“c �N•F
�[‘wŠw�K‚ð—p‚¢‚½‰i‹vŽ¥�Î MRI ‚É‚¨‚¯‚é Spiral ‰æ‘œƒA�[ƒ`ƒtƒ@ƒNƒg•â�³–@‚ÌŠJ”­
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510. �²�X–Ø –¸ˆê�CŽ›“c �N•F
�[‘wŠw�K‚ð—p‚¢‚½ MR fingerprinting ‚É‚¨‚¯‚鎞ŠÔ’Z�k‚Æ�„’è�¸“xŒü�ã‚ÌŒŸ“¢
—ߘa1”N8ŒŽ8“ú�C‘æ23‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�C‰¡•l�iP2)P29-32

 

509. Š�Œ´ �¬�¶�C‹{�â ’mŽ÷�C‰ª–{ ‰Ãˆê�CŽ›“c �N•F
ƒ|�[ƒ^ƒuƒ‹ MRI ‚Ì–ì‹…�ê‚ł̎B‘œŽŽŒ±
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508.‘��…�@‹I‹vŽq�CŽ›“c�@�N•F�C•Љª�@–MŒõ�C�H–{�@�‡“ñ�C”qŽt�@’q”V
PGSE-NMR‚ÅŠÏ‘ª‚·‚éƒK�[ƒlƒbƒg“d‰ðŽ¿’†‚ÌLi+‚Ì�Õ“Ë�E‰ñ�ÜŒ»�Û
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507.ûü�ì�@’¼–ç�CŽ›“c�@�N•F
•X“_‰º‚ł̎B‘œ‚ð–Ú“I‚Æ‚µ‚½4.7T‰·“x�§ŒäMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOƒVƒXƒeƒ€‚ÌŠJ”­
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506�@�âŒû�@˜a–ç�CŽ›“c�@�N•F
“ÁˆÙ’l•ª‰ð–@‚ÆABCƒAƒ‹ƒSƒŠƒYƒ€‚ð‘g‚Ý�‡‚킹‚½‰~“›Œ^‚̃V�[ƒ‹ƒhŒù”zŽ¥�êƒRƒCƒ‹‚Ì�Å“K‰»
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505 Ž›“c�@�N•F�C’†”ö�@ˆ¤�C‹Ê“c�@‘å‹P�C–{�™�@‰F‘¾˜Y
�[‘wŠw�K‚ð—p‚¢‚½—Õ�°”]‰æ‘œ‚ÌŽB‘œŽžŠÔ‚Ì’Z�k‰»
—ߘa1”N8ŒŽ8“ú�C‘æ23‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�C‰¡•l�iO1)P1-4

 

504. •½�ì�@‰ë•¶�C�¼‰º�@”Í‹v�C•Ÿ“c�@Œ’“ñ�CŽ›“c�@�N•F
MRI‚ð—p‚¢‚½ƒGƒ“ƒ{ƒŠƒYƒ€‚Ì”­�¶�E‰ñ•œ‰ß’ö‚É‚¨‚¯‚é�…•ª’Ê“±‚̉Ž‹‰»
•½�¬31”N3ŒŽ20-23�C‘æ130‰ñ“ú–{�X—ÑŠw‰ï‘å‰ï�C�VŠƒ�iP1-087)
https://doi.org/10.11519/jfsc.130.0_296

 

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

 

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

 

501.�¼ú±�@�~•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“ƒgŠî”‚ð—p‚¢‚½‰~“›Œ^Œù”zŽ¥�êƒRƒCƒ‹‚ÌŠJ”­�i2�j�F�«”\•]‰¿‚Ɖž—p
•½�¬30”N9ŒŽ7“ú�C‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�C‹à‘ò�@�iO1-107) P184
—D�G‰‰‘è�Ü

 

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

 

499.’†�ž�@�^—D�C“c•Ó�@—º�Ÿ�C�¯�‡ ‘s‘å�C‰ª–{�@‰Ãˆê�CŽ›“c�@�N•F
Deep convolutional neural network for denoising images of low-field scanners
’Ꭵ�êMRI‚É‚¨‚¯‚édeep learning‚ð—p‚¢‚½ƒmƒCƒY�œ‹Ž‚ÌŒŸ“¢
•½�¬30”N9ŒŽ8“ú�C‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�C‹à‘ò
�iP2-A2-013)P252

 

498.�âŒû�@˜a–ç�C�¼àV�@�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‚Ý�‡‚킹‚½‰~“›Œ^Œù”zŽ¥�êƒRƒCƒ‹‚ÌŠJ”­
•½�¬30”N9ŒŽ8“ú�C‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�C‹à‘ò�@
�iP2-B2-007)P276

 

497.�‚�ì�@’¼–ç�CŽ›“c�@�N•F
Development of temperature-variable MR microimaging system (I) ‰·“x‰Â•ÏMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOƒVƒXƒeƒ€‚ÌŠJ”­�iI�j
•½�¬30”N9ŒŽ8“ú�C‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�C‹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“ú�C‘æ46‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�C‹à‘ò�@
�iP2-A8-057)P266

 

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

 

494.�Z�¼�è�@�~•½�C”qŽt�@’q”V�CŽ›“c�@�N•F
ƒvƒŠƒ“ƒgŠî”‚ð—p‚¢‚½Œù”zŽ¥�êƒRƒCƒ‹‚ÌŠJ”­
•½�¬‚R‚O”N‚WŒŽ‚Q‚P“ú�C‘æ22‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�C“Œ–k‘åŠw�C
�iO10)P55-58

 

493.�ZŽ›“c�@�N•F�C’†�ž�@�^—D�C‰ª–{�@‰Ãˆê
‰i‹vŽ¥�Î MRI ‚É‚¨‚¯‚é�[‘wŠw�K‚ð—p‚¢‚½ƒmƒCƒY�œ‹Ž‚ÌŒŸ“¢
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492.�Z�‚�ì�@’¼–ç�CŽ›“c�@�N•F
4.7T/89mm ŠJŒû�cŒ^’´“`“±Ž¥�΂ð—p‚¢‚½‰·“x‰Â•ÏMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOƒVƒXƒeƒ€‚ÌŠJ”­
•½�¬‚R‚O”N‚WŒŽ‚Q‚O“ú�C‘æ22‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�C“Œ–k‘åŠw�C
�iP4�jP37-40

 

491.�Z�âŒû�@˜a–ç�C�¼àV�@�WŽ÷�CŽ›“c�@�N•F
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�iP3�jP33-36

 

490.�Z�²�X–Ø�@–¸ˆê�CŽ›“c�@�N•F
1.5TŽlŽˆ—p MRI ‚É‚¨‚¯‚é Cartesian MR fingerprinting ‚ÌŽB‘œ�‚‘¬‰»
•½�¬‚R‚O”N‚WŒŽ‚Q‚O“ú�C‘æ22‰ñNMRƒ}ƒCƒNƒ�ƒCƒ��[ƒWƒ“ƒOŒ¤‹†‰ï�C“Œ–k‘åŠw�C
�iP2�jP29-32

 

489.�›’†”ö ˆ¤�CŽ›“c �N•F
ŽlŽˆ—p MRI ‚É‚¨‚¯‚é non-Cartesian imaging –@‚ÌŠJ”­
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�iP1�jP25-28

 

488.�›–x�ì —F•ã�C•Ÿ“c Œ’“ñ�CŽ›“c �N•F
MRI ‚ð—p‚¢‚½‰®ŠOŽ÷–؂̎÷‰t—¬ƒCƒ��[ƒWƒ“ƒO�F�L—tŽ÷‚Æ�j—tŽ÷‚Ì”äŠr
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�iO2�jP5-8

 

487.�›’†�ž �^—D�C“c•Ó —º�Ÿ�C‰ª–{ ‰Ãˆê�C�¯�‡ ‘s‘å�CŽ›“c �N•F
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486.’·“c�W‰À,�›•Ÿ“cŒ’“ñ,Ž›“c�N•F
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485.�¬—Ñ —D‘¾�CŽ›“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.�²�X–Ø –¸ˆê�C�¬—Ñ�@—D‘¾�CŽ›“c�@�N•F
Musculoskeletal MR Fingerprinting using a 1.5T/280mm small-bore MRI 1.5T/280mmƒXƒ‚�[ƒ‹ƒ{ƒAMRI‚ð—p‚¢‚½�œ“î•”‚Ö‚ÌMR Fingerprinting‚̉ž—p
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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‚æ‚é–ƒ�Œ‰ºƒ}ƒEƒX�^ƒ‰ƒbƒg‚̊ɘaŽžŠÔ�^ADCƒ}ƒbƒsƒ“ƒO‚Ì�‰ŠúŒŸ“¢
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P2-B6-191(p394)

 

482.–x�ì —F•ã�C‹��£�@�Ÿ”ü�CŽ›“c�@�N•F
Error evaluation of QSI measurements for widely-distributed flow �L‚¢‘¬“x•ª•z‚ð‚à‚—¬‚ê‚ÌQSIŒv‘ª‚ÌŒë�·•]‰¿
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P2-B6-189 (p393)

 

481.�âŒû ˜a–ç�C�¼àV�@�WŽ÷�C‹��£�@�Ÿ”ü�CŽ›“c�@�N•F
New method for designing gradients using singular value decomposition and genetic algorithm “ÁˆÙ’l•ª‰ð–@‚ƈâ“`“IƒAƒ‹ƒSƒŠƒYƒ€‚ð‘g‚Ý�‡‚킹‚½Œù”zŽ¥�êƒRƒCƒ‹�«”\‚Ì�Å“K‰»Žè–@‚ÌŠJ”­
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P2-B2-186(p391)

 

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446.�£ŒËˆäˆ»�Ø�C‹��£�Ÿ”ü
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443.ŽR“c—È‘¾�C‹��£�Ÿ”ü�CŽ›“c�N•F
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327.‹��£�Ÿ”ü
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324.–x‰ê‰ëŽj�C�ÎàVˆêŒ›�C”¼“c�W–ç�C‹��£�Ÿ”ü
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322.ˆî‘º�^–ç�CŽ›“c�N•F�C‹��£�Ÿ”ü
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320.‹Ê“c‘å‹P�C‹��£�Ÿ”ü
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318.“àŠC’m”ü�C‰Í–ì�Ê‹L�CŽ›“c�N•F�C‹��£�Ÿ”ü�C‹{�é�@—º�CŽR•”‰p�s�C‹g‰ª�@‘å
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316.‹Ê“c‘å‹P�C‹��£�Ÿ”ü�C”qŽt’q”V
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315.‹��£�Ÿ”ü�C–x‰ê‰ëŽj�C‹Ê“c‘å‹P�C‰º‰Æ—S�l�CŽ›“c�N•F�C‹´–{�ª‘¾˜Y�C”qŽt’q”V
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314.‰º‰Æ—S�l�C–x‰ê‰ëŽj�CŽ›“c�N•F�C‹��£�Ÿ”ü�C”qŽt’q”V�CŒ·ŠÔ�@—m�C�£ŒÃ‘ò—R•F.
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•½�¬24”N9ŒŽ8“ú�C‘æ40‰ñ“ú–{Ž¥‹C‹¤–ˆãŠw‰ï‘å‰ï�C‹ž“s(p-3-238)

 

313.‰º‰Æ—S�l�C–x‰ê‰ëŽj�CŽ›“c�N•F�C‹��£�Ÿ”ü�C”qŽt’q”V�CŒ·ŠÔ�@—m�C�£ŒÃ‘ò—R•F
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311.‹Ê“c‘å‹P�C‹��£�Ÿ”ü
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309. ‰Í–ì�Ê‹L�C“àŠC’m”ü�CŽ›“c�N•F�C‹��£�Ÿ”ü�C‹{�é�@—º�CŽR•”‰p�s�C‹g‰ª�@‘å
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307.”qŽt’q”V
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299.‹Ê“c‘å‹P�CŽ›“c�N•F�C‹��£�Ÿ”ü
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298.‹´–{�ª‘¾˜Y�C‹��£�Ÿ”ü�C”qŽt’q”V
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297.”qŽt’q”V
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296.–Ø‘º•�Žj�C‹��£�Ÿ”ü�C‰º‰Æ—S�l�CŽ›“c�N•F�C”qŽt’q”V�CŒ·ŠÔ�@—m�C�£ŒÃàV—R•F
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294.ŠÛŽRŒ\‰î�C‹Ê“c‘å‹P�CŽ›“c�N•F�C‹��£�Ÿ”ü
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