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Multiview light-sheet microscope for rapid in toto imaging

Authors:

Abstract

We present a multiview selective-plane illumination microscope (MuVi-SPIM), comprising two detection and illumination objective lenses, that allows rapid in toto fluorescence imaging of biological specimens with subcellular resolution. The fixed geometrical arrangement of the imaging branches enables multiview data fusion in real time. The high speed of MuVi-SPIM allows faithful tracking of nuclei and cell shape changes, which we demonstrate through in toto imaging of the embryonic development of Drosophila melanogaster.
Multi-view light-sheet microscope for rapid in toto imaging
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We present a multi-view selective-plane illumination microscope (MuVi-SPIM), comprised of two
detection and illumination objective lenses, that allows rapid in toto fluorescence imaging of
biological specimens with subcellular resolution. The fixed geometrical arrangement of the
imaging branches enables multi-view data fusion in real-time. The high speed of MuVi-SPIM
enables faithful tracking of nuclei and cell shape changes, which we demonstrate by in toto
imaging the embryonic development of Drosophila melanogaster.
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3@-"07(S#$S#E($)4."% 6.(@)4.1+% 7(-% ;.$#1:."."% Q1M.1:31?%03/% 2#@:"3;(1?@u$."B% Ann. Phys.%315+%
*TUJ%V*J\5WB%
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:(7.1$(#10@%(70?(1?%#/%70)"#$)#E()%;(#@#?()0@%$E.)(7.1$B%J Microsc%170+%55JTUI%V*JJUWB%
IB% >3($S.1+%<B+%,M#?."+%<B+%G.@%D.1.+%]B+%f(--;"#:-+%<B%m%,-.@'."+%9B%>B%&B%_E-()0@%,.)-(#1(1?%G..E%Y1$(:.%
=(P.%97;"8#$%;8%,.@.)-(P.%X@01.%Y@@37(10-(#1%F()"#$)#E8B%Science%305+%*\\KT*\\J%V5\\ZWB%
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M4#@.%7#3$.%;"0(1B%Nat Meth%4+%UU*TUUI%V5\\KWB%
[B% &.@@."+% XB% <B+% ,)47(:-+% QB% GB+% f(--;"#:-+% <B% m% ,-.@'."+% 9B% >B% &B% d.)#1$-"3)-(#1% #/% n.;"0/($4% 90"@8%
97;"8#1()%G.P.@#E7.1-%;8%,)011.:%=(?4-%,4..-%F()"#$)#E8B%Science%322+%*\I^T*\IJ%V5\\[WB%
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*\B%6"3#1?+%6B%`B+%,3E0--#+%fB+% &##$+%GB%,B+%C4#(+%<B% FB% m% ]"0$."+%,B%9B%G..E%01:% /0$-%@(P.%(70?(1?%M(-4%
-M#HE4#-#1%$)011.:%@(?4-H$4..-%7()"#$)#E8B%Nat Meth%8+%K^KTKI\%V5\**WB%
**B%C0E#3@0:.+% <B+% f0)4$73-4+% FB+% >3/10?.@+% =B% m% &1#E+%FB% l301-(-0-(P.% /@3#".$).1).% (70?(1?% #/%
E"#-.(1%:(//3$(#1%01:%(1-."0)-(#1%(1%@(P(1?%).@@$B%Nat Biotech%29+%[U^T[UJ%V5\**WB%
*5B%C.@@0%n010))4(+%]B%et al.%=(P.H).@@%UG%$3E."H".$#@3-(#1%(70?(1?%(1%-4()S%;(#@#?()0@%$07E@.$B%Nat Meth%
8+%*\ZKT*\ZJ%V5\**WB%
*UB%,M#?."+%<B+%`."P.."+%XB+%2".?."+%&B+%>3($S.1+%<B%m%,-.@'."+%9B%>B%&B%F3@-(HP(.M%(70?.%/3$(#1%(7E"#P.$%
".$#@3-(#1%(1%-4"..H:(7.1$(#10@%7()"#$)#E8B%Opt. Express%15+%[\5JT[\Z5%V5\\KWB%
*ZB%X".(;($)4+% ,B+% ,00@/.@:+% ,B+% ,)4(1:.@(1+% <B% m% 6#701)0S+% XB% ,#/-M0".%/#"% ;.0:H;0$.:% ".?($-"0-(#1% #/%
$.@.)-(P.%E@01.%(@@37(10-(#1%7()"#$)#E8%:0-0B%Nat Meth%7+%Z*[TZ*J%V5\*\WB%
*^B%&"'()+% !B% F3@-(E@.HP(.M%7()"#$)#E8% M(-4% @(?4-H$4..-% ;0$.:%/@3#".$).1).% 7()"#$)#E.B% X4G% 64.$($+%
!1(P."$(-8%#/%>.(:.@;."?%V5\\JW%
*IB%>3($S.1+% <B% m% ,-0(1(."+% GB% oB% dB% 9P.1% /@3#".$).1).% .N)(-0-(#1% ;8% 73@-(:(".)-(#10@% $.@.)-(P.% E@01.%
(@@37(10-(#1%7()"#$)#E8%V7,XYFWB%Opt. Lett.%32+%5I\[T5I*\%V5\\KWB%
*KB%]#.+%`B%9B% m% Q@;."-$+%DB% FB% ,-3:(.$%#/% 13)@.0"% 01:% )8-#E@0$7()%;.40P(#3"%:3"(1?%-4.%/(P.%7(-#-()%
)8)@.$%-40-% E".).:.% ?0$-"3@0-(#1%(1%G"#$#E4(@0%.7;"8#?.1.$($B%Journal of Cell Science%61+%U*TK\%
V*J[UWB%
*[B%2#$4-0$;8+% QB% 2-D and 3-D image registration for medical, remote sensing, and industrial
applicationsB%V<#41%f(@.8%01:%,#1$c%5\\^WB%
*JB%X@01)4#1+%6B% QB% et al.%d0E(:%-4"..H:(7.1$(#10@%($#-"#E()%(70?(1?%#/%@(P(1?%).@@$%3$(1?%D.$$.@%;.07%
E@01.%(@@37(10-(#1B%Nat Meth%8+%Z*KTZ5U%V5\**WB%
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@0"?.%$#@(:%01?@.B%J. Opt. Soc. Am. A%16+%5I^[T5II^%V*JJJWB%
5*B%l3077.1+% CB% fB+% ].1?+% GB% m% 608@#"% YY+% dB% FB% X."/#"701).% #/% UG% G.)#1P#@3-(#1% Q@?#"(-47$% #1%
F3@-(HC#".%01:%F018HC#".%Q")4(-.)-3".$B%University of North Carolina at Chapel Hill, Department
of Computer Science, Tech. Rep%V5\\JWB%
55B%d()40":$#1+%fB%>B%D08.$(01HD0$.:%Y-."0-(P.%F.-4#:%#/%Y70?.%d.$-#"0-(#1B%J. Opt. Soc. Am.%62+%^^T
^J%V*JK5WB%
5UB%2#1'0@.'+% dB% CB+% f##:$+% dB% 9B% m%9::(1$+% ,B% =B% Digital image processing using MATLABB% VX".1- ().%
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5^B%d0//.@+%FB+%f(@@."-+%CB%9B%m%&#7E.1401$+%<B%Particle image velocimetry: a practical guideB%V,E"(1?."c%
D."@(1R%e.M%o#"S+%*JJ[WB%
List of supplementary items
Supplementary File Title
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,3EE@.7.1-0"8%](?3".%Z% F3`(H,XYF%(70?.%$81-4.$($
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).@@%7.7;"01.$%(1%G"#$#E4(@0%.7;"8#%
,3EE@.7.1-0"8%](?3".%J% G.-0(@.:%P(.M%#/%-4.%?0EZUH7C4.""8%G"#$#E4(@0%.7;"8#%
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;0"%($%SZ%e7E%c(?4-%C01.@_%)@#$.G3C%#/%-4.%U4(-.%".)-01?3@0"%;#R%(1%O0PE%N1:(M(:30@%-(7.%
C#(1-$%HWZ%$+%HJW%$+%JZZ%$%01:%J5W%$%0".%$4#U1+%U(-4%$.@.)-.:%13)@.(%)#@#3".:E%!C#1%
:(M($(#1+%#1.%#/%-4.%:03?4-."%).@@$%($%0$$(?1.:%0%1.U%)#@#3"E%6"0)X$%0".%)#@#3"%)#:.:%/#"%
-(7.%0$%(1:()0-.:%;8%-4.%$)0@.%;0"%O;#--#7P%"01?(1?%/"#7%WZZ%$%-#%J5W%$E%(d)%64.%$4#"-.$-%
:($-01).% %;.-U..1%018%-U#%13)@.(%0$%0%/31)-(#1%#/%-(7.%O;@0)X%:(07#1:$P%01:%:#3;@.%
70R(737%:($C@0).7.1-% %:3"(1?%0%-(7.%$-.C% %O".:%$]30".$PE%D.01%:($-01).% %
($%$4#U1%0$%;@0)X%$#@(:%@(1.%U(-4%?"08%$40:.%$4#U(1?%-4.%$-01:0":%:.M(0-(#1% E%(e)%
D.:(01%13)@.0"%$C..:%0$%0%/31)-(#1%#/%-(7.E%%c.:+%;@3.%01:%?"..1%)#@#3"$%)#"".$C#1:%-#%
7(-#-()%)8)@.$%**+%*5%01:%*S+%".$C.)-(M.@8E%,..%K(?E%5;%/#"%)#7C0"($#1E%%%
%
Supplementary!Figure!7!
A.@@%-"0)X(1?%(1%-4.%Drosophila%M.1-"0@%.)-#:."7E%=./-%)#@371%$4#U$%70R(737%(1-.1$(-8%
C"#^.)-(#1$%#/%-4.%M.1-"0@%$(:.%#/%0%4($5\MG7A4.""8%Drosophila%.7;"8#%0-%/#3"%:(//.".1-%
-(7.$%O6% g%Z% 4%:./(1.:% 0$%$-0"-% #/%(70?(1?PE% ,(R%13)@.(% 0".%)4#$.1% /#"%-"0)X(1?% O/(M.%
?"#3C.:% )@#$.% -#?.-4."% 01:% #1.% /3"-4."% 0C0"-P% 01:% /#@@#U.:% -4"#3?4% /(M.% 4#3"$% #/%
:.M.@#C7.1-E%F03?4-."%13)@.(%0".%@0;.@@.:%(1%-4.%$07.%)#@#3"%0$%-4.%C0".1-%13)@.3$E%
D(::@.% )#@371% )#"".$C#1:$% -#% -4.% ".?(#1% 4(?4@(?4-.:% ;8% -4.% U4(-.% ;#R% (1% -4.% @./-%
)#@371E%6"0).$%#/% -4.%13)@.0"%-"0^.)-#"(.$% 0".%0@$#%$4#U1% (1%-4.%$07.%)#@#3"%0$% -4.%
)#"".$C#1:(1?%13)@.3$E% 64.% "(?4-% )#@371% $4#U$% 0% )@#$.% 3C% M(.U% #/% -4.%/(M.%)@#$.@8%
?"#3C.:%).@@$E%A.@@%70"X."$%0".%).1-".:%#1%-4.%C#$(-(#1%#/%70R(737%(1-.1$(-8%/#"%.0)4%
).@@E%%
%
%
Supplementary!Figure!8!
9//.)-$% #/% 01($#-"#C()% C#(1-G$C".0:% /31)-(#1% #1% -4.% (70?(1?% #/% ).@@% 7.7;"01.$% (1%
Drosophila%.7;"8#E%(a)%L#(1-G$C".0:%/31)-(#1%OL,KP%#/%-4.%D3Q(G,LND%($%.@#1?0-.:%0@#1?%
-4.%7()"#$)#C.h$% :.-.)-(#1%0R($E%(b)% N1%01% .0"@8% Drosophila%.7;"8#+%7#$-% ).@@$%0/-."%
).@@3@0"($0-(#1%@(.%)@#$.%-#%-4.%.7;"8#h$%$3"/0).i%-4.%7.7;"01.$%;.-U..1%-4.%).@@$%0".%
-43$% 1.0"@8% C."C.1:()3@0"% -#% -4.% .7;"8#h$% $3"/0).E% 64.% (@@3$-"0-(#1% $4#U$% 0% )"#$$G
$.)-(#1%-4"#3?4%0%Drosophila%.7;"8#%C."C.1:()3@0"%-#%-4.%.7;"8#h$%@#1?%0R.$E%=./-%-U#%
)#@371$%$4#U% -4.%#"(.1-0-(#1% #/%-4.%L,K% ".@0-(M.%-#% -4.%.7;"8#+% U(-4% -4.%.7;"8#h$%
01?3@0"% C#$(-(#1% #/% Zj% 01:% IZj+% ".$C.)-(M.@8E% 64.% /3$.:% (70?.% O"(?4-% )#@371P% ($%
)#1$-"3)-.:%/"#7%-4.%(1C3-%(70?.$%$3)4%-40-%-4.%7.7;"01.$%01:%-4.%7()"#$)#C.h$%L,K%
0".%.M."8U4.".%)@#$.%-#%C0"0@@.@E%(c)%k"(.1-0-(#1%#/%-4.%7()"#$)#C.h$%L,K%".@0-(M.%-#%-4.%
#"(.1-0-(#1%#/%-4.%/@3#".$).1-% 7.7;"01.$%:.-."7(1.$%(70?.%]30@(-8%O.7;"8#%$3"/0).%
U0$%l31"#@@.:l%0$%:.$)"(;.:%(1%,3CC@.7.1-0"8%1#-.%WPE%N1%0%$(1?@.%D3Q(G,LND%(70?.%$.-%
U(-4#3-%$C.)(7.1%"#-0-(#1%O@./-%-U#%)#@371$P+%7.7;"01.$%U(@@%;.%;.$-%".$#@M.:%U4.".%
-4.8% 0".% 1.0"@8% C0"0@@.@% -#% -4.% 7()"#$)#C.h$% L,K+% U4(@.% (1% -4.% 0".0$% U4.".% -4.%
7.7;"01.$%0".% 1.0"@8% C."C.1:()3@0"% -#% -4.% L,K+% (70?.% )#1-"0$-% 01:%".$#@3-(#1%0".%
$(?1(/()01-@8%".:3).:E%N1%0%/3$.:%(70?.%C"#:3).:%/"#7%-U#%"#-0-.:%D3Q(G,LND%:0-0$.-$%
O"(?4-%)#@371P+%-4.%L,K% ($%1.M."%C."C.1:()3@0"%-#% -4.%).@@% 7.7;"01.$i%-4.% ".$3@-(1?%
(70?.%)#1-"0$-%01:%".$#@3-(#1%0".%-4."./#".%$3//()(.1-%-#%".$#@M.%(1:(M(:30@%).@@$%0@7#$-%
018U4.".%#1%-4.%.7;"8#h$%$3"/0).%O,..%,3CC@.7.1-0"8%Q(:.#%*ZPE%
Supplementary!Figure!9!
F.-0(@.:%M(.U%#/%-4.%?0CVSG7A4.""8%Drosophila%.7;"8#%$3"/0).E%(a)",.?7.1-0-(#1%#/%-4.%
7.7;"01.$%(1%01% .7;"8#%:3"(1?%.0"@8% )8)@.%*V%O.R)@3:(1?% C#@0"%".?(#1$PE%2".8$)0@.%
:0-0%($%-4.%/3$.:%01:%31"#@@.:%(70?.%U(-4%".:%@(1.$%:.1#-(1?%$.?7.1-.:%).@@%;#":."$E%(b)"
A@#$.G3C%#/%-4.%4(?4@(?4-.:%".?(#1%(1%O0PE%(c)"6(7.G@0C$.%M(.U%#/%).@@%7#M.7.1-$%01:%
$40C.$% :3"(1?% .0"@8% Drosophila!7#"C4#?.1.$($E% 64.% @./-% $)4.70-()% :.$)"(;.$% -4.%
M(.UC#(1-$% O01:% )#@#"% $)4.7.P% $4#U1% (1% "(?4-% C01.@$% O(GR((PE% O(GR((P% A#@#".:% :#-$%
)#"".$C#1:%-#% ).@@%).1-".$% 0$% /#31:%/"#7% (70?.% $.?7.1-0-(#1E%64.% $3;$.]3.1-% -(7.%
C#(1-$%$4#U%).@@$%-"0)X.:%).@@$%/#"%3C%-#%5Z%7(13-.$E%(d)"K"#7%-4.%-"0)X(1?%:0-0%$4#U1%
(1%O)P+%).@@%$C..:$%)01%;.%)0@)3@0-.:E%6(7.%($%:./(1.:%/"#7%-4.%/("$-%(70?.%#/%-4.%-(7.G
@0C$.%O/#3"%7(13-.$%;./#".%#1$.-%#/%M.1-"0@%/3""#U%/#"70-(#1PE%\""#U$%@0;.@@.:%O*GSP%
:.1#-.%-(7.$%U4.".%:(//.".1-%7#"C4#@#?()0@%.M.1-$%#))3"_%-4.%#1$.-%#/%M.1-"0@%/3""#U%
/#"70-(#1% O*P+% -4.% #1$.-% #/% ).C40@()% /3""#U% /#"70-(#1% O5P% 01:% -4.% ;.?(11(1?% #/%
?."7;01:%.R-.1$(#1%OSPE%%
!
Supplementary!Figure!10!
=(?4-G$4..-%0@(?17.1-%3$(1?%0%/@3#".$).1-%7.:(37E%(a)%f4.1%1#%.7($$(#1%/(@-."%($%3$.:%
(1%-4.%:.-.)-(#1%0"7+%C0"-()@.$%/@#0-(1?%-4"#3?4%-4.%C0"X.:%(@@37(10-(#1%;.07%?.1."0-.%
)(")3@0"%$40C.$%(1%-4.%(70?.E%,('.%#/%-4.%)(")@.$%($%"#3?4@8%C"#C#"-(#10@%-#%-4.%:($-01).%#/%
-4.%C0"-()@.%O01:% -4."./#".%/"#7%-4.% C0"X.:%@0$."%;.07P% /"#7%-4.%/#)0@%C@01.%#/%-4.%
:.-.)-(#1%0"7E%=0"?.%M0"(0-(#1%(1% -4.%)(")@.$`% $('.$%(7C@(.$% 0R(0@%7($0@(?17.1-%#/% -4.%
(@@37(10-(#1%#;^.)-(M.%@.1$+%U4(@.%31(/#"7@8%$('.:%)(")@.$%O0$%($%-4.%)0$.%(1%-4.%/(?3".P%
C#(1-%-#%0R(0@%7($0@(?17.1-%#/%-4.%:.-.)-(#1%#;^.)-(M.%@.1$E%(b!c)%k1).%01%.7($$(#1%/(@-."%
($%(1$."-.:+%/@3#".$).1).%?.1."0-.:%(1%-4.%C0"X.:%@0$."%;.07%".M.0@$%C".)($.%C#$(-(#1%
01:%$40C.%#/%-4.%;.07`$%U0($-E%\R(0@@8%0$877.-"()%;.07%(7C@(.$%0R(0@%7($0@(?17.1-%#/%
-4.%(@@37(10-(#1%#;^.)-(M.%@.1$%O;P+%U4(@.% 0%$877.-"()%;3-%-4()X% ;.07%(1:()0-.$%0R(0@%
7($0@(?17.1-%#/%-4.%:.-.)-(#1%@.1$%O)PE%(d)%N1%0%U.@@%0@(?1.:%,LND+%0%C0"X.:%@0$."%;.07%
?.1."0-.$%01%(70?.%#/%-4.%;.07%U(-4%(-$%U0($-%1.0"%-4.%).1-".%#/%-4.%(70?.E%64()X1.$$%#/%
-4.%U0($-%($%7(1(70@+%(E.E%018%0R(0@%7#M.7.1-%#/%-4.%:.-.)-(#1%#;^.)-(M.%@.1$%U(@@%(1)".0$.%
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".0:(@8%7.0$3".:E%K#"%:.7#1$-"0-(#1%C3"C#$.$+%-4.%;.07%U0($-% (1%-4($%/(?3".%($%$3;G
#C-(70@@8% -4(1+% U4()4% ".$3@-$% (1% 0% 4(?4% ;.07% :(M."?.1).% 01:% 0% @(?4-G$4..-% -40-% ($%
)#1$(:."0;@8%-4()X."%0-%-4.%$(:.$%#/%-4.%/(.@:%#/%M(.U%-401%(1%-4.%7(::@.E%(e)%N-%($%#/-.1%
3$./3@%-#%:($C@0).%-4.%;#-4%@(?4-G$4..-$+%7#M(1?%.0)4%U0($-%/#"%0%/"0)-(#1%#/%-4.%@(?4-G
$4..-`$%3$0;@.%@.1?-4%(1%-4.%:(".)-(#1%#CC#$(-.%-#%-4.%:(".)-(#1%#/%@(?4-E%64($%-"()X%)01%3C%
-#%:#3;@.%@(?4-G$4..-`$%3$0;@.%@.1?-4%U(-4#3-%0//.)-(1?%(-$%-4()X1.$$E%N1%-4($%.R07C@.+%-U#%
@(?4-G$4..-$%U(-4%3$0;@.%@.1?-4$%O:./(1.:%0$%-U().%-4.%c08@.(?4%"01?.1P%#/%*SSe7%0".%
:($C@0).:%/#"%0CC"#R(70-.@8%*ZZe7+%".$3@-(1?%(1%01%.//.)-(M.%@(?4-G$4..-%U(-4%-4()X1.$$%
;.-U..1%5ESe7% 01:%SE5e7% #M."%-4.% U4#@.%Drosophila%.7;"8#E%(f)%k1).%-4.%,LND%($%
0@(?1.:+% ?0@M01#7.-"()% $)011."% ($% 3$.:% -#% $)01% -4.% ;.07% #M."% -4.% /(.@:% #/% M(.U+%
?.1."0-(1?% 0% 4#7#?.1.#3$% @(?4-G$4..-E% K#"% :.7#1$-"0-(#1% C3"C#$.$+% $)011(1?%
07C@(-3:.% ($% $.-% 0% ;(-% ;.@#U% #C-(70@% (1% -4($% /(?3".+% @.0M(1?% C0"-$% #/% -4.% (70?.% 1#-%
(@@37(10-.:E%%
%
Supplementary!Note!1!
Light!sheet"properties"
L"#C."-(.$%#/%-4.%@(?4-G$4..-%)01%;.%.0$(@8%#;-0(1.:%/"#7%-4.%(70?.%?.1."0-.:%;8%0%U.@@%
/#)3$.:% C0"X.:% ;.07% (@@37(10-(1?% 0% /@3#".$).1-% 7.:(37% O$..% 0@$#% ,3CC@.7.1-0"8%
L"#-#)#@% *% 01:% ,3CC@.7.1-0"8% K(?E% *Z:PE% N1% #3"% )0$.+% -4.% ;.07% U0($-% "0:(3$% U0$%
7.0$3".:%-#%;.%mno5ES%pq%Orst%"0:(3$*P+%U4()4%)#"".$C#1:$%-#%:.C-4%#/%/#)3$%O-U().%
-4.%c08@.(?4%"01?.P%#/%5uno *SS%pqE%\/-."%-4.%U0($-$%#/%-4.%;#-4%;.07$%0".%:($C@0).:%
/#"%"#3?4@8%*ZZ%e7%O,3CC@.7.1-0"8%K(?E%*Z.P+%.//.)-(M.%@(?4-G$4..-%-4()X1.$$%($%;.-U..1%
5ES%e7%01:%SE5%e7%#M."%-4.%U4#@.%Drosophila%.7;"8#%O0"#31:%5ZZe7%(1%:(07.-."PE%,('.%
#/%-4.%@(?4-G$4..-%(1%-4.%:(".)-(#1%C."C.1:()3@0"%-#%-4.%(@@37(10-(#1%0R($%($%:./(1.:%;8%-4.%
$)011(1?%"01?.%#/%-4.%?0@M01#7.-"()%$)011."$%01:%U0$%$.-%0-%0"#31:%WWZe7%/#"%(70?(1?%
#/%Drosophila%.7;"8#$%O0"#31:%WZZ%e7%(1%@.1?-4PE%
=0-."0@% ".$#@3-(#1% OU(-4(1% -4.% /#)0@% C@01.P% #/% -4.% 7()"#$)#C.% ($% :.-."7(1.:% ;8% -4.%
137."()0@%0C."-3".%#/%-4.%:.-.)-(#1%#;^.)-(M.%@.1$%01:%U(@@%;.%0"#31:%ZEVS%e72%(1%)0$.%#/%
-4.% VZRvZEYf% @.1$% 01:% H*Z% 17% .7($$(#1% O0$% 3$.:% /#"% Drosophila%(70?(1?PE%\R(0@%
".$#@3-(#1%O0@#1?%-4.%:.-.)-(#1%0R($P%U(@@%;.%:"(M.1%C"(70"(@8%;8%-4.%0R(0@%".$#@3-(#1%#/%
-4.%:.-.)-(#1%@.1$%O5E5Y%e7%U(-4%VZRvZEYfP+%0@-4#3?4%0%7(1#"%(7C"#M.7.1-%O0"#31:%
*Zw%0-%-4.%U0($-%#/%.0)4%@(?4-G$4..-P%($%.RC.)-.:%:3.%-#%-4.%@(?4-G$4..-%(@@37(10-(#13E%
!
Supplementary!Note!2!
Microscope"instrument"control"architecture"
64.%7()"#$)#C.%)#1-"#@%.@.)-"#1()$%($%;3(@:%0"#31:%-U#%4(?4G$C..:%010@#?%#3-C3-%)0":$%
O[N%LANGHJSS%01:%LAN.GH5WI+%[0-(#10@%N1$-"37.1-$%A#"CEPE%\10@#?%#3-C3-%)4011.@$%0".%
3$.:%-#% :(".)-@8%$.-% -4.%@0$."% (1-.1$(-(.$%O3$(1?%01% 0)#3$-#G#C-()0@%-310;@.% /(@-."+%$..%
kC-()0@% $.-3C"(1% D.-4#:$P+% C#$(-(#1$% #/% abGL(.'#% $-0?.% 01:% ?0@M01#7.-"()% $)011."$%
O-4"#3?4%D(1(,0R%B#0":$+%A07;"(:?.%6.)41#@#?8%N1)EPE%6#%.1$3".%7()"#$.)#1:%C".)($.%
-(7(1?+%M#@-0?.%-"0).$%/#"%-4.$.%:.M().$%0".%C".G)#7C3-.:%01:%-4.1%3C@#0:.:%#1-#%-4.%
)0":$%-#?.-4."%U(-4%)#"".$C#1:(1?%:(?(-0@%-"0).$%-#%-"(??."%)07."0+%\k6K%;@01X(1?%01:%
(@@37(10-(#1%$-.."(1?%/@(CG7(""#"E%K(@-."%U4..@+%$C.)(7.1%"#-0-(#1%01:%$C.)(7.1%M."-()0@%
-"01$@0-(#1%0".%)#1-"#@@.:%#M."%c,5S5%C#"-$+%0$%-4.("%#C."0-(#1%($%1#-%-(7.%)"(-()0@E%
\1%(7C#"-01-%C0"-%#/%D3Q(G,LND%($%-4.%.//()(.1-%01:%/0$-%401:@(1?%#/%-4.%@0"?.%07#31-$%#/%
:0-0E%64.%/3@@%(70?.%0)]3($(-(#1%01:%C"#).$$(1?%C(C.@(1.%)#7C"($.$%#/%/#3"%)#7C3-."$_%0%
7()"#$)#C.%)#1-"#@%)#7C3-."%ON1-.@%A#".5F3#%"311(1?%0-% SE*H%2>'+%V%2B%c\DPi%-U#%
)07."0%)#7C3-."$%O.0)4%/.0-3"(1?%-U#%]30:G)#".%5EV2>'%N1-.@%a.#1%AL!$+%5V%2B%#/%c\D%
01:%\".)0%\cA%*YYZN%c\NF%)#1-"#@@."%U(-4%/#3"%5%6B%40":%:"(M.$Pi%01:%0%:0-0%010@8$($%
)#7C3-."%O$..%N70?.%/3$(#1"(1%D.-4#:$PE%\1%YZ%6B%c\NFH%;0$.:%$-#"0?.%$8$-.7%O3$(1?%0%
Sf0".%IJWZGY(%c\NF%)#1-"#@@."P%($%3$.:%-#%$-#".%-4.%@0"?.%07#31-$%#/%:0-0%C"#:3).:%;8%
D3Q(G,LNDE%\%C"(M0-.% *Z%?(?0;(-%9-4."1.-% #C-()0@%1.-U#"X% 0@@#U$%/#"%/0$-% -"01$/."%#/%
(70?.$%;.-U..1%-4.%7()"#$)#C.+%$-#"0?.%$8$-.7%01:%(70?.%C"#).$$(1?%)#7C3-."+%01:%
)#7731()0-(#1%;.-U..1%-4.%7()"#$)#C.%)#7C3-."$E%
%
64.%7()"#$)#C.%($%)#1-"#@@.:%;8%0%)3$-#7GU"(--.1%C"#?"07%)#:.:%(1%=0;Q(.U%O[0-(#10@%
N1$-"37.1-$%A#"CEP%-40-%401:@.$%-4.%)#7731()0-(#1%U(-4%-4.%.@.)-"#1()$%01:%-4.%#-4."%
)#7C3-."$E%
!
Supplementary!Note!3!
Assessment"of"imaging"time"
\@@G0"#31:%(70?(1?%U(-4%D3Q(G,LND%($%/0$-."%-401%U(-4%0%".?3@0"%,LND4%#"%7,LND5%/#"%
-U#%:($-(1)-%".0$#1$E%64.%/("$-%($$3.%)#1)."1$%-4.%-(7.%-4.%$C.)(7.1%($%0)-30@@8%(70?.:%
O.R)@3:(1?% -4.% -(7.% ".]3(".:% /#"% "#-0-(#1P% 0-% .0)4% -(7.% C#(1-E% D3Q(G,LND% C"#:3).$%
M("-30@@8% ".?($-.".:% (70?.$+% U4(@.% "#-0-(#1% ;0$.:% 73@-(GM(.U% (70?(1?% U(-4% ,LND% #"%
7,LND% ".]3(".$% (70?.G;0$.:% (70?.% ".?($-"0-(#1E% 64.% @0--."% 1.).$$(-0-.$% $3//()(.1-%
#M."@0C%;.-U..1%-4.%:(//.".1-%M(.U$+%U4()4%(1.M(-0;@8%@.0:$%-#%$#7.%0".0$%;.(1?%(70?.:%
73@-(C@.%-(7.$E%64.%.R0)-%$('.%#/%-4.%1.).$$0"8%#M."@0C%:.C.1:$%#1%-4.%C"#C."-(.$%#/%-4.%
$C.)(7.1%O$C.)(7.1%U(-4%@#U% C.1.-"0-(#1% :.C-4% 01:v#"% @0)X(1?% $40"C% /.0-3".$% U(@@%
?.1."0@@8%".]3(".%@0"?."%#M."@0C%;.-U..1%-4.%M(.U$PE%N1%-4.#"8+%-4($%#M."@0C%)#3@:%;.%
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Supplementary!Note!4!
Calculation"of"transformation"parameters"
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Supplementary!Note!5!
Equirectangular"projection"of"Drosophila"embryo"surface"""
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Supplementary!Note!6!
Further"details"on"analysis"of"Drosophila"embryo"images"
Early!Drosophila!development!
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Supplementary!Protocol!2!
Diagnostic"specimen"preparation"
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Supplementary!Protocol!3!
Drosophila"embryo!preparation!and!mounting!!
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References!
*E% ,0@.4+%BE%9E%\E%³%6.()4+%DE%AE%Fundamentals!of!PhotonicsE%Of(@.8%³%,#1$_%5ZZJPE%
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... AO dynamically corrects wavefront distortions caused by tissue heterogeneity, improving resolution at greater depths [61]. Multiview imaging, where the sample is rotated for imaging from multiple angles, complements AO by reconstructing comprehensive 3D views of thicker specimens [33]. Together, these advancements enable LSFM to overcome some of its depth-related limitations. ...
... Adaptive optics (AO) has emerged as a promising solution, dynamically correcting wavefront distortions caused by tissue heterogeneity and enabling sharper imaging at greater depths [61]. Additionally, multiview imaging approaches, where the sample is rotated to capture images from multiple angles, have been employed to overcome depth limitations and reconstruct complete 3D views of thicker specimens [33]. ...
... For instance, in terms of engineering the laser intensity profile, lattice light sheets provide improved axial resolution, rapid imaging with reduced phototoxicity/damage to the sample 18 , while Airy/Bessel beams, especially with two-photon excitation, provide a superior axial resolution across a wider lateral extent 19,20 . In terms of engineering the optical geometry, dual excitation/detection 21,22 , line confocal approaches [23][24][25] and multi-modal integration OPTiSPIM 26 can effectively reduce artifacts/blurring and alleviate the scattering problems associated with light sheet imaging. In terms of post-processing, deconvolution methods applied in LSFM with propagation-invariant beams 27,28 can provide improved isotropic resolution and larger field of view. ...
Article
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Time-resolved volumetric fluorescence imaging over an extended duration with high spatial/temporal resolution is a key driving force in biomedical research for investigating spatial-temporal dynamics at organism-level systems, yet it remains a major challenge due to the trade-off among imaging speed, light exposure, illumination power, and image quality. Here, we present a deep-learning enhanced light sheet fluorescence microscopy (LSFM) approach that addresses the restoration of rapid volumetric time-lapse imaging with less than 0.03% light exposure and 3.3% acquisition time compared to a typical standard acquisition. We demonstrate that the convolutional neural network (CNN)-transformer network developed here, namely U-net integrated transformer (UI-Trans), successfully achieves the mitigation of complex noise-scattering-coupled degradation and outperforms state-of-the-art deep learning networks, due to its capability of faithfully learning fine details while comprehending complex global features. With the fast generation of appropriate training data via flexible switching between confocal line-scanning LSFM (LS-LSFM) and conventional LSFM, this method achieves a three- to five-fold signal-to-noise ratio (SNR) improvement and ~1.8 times contrast improvement in ex vivo zebrafish heart imaging and long-term in vivo 4D (3D morphology + time) imaging of heartbeat dynamics at different developmental stages with ultra-economical acquisitions in terms of light dosage and acquisition time.
... Light sheet fluorescence microscopy (LSFM) has become a key instrument in modern biology with its confined excitaGon and its capability to image live, intact biological samples fast and gently [1][2][3][4] . In LSFM, opGcal secGoning is achieved by two orthogonal opGcal arms 5 : one projects a laser light sheet into the sample, illuminaGng a thin secGon, and the other records the emimed fluorescence light perpendicularly with a camera. ...
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Light sheet microscopy is the ideal technique for multiscale imaging of large and cleared tissues, and it is desirable to achieve the highest possible isotropic resolution across the entire sample. However, isotropic resolution for a centimeter-sized sample has only been achieved with slow and often aberrated, axially scanned light sheets, resulting in a low resolution of several micrometers. Here, we introduce a compact, high-speed light sheet fluorescence microscope with isotropic sub-micron resolution optimized for cleared tissue. We introduce three major opto-mechanical innovations using off-the-shelf optics to achieve an isotropic resolution of 850 nm across samples up to 1 cm3 and refractive indices ranging from 1.33 to 1.56. We show that combining an air objective and a meniscus lens achieves an axially swept light sheet with sub-micron diffraction-limited resolution, free of aberrations. The effective field of view is increased 2-fold by correcting the field curvature of the light sheet with a concave mirror in the remote focusing unit. Furthermore, the imaging speed is enhanced 10-fold by adapting the light sheet's motion with a closed-loop feedback, reaching 100 frames per second while maintaining isotropic resolution across the large field of view. Finally, we showcase the performance of our light sheet system for imaging from subcellular up to centimeter scale in cleared mouse cochlea and brain.
... Traditional experimental techniques used in previous research involve pathology spot counting in thin two-dimensional (2D) sections of localized subregions, which limits the assessment of the three-dimensional (3D) spatial distribution of amyloid-beta (Aβ) particles within whole, intact brain structures. In contrast, Macro-LSFM, combined with tissue-clearing technologies, offers an expansive field of view without compromising spatial resolution, enabling imaging of entire brain structures while preserving their 3D integrity and providing comprehensive insights into the 3D spatial distribution of complex neuropathological networks within the fully transparent brain [16][17][18][19]. In addition to the significant reduction in total amyloid surface volume, the swim exercise regimen slightly reduced the total plaque number, total surface area, and area or volume of individual plaques. ...
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Emerging evidence from observational studies suggests that lifestyle modifications, particularly moderate-intensity exercise, may confer neuroprotective benefits against dementia, potentially by enhancing brain resistance through clearance mechanisms. Using light-sheet fluorescence microscopy (LSFM) with tissue clearing, we investigated the role of voluntary swimming in ameliorating β-amyloid pathology in a transgenic Alzheimer’s disease (AD) mouse model. Twenty 52-week-old hAPPsw mice were randomly divided into a 5-week voluntary swimming intervention group and a control group (each n = 10). Each session included a 10-min swim followed by a 10-min rest, escalating from one session per day in the first week to three sessions per day by the fifth week. The excised brains were prepared using tissue-clearing and volume immunostaining with thioflavin-S for β-amyloid. For LSFM imaging, the individual plaque area and volume, total plaque load, and morphological parameters were quantified via an Imaris-based three-dimensional (3D) volumetric surface model. Visual comparison revealed that the intervention group presented significantly lower β-amyloid accumulation. The total surface volume of β-amyloid accumulation in the intervention group was significantly lower than that of the control group (intervention, 122,180,948 μm³ [105,854,660–169,063,081]; control, 167,201,016 μm³ [139,367,765–193,535,450]; p = 0.043). There were no significant differences in the morphological parameters, such as ellipticity and sphericity. Our LSFM study demonstrated notable reductions in β-amyloid, as evidenced by a decrease in total surface volume, in 52-week-old transgenic mice after a 5-week structured swimming program, supporting the notion that even in advanced AD stages, leisure-time voluntary swimming serves as an efficacious intervention for augmenting resistance to pathology.
... Similarly, the sectioning strength and field of view are inversely related due to the constraints on the Gaussian beam thickness and transmission distance 123,124 . As such, methods such as multi-view imaging or scanning the illumination across the field are adopted to extend the field of view substantially [125][126][127][128][129] . However, the effectiveness can be affected by sample rotation or the capabilities of the scanning device. ...
Article
Full-text available
In recent advancements in life sciences, optical microscopy has played a crucial role in acquiring high-quality three-dimensional structural and functional information. However, the quality of 3D images is often compromised due to the intense scattering effect in biological tissues, compounded by several issues such as limited spatiotemporal resolution, low signal-to-noise ratio, inadequate depth of penetration, and high phototoxicity. Although various optical sectioning techniques have been developed to address these challenges, each method adheres to distinct imaging principles for specific applications. As a result, the effective selection of suitable optical sectioning techniques across diverse imaging scenarios has become crucial yet challenging. This paper comprehensively overviews existing optical sectioning techniques and selection guidance under different imaging scenarios. Specifically, we categorize the microscope design based on the spatial relationship between the illumination and detection axis, i.e., on-axis and off-axis. This classification provides a unique perspective to compare the implementation and performances of various optical sectioning approaches. Lastly, we integrate selected optical sectioning methods on a custom-built off-axis imaging system and present a unique perspective for the future development of optical sectioning techniques.
... As long-term fluorescence live imaging data from multiple insect species become accessible, morphological structures and the associated morphogenic processes can be thoroughly analyzed within an evolutionary context. Currently, LSFM data for four insect species-the fruit fly [76][77][78][79], the scuttle fly [70], the red flour beetle [80][81][82] and now the medfly [42]-are already available, facilitating comparative research on the embryonic morphogenesis of insects. ...
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Comparative studies across multiple species provide valuable insights into the evolutionary diversification of developmental strategies. While the fruit fly Drosophila melanogaster has long been the primary insect model organism for understanding molecular genetics and embryonic development, the Mediterranean fruit fly Ceratitis capitata, also known as medfly, presents a promising complementary model for studying developmental biology. With its sequenced genome and a diverse array of molecular techniques, the medfly is well-equipped for study. However, an integrative framework for studying its embryogenesis is currently lacking. In this study, we present a two-level staging system for the medfly based on nine datasets recorded using light sheet fluorescence microscopy. The upper level features of six consecutive embryogenetic events, facilitating comparisons between insect orders, while the lower level consists of seventeen stages, adapted from the fruit fly, allowing for comparisons within the Diptera. We provide detailed descriptions of all identifiable characteristics in multiple formats, including a detailed timetable, comprehensively illustrated figures for all embryogenetic events, glossary-like tables for selected structures and processes, as well as a stage-based quick lookup chart. One remarkable difference between the fruit fly and the medfly is that in the latter, the amnioserosa differentiates and unfolds already during gastrulation. Our staging system, which is based on systematically acquired fluorescence live imaging data, provides standard deviations for each developmental time point and serves as a template for future studies seeking to establish an integrative morphogenic framework for other emerging model insect species.
... The Drosophila embryo is one of the best-established models of developmental mechanics as it is ideal for live imaging and offers an extensive set of genetic tools (Hales et al., 2015). Progress in live imaging and computational image analysis has produced remarkably quantitative data (Krzic et al., 2012;Mitchell et al., 2022). In this work, we will take advantage of a previously published dataset which we will reanalyze and use as a test-bed for theory development. ...
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Shape changes of epithelia during animal development, such as convergent extension, are achieved through the concerted mechanical activity of individual cells. While much is known about the corresponding large-scale tissue flow and its genetic drivers, fundamental questions regarding local control of contractile activity on the cellular scale and its embryo-scale coordination remain open. To address these questions, we develop a quantitative, model-based analysis framework to relate cell geometry to local tension in recently obtained time-lapse imaging data of gastrulating Drosophila embryos. This analysis systematically decomposes cell shape changes and T1 rearrangements into internally driven, active, and externally driven, passive, contributions. Our analysis provides evidence that germ band extension is driven by active T1 processes that self-organize through positive feedback acting on tensions. More generally, our findings suggest that epithelial convergent extension results from the controlled transformation of internal force balance geometry which combines the effects of bottom-up local self-organization with the top-down, embryo-scale regulation by gene expression.
Chapter
The pursuit of exploring cardiac morphogenesis and remodeling in its entirety and in real time has posed a persistent optical challenge in cardiac development and regeneration research. The emergence of light-sheet microscopy (LSM) has provided unparalleled advantages in spatial resolution, imaging speed, field of view, and depth of field. The representative design of decoupled illumination and detection further allows for minimal photobleaching and phototoxicity, in contrast to conventional optical imaging approaches. This strategy enhances in vivo visualization from the developing circulation system in live zebrafish embryos to ex vivo investigation of the micro-structure of optically cleared rodent cardiovascular anatomy. We hereby introduce basic concepts of LSM and its numerous applications, showcasing representative studies that have advanced the understanding of developmental and pathological processes in hearts, as well as vascular networks across various levels. Through these investigations, we discuss the potential and limitations of multi-scale LSM in unveiling cardiovascular structure and function. Overall, the versatile framework of LSM holds promise for elucidating intricate cardiovascular systems, providing insights into cardiac morphogenesis and disease pathogenesis.
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Colorectal epithelium was the first long-term 3D organoid culture established in vitro. Identification of the key components essential for the long-term survival of the stem cell niche allowed an indefinite propagation of these cultures and the modulation of their differentiation into various lineages of mature intestinal epithelial cells. While these methods were eventually adapted to establish organoids from different organs, colorectal organoids remain a pioneering model for the development of new applications in health and disease. Several basic and applicative aspects of organoid culture, modeling, monitoring and testing are analyzed in this review. We also tackle the ethical problems of biobanking and distribution of these precious research tools, frequently confined in the laboratory of origin or condemned to destruction at the end of the project.
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During embryogenesis, the first cell fate decision—whether the cell participates in development of the embryo or not—is often linked to the positioning of the nucleus. The cell cycle oscillator and associated cytoskeletal dynamics contribute to the control of nuclear positioning. However, the mechanisms that ensure that the correct number of nuclei move to their appropriate place remain poorly understood. Here we show that the orientation of the mitotic spindle controls the first fate decision, embryonic or yolk cell fate, in Drosophila embryos using light sheet microscopy experiments. Combining computational methods inspired by integral geometry, manipulation of cell cycle genes, and investigation of the relationship between geometry and topology, we show that spindle orientation is controlled by topological interactions with neighbouring nuclei and not by internuclear distance. Leveraging the physics of space-filling systems, we develop a theory for topological dependency in microtubule structures. Our work shows how the topological interplay of microtubule mechanics can ensure robust control of nuclear density and determine cell fate.
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A long-standing goal of biology is to map the behavior of all cells during vertebrate embryogenesis. We developed digital scanned laser light sheet fluorescence microscopy and recorded nuclei localization and movement in entire wild-type and mutant zebrafish embryos over the first 24 hours of development. Multiview in vivo imaging at 1.5 billion voxels per minute provides “digital embryos,” that is, comprehensive databases of cell positions, divisions, and migratory tracks. Our analysis of global cell division patterns reveals a maternally defined initial morphodynamic symmetry break, which identifies the embryonic body axis. We further derive a model of germ layer formation and show that the mesendoderm forms from one-third of the embryo's cells in a single event. Our digital embryos, with 55 million nucleus entries, are provided as a resource.
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: Report developed under SBIR contract. A two-color particle imaging velocimetry system was demonstrated for obtaining two-dimensional velocity measurements in a subsonic reacting flow and in a supersonic flowfield. Data was obtained using three different camera systems, an 8-mm video camera, a digital color CCD camera system and a 35-mm camera. The data obtained from the wall boundary layer region in the supersonic wind tunnel showed good correlation with LDV data. The flow in the wake region of the flame holder was investigated using the PIV system developed. The results obtained through these experiments showed that the PIV system was capable of resolving the flow information in the highly turbulent region with a high degree of accuracy. Values of shear layer stresses behind the bluff body region were also calculated from the PIV data. Reacting flow experiments showed the ability of the PIV system to resolve velocity fields in a highly turbulent, large gradient flow regime. The software yielded RMS fluctuations in the 1.5% range which is less than the 3% limit widely reported in the literature. Taitech, Inc. is currently commercializing the PIV system developed in this contract.
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To master the fundamentals of image registration, there is no more comprehensive source than 2-D and 3-D Image Registration. In addition to delving into the relevant theories of image registration, the author presents their underlying algorithms. You'll also discover cutting-edge techniques to use in remote sensing, industrial, and medical applications. Examples of image registration are presented throughout, and the companion Web site contains all the images used in the book and provides links to software and algorithms discussed in the text, allowing you to reproduce the results in the text and develop images for your own research needs. 2-D and 3-D Image Registration serves as an excellent textbook for classes in image registration as well as an invaluable working resource.
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