Boundary　condition　settings　are　key　risk　factors　for　the　accuracy　of　noninvasive　quantification　of　fractional　flow　reserve　(FFR)　based　on　computed　tomography　angiography　(i.e.,　FFRCT).　However,　transient　numerical　simulation-based　FFRCT　often　ignores　the　three-dimensional　(3D)　model　of　coronary　artery　and　clinical　statistics　of　hyperemia　state　set　by　boundary　conditions,　resulting　in　insufficient　computational　accuracy　and　high　computational　cost.　Therefore,　it　is　necessary　to　develop　the　custom　function　that　combines　the　3D　model　of　the　coronary　artery　and　clinical　statistics　of　hyperemia　state　for　boundary　condition　setting,　to　accurately　and　quickly　quantify　FFRCT　under　steady-state　numerical　simulations.　The　3D　model　of　the　coronary　artery　was　reconstructed　by　patient　computed　tomography　angiography　(CTA),　and　coronary　resting　flow　was　determined　from　the　volume　and　diameter　of　the　3D　model.　Then,　we　developed　the　custom　function　that　took　into　account　the　interaction　of　stenotic　resistance,　microcirculation　resistance,　inlet　aortic　pressure,　and　clinical　statistics　of　resting　to　hyperemia　state　due　to　the　effect　of　adenosine　on　boundary　condition　settings,　to　accurately　and　rapidly　identify　coronary　blood　flow　for　quantification　of　FFRCT　calculation　(FFRU).　We　tested　the　diagnostic　accuracy　of　FFRU　calculation　by　comparing　it　with　the　existing　methods　(CTA,　coronary　angiography　(QCA),　and　diameter-flow　method　for　calculating　FFR　(FFRD))　based　on　invasive　FFR　of　86　vessels　in　73　patients.　The　average　computational　time　for　FFRU　calculation　was　greatly　reduced　from　1–4 h　for　transient　numerical　simulations　to　5 min　per　simulation,　which　was　2-fold　less　than　the　FFRD　method.　According　to　the　results　of　the　Bland-Altman　analysis,　the　consistency　between　FFRU　and　invasive　FFR　of　86　vessels　was　better　than　that　of　FFRD.　The　area　under　the　receiver　operating　characteristic　curve　(AUC)　for　CTA,　QCA,　FFRD　and　FFRU　at　the　lesion　level　were　0.62　(95%　CI:　0.51–0.74),　0.67　(95%　CI:　0.56–0.79),　0.85　(95%　CI:　0.76–0.94),　and　0.93　(95%　CI:　0.87–0.98),　respectively.　At　the　patient　level,　the　AUC　was　0.61　(95%　CI:　0.48–0.74)　for　CTA,　0.65　(95%　CI:　0.53–0.77)　for　QCA,　0.83　(95%　CI:　0.74–0.92)　for　FFRD,　and　0.92　(95%　CI:　0.89–0.96)　for　FFRU.　The　proposed　novel　method　might　accurately　and　rapidly　identify　coronary　blood　flow,　significantly　improve　the　accuracy　of　FFRCT　calculation,　and　support　its　wide　application　as　a　diagnostic　indicator　in　clinical　practice.　Copyright　©　2023　Zhang,　Song,　Wu,　Li,　Hou,　Xie,　Hou　and　Qiao.