海南医学
海南醫學
해남의학
HAINAN MEDICAL JOURNAL
2014年
21期
3163-3165,3166
,共4页
张德军%马春%张祖建%明兵
張德軍%馬春%張祖建%明兵
장덕군%마춘%장조건%명병
血管成像%卷积核%颈内动脉%椎动脉%X线计算机%体层摄影术
血管成像%捲積覈%頸內動脈%椎動脈%X線計算機%體層攝影術
혈관성상%권적핵%경내동맥%추동맥%X선계산궤%체층섭영술
Angiography%Kernel algorithm%Internal carotid artery%Vertebral artery%X-ray computed%To-mography
目的:应用不同的卷积核算法,使用高级血管分析软件的半自动追踪曲面重组功能显示颈内动脉颅内段及椎动脉,探讨最容易曲面重组与骨质结构关系密切血管的卷积核算法。方法对32例临床疑有脑缺血或蛛网膜下腔出血病例进行头颈部Neuro DSA CTA扫描。默认图像重建卷积核算法为H10f,然后分别选择H48f、H50f及H60f卷积核算法进行重建。使用Inspace软件下的AVA软件进行血管分析,计数载入不同卷积核算法图像半自动追踪成功曲面重组颈内动脉及椎动脉支数。结果32例共128支血管。H10f算法图像半自动追踪曲面重组显示0支血管,成功率为0;H48f算法图像显示44支血管,成功率为34%;H50f算法图像显示68支血管,成功率为53%;H60f算法图像显示108支血管,成功率为84%,两两比较差异均有统计学意义(P<0.01);H60f算法图像半自动追踪曲面重组成功率明显高于其他两种,H60f算法图像对颈内动脉曲面重组成功率明显高于椎动脉。结论使用AVA软件对颈内动脉颅内段及椎动脉进行血管分析时,应用较高卷积核(H60f)算法能明显改善半自动追踪血管曲面重组成功率,利于进一步进行血管分析,有较大临床实用价值。
目的:應用不同的捲積覈算法,使用高級血管分析軟件的半自動追蹤麯麵重組功能顯示頸內動脈顱內段及椎動脈,探討最容易麯麵重組與骨質結構關繫密切血管的捲積覈算法。方法對32例臨床疑有腦缺血或蛛網膜下腔齣血病例進行頭頸部Neuro DSA CTA掃描。默認圖像重建捲積覈算法為H10f,然後分彆選擇H48f、H50f及H60f捲積覈算法進行重建。使用Inspace軟件下的AVA軟件進行血管分析,計數載入不同捲積覈算法圖像半自動追蹤成功麯麵重組頸內動脈及椎動脈支數。結果32例共128支血管。H10f算法圖像半自動追蹤麯麵重組顯示0支血管,成功率為0;H48f算法圖像顯示44支血管,成功率為34%;H50f算法圖像顯示68支血管,成功率為53%;H60f算法圖像顯示108支血管,成功率為84%,兩兩比較差異均有統計學意義(P<0.01);H60f算法圖像半自動追蹤麯麵重組成功率明顯高于其他兩種,H60f算法圖像對頸內動脈麯麵重組成功率明顯高于椎動脈。結論使用AVA軟件對頸內動脈顱內段及椎動脈進行血管分析時,應用較高捲積覈(H60f)算法能明顯改善半自動追蹤血管麯麵重組成功率,利于進一步進行血管分析,有較大臨床實用價值。
목적:응용불동적권적핵산법,사용고급혈관분석연건적반자동추종곡면중조공능현시경내동맥로내단급추동맥,탐토최용역곡면중조여골질결구관계밀절혈관적권적핵산법。방법대32례림상의유뇌결혈혹주망막하강출혈병례진행두경부Neuro DSA CTA소묘。묵인도상중건권적핵산법위H10f,연후분별선택H48f、H50f급H60f권적핵산법진행중건。사용Inspace연건하적AVA연건진행혈관분석,계수재입불동권적핵산법도상반자동추종성공곡면중조경내동맥급추동맥지수。결과32례공128지혈관。H10f산법도상반자동추종곡면중조현시0지혈관,성공솔위0;H48f산법도상현시44지혈관,성공솔위34%;H50f산법도상현시68지혈관,성공솔위53%;H60f산법도상현시108지혈관,성공솔위84%,량량비교차이균유통계학의의(P<0.01);H60f산법도상반자동추종곡면중조성공솔명현고우기타량충,H60f산법도상대경내동맥곡면중조성공솔명현고우추동맥。결론사용AVA연건대경내동맥로내단급추동맥진행혈관분석시,응용교고권적핵(H60f)산법능명현개선반자동추종혈관곡면중조성공솔,리우진일보진행혈관분석,유교대림상실용개치。
Objective To investigate the correct kernel algorithm for vessels close to adjacent bone when semi-automated tracing internal carotid artery (ICA) intracranial segment and vertebral artery (VA) with curved planar reformation (CPR) in advanced vessels analysis (AVA). Methods Thirty-two patients were enrolled and were scanned by Neuro DSA CT angiograply. Algorithms of H10f, H48f, H50f, H60f were selected. Inspace AVA was applied for ves-sels analysis, the number of ICA and VA was counted, which were semi-automated traced well. Results The 32 pa-tients had a total of 128 vessels. No vessel was successfully curve planar reformatted with H10f. Forty-four vessels (34%) was successfully displayed with H48f and 68 vessels (53%) with H50f, 108 vessels (84%) with H60f. Signifi-cant difference was found between different groups (P<0.05). The achievement ratio of H60f was significantly higher than H48f and H50f, and achievement ratio of H60f was higher in ICA than VA. Conclusion High kernel algorithm could improve achievement ratio in semi-automated traced CPR for advanced vessel analysis, when analyzing ICA in-tracranial segment and VA with AVA.