Background: The LiBackpack is a recently developed backpack light detection and ranging(LiDAR) system that combines the flexibility of human walking with the nearby measurement in all directions to provide a novel and...Background: The LiBackpack is a recently developed backpack light detection and ranging(LiDAR) system that combines the flexibility of human walking with the nearby measurement in all directions to provide a novel and efficient approach to LiDAR remote sensing, especially useful for forest structure inventory. However, the measurement accuracy and error sources have not been systematically explored for this system.Method: In this study, we used the LiBackpack D-50 system to measure the diameter at breast height(DBH) for a Pinus sylvestris tree population in the Saihanba National Forest Park of China, and estimated the accuracy of LiBackpack measurements of DBH based on comparisons with manually measured DBH values in the field. We determined the optimal vertical slice thickness of the point cloud sample for achieving the most stable and accurate LiBackpack measurements of DBH for this tree species, and explored the effects of different factors on the measurement error.Result: 1) A vertical thickness of 30 cm for the point cloud sample slice provided the highest fitting accuracy(adjusted R2= 0.89, Root Mean Squared Error(RMSE) = 20.85 mm);2) the point cloud density had a significant negative, logarithmic relationship with measurement error of DBH and it explained 35.1% of the measurement error;3) the LiBackpack measurements of DBH were generally smaller than the manually measured values, and the corresponding measurement errors increased for larger trees;and 4) by considering the effect of the point cloud density correction, a transitional model can be fitted to approximate field measured DBH using LiBackpackscanned value with satisfactory accuracy(adjusted R2= 0.920;RMSE = 14.77 mm), and decrease the predicting error by 29.2%. Our study confirmed the reliability of the novel LiBackpack system in accurate forestry inventory, set up a useful transitional model between scanning data and the traditional manual-measured data specifically for P.sylvestris, and implied the applicable substitution of this new approach fo展开更多
目的比较乳腺同时多层单次激发平面回波成像(SMS+SS-EPI)和分段读出平面回波(RESOLVE)扩散加权成像(DWI)的图像质量及其诊断乳腺恶性病灶的效能。方法该研究为横断面研究。前瞻性分析2021年3月至2023年2月宁波大学附属第一医院因乳腺病...目的比较乳腺同时多层单次激发平面回波成像(SMS+SS-EPI)和分段读出平面回波(RESOLVE)扩散加权成像(DWI)的图像质量及其诊断乳腺恶性病灶的效能。方法该研究为横断面研究。前瞻性分析2021年3月至2023年2月宁波大学附属第一医院因乳腺病变而接受MRI检查并最终经病理证实的102例患者的临床及影像资料。所有患者均接受乳腺MRI常规扫描以及横断面RESOLVE和SMS+SS-EPI序列DWI,评估2种序列成像的图像质量。主观评价采用5分法,评价内容包括乳腺的几何畸变、伪影模糊、脂肪抑制、整体图像质量和病灶显著性;客观评价包括病灶信噪比(SNR)、对比噪声比(CNR)、病灶表观扩散系数(ADC)值。采用Wilcoxon符号秩和检验比较2种序列成像的主、客观评价指标。最后采用受试者操作特征曲线及曲线下面积(AUC)评估2种序列图像上病灶ADC值诊断乳腺恶性病灶的效能。结果102例患者均为单发病变,均为女性患者,年龄25~68岁。乳腺恶性病灶60例,良性病变42例。SMS+SS-EPI序列成像的采集时间为1 min 50 s,RESOLVE序列成像的采集时间为3 min 43 s。SMS+SS-EPI和RESOLVE序列成像的图像质量主观评分均≥3分,RESOLVE序列图像的几何畸变、伪影模糊、整体图像质量评分均高于SMS+SS-EPI序列(P均<0.001)。SMS+SS-EPI序列的DWI图像和ADC图像的总体病灶显著性评分、恶性病灶显著性评分、良性病灶的显著性评分均高于RESOLVE序列(P均<0.05)。SMS+SS-EPI和RESOLVE序列DWI图像乳腺病灶的SNR、CNR、总体ADC值及恶性病灶ADC值、良性病灶ADC值差异均无统计学意义(P均>0.05)。RESOLVE序列成像的病灶ADC值诊断乳腺恶性病灶的AUC为0.973,灵敏度为0.929,特异度为0.915;SMS+SS-EPI序列成像的病灶ADC值诊断乳腺恶性病灶的AUC为0.956,灵敏度为0.977,特异度为0.850。结论在乳腺DWI中,SMS+SS-EPI图像质量主观评分能基本满足诊断要求且病变显著性好于RESOLVE序列�展开更多
基金supported by the projects (41790425,41971228) of Natural Science Foundation of China。
文摘Background: The LiBackpack is a recently developed backpack light detection and ranging(LiDAR) system that combines the flexibility of human walking with the nearby measurement in all directions to provide a novel and efficient approach to LiDAR remote sensing, especially useful for forest structure inventory. However, the measurement accuracy and error sources have not been systematically explored for this system.Method: In this study, we used the LiBackpack D-50 system to measure the diameter at breast height(DBH) for a Pinus sylvestris tree population in the Saihanba National Forest Park of China, and estimated the accuracy of LiBackpack measurements of DBH based on comparisons with manually measured DBH values in the field. We determined the optimal vertical slice thickness of the point cloud sample for achieving the most stable and accurate LiBackpack measurements of DBH for this tree species, and explored the effects of different factors on the measurement error.Result: 1) A vertical thickness of 30 cm for the point cloud sample slice provided the highest fitting accuracy(adjusted R2= 0.89, Root Mean Squared Error(RMSE) = 20.85 mm);2) the point cloud density had a significant negative, logarithmic relationship with measurement error of DBH and it explained 35.1% of the measurement error;3) the LiBackpack measurements of DBH were generally smaller than the manually measured values, and the corresponding measurement errors increased for larger trees;and 4) by considering the effect of the point cloud density correction, a transitional model can be fitted to approximate field measured DBH using LiBackpackscanned value with satisfactory accuracy(adjusted R2= 0.920;RMSE = 14.77 mm), and decrease the predicting error by 29.2%. Our study confirmed the reliability of the novel LiBackpack system in accurate forestry inventory, set up a useful transitional model between scanning data and the traditional manual-measured data specifically for P.sylvestris, and implied the applicable substitution of this new approach fo
文摘目的比较乳腺同时多层单次激发平面回波成像(SMS+SS-EPI)和分段读出平面回波(RESOLVE)扩散加权成像(DWI)的图像质量及其诊断乳腺恶性病灶的效能。方法该研究为横断面研究。前瞻性分析2021年3月至2023年2月宁波大学附属第一医院因乳腺病变而接受MRI检查并最终经病理证实的102例患者的临床及影像资料。所有患者均接受乳腺MRI常规扫描以及横断面RESOLVE和SMS+SS-EPI序列DWI,评估2种序列成像的图像质量。主观评价采用5分法,评价内容包括乳腺的几何畸变、伪影模糊、脂肪抑制、整体图像质量和病灶显著性;客观评价包括病灶信噪比(SNR)、对比噪声比(CNR)、病灶表观扩散系数(ADC)值。采用Wilcoxon符号秩和检验比较2种序列成像的主、客观评价指标。最后采用受试者操作特征曲线及曲线下面积(AUC)评估2种序列图像上病灶ADC值诊断乳腺恶性病灶的效能。结果102例患者均为单发病变,均为女性患者,年龄25~68岁。乳腺恶性病灶60例,良性病变42例。SMS+SS-EPI序列成像的采集时间为1 min 50 s,RESOLVE序列成像的采集时间为3 min 43 s。SMS+SS-EPI和RESOLVE序列成像的图像质量主观评分均≥3分,RESOLVE序列图像的几何畸变、伪影模糊、整体图像质量评分均高于SMS+SS-EPI序列(P均<0.001)。SMS+SS-EPI序列的DWI图像和ADC图像的总体病灶显著性评分、恶性病灶显著性评分、良性病灶的显著性评分均高于RESOLVE序列(P均<0.05)。SMS+SS-EPI和RESOLVE序列DWI图像乳腺病灶的SNR、CNR、总体ADC值及恶性病灶ADC值、良性病灶ADC值差异均无统计学意义(P均>0.05)。RESOLVE序列成像的病灶ADC值诊断乳腺恶性病灶的AUC为0.973,灵敏度为0.929,特异度为0.915;SMS+SS-EPI序列成像的病灶ADC值诊断乳腺恶性病灶的AUC为0.956,灵敏度为0.977,特异度为0.850。结论在乳腺DWI中,SMS+SS-EPI图像质量主观评分能基本满足诊断要求且病变显著性好于RESOLVE序列�
