两种基于模型迭代重建算法在腹部低剂量CT中的应用
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摘要
目的以腹部常规剂量40%ASIR重建来评价两种基于模型迭代重建算法(MBIRSTND与MBIRNR40)在低辐射剂量扫描时的图像质量及辐射剂量减低率。方法连续纳入接受腹部增强复查的患者20例,首次检查行常规剂量(NI=10)扫描并ASIR40%重建;复查时,采用低剂量扫描(NI=20),并采用MBIRSTND与MBIRNR40重建。客观测量图像噪声值(SD)和CT值,计算SNR、CNR,比较各参数之间差异;由2名医师对通图像进行主观评分,包括图像锐利度、噪声、伪影和诊断接受度,并进行对比分析。记录每例患者每次检查的剂量长度乘积(DLP)和CT剂量指数(CTDIVOL),计算剂量减低率。结果对于腹部脏器,低剂量MBIRSTND(NI=20)图像与常规剂量40%ASIR(NI=10)在图像噪声(SD)、信噪比(SNR)和对比噪声比(CNR)方面无统计学差异(P>0.05),低剂量MBIRSTND、MBIRNR40重建比40%ASIR图像有更低伪影和噪声,MBIRNR40图像SNR、CNR最高,主观评价结果更佳(P<0.05)。低剂量扫描的DLP值和CTDIVOL值分别为(95.56±47.17)mGy*cm和(3.04±1.48)mGy,而常规剂量ASIR40%重建的DLP值和CTDIVOL分别为(376.39±160.40)mGy*cm和(12.16±5.18)mGy,辐射剂量减低约75%。结论相比常规剂量扫描(NI=10)ASIR40%重建,低剂量扫描(NI=20)MBIRSTND、MBIRNR40重建在扫描辐射剂量降低75%的情况下,能有效降低噪声,提高图像SNR及CNR。
Objective To evaluate the dose reduction and image quality potential in abdominal low-dose CT reconstructed with model-based iterative reconstruction(MBIR) with two different pre-settings(MBIRSTND and MBIRNR40), in comparison with the state-of-the-art adaptive statistical interactive reconstruction(ASIR) in routine-dose CT. Methods Twenty patients underwent two contrast-enhanced abdominal CT scans within two weeks on a 64-slice CT(Discovery CT750HD) with standard-dose(noise index=10HU) and low-dose(noise index=20HU) in the follow-up scan. The routinedose CT was reconstructed with ASIR40(40% ASIR and 60% FBP mix) and the lowdose CT was reconstructed with both the ASIR40 and the two types of MBIR algorithms. Two radiologists assessed the images blindly according to the sharpness, image noise, diagnostic acceptability and artifacts with 5-point scoring. Image noise, signal-to-noise ratio(SNR) and contrast-to-noise ratio(CNR) of abdominal organs relative to erector spinae were assessed. The volume CT dose index(CTDIvol), dose-length product(DLP) and dose reduction rate were also determined. Results CTDIvol and DLP for the lowdose CT were(95.56±47.17m)Gy*cm and(3.04±1.48)mGy, respectively, compared with(376.39±160.40)mGy*cm and(12.16±5.18)mGy in the routine-dose CT with 75% reduction(P<0.001). Low-dose(NI=20HU) MBIR images with MBIRSTND setting had similar image noise, SNR and CNR as the routine-dose(NI=10HU) ASIR40 images(all P>0.05). On the other hand, the low-dose MBIR images with MBIRNR40 setting further improved image quality with higher image quality score, reduced image noise and improved SNR and CNR compared with the routine-dose ASIR40 images(all P<0.001). Conclusion MBIR significantly reduced image noise and improved SNR and CNR even at 75% dose reduction, compared with the routine-dose, state-of-the-art ASIR images.
Objective To evaluate the dose reduction and image quality potential in abdominal low-dose CT reconstructed with model-based iterative reconstruction(MBIR) with two different pre-settings(MBIRSTND and MBIRNR40), in comparison with the state-of-the-art adaptive statistical interactive reconstruction(ASIR) in routine-dose CT. Methods Twenty patients underwent two contrast-enhanced abdominal CT scans within two weeks on a 64-slice CT(Discovery CT750HD) with standard-dose(noise index=10HU) and low-dose(noise index=20HU) in the follow-up scan. The routinedose CT was reconstructed with ASIR40(40% ASIR and 60% FBP mix) and the lowdose CT was reconstructed with both the ASIR40 and the two types of MBIR algorithms. Two radiologists assessed the images blindly according to the sharpness, image noise, diagnostic acceptability and artifacts with 5-point scoring. Image noise, signal-to-noise ratio(SNR) and contrast-to-noise ratio(CNR) of abdominal organs relative to erector spinae were assessed. The volume CT dose index(CTDIvol), dose-length product(DLP) and dose reduction rate were also determined. Results CTDIvol and DLP for the lowdose CT were(95.56±47.17m)Gy*cm and(3.04±1.48)mGy, respectively, compared with(376.39±160.40)mGy*cm and(12.16±5.18)mGy in the routine-dose CT with 75% reduction(P<0.001). Low-dose(NI=20HU) MBIR images with MBIRSTND setting had similar image noise, SNR and CNR as the routine-dose(NI=10HU) ASIR40 images(all P>0.05). On the other hand, the low-dose MBIR images with MBIRNR40 setting further improved image quality with higher image quality score, reduced image noise and improved SNR and CNR compared with the routine-dose ASIR40 images(all P<0.001). Conclusion MBIR significantly reduced image noise and improved SNR and CNR even at 75% dose reduction, compared with the routine-dose, state-of-the-art ASIR images.
引文
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[12]Koichiro Yasaka,Masaki Katsura,Shouhei Hanaoka,Jiro Sato,Kuni Ohtomo.High-resolution CT with new model-based iterative reconstruction with resolution preference algorithm in evaluations of lung nodules:Comparison with conventional model-based iterative reconstruction and adaptive statisticaliterative reconstruction[J]. European Journal of Radiology,2016,85(3).
[13]贾永军,于勇,张喜荣,等.新一代基于模型的迭代重建中肺特异性设置在亚mSv胸部CT检查中的应用[J].中国医学影像技术,2017,33(10):1545-1549.
[14]Kahn J,Grupp U,Rotzinger R, et a1.CT for evaluation of potential renal donors-how does iterative reconstruction influence image quality and dose[J].European Journal of Radiology,2014,83(8):1332-1336.
[15]赵晶,徐飞,李晓璐,王慧慧,赵心明.不同水平的自适应统计迭代重建(ASIR)算法在能谱CT门静脉成像中的图像质量比较[J].临床放射学杂志,2016,35(2):282-287.
[2]白玫,郑钧正.多排(层)螺旋CT的辐射剂量表达及其影响因素探讨[J].辐射防护,2008,28(1):1-12.
[3]Desai G S,Uppot R N,Yu E W,et al.Impact of iterative reconstruction on image quality and radiation dose in multidetector CT of large body size adults[J].European Radiology,2012,22(8):1631-1640.
[4]Hansmann J,Schoenberg G M,Brix G,et al.CT of urolithiasis:comparison of image quality and diagnostic confidence using filtered back projection and iterative reconstruction techniques[J].Academic Radiology,2013,20(9):1162-1167.
[5]刘卓,李超.多模型迭代重建算法对CT图像质量影响的模型研究[J].中国医学影像学杂志,2016,24(7):553-556.
[6]Yasaka K,Katsura M,Hanaoka S,et al.High-resolution CT with new model-based iterative reconstruction with resolution preference algorithm in evaluations of lung nodules:Comparison with conventional model-based iterative reconstruction and adaptive statistical iterative reconstruction[J].European Journal of Radiology,2016,85(3):599-606.
[7]Naidich D P,Marshall C H.Gribbin C,et al.Low-dose CT of the lungs:preliminary observations[J].Radiology, 1990,175(3):729-731.
[8]杜贺钦,张镇滔,吴玉仪等.双低剂量CT扫描及迭代重建技术对肺癌早期检出和定性的可行性研究[J].中国CT和MRI杂志,2017,15(9):77-79.
[9]Rampinelli C,Origgi D,Vecchi V,et al.Ultra-low-dose CT with modelbased iterative reconstruction(MBIR):detection of ground-glass nodules in an anthropomorphic phantom study[J].Radiologia Medica,2015, 120(7):611-617.
[10]Katsura M,Matsuda I,Akahane M,et al.Model-based iterative reconstruction technique for radiation dose reduction in chest CT:comparison with the adaptive statistical iterative reconstruction technique[J]. European Radiology,2012, 22(8):1613-1623.
[11]陈学力,王战.宝石能谱C T低剂量扫描对早期肺癌诊断的临床价值[J].中国CT和MRI杂志,2018,16(3):30-32.
[12]Koichiro Yasaka,Masaki Katsura,Shouhei Hanaoka,Jiro Sato,Kuni Ohtomo.High-resolution CT with new model-based iterative reconstruction with resolution preference algorithm in evaluations of lung nodules:Comparison with conventional model-based iterative reconstruction and adaptive statisticaliterative reconstruction[J]. European Journal of Radiology,2016,85(3).
[13]贾永军,于勇,张喜荣,等.新一代基于模型的迭代重建中肺特异性设置在亚mSv胸部CT检查中的应用[J].中国医学影像技术,2017,33(10):1545-1549.
[14]Kahn J,Grupp U,Rotzinger R, et a1.CT for evaluation of potential renal donors-how does iterative reconstruction influence image quality and dose[J].European Journal of Radiology,2014,83(8):1332-1336.
[15]赵晶,徐飞,李晓璐,王慧慧,赵心明.不同水平的自适应统计迭代重建(ASIR)算法在能谱CT门静脉成像中的图像质量比较[J].临床放射学杂志,2016,35(2):282-287.