اکثر مکاتبات کومش از طریق ایمیل سایت می باشد.
لطفا Spam ایمیل خود را نیز چک نمایید.
   [صفحه اصلی ]   [Archive] [ English ]  
:: صفحه اصلي :: درباره نشريه :: آخرين شماره :: تمام شماره‌ها :: جستجو :: ثبت نام :: ارسال مقاله :: تماس با ما ::
:: جلد 23، شماره 1 - ( زمستان 1399 ) ::
جلد 23 شماره 1 صفحات 64-71 برگشت به فهرست نسخه ها
مقایسه روش‌های پیشرفته جداسازی استخوان در تصاویر رزونانس مغناطیسی مغز، مبتنی بر روش اطلس برای طراحی درمان با تصاویر MR و تصحیح تضعیف تصاویر PET-MR
سمانه مصطفی پور ، حسین عربی
چکیده:   (421 مشاهده)
هدف: تصاویر رزنانس مغناطیسی (Magnetic Resonance, MR) کاربرد گسترده‌ای در طراحی درمان برای رادیوتراپی و یا اصلاح تضعیف تصاویر PET (Positron Emission Tomography) دارند؛ اما عدم امکان تبدیل مقادیر تصاویر MR به دانسیته الکترونی، ساخت تصاویر مصنوعی را مطرح می‌سازد. از جمله روش‌های مناسب، روش‌های مبتنی بر اطلس است که در این مطالعه، عملکرد دو روش جداسازی استخوان بر مبنای روش اطلس برای تهیه تصاویر CT (Computed Tomography) مصنوعی، از تصاویر MR مغزی، بررسی و مقایسه شده است.
مواد و روشها: جداسازی استخوان از تصاویر MR مغزی مربوط به 43 بیمار، با استفاده از دو روش وزن‌دهی محلی بر مبنای اطلس (Atlas-LW (Local weighting atlas-based)) و روش تشخیص الگو بر مبنای اطلس AT-PR (Atlas) registration & Pattern recognition)، انجام شد. صحت این دو روش، برای جداسازی کل استخوان (Bone total) و قسمت قشری (Cortical bone or compact bone) آن با استفاده از پارامترهایی نظیر شباهت Dice (Dice similarity (DSC))، بررسی شد. هم‌چنین صحت مقادیر CT استخراج شده با استفاده از خطای مطلق میانگین (MAE) و خطای جذر میانگین مربعات (RMSE) به‌دست آمد.
یافتهها: روش Atlas-LW با مقادیر 61/0±79/0=DSC برای استخوان قشری و 03/0±84/0=DSC برای کل استخوان، صحت جداسازی بیش‌تری را نشان داد. این مقادیر برای روش AT-PR به ترتیب از راست به چپ، 8/0±72/0=DSC و 05/0±77/0=DSC بود.
نتیجهگیری: مقادیر خطای بهتر به‌دست آمده توسط روش Atlas-LW در این مطالعه، نشان‌دهنده توانایی بالقوه این روش به منظور استفاده در طراحی درمان و اصلاح تضعیف تصاویر PET-MR است.
 
واژه‌های کلیدی: استخوان، مغز، تصویربرداری تشدید مغناطیسی، برش نگاری نشر پوزیترون، رادیو تراپی
متن کامل [PDF 1125 kb]   (64 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: عمومى
دریافت: 1398/10/30 | پذیرش: 1399/4/22 | انتشار: 1399/10/23
فهرست منابع
1. [1] Chandarana H, Wang H, Tijssen RHN, Das IJ. Emerging role of MRI in radiation therapy. J Magn Reson Imaging 2018; 48: 1468-1478. [DOI:10.1002/jmri.26271] [PMID] [PMCID]
2. [2] Ulin K, Urie MM, Cherlow JM. Results of a multi-institutional benchmark test for cranial CT/MR image registration. Int J Radiat Oncol Biol Phys 2010; 77: 1584-1589. [DOI:10.1016/j.ijrobp.2009.10.017] [PMID] [PMCID]
3. [3] Arabi H, Dowling JA, Burgos N, Han X, Greer PB, Koutsouvelis N, et al. Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI-guided radiation planning in the pelvic region. Med Phys 2018; 45: 5218-5233. [DOI:10.1002/mp.13187] [PMID]
4. [4] Mehranian A, Arabi H, Zaidi H. Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities. Med Phys 2016; 43: 1130-1155. [DOI:10.1118/1.4941014] [PMID]
5. [5] Chen Y, An H. Attenuation Correction of PET/MR Imaging. Magn Reson Imaging Clin N Am 2017; 25: 245-255. [DOI:10.1016/j.mric.2016.12.001] [PMID] [PMCID]
6. [6] Mehranian A, Arabi H, Zaidi H. Quantitative analysis of MRI-guided attenuation correction techniques in time-of-flight brain PET/MRI. Neuroimage 2016; 130: 123-133. [DOI:10.1016/j.neuroimage.2016.01.060] [PMID]
7. [7] Arabi H, Zaidi H. Whole-body bone segmentation from MRI for PET/MRI attenuation correction using shape-based averaging. Med Phys 2016; 43: 5848. [DOI:10.1118/1.4963809] [PMID]
8. [8] Arabi H, Zaidi H. One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI. Eur J Nucl Med Mol Imaging 2016; 43: 2021-2035. [DOI:10.1007/s00259-016-3422-5] [PMID]
9. [9] Arabi H, Rager O, Alem A, Varoquaux A, Becker M, Zaidi H. Clinical assessment of MR-guided 3-class and 4-class attenuation correction in PET/MR. Mol Imaging Biol 2015; 17: 264-276. [DOI:10.1007/s11307-014-0777-5] [PMID]
10. [10] Bortolin K, Arabi H, Zaidi H. Deep learning-guided attenuation and scatter correction in brain PET/MRI without using anatomical images. IEEE Nucl Sci Sympos Med Imaging Confer (NSS/MIC), Manchester, UK. 2019. [DOI:10.1109/NSS/MIC42101.2019.9059943]
11. [11] Bahrami A, Karimian A, Fatemizadeh E, Arabi H. Zaidi H. A novel convolutional neural network with high convergence rate: Application to CT synthesis from MR images. IEEE Nucl Sci Sympos Med Imaging Confer (NSS/MIC), Manchester, UK. 2019. [DOI:10.1109/NSS/MIC42101.2019.9059908] [PMCID]
12. [12] Arabi H, Zaidi H. Three-dimensional shape completion using deep convolutional neural networks: Application to truncation compensation and metal artifact reduction in PET/MRI attenuation correction. IEEE Nucl Sci Sympos Med Imaging Confer (NSS/MIC), Manchester, UK. 2019. [DOI:10.1109/NSS/MIC42101.2019.9059660]
13. [13] Arabi H, Bortolin K, Ginovart N, Garibotto V, Zaidi H. Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies. Hum Brain Mapp 2020; 41: 3667-3679. [DOI:10.1002/hbm.25039] [PMID] [PMCID]
14. [14] Arabi H, Zaidi H. Magnetic resonance imaging-guided attenuation correction in whole-body PET/MRI using a sorted atlas approach. Med Image Anal 2016; 31: 1-15. [DOI:10.1016/j.media.2016.02.002] [PMID]
15. [15] Arabi H, Koutsouvelis N, Rouzaud M, Miralbell R, Zaidi H. Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning. Phys Med Biol 2016; 61: 6531-6552. [DOI:10.1088/0031-9155/61/17/6531] [PMID]
16. [16] Arabi H, Zaidi H, editors. MRI-based pseudo-CT generation using sorted atlas images in whole-body PET/MRI. IEEE Nucl Sci Sympos Med Imaging Confer (NSS/MIC); 2014. [DOI:10.1109/NSSMIC.2014.7430833] [PMCID]
17. [17] Burgos N, Cardoso MJ, Thielemans K, Modat M, Pedemonte S, Dickson J, et al. Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies. IEEE Trans Med Imaging 2014; 33: 2332-2341. [DOI:10.1109/TMI.2014.2340135] [PMID]
18. [18] Andreasen D, Van Leemput K, Hansen RH, Andersen JA, Edmund JM. Patch-based generation of a pseudo CT from conventional MRI sequences for MRI-only radiotherapy of the brain. Med phys 2015; 42: 1596-1605. [DOI:10.1118/1.4914158] [PMID]
19. [19] Hofmann M, Steinke F, Scheel V, Charpiat G, Farquhar J, Aschoff P, et al. MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration. J Nucl Med 2008; 49: 1875-1883. [DOI:10.2967/jnumed.107.049353] [PMID]
20. [20] Sekine T, Ter Voert EE, Warnock G, Buck A, Huellner M, Veit-Haibach P, et al. Clinical evaluation of Zero-Echo-Time attenuation correction for brain 18F-FDG PET/MRI: comparison with Atlas attenuation correction. J Nucl Med 2016; 57: 1927-1932. https://doi.org/10.2967/jnumed.115.159228 https://doi.org/10.2967/jnumed.116.175398 [DOI:10.2967/jnumed.115.169045] [PMID]
21. [21] Montazeri M. Machine learning models for predicting the diagnosis of liver disease. Koomesh 2014; 16. (Persian).
22. [22] Largent A, Barateau A, Nunes JC, Mylona E, Castelli J, Lafond C, et al. Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning. Int J Radiat Oncol Biol Phys 2019; 105: 1137-1150. [DOI:10.1016/j.ijrobp.2019.08.049] [PMID]
23. [23] Klein S, Staring M, Murphy K, Viergever MA, Pluim JP. elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 2010; 29: 196-205. [DOI:10.1109/TMI.2009.2035616] [PMID]
24. [24] Akbarzadeh A, Gutierrez D, Baskin A, Ay MR, Ahmadian A, Riahi Alam N, et al. Evaluation of whole-body MR to CT deformable image registration. J Appl Clin Med Phys 2013; 14: 4163. [DOI:10.1120/jacmp.v14i4.4163] [PMID] [PMCID]
25. [25] Hofmann M, Bezrukov I, Mantlik F, Aschoff P, Steinke F, Beyer T, et al. MRI-based attenuation correction for whole-body PET/MRI: quantitative evaluation of segmentation-and atlas-based methods. J Nucl Med 2011; 52: 1392-1399. [DOI:10.2967/jnumed.110.078949] [PMID]
26. [26] Haghparast A, Hashemi B, Eivazi MT. An assessment of the factors involved in effective attenuation coefficient of the compensator material for the treatment with 6MV photons using intensity modulated radiation therapy method. Koomesh 2011; 12: 279-184.
27. [27] Arabi H, Zaidi H. Comparison of atlas-based techniques for whole-body bone segmentation. Med Image Anal 2017; 36: 98-112. [DOI:10.1016/j.media.2016.11.003] [PMID]
28. [28] Arabi H, Zeng G, Zheng G, Zaidi H. Does deep learning approaches outperform atlas-guided attenuation correction in brain PET/MRI? J Nucl Med 2019; 60: 175.
29. [29] Arabi H, Zaidi H. Deep learning-guided estimation of attenuation correction factors from time-of-flight PET emission data. Med Image Anal 2020; 64: 101718. [DOI:10.1016/j.media.2020.101718] [PMID]
30. [1] Chandarana H, Wang H, Tijssen RHN, Das IJ. Emerging role of MRI in radiation therapy. J Magn Reson Imaging 2018; 48: 1468-1478. [DOI:10.1002/jmri.26271] [PMID] [PMCID]
31. [2] Ulin K, Urie MM, Cherlow JM. Results of a multi-institutional benchmark test for cranial CT/MR image registration. Int J Radiat Oncol Biol Phys 2010; 77: 1584-1589. [DOI:10.1016/j.ijrobp.2009.10.017] [PMID] [PMCID]
32. [3] Arabi H, Dowling JA, Burgos N, Han X, Greer PB, Koutsouvelis N, et al. Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI-guided radiation planning in the pelvic region. Med Phys 2018; 45: 5218-5233. [DOI:10.1002/mp.13187] [PMID]
33. [4] Mehranian A, Arabi H, Zaidi H. Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities. Med Phys 2016; 43: 1130-1155. [DOI:10.1118/1.4941014] [PMID]
34. [5] Chen Y, An H. Attenuation Correction of PET/MR Imaging. Magn Reson Imaging Clin N Am 2017; 25: 245-255. [DOI:10.1016/j.mric.2016.12.001] [PMID] [PMCID]
35. [6] Mehranian A, Arabi H, Zaidi H. Quantitative analysis of MRI-guided attenuation correction techniques in time-of-flight brain PET/MRI. Neuroimage 2016; 130: 123-133. [DOI:10.1016/j.neuroimage.2016.01.060] [PMID]
36. [7] Arabi H, Zaidi H. Whole-body bone segmentation from MRI for PET/MRI attenuation correction using shape-based averaging. Med Phys 2016; 43: 5848. [DOI:10.1118/1.4963809] [PMID]
37. [8] Arabi H, Zaidi H. One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI. Eur J Nucl Med Mol Imaging 2016; 43: 2021-2035. [DOI:10.1007/s00259-016-3422-5] [PMID]
38. [9] Arabi H, Rager O, Alem A, Varoquaux A, Becker M, Zaidi H. Clinical assessment of MR-guided 3-class and 4-class attenuation correction in PET/MR. Mol Imaging Biol 2015; 17: 264-276. [DOI:10.1007/s11307-014-0777-5] [PMID]
39. [10] Bortolin K, Arabi H, Zaidi H. Deep learning-guided attenuation and scatter correction in brain PET/MRI without using anatomical images. IEEE Nucl Sci Sympos Med Imaging Confer (NSS/MIC), Manchester, UK. 2019. [DOI:10.1109/NSS/MIC42101.2019.9059943]
40. [11] Bahrami A, Karimian A, Fatemizadeh E, Arabi H. Zaidi H. A novel convolutional neural network with high convergence rate: Application to CT synthesis from MR images. IEEE Nucl Sci Sympos Med Imaging Confer (NSS/MIC), Manchester, UK. 2019. [DOI:10.1109/NSS/MIC42101.2019.9059908] [PMCID]
41. [12] Arabi H, Zaidi H. Three-dimensional shape completion using deep convolutional neural networks: Application to truncation compensation and metal artifact reduction in PET/MRI attenuation correction. IEEE Nucl Sci Sympos Med Imaging Confer (NSS/MIC), Manchester, UK. 2019. [DOI:10.1109/NSS/MIC42101.2019.9059660]
42. [13] Arabi H, Bortolin K, Ginovart N, Garibotto V, Zaidi H. Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies. Hum Brain Mapp 2020; 41: 3667-3679. [DOI:10.1002/hbm.25039] [PMID] [PMCID]
43. [14] Arabi H, Zaidi H. Magnetic resonance imaging-guided attenuation correction in whole-body PET/MRI using a sorted atlas approach. Med Image Anal 2016; 31: 1-15. [DOI:10.1016/j.media.2016.02.002] [PMID]
44. [15] Arabi H, Koutsouvelis N, Rouzaud M, Miralbell R, Zaidi H. Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning. Phys Med Biol 2016; 61: 6531-6552. [DOI:10.1088/0031-9155/61/17/6531] [PMID]
45. [16] Arabi H, Zaidi H, editors. MRI-based pseudo-CT generation using sorted atlas images in whole-body PET/MRI. IEEE Nucl Sci Sympos Med Imaging Confer (NSS/MIC); 2014. [DOI:10.1109/NSSMIC.2014.7430833] [PMCID]
46. [17] Burgos N, Cardoso MJ, Thielemans K, Modat M, Pedemonte S, Dickson J, et al. Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies. IEEE Trans Med Imaging 2014; 33: 2332-2341. [DOI:10.1109/TMI.2014.2340135] [PMID]
47. [18] Andreasen D, Van Leemput K, Hansen RH, Andersen JA, Edmund JM. Patch-based generation of a pseudo CT from conventional MRI sequences for MRI-only radiotherapy of the brain. Med phys 2015; 42: 1596-1605. [DOI:10.1118/1.4914158] [PMID]
48. [19] Hofmann M, Steinke F, Scheel V, Charpiat G, Farquhar J, Aschoff P, et al. MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration. J Nucl Med 2008; 49: 1875-1883. [DOI:10.2967/jnumed.107.049353] [PMID]
49. [20] Sekine T, Ter Voert EE, Warnock G, Buck A, Huellner M, Veit-Haibach P, et al. Clinical evaluation of Zero-Echo-Time attenuation correction for brain 18F-FDG PET/MRI: comparison with Atlas attenuation correction. J Nucl Med 2016; 57: 1927-1932. https://doi.org/10.2967/jnumed.115.159228 https://doi.org/10.2967/jnumed.116.175398 [DOI:10.2967/jnumed.115.169045] [PMID]
50. [21] Montazeri M. Machine learning models for predicting the diagnosis of liver disease. Koomesh 2014; 16. (Persian).
51. [22] Largent A, Barateau A, Nunes JC, Mylona E, Castelli J, Lafond C, et al. Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning. Int J Radiat Oncol Biol Phys 2019; 105: 1137-1150. [DOI:10.1016/j.ijrobp.2019.08.049] [PMID]
52. [23] Klein S, Staring M, Murphy K, Viergever MA, Pluim JP. elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 2010; 29: 196-205. [DOI:10.1109/TMI.2009.2035616] [PMID]
53. [24] Akbarzadeh A, Gutierrez D, Baskin A, Ay MR, Ahmadian A, Riahi Alam N, et al. Evaluation of whole-body MR to CT deformable image registration. J Appl Clin Med Phys 2013; 14: 4163. [DOI:10.1120/jacmp.v14i4.4163] [PMID] [PMCID]
54. [25] Hofmann M, Bezrukov I, Mantlik F, Aschoff P, Steinke F, Beyer T, et al. MRI-based attenuation correction for whole-body PET/MRI: quantitative evaluation of segmentation-and atlas-based methods. J Nucl Med 2011; 52: 1392-1399. [DOI:10.2967/jnumed.110.078949] [PMID]
55. [26] Haghparast A, Hashemi B, Eivazi MT. An assessment of the factors involved in effective attenuation coefficient of the compensator material for the treatment with 6MV photons using intensity modulated radiation therapy method. Koomesh 2011; 12: 279-184.
56. [27] Arabi H, Zaidi H. Comparison of atlas-based techniques for whole-body bone segmentation. Med Image Anal 2017; 36: 98-112. [DOI:10.1016/j.media.2016.11.003] [PMID]
57. [28] Arabi H, Zeng G, Zheng G, Zaidi H. Does deep learning approaches outperform atlas-guided attenuation correction in brain PET/MRI? J Nucl Med 2019; 60: 175.
58. [29] Arabi H, Zaidi H. Deep learning-guided estimation of attenuation correction factors from time-of-flight PET emission data. Med Image Anal 2020; 64: 101718. [DOI:10.1016/j.media.2020.101718] [PMID]
ارسال پیام به نویسنده مسئول

ارسال نظر درباره این مقاله
نام کاربری یا پست الکترونیک شما:

CAPTCHA

Ethics code: 241345CH



XML   English Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Mostafapour S, Arabi H. Comparison of state-of-the-art atlas-based bone segmentation approaches from brain MR images for MR-only radiation planning and PET/MR attenuation correction. Koomesh. 2021; 23 (1) :64-71
URL: http://koomeshjournal.semums.ac.ir/article-1-6171-fa.html

مصطفی پور سمانه، عربی حسین. مقایسه روش‌های پیشرفته جداسازی استخوان در تصاویر رزونانس مغناطیسی مغز، مبتنی بر روش اطلس برای طراحی درمان با تصاویر MR و تصحیح تضعیف تصاویر PET-MR. كومش. 1399; 23 (1) :64-71

URL: http://koomeshjournal.semums.ac.ir/article-1-6171-fa.html



جلد 23، شماره 1 - ( زمستان 1399 ) برگشت به فهرست نسخه ها
کومش Koomesh
Persian site map - English site map - Created in 0.06 seconds with 30 queries by YEKTAWEB 4256