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Feasibility study on the clinical application of CT-based synthetic brain T1-weighted MRI: comparison with conventional T1-weighted MRI

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

This study aimed to examine the equivalence of computed tomography (CT)–based synthetic T1-weighted imaging (T1WI) to conventional T1WI for the quantitative assessment of brain morphology.

Materials and methods

This prospective study examined 35 adult patients undergoing brain magnetic resonance imaging (MRI) and CT scans. An image synthesis method based on a deep learning model was used to generate synthetic T1WI (sT1WI) from CT data. Two senior radiologists used sT1WI and conventional T1WI on separate occasions to independently measure clinically relevant brain morphological parameters. The reliability and consistency between conventional and synthetic T1WI were assessed using statistical consistency checks, comprising intra-reader, inter-reader, and inter-method agreement.

Results

The intra-reader, inter-reader, and inter-method reliability and variability mostly exhibited the desired performance, except for several poor agreements due to measurement differences between the radiologists. All the measurements of sT1WI were equivalent to that of T1WI at 5% equivalent intervals.

Conclusion

This study demonstrated the equivalence of CT-based sT1WI to conventional T1WI for quantitatively assessing brain morphology, thereby providing more information on imaging diagnosis with a single CT scan.

Clinical relevance statement

Real-time synthesis of MR images from CT scans reduces the time required to acquire MR signals, improving the efficiency of the treatment planning system and providing benefits in the clinical diagnosis of patients with contraindications such as presence of metal implants or claustrophobia.

Key Points

• Deep learning–based image synthesis methods generate synthetic T1-weighted imaging from CT scans.

• The equivalence of synthetic T1-weighted imaging and conventional MRI for quantitative brain assessment was investigated.

• Synthetic T1-weighted imaging can provide more information per scan and be used in preoperative diagnosis and radiotherapy.

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Abbreviations

AIS:

Acute ischemic stroke

BAHLV:

Angle formed by the bilateral anterior horns of the lateral ventricles

BPHLV:

Angle formed by the bilateral posterior horns of the lateral ventricles

CT:

Computed tomography

CTLCC:

Cortical thickness of the left cingulate cortex

CTRCC:

Cortical thickness of the right cingulate cortex

DKCCPP:

Distance between knee of the corpus callosum and pressing part

GAN:

Generative adversarial network

HCC:

Height of the corpus callosum

ICC:

Intra-class correlation coefficient

IH:

Intracranial height

IL:

Intracranial length

IW:

Intracranial width

LEL:

Left eyeball length

LEW:

Left eyeball width

LLVAAD:

Left lateral ventricle anteroposterior angle distance

LTL:

Left thalamus length

LTW:

Left thalamus width

MPR:

Multi-planar reconstruction

MRI:

Magnetic resonance imaging

PL:

Pontine length

PW:

Pontine width

REL:

Right eyeball length

REW:

Right eyeball width

RLVAAD:

Right lateral ventricle anteroposterior angle distance

RTL:

Right thalamus length

RTW:

Right thalamus width

sMRI:

Synthetic magnetic resonance imaging

sT1WI:

Synthetic T1-weighted image

TPS:

Treatment planning system

References

  1. Zhang W, Cheng J, Zhang Y, Wang K, Jin H (2019) Analysis of CT and MRI combined examination for the diagnosis of acute cerebral infarction. J Coll Physicians Surg Pak 29:898–899

    Article  PubMed  Google Scholar 

  2. Lee HJ, Kim MJ, Choi JY, Hong HS, Kim KA (2011) Relative accuracy of CT and MRI in the differentiation of benign from malignant pancreatic cystic lesions. Clin Radiol 66:315–321

    Article  PubMed  Google Scholar 

  3. Schmidt MA, Payne GS (2015) Radiotherapy planning using MRI. Phys Med Biol 60:R323–R361

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Glide-Hurst CK, Low DA, Orton CG (2014) MRI/CT is the future of radiotherapy treatment planning. Med Phys 41:110601

    Article  PubMed  Google Scholar 

  5. Fox T, Elder E, Crocker I (2008) Chapter 3 - Image registration and fusion techniques. In: Paulino AC, Teh BS (eds) PET-CT in Radiotherapy Treatment Planning. Elsevier, Philadelphia, pp 35–51

    Chapter  Google Scholar 

  6. Hsu S-H, Cao Y, Huang K, Feng M, Balter JM (2013) Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy. Phys Med Biol 58:8419–8435

    Article  PubMed  Google Scholar 

  7. Lei Y, Harms J, Wang T et al (2019) MRI-based synthetic CT generation using semantic random forest with iterative refinement. Phys Med Biol 64:085001

    Article  PubMed  PubMed Central  Google Scholar 

  8. Hsu SH, Dupre P, Peng Q, Tomé WA (2020) A technique to generate synthetic CT from MRI for abdominal radiotherapy. J Appl Clin Med Phys 21:136–143

    Article  PubMed  PubMed Central  Google Scholar 

  9. Han X (2017) MR-based synthetic CT generation using a deep convolutional neural network method. Med Phys 44:1408–1419

    Article  CAS  PubMed  Google Scholar 

  10. Yang H, Sun J, Carass A et al (2020) Unsupervised MR-to-CT synthesis using structure-constrained CycleGAN. IEEE Trans Med Imaging 39:4249–4261

    Article  PubMed  Google Scholar 

  11. Florkow MC, Zijlstra F, Willemsen K et al (2020) Deep learning–based MR-to-CT synthesis: the influence of varying gradient echo–based MR images as input channels. Magn Reson Med 83:1429–1441

    Article  CAS  PubMed  Google Scholar 

  12. Li Y, Li W, Xiong J, Xia J, Xie Y (2020) Comparison of supervised and unsupervised deep learning methods for medical image synthesis between computed tomography and magnetic resonance images. Biomed Res Int 2020:5193707

    PubMed  PubMed Central  Google Scholar 

  13. Morbée L, Chen M, Van Den Berghe T et al (2022) MRI-based synthetic CT of the hip: can it be an alternative to conventional CT in the evaluation of osseous morphology? Eur Radiol 32:3112–3120

    Article  PubMed  Google Scholar 

  14. Morbée L, Chen M, Herregods N, Pullens P, Jans LBO (2021) MRI-based synthetic CT of the lumbar spine: geometric measurements for surgery planning in comparison with CT. Eur J Radiol 144:109999

    Article  PubMed  Google Scholar 

  15. Jin C-B, Kim H, Liu M et al (2019) Deep CT to MR synthesis using paired and unpaired data. Sensors (Basel) 19:2361

    Article  PubMed  Google Scholar 

  16. Li W, Li Y, Qin W et al (2020) Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR)-guided radiotherapy. Quant Imaging Med Surg 10:1223–1236

    Article  PubMed  PubMed Central  Google Scholar 

  17. Jin C-B, Kim H, Liu M et al (2019) DC2Anet: generating lumbar spine MR images from cT scan data based on semi-supervised learning. Appl Sci 9:2521

    Article  Google Scholar 

  18. Gu S, Timofte R (2019) A brief review of image denoising algorithms and beyond. Springer International Publishing, Cham, pp 1–21

    Google Scholar 

  19. Wang H, Yang P, Xu C, Min L, Wang S, Xu B (2022) Lung CT image enhancement based on total variational frame and wavelet transform. Int J Imaging Syst Technol 32:1604–1614

    Article  Google Scholar 

  20. Li Z, Huang X, Zhang Z et al (2022) Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function. Quant Imaging Med Surg 12:3151–3169

    Article  PubMed  PubMed Central  Google Scholar 

  21. Marques JP, Kober T, Krueger G, van der Zwaag W, Van de Moortele P-F, Gruetter R (2010) MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 49:1271–1281

    Article  PubMed  Google Scholar 

  22. Benchoufi M, Matzner-Lober E, Molinari N, Jannot AS, Soyer P (2020) Interobserver agreement issues in radiology. Diagn Interv Imaging 101:639–641

    Article  CAS  PubMed  Google Scholar 

  23. Engesæter IØ, Laborie LB, Lehmann TG et al (2012) Radiological findings for hip dysplasia at skeletal maturity. Validation of digital and manual measurement techniques. Skelet Radiol 41:775–785

    Article  Google Scholar 

  24. de Vet HCW, Terwee CB, Knol DL, Bouter LM (2006) When to use agreement versus reliability measures. J Clin Epidemiol 59:1033–1039

    Article  PubMed  Google Scholar 

  25. Vogrig C, Louis JS, Avila F et al (2021) Synthetic MRI is not yet ready for morphologic and functional assessment of patellar cartilage at 1.5Tesla. Diagn Interv Imaging 102:181–187

    Article  CAS  PubMed  Google Scholar 

  26. Spadea MF, Maspero M, Zaffino P, Seco J (2021) Deep learning based synthetic-CT generation in radiotherapy and PET: a review. Med Phys 48:6537–6566

    Article  PubMed  Google Scholar 

  27. Cusumano D, Placidi L, Teodoli S et al (2020) On the accuracy of bulk synthetic CT for MR-guided online adaptive radiotherapy. Radiol Med 125:157–164

    Article  PubMed  Google Scholar 

  28. Florkow MC, Willemsen K, Zijlstra F et al (2022) MRI-based synthetic CT shows equivalence to conventional CT for the morphological assessment of the hip joint. J Orthop Res 40:954–964

    Article  PubMed  Google Scholar 

  29. Liu L, Johansson A, Cao Y, Dow J, Lawrence TS, Balter JM (2020) Abdominal synthetic CT generation from MR Dixon images using a U-net trained with ‘semi-synthetic’CT data. Phys Med Biol 65:125001

    Article  PubMed  PubMed Central  Google Scholar 

  30. Wang Y, Liu C, Zhang X, Deng W (2019) Synthetic CT generation based on T2 weighted MRI of nasopharyngeal carcinoma (NPC) using a deep convolutional neural network (DCNN). Front Oncol 9:1333

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hu N, Zhang T, Wu Y et al (2022) Detecting brain lesions in suspected acute ischemic stroke with CT-based synthetic MRI using generative adversarial networks. Ann Transl Med 10:35

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the Institute of Medical Technology, Peking University Health Science Center for technical support and Shenzhen Second People’s Hospital for data collection.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 12075011, No. 82071280, and No. 82171913), the Natural Science Research of Jiangsu Higher Education Institutions of China (No. 23KJB310019), and the Key Research and Development Program of Science and Technology Planning Project of Tibet Autonomous Region, China (Grant No. XZ202001ZY0005G).

Author information

Author notes

  1. Zhaotong Li and Gan Cao contributed equally to this work and should be regarded as co-first authors.

Authors and Affiliations

  1. Laboratory of Digital Medicine, Department of Medical Informatics, Medical School of Nantong University, Nantong, China

    Zhaotong Li

  2. Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, China

    Gan Cao

  3. Department of Radiology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, People’s Republic of China

    Li Zhang

  4. Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen, China

    Jichun Yuan & Jun Xia

  5. Institute of Medical Technology, Peking University Health Science Center, Beijing, China

    Sha Li, Zeru Zhang & Song Gao

  6. Beijing Key Laboratory of Spinal Disease Research, Engineering Research Center of Bone and Joint Precision Medicine, Department of Orthopedics, Peking University Third Hospital, Beijing, China

    Fengliang Wu

Authors
  1. Zhaotong Li
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Zhaotong Li, Gan Cao, Li Zhang, and Jichun Yuan. The first draft of the manuscript was written by Zhaotong Li and Gan Cao, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Song Gao or Jun Xia.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Dr. Jun Xia.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

This study was performed in line with the principles of the Declaration of Helsinki. The study’s protocol was also approved by the local medical ethics committee of Shenzhen Second People’s Hospital.

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

Methodology

• prospective

• cross-sectional study/observational study

• multi-center study

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Li, Z., Cao, G., Zhang, L. et al. Feasibility study on the clinical application of CT-based synthetic brain T1-weighted MRI: comparison with conventional T1-weighted MRI. Eur Radiol (2024). https://doi.org/10.1007/s00330-023-10534-1

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  • DOI: https://doi.org/10.1007/s00330-023-10534-1

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