Literature DB >> 29842815

Proton range shift analysis on brain pseudo-CT generated from T1 and T2 MR.

Giampaolo Pileggi1,2, Christoph Speier2,3, Gregory C Sharp2, David Izquierdo Garcia4, Ciprian Catana4, Jennifer Pursley2, Francesco Amato1, Joao Seco5, Maria Francesca Spadea1.   

Abstract

BACKGROUND: In radiotherapy, MR imaging is only used because it has significantly better soft tissue contrast than CT, but it lacks electron density information needed for dose calculation. This work assesses the feasibility of using pseudo-CT (pCT) generated from T1w/T2w MR for proton treatment planning, where proton range comparisons are performed between standard CT and pCT.
MATERIAL AND METHODS: MR and CT data from 14 glioblastoma patients were used in this study. The pCT was generated by using conversion libraries obtained from tissue segmentation and anatomical regioning of the T1w/T2w MR. For each patient, a plan consisting of three 18 Gy beams was designed on the pCT, for a total of 42 analyzed beams. The plan was then transferred onto the CT that represented the ground truth. Range shift (RS) between pCT and CT was computed at R80 over 10 slices. The acceptance threshold for RS was according to clinical guidelines of two institutions. A γ-index test was also performed on the total dose for each patient.
RESULTS: Mean absolute error and bias for the pCT were 124 ± 10 and -16 ± 26 Hounsfield Units (HU), respectively. The median and interquartile range of RS was 0.5 and 1.4 mm, with highest absolute value being 4.4 mm. Of the 42 beams, 40 showed RS less than the clinical range margin. The two beams with larger RS were both in the cranio-caudal direction and had segmentation errors due to the partial volume effect, leading to misassignment of the HU.
CONCLUSIONS: This study showed the feasibility of using T1w and T2w MRI to generate a pCT for proton therapy treatment, thus avoiding the use of a planning CT and allowing better target definition and possibilities for online adaptive therapies. Further improvements of the methodology are still required to improve the conversion from MRI intensities to HUs.

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Year:  2018        PMID: 29842815     DOI: 10.1080/0284186X.2018.1477257

Source DB:  PubMed          Journal:  Acta Oncol        ISSN: 0284-186X            Impact factor:   4.089


  6 in total

Review 1.  Online daily adaptive proton therapy.

Authors:  Francesca Albertini; Michael Matter; Lena Nenoff; Ye Zhang; Antony Lomax
Journal:  Br J Radiol       Date:  2019-11-11       Impact factor: 3.039

2.  MRI-Based Proton Treatment Planning for Base of Skull Tumors.

Authors:  Ghazal Shafai-Erfani; Yang Lei; Yingzi Liu; Yinan Wang; Tonghe Wang; Jim Zhong; Tian Liu; Mark McDonald; Walter J Curran; Jun Zhou; Hui-Kuo Shu; Xiaofeng Yang
Journal:  Int J Part Ther       Date:  2019-09-30

Review 3.  MR-guided proton therapy: a review and a preview.

Authors:  Aswin Hoffmann; Bradley Oborn; Maryam Moteabbed; Susu Yan; Thomas Bortfeld; Antje Knopf; Herman Fuchs; Dietmar Georg; Joao Seco; Maria Francesca Spadea; Oliver Jäkel; Christopher Kurz; Katia Parodi
Journal:  Radiat Oncol       Date:  2020-05-29       Impact factor: 3.481

4.  MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method.

Authors:  Yingzi Liu; Yang Lei; Yinan Wang; Tonghe Wang; Lei Ren; Liyong Lin; Mark McDonald; Walter J Curran; Tian Liu; Jun Zhou; Xiaofeng Yang
Journal:  Phys Med Biol       Date:  2019-07-16       Impact factor: 3.609

5.  Introduction of a Simple Algorithm to Create Synthetic-computed Tomography of the Head from Magnetic Resonance Imaging.

Authors:  Nahid Chegeni; Mohamad Javad Tahmasebi Birgani; Fariba Farhadi Birgani; Daryoush Fatehi; Gholamreza Akbarizadeh; Marziyeh Tahmasbi
Journal:  J Med Signals Sens       Date:  2019 Apr-Jun

6.  Generation of Pseudo-CT using High-Degree Polynomial Regression on Dual-Contrast Pelvic MRI Data.

Authors:  Samuel C Leu; Zhibin Huang; Ziwei Lin
Journal:  Sci Rep       Date:  2020-05-15       Impact factor: 4.379

  6 in total

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