Sorting lung tumor volumes from 4D-MRI data using an automatic tumor-based signal reduces stitching artifacts.
4D-MRI
NSCLC
principal components
respiratory sorting
stitching artifacts
Journal
Journal of applied clinical medical physics
ISSN: 1526-9914
Titre abrégé: J Appl Clin Med Phys
Pays: United States
ID NLM: 101089176
Informations de publication
Date de publication:
17 Jan 2024
17 Jan 2024
Historique:
revised:
30
10
2023
received:
31
08
2023
accepted:
18
12
2023
medline:
18
1
2024
pubmed:
18
1
2024
entrez:
18
1
2024
Statut:
aheadofprint
Résumé
To investigate whether a novel signal derived from tumor motion allows more precise sorting of 4D-magnetic resonance (4D-MR) image data than do signals based on normal anatomy, reducing levels of stitching artifacts within sorted lung tumor volumes. (4D-MRI) scans were collected for 10 lung cancer patients using a 2D T2-weighted single-shot turbo spin echo sequence, obtaining 25 repeat frames per image slice. For each slice, a tumor-motion signal was generated using the first principal component of movement in the tumor neighborhood (TumorPC1). Signals were also generated from displacements of the diaphragm (DIA) and upper and lower chest wall (UCW/LCW) and from slice body area changes (BA). Pearson r coefficients of correlations between observed tumor movement and respiratory signals were determined. TumorPC1, DIA, and UCW signals were used to compile image stacks showing each patient's tumor volume in a respiratory phase. Unsorted image stacks were also built for comparison. For each image stack, the presence of stitching artifacts was assessed by measuring the roughness of the compiled tumor surface according to a roughness metric (Rg). Statistical differences in weighted means of Rg between any two signals were determined using an exact permutation test. The TumorPC1 signal was most strongly correlated with superior-inferior tumor motion, and had significantly higher Pearson r values (median 0.86) than those determined for correlations of UCW, LCW, and BA with superior-inferior tumor motion (p < 0.05). Weighted means of ratios of Rg values in TumorPC1 image stacks to those in unsorted, UCW, and DIA stacks were 0.67, 0.69, and 0.71, all significantly favoring TumorPC1 (p = 0.02-0.05). For other pairs of signals, weighted mean ratios did not differ significantly from one. Tumor volumes were smoother in 3D image stacks compiled using the first principal component of tumor motion than in stacks compiled with signals based on normal anatomy.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e14262Subventions
Organisme : Cancer Research UK
ID : 28990
Pays : United Kingdom
Informations de copyright
© 2024 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.
Références
Kumar S, Holloway L, Boxer M, et al. Variability of gross tumour volume delineation: MRI and CT based tumour and lymph node delineation for lung radiotherapy. Radiother Oncol. 2021;167:292-299.
Noel CE, Parikh PJ, Spencer CR, et al. Comparison of onboard low-field magnetic resonance imaging versus onboard computed tomography for anatomy visualization in radiotherapy. Acta Oncol (Madr). 2015;54(9):1474-1482.
Steiner E, Shieh CC, Caillet V, et al. 4-Dimensional cone beam computed tomography-measured target motion underrepresents actual motion. Int J Radiat Oncol Biol Phys. 2018;102(4):932-940.
Steiner E, Shieh CC, Caillet V, et al. Both four-dimensional computed tomography and four-dimensional cone beam computed tomography under-predict lung target motion during radiotherapy. Radiother Oncol. 2019;135:65-73.
Bertholet J, Knopf A, Eiben B, et al. Real-time intrafraction motion monitoring in external beam radiotherapy. Phys Med Biol. 2019;64(15). 15TR01.
Cai J, Read PW, Baisden JM, Larner JM, Benedict SH, Sheng K. Estimation of error in maximal intensity projection-based internal target volume of lung tumors: a simulation and comparison study using dynamic magnetic resonance imaging. Int J Radiat Oncol Biol Phys. 2007;69(3):895-902.
Rabe M, Thieke C, Düsberg M, et al. Real-time 4DMRI-based internal target volume definition for moving lung tumors. Med Phys. 2020;47(4):1431-1442.
van de Lindt TN, Fast MF, van den Wollenberg W, et al. Validation of a 4D-MRI guided liver stereotactic body radiation therapy strategy for implementation on the MR-linac. Phys Med Biol. 2021;66:105010.
van de Lindt TN, Nowee ME, Janssen T, et al. Technical feasibility and clinical evaluation of 4D-MRI guided liver'2 SBRT on the MR-linac. Radiother Oncol. 2022;167:285-291.
Paulson ES, Ahunbay E, Chen X, et al. 4D-MRI driven MR-guided online adaptive radiotherapy for abdominal stereotactic body radiation therapy on a high field MR-Linac: implementation and initial clinical experience. Clin Transl Radiat Oncol. 2020;23:72-79.
Keijnemans K, Borman PTS, Uijtewaal P, Woodhead PL, Raaymakers BW, Fast MF. A hybrid 2D/4D-MRI methodology using simultaneous multislice imaging for radiotherapy guidance. Med Phys. 2022;49(9):6068-6081.
Stemkens B, Paulson ES, Tijssen RHN. Nuts and bolts of 4D-MRI for radiotherapy. Phys Med Biol. 2018;63(21):21tr01.
Mickevicius NJ, Paulson ES. Investigation of undersampling and reconstruction algorithm dependence on respiratory correlated 4D-MRI for online MR-guided radiation therapy. Phys Med Biol. 2017;62(8):2910-2921.
Paganelli C, Kipritidis J, Lee D, Baroni G, Keall P, Riboldi M. Image-based retrospective 4D MRI in external beam radiotherapy: a comparative study with a digital phantom. Med Phys. 2018;45(7):3161-3172.
Li G, Wei J, Olek D, et al. Direct comparison of respiration-correlated four-dimensional magnetic resonance imaging reconstructed using concurrent internal navigator and external bellows. Int J Radiat Oncol Biol Phys. 2017;97(3):596-605.
Koch N, Liu HH, Starkschall G, et al. Evaluation of internal lung motion for respiratory-gated radiotherapy using MRI: part I-correlating internal lung motion with skin fiducial motion. Int J Radiat Oncol Biol Phys. 2004;60(5):1459-1472.
van de Lindt T, Sonke JJ, Nowee M, et al. A self-sorting coronal 4D-MRI method for daily image guidance of liver lesions on an MR-LINAC. Int J Radiat Oncol Biol Phys. 2018;102(4):875-884.
Liu Y, Yin F-F, Rhee D, Cai J. Accuracy of respiratory motion measurement of 4D-MRI: a comparison between cine and sequential acquisition. Med Phys. 2016;43(1):179-179.
Liu Y, Yin FF, Czito BG, Bashir MR, Cai J. T2-weighted four dimensional magnetic resonance imaging with result-driven phase sorting. Med Phys. 2015;42(8):4460-4471.
Brunton SL, Kutz JN, et al. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press; 2019. doi:10.1017/9781108380690
Liu Y, Yin FF, Chang Z, et al. Investigation of sagittal image acquisition for 4D-MRI with body area as respiratory surrogate. Med Phys. 2014;41(10):101902.
Uh J, Ayaz Khan M, Hua C. Four-dimensional MRI using an internal respiratory surrogate derived by dimensionality reduction. Phys Med Biol. 2016;61(21):7812-7832.
Gadelmawla ES, Koura MM, Maksoud TMA, Elewa IM, Soliman HH. Roughness parameters. J Mater Process Technol. 2002;123(1):133-145.
Tonietto L, Gonzaga L, Veronez MR, de Souza Kazmierczak C, Arnold DCM, da Costa CA. New method for evaluating surface roughness parameters acquired by laser scanning. Sci Rep. 2019;9(1):15038.
Tran EH, Eiben B, Wetscherek A, et al. Evaluation of MRI-derived surrogate signals to model respiratory motion. Biomed Phys Eng Express. 2020;6(4):045015.
Dhont J, Vandemeulebroucke J, Burghelea M, et al. The long- and short-term variability of breathing induced tumor motion in lung and liver over the course of a radiotherapy treatment. Radiother Oncol. 2018;126(2):339-346.
Seppenwoolde Y, Shirato H, Kitamura K, et al. Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. Int J Radiat Oncol Biol Phys. 2002;53(4):822-834.
Blackall JM, Ahmad S, Miquel ME, McClelland JR, Landau DB, Hawkes DJ. MRI-based measurements of respiratory motion variability and assessment of imaging strategies for radiotherapy planning. Phys Med Biol. 2006;51(17):4147-4169.
Amin MB, Greene FL, Byrd DR, eds. JCC Cancer Staging Manual. 8th ed. Springer International Publishing: American Joint Commission on Cancer; 2017.