Non-rigid registration of longitudinal brain tumor treatment MRI

Chitphakdithai, Nicha; Chiang, Veronica; Duncan, James S.

doi:10.1109/iembs.2011.6091212

Cited by 7 publications

(10 citation statements)

References 13 publications

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“…Moreover, technical papers are often complex and use only a small number of patient datasets, e.g. up to two patients with brain tumors and up to six patients with epilepsy surgery . We have created an easily reproducible method (see Supporting information for the code) and have tested it in a large (n = 32) population.…”

Section: Discussionmentioning

confidence: 99%

“…]. Technical studies suffer from other issues, such as small sample size . Another study has tested methods only on epilepsy patients with whole lobe resection , where fewer signal changes and mass effects are expected.…”

Section: Introductionmentioning

confidence: 99%

“…Another study has tested methods only on epilepsy patients with whole lobe resection , where fewer signal changes and mass effects are expected. Furthermore, these and other technical papers do not provide comprehensible co‐registration guidelines, thereby hindering wide applicability, especially for clinical researchers .…”

Section: Introductionmentioning

confidence: 99%

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Validation of a semi-automatic co-registration of MRI scans in patients with brain tumors during treatment follow-up

et al. 2016

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There is an expanding research interest in high‐grade gliomas because of their significant population burden and poor survival despite the extensive standard multimodal treatment. One of the obstacles is the lack of individualized monitoring of tumor characteristics and treatment response before, during and after treatment. We have developed a two‐stage semi‐automatic method to co‐register MRI scans at different time points before and after surgical and adjuvant treatment of high‐grade gliomas. This two‐stage co‐registration includes a linear co‐registration of the semi‐automatically derived mask of the preoperative contrast‐enhancing area or postoperative resection cavity, brain contour and ventricles between different time points. The resulting transformation matrix was then applied in a non‐linear manner to co‐register conventional contrast‐enhanced T1‐weighted images. Targeted registration errors were calculated and compared with linear and non‐linear co‐registered images. Targeted registration errors were smaller for the semi‐automatic non‐linear co‐registration compared with both the non‐linear and linear co‐registered images. This was further visualized using a three‐dimensional structural similarity method. The semi‐automatic non‐linear co‐registration allowed for optimal correction of the variable brain shift at different time points as evaluated by the minimal targeted registration error. This proposed method allows for the accurate evaluation of the treatment response, essential for the growing research area of brain tumor imaging and treatment response evaluation in large sets of patients. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validation of a semi-automatic co-registration of MRI scans in patients with brain tumors during treatment follow-up

et al. 2016

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“…25,26 In general, deformable registration approaches assume that all structures in the moving image can find their correspondence in the fixed image. 28 However, this assumption may not be valid in the case of tumour response studies. The changes in tumour size, that is, tumour shrinkage or even disappearance, can occur as the treatment progresses, producing uncertainty in the correspondence between the baseline and the evaluation scans.…”

Section: Deformable Registrationmentioning

confidence: 99%

Computerized PET/CT image analysis in the evaluation of tumour response to therapy

Wang²,

Zhang³

2015

BJR

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Current cancer therapy strategy is mostly population based, however, there are large differences in tumour response among patients. It is therefore important for treating physicians to know individual tumour response. In recent years, many studies proposed the use of computerized positron emission tomography/CT image analysis in the evaluation of tumour response. Results showed that computerized analysis overcame some major limitations of current qualitative and semiquantitative analysis and led to improved accuracy. In this review, we summarize these studies in four steps of the analysis: image registration, tumour segmentation, image feature extraction and response evaluation. Future works are proposed and challenges described.

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“…An automated 3D deformable registration (BioImage Suite, Yale University, New Haven, CT, USA) was used to register the pre-and 20 second post-contrast scans as a preprocessing step, 15,16 in order to reduce inter-phase hepatic displacement and improve image quality. The whole liver from the arterial phase MR image was segmented in 3D semi-automatically using a prototype software which generated a 3D segmentation mask.…”

Section: Mr Image Processing: Registration Segmentation and Bias Fiementioning

confidence: 99%

Predicting Infiltrative Hepatocellular Carcinoma Patient Outcome Post-TACE: MR Bias Field Correction Effect on 3D-quantitative Image Analysis

Liu

Smolka

Papademetris

et al. 2020

Journal of Clinical and Translational Hepatology

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Background and Aims: To investigate the impact of MR bias field correction on response determination and survival prediction using volumetric tumor enhancement analysis in patients with infiltrative hepatocellular carcinoma, after transcatheter arterial chemoembolization (TACE). Methods: This study included 101 patients treated with conventional or drug-eluting beads TACE between the years of 2001 and 2013. Semi-automated 3D quantification software was used to segment and calculate the enhancing tumor volume (ETV) of the liver with and without bias-field correction on multiphasic contrast-enhanced MRI before and 1-month after initial TACE. ETV (expressed as cm 3) at baseline imaging and the relative change in ETV (as % change, ETV%) before and after TACE were used to predict response and survival, respectively. Statistical survival analyses included Kaplan-Meier curve generation and Cox proportional hazards modeling. Q statistics were calculated and used to identify the best cutoff value for ETV to separate responders and non-responders (ETV cm 3). The difference in survival was evaluated between responders and non-responders using Kaplan-Meier and Cox models. Results: MR bias field correction correlated with improved response calculation from baseline MR as well as survival after TACE; using a 415 cm 3 cutoff for ETV at baseline (hazard ratio: 2.00, 95% confidence interval: 1.23-3.26, p=0.01) resulted in significantly improved response prediction (median survival in patients with baseline ETV <415 cm 3 : 19.66 months vs. $415 cm 3 : 9.21 months, p<0.001, logrank test). A $41% relative decrease in ETV (hazard ratio: 0.58, 95%confidence interval: 0.37-0.93, p=0.02) was significant in predicting survival (ETV $41%: 19.20 months vs. ETV <41%: 8.71 months, p=0.008, log-rank test). Without MR bias field correction, response from baseline ETV could be predicted but survival after TACE could not. Conclusions: MR bias field correction improves both response assessment and accuracy of survival prediction using whole liver tumor enhancement analysis from baseline MR after initial TACE in patients with infiltrative hepatocellular carcinoma.

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Non-rigid registration of longitudinal brain tumor treatment MRI

Cited by 7 publications

References 13 publications

Validation of a semi-automatic co-registration of MRI scans in patients with brain tumors during treatment follow-up

Validation of a semi-automatic co-registration of MRI scans in patients with brain tumors during treatment follow-up

Computerized PET/CT image analysis in the evaluation of tumour response to therapy

Predicting Infiltrative Hepatocellular Carcinoma Patient Outcome Post-TACE: MR Bias Field Correction Effect on 3D-quantitative Image Analysis

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