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Evaluation of elastix-based propagated align algorithm for VOI- and voxel-based analysis of longitudinal (18)F-FDG PET/CT data from patients with non-small cell lung cancer (NSCLC).

Kerner GS, Fischer A, Koole MJ, Pruim J, Groen HJ - EJNMMI Res (2015)

Bottom Line: Lesion statistics were compared to assess the impact on therapy response assessment.The elastix toolbox impacts lesion statistics and therefore therapy response assessment in a clinically significant way.Further optimization and validation of this technique is necessary prior to clinical implementation.

View Article: PubMed Central - PubMed

Affiliation: University of Groningen and Department of Pulmonary Diseases, University Medical Center Groningen, Hanzeplein 1, P.O. Box 30.001, , 9700 RB Groningen, The Netherlands.

ABSTRACT

Background: Deformable image registration allows volume of interest (VOI)- and voxel-based analysis of longitudinal changes in fluorodeoxyglucose (FDG) tumor uptake in patients with non-small cell lung cancer (NSCLC). This study evaluates the performance of the elastix toolbox deformable image registration algorithm for VOI and voxel-wise assessment of longitudinal variations in FDG tumor uptake in NSCLC patients.

Methods: Evaluation of the elastix toolbox was performed using (18)F-FDG PET/CT at baseline and after 2 cycles of therapy (follow-up) data in advanced NSCLC patients. The elastix toolbox, an integrated part of the IMALYTICS workstation, was used to apply a CT-based non-linear image registration of follow-up PET/CT data using the baseline PET/CT data as reference. Lesion statistics were compared to assess the impact on therapy response assessment. Next, CT-based deformable image registration was performed anew on the deformed follow-up PET/CT data using the original follow-up PET/CT data as reference, yielding a realigned follow-up PET dataset. Performance was evaluated by determining the correlation coefficient between original and realigned follow-up PET datasets. The intra- and extra-thoracic tumors were automatically delineated on the original PET using a 41% of maximum standardized uptake value (SUVmax) adaptive threshold. Equivalence between reference and realigned images was tested (determining 95% range of the difference) and estimating the percentage of voxel values that fell within that range.

Results: Thirty-nine patients with 191 tumor lesions were included. In 37/39 and 12/39 patients, respectively, thoracic and non-thoracic lesions were evaluable for response assessment. Using the EORTC/SUVmax-based criteria, 5/37 patients had a discordant response of thoracic, and 2/12 a discordant response of non-thoracic lesions between the reference and the realigned image. FDG uptake values of corresponding tumor voxels in the original and realigned reference PET correlated well (R (2)=0.98). Using equivalence testing, 94% of all the voxel values fell within the 95% range of the difference between original and realigned reference PET.

Conclusions: The elastix toolbox impacts lesion statistics and therefore therapy response assessment in a clinically significant way. The elastix toolbox is therefore not applicable in its current form and/or standard settings for PET response evaluation. Further optimization and validation of this technique is necessary prior to clinical implementation.

No MeSH data available.


Related in: MedlinePlus

Fusion steps of the propagated align algorithm. Reg-rigid: rigidly registered image. Reg-elastix: rigid and non-rigidly registered image. Step 1: rigid CT to CT alignment of target to reference. Step 2: translation of step 1 on target PET. Step 3: CT to rigid aligned CT non-rigid alignment using elastix toolbox. Step 4: translation of step 3 to PET of step 2. QA: resample of image as quality assurance, so voxel size matches prior to voxel-by-voxel comparison.
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Fig1: Fusion steps of the propagated align algorithm. Reg-rigid: rigidly registered image. Reg-elastix: rigid and non-rigidly registered image. Step 1: rigid CT to CT alignment of target to reference. Step 2: translation of step 1 on target PET. Step 3: CT to rigid aligned CT non-rigid alignment using elastix toolbox. Step 4: translation of step 3 to PET of step 2. QA: resample of image as quality assurance, so voxel size matches prior to voxel-by-voxel comparison.

Mentions: The fusion steps are also detailed in Figure 1. All transformation calculations used a four-level multi-resolution approach while mutual information as image similarity measure, and an adaptive stochastic gradient descent optimizer [21] was used to maximize image similarity. The control point spacing of the B-spline transformation was 16 mm. The maximum number of iterations was set to 250 (rigid) and 500 (B-spline). Because of computation time, data were processed in batch mode using python scripting.Figure 1


Evaluation of elastix-based propagated align algorithm for VOI- and voxel-based analysis of longitudinal (18)F-FDG PET/CT data from patients with non-small cell lung cancer (NSCLC).

Kerner GS, Fischer A, Koole MJ, Pruim J, Groen HJ - EJNMMI Res (2015)

Fusion steps of the propagated align algorithm. Reg-rigid: rigidly registered image. Reg-elastix: rigid and non-rigidly registered image. Step 1: rigid CT to CT alignment of target to reference. Step 2: translation of step 1 on target PET. Step 3: CT to rigid aligned CT non-rigid alignment using elastix toolbox. Step 4: translation of step 3 to PET of step 2. QA: resample of image as quality assurance, so voxel size matches prior to voxel-by-voxel comparison.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4385310&req=5

Fig1: Fusion steps of the propagated align algorithm. Reg-rigid: rigidly registered image. Reg-elastix: rigid and non-rigidly registered image. Step 1: rigid CT to CT alignment of target to reference. Step 2: translation of step 1 on target PET. Step 3: CT to rigid aligned CT non-rigid alignment using elastix toolbox. Step 4: translation of step 3 to PET of step 2. QA: resample of image as quality assurance, so voxel size matches prior to voxel-by-voxel comparison.
Mentions: The fusion steps are also detailed in Figure 1. All transformation calculations used a four-level multi-resolution approach while mutual information as image similarity measure, and an adaptive stochastic gradient descent optimizer [21] was used to maximize image similarity. The control point spacing of the B-spline transformation was 16 mm. The maximum number of iterations was set to 250 (rigid) and 500 (B-spline). Because of computation time, data were processed in batch mode using python scripting.Figure 1

Bottom Line: Lesion statistics were compared to assess the impact on therapy response assessment.The elastix toolbox impacts lesion statistics and therefore therapy response assessment in a clinically significant way.Further optimization and validation of this technique is necessary prior to clinical implementation.

View Article: PubMed Central - PubMed

Affiliation: University of Groningen and Department of Pulmonary Diseases, University Medical Center Groningen, Hanzeplein 1, P.O. Box 30.001, , 9700 RB Groningen, The Netherlands.

ABSTRACT

Background: Deformable image registration allows volume of interest (VOI)- and voxel-based analysis of longitudinal changes in fluorodeoxyglucose (FDG) tumor uptake in patients with non-small cell lung cancer (NSCLC). This study evaluates the performance of the elastix toolbox deformable image registration algorithm for VOI and voxel-wise assessment of longitudinal variations in FDG tumor uptake in NSCLC patients.

Methods: Evaluation of the elastix toolbox was performed using (18)F-FDG PET/CT at baseline and after 2 cycles of therapy (follow-up) data in advanced NSCLC patients. The elastix toolbox, an integrated part of the IMALYTICS workstation, was used to apply a CT-based non-linear image registration of follow-up PET/CT data using the baseline PET/CT data as reference. Lesion statistics were compared to assess the impact on therapy response assessment. Next, CT-based deformable image registration was performed anew on the deformed follow-up PET/CT data using the original follow-up PET/CT data as reference, yielding a realigned follow-up PET dataset. Performance was evaluated by determining the correlation coefficient between original and realigned follow-up PET datasets. The intra- and extra-thoracic tumors were automatically delineated on the original PET using a 41% of maximum standardized uptake value (SUVmax) adaptive threshold. Equivalence between reference and realigned images was tested (determining 95% range of the difference) and estimating the percentage of voxel values that fell within that range.

Results: Thirty-nine patients with 191 tumor lesions were included. In 37/39 and 12/39 patients, respectively, thoracic and non-thoracic lesions were evaluable for response assessment. Using the EORTC/SUVmax-based criteria, 5/37 patients had a discordant response of thoracic, and 2/12 a discordant response of non-thoracic lesions between the reference and the realigned image. FDG uptake values of corresponding tumor voxels in the original and realigned reference PET correlated well (R (2)=0.98). Using equivalence testing, 94% of all the voxel values fell within the 95% range of the difference between original and realigned reference PET.

Conclusions: The elastix toolbox impacts lesion statistics and therefore therapy response assessment in a clinically significant way. The elastix toolbox is therefore not applicable in its current form and/or standard settings for PET response evaluation. Further optimization and validation of this technique is necessary prior to clinical implementation.

No MeSH data available.


Related in: MedlinePlus