Evaluation of Four Volume-Based Image Registration Algorithms

Zhang, Yunkai; Chu, James C.H.; Hsi, W; Khan, Atif J.; Mehta, P.; Bernard, D.; Abrams, Ross A.

doi:10.1016/j.meddos.2008.12.004

Cited by 9 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accuracy of automated fusion between CT and anatomic MR images is given to be submillimeter and subdegree with Syntegra toolbox (Pinnacle software version 8.0 m, Philips Medical Systems, Milpitas, CA) [23]. We found it relevant to check all 16 patients’ data sets to determine if normalization and threshold-based segmentation could wrongly influence the fusion process between CT scans and anatomic-metabolic images.…”

Section: Methodsmentioning

confidence: 99%

Integration method of 3D MR spectroscopy into treatment planning system for glioblastoma IMRT dose painting with integrated simultaneous boost

et al. 2013

View full text Add to dashboard Cite

BackgroundTo integrate 3D MR spectroscopy imaging (MRSI) in the treatment planning system (TPS) for glioblastoma dose painting to guide simultaneous integrated boost (SIB) in intensity-modulated radiation therapy (IMRT).MethodsFor sixteen glioblastoma patients, we have simulated three types of dosimetry plans, one conventional plan of 60-Gy in 3D conformational radiotherapy (3D-CRT), one 60-Gy plan in IMRT and one 72-Gy plan in SIB-IMRT. All sixteen MRSI metabolic maps were integrated into TPS, using normalization with color-space conversion and threshold-based segmentation. The fusion between the metabolic maps and the planning CT scans were assessed. Dosimetry comparisons were performed between the different plans of 60-Gy 3D-CRT, 60-Gy IMRT and 72-Gy SIB-IMRT, the last plan was targeted on MRSI abnormalities and contrast enhancement (CE).ResultsFusion assessment was performed for 160 transformations. It resulted in maximum differences <1.00 mm for translation parameters and ≤1.15° for rotation. Dosimetry plans of 72-Gy SIB-IMRT and 60-Gy IMRT showed a significantly decreased maximum dose to the brainstem (44.00 and 44.30 vs. 57.01 Gy) and decreased high dose-volumes to normal brain (19 and 20 vs. 23% and 7 and 7 vs. 12%) compared to 60-Gy 3D-CRT (p < 0.05).ConclusionsDelivering standard doses to conventional target and higher doses to new target volumes characterized by MRSI and CE is now possible and does not increase dose to organs at risk. MRSI and CE abnormalities are now integrated for glioblastoma SIB-IMRT, concomitant with temozolomide, in an ongoing multi-institutional phase-III clinical trial. Our method of MR spectroscopy maps integration to TPS is robust and reliable; integration to neuronavigation systems with this method could also improve glioblastoma resection or guide biopsies.

show abstract

Section: Methodsmentioning

confidence: 99%

Integration method of 3D MR spectroscopy into treatment planning system for glioblastoma IMRT dose painting with integrated simultaneous boost

et al. 2013

View full text Add to dashboard Cite

show abstract

“…There are many intensity based similarity measures that have been used for multi-modal image registration including: mutual information (MI) [3]- [6], cross-correlation (CC) and local correlation (LC) [5] and an adaptive combination of MI and gradient coded mutual information (GCMI) known as ACMI [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The registration technique proposed in [5] was used for matching 3D CT to MR brain images. The normalized mutual information (NMI), LC and CC similarity measures were evaluated in a multi-resolution approach to determine the six 3D rigid body transformation parameters for head phantom data.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Robust 3D Multi-Modal Registration of MRI Volumes Using the Sum of Conditional Variance

Aktar

Alam

Lambert

et al. 2013

2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

View full text Add to dashboard Cite

Multi-modal registration is a fundamental step for many medical imaging procedures. In this paper, the sum of conditional variance (SCV) similarity measure is proposed for 3D multi-modal medical image registration. The SCV similarity measure is based on minimizing the sum of conditional variances that are calculated using the joint histogram of the two images to be registered. Standard Gauss-Newton optimization is used to automatically minimize this measure which allows fast computational time and high accuracy. Experimental results show that our proposed approach is robust, computationally efficient and also more accurate when compared with the standard mutual information (MI) based approach and also the recently proposed sum-of-squared-difference on entropy images (eSSD) approach.

show abstract

“…Quantification of registration accuracy in patient data sets is done by comparing the automatically registered data with manually registered data using anatomical landmarks (Nelles et al, 2004). Alternatively, registration accuracy can be determined by using homogeneous phantoms with reference points inside a head contour (Isambert et al, 2008;Zhang et al, 2009). But after adding up the maximal errors of each image processing step, the resulting theoretical overall error is bigger than that which is clinically acceptable.…”

Section: Introductionmentioning

confidence: 99%

A head phantom prototype to verify subdural electrode localization tools in epilepsy surgery

Kuß¹,

Wagner²,

Meyer³

et al. 2011

NeuroImage

View full text Add to dashboard Cite

Evaluation of Four Volume-Based Image Registration Algorithms

Cited by 9 publications

References 21 publications

Integration method of 3D MR spectroscopy into treatment planning system for glioblastoma IMRT dose painting with integrated simultaneous boost

Integration method of 3D MR spectroscopy into treatment planning system for glioblastoma IMRT dose painting with integrated simultaneous boost

Robust 3D Multi-Modal Registration of MRI Volumes Using the Sum of Conditional Variance

A head phantom prototype to verify subdural electrode localization tools in epilepsy surgery

Contact Info

Product

Resources

About