Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets

Robitaille, Nicolas; Mouiha, Abderazzak; Crépeault, Burt; Valdivia, Fernando; Duchesne, Simon

doi:10.1155/2012/347120

Cited by 42 publications

(49 citation statements)

References 23 publications

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“…Here, the SBST results are compared with those obtained by its parent standardization technique, i.e., L4, and another tissue-based standardization technique called STandardization of Intensities (STI) [11]. The measure employed is the voxelwise MAE [11], computed on different voxel sets.…”

Section: Comparison By Using the Maementioning

confidence: 99%

“…where N is the number of voxels in the considered regions (e.g., CSF, WM…), and I o,v and I s,v are intensity values for the template and the nonlinearly registered images (NS, SBST, or L4-standardized), respectively, at voxel v. MAE can be expressed in percentage [11]. The STI technique uses spatial correspondence between an input image and a standard one, determined via global linear and nonlinear registration.…”

Section: Comparison By Using the Maementioning

confidence: 99%

“…Leung et al [1] proposed a semiautomated segmentation technique that delineates CSF/WM/GM tissue components, for which they computed mean intensities. However, this technique yields a linear transformation, which does not completely address the problem, guaranteeing the standardization of spatially corresponding tissue intensities [11].…”

Section: Introductionmentioning

confidence: 99%

“…Standardization techniques that employ histograms are indeed largely used in the literature [11,[13][14][15][16], we attempted to improve them, enhancing robustness, by considering tissuespecific information. The main novelty consists of computing three piecewise-linear standardizing transformations for the three main brain tissues, for each tissue class separately.…”

Section: Introductionmentioning

confidence: 99%

“…MAE was calculated on different voxel sets of the single brain tissues and the corresponding template images, in case of SBST and with other standardization procedures available in the literature [11,14].…”

Section: Introductionmentioning

confidence: 99%

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Robust Intensity Standardization in Brain Magnetic Resonance Images

2015

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The paper is focused on a tiSsue-Based Standardization Technique (SBST) of magnetic resonance (MR) brain images. Magnetic Resonance Imaging intensities have no fixed tissue-specific numeric meaning, even within the same MRI protocol, for the same body region, or even for images of the same patient obtained on the same scanner in different moments. This affects postprocessing tasks such as automatic segmentation or unsupervised/supervised classification methods, which strictly depend on the observed image intensities, compromising the accuracy and efficiency of many image analyses algorithms. A large number of MR images from public databases, belonging to healthy people and to patients with different degrees of neurodegenerative pathology, were employed together with synthetic MRIs. Combining both histogram and tissue-specific intensity information, a correspondence is obtained for each tissue across images. The novelty consists of computing three standardizing transformations for the three main brain tissues, for each tissue class separately. In order to create a continuous intensity mapping, spline smoothing of the overall slightly discontinuous piecewise-linear intensity transformation is performed. The robustness of the technique is assessed in a post hoc manner, by verifying that automatic segmentation of images before and after standardization gives a high overlapping (Dice index >0.9) for each tissue class, even across images coming from different sources. Furthermore, SBST efficacy is tested by evaluating if and how much it increases intertissue discrimination and by assessing gaussianity of tissue gray-level distributions before and after standardization. Some quantitative comparisons to already existing different approaches available in the literature are performed.

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Section: Comparison By Using the Maementioning

confidence: 99%

Section: Comparison By Using the Maementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Intensity Standardization in Brain Magnetic Resonance Images

2015

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Multiatlas approach with local registration goodness weighting for MRI-based electron density mapping of head and neck anatomy

et al. 2017

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Purpose The growing use of magnetic resonance imaging (MRI) as a substitute for computed tomography-based treatment planning requires the development of effective algorithms to generate electron density maps for treatment planning and patient setup verification. The purpose of this work was to develop a method to synthesize computerized tomography (CT) for MR-only radiotherapy of head and neck cancer patients. Methods The algorithm is based on registration of multiple patient datasets containing both MRI and CT images (a ”multi-atlas” algorithm). Twelve matched pairs of good quality CT and MRI scans (those without apparent motion and blurring artifacts) were selected from a pool of head and neck cancer patients to form the atlas. All atlas MRI scans were pre-processed to reduce scanner- and patient-induced intensity inhomogeneities and to standardize their intensity histograms. Atlas CT and MRIs were co-registered using a novel bone-to-air replacement technique applied to the CT scans that improves the similarity between CTs and MRIs and facilitates the registration process. For each new patient, all atlas MRIs are deformed initially onto the new patients’ MRI. We introduce a generalized registration error (GRE) metric that automatically measures the goodness of local registration between MRI pairs. The final synthetic CT value at each point is a nonlinear GRE-weighted average of the atlas CTs. For evaluation, the leave-one-out technique was used for synthetic CT generation and the mean absolute error (MAE) between the original and synthetic CT was computed over the entire CT image. The impact of our proposed CT-MR registration scheme on the accuracy of the final synthetic CT was also studied. The original treatment plans were also recomputed on the new synthetic CTs and dose-volume histogram metrics were compared. In addition, the two-dimensional (2D) gamma analysis at 1%/1mm and 2%/2 mm dose difference/distance to agreement was also performed to study the dose distribution at the isocenter. Results MAE error (± standard deviation) between the original and the synthetic CTs was 64 ± 10, 113 ± 12, and 130 ± 28 Hounsfield Unit (HU) for the entire image, air, and bone regions, respectively. Our results showed that our proposed bone-suppression based CT-MR fusion and GRE-weighted strategy could lower the overall MAE error between the original and synthetic CTs by ~69% and ~34%, respectively. Dose re-calculation comparison showed highly consistent results between plans based on the synthetic vs. the original CTs. The 2D gamma analysis revealed the pass rate of 95.44 ± 2.5 and 99.36 ± 0.71 for 1%/1mm and 2%/2 mm criteria, respectively. Due to local registration weighting, the method is robust with respect to MRI imaging artifacts. Conclusion We developed a novel image analysis technique to synthesize CT for head and neck anatomy. Novel methods were introduced to accurately register atlas CTs and MRIs as well as to weight the final electron density maps using local registration goodness estimates. The resulting...

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Intensity Standardization of Skeleton in Follow-Up Whole-Body MRI

Ceranka

Verga

Lecouvet

et al. 2019

Lecture Notes in Computer Science

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The value of whole-body MRI is constantly growing and is currently employed in several bone pathologies including diagnosis and prognosis of multiple myeloma, musculoskeletal imaging and evaluation of treatment response assessment in bone metastases. Intra-patient follow-up MR images acquired over time do not only suffer from spatial misalignments caused by change in patient positioning and body composition, but also intensity inhomogeneities, making the absolute MR intensity values inherently non-comparable. The non-quantitative nature of whole-body MRI makes it difficult to derive reproducible measurement and limits the use of treatment response maps. In this work, we have investigated and compared the performance of several standardization algorithms for skeletal tissue in anatomical and diffusion-weighted whole-body MRI. The investigated method consists of two steps. First, the follow-up whole-body image is spatially registered to a baseline image using B-spline deformable registration. Secondly, an intensity standardization algorithm based on a histogram matching is applied to the followup image. Additionally, the use of a skeleton mask was introduced, in order to focus the accuracy of algorithms on a tissue of interest. A linear piecewise matching method using masked skeletal region showed a superior performance in comparison to the other evaluated intensity standardization methods. The proposed work helps to overcome the nonquantitative nature of whole-body MRI images, allowing for extraction of important image parameters, visualization of whole-body MR treatment response maps and assessment of severity of bone pathology based on MR intensity profile.

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Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets

Cited by 42 publications

References 23 publications

Robust Intensity Standardization in Brain Magnetic Resonance Images

Robust Intensity Standardization in Brain Magnetic Resonance Images

Multiatlas approach with local registration goodness weighting for MRI-based electron density mapping of head and neck anatomy

Intensity Standardization of Skeleton in Follow-Up Whole-Body MRI

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