A multi-metric registration strategy for the alignment of longitudinal brain images in pediatric oncology

Montin, Eros; Belfatto, Antonella; Bologna, Marco; Meroni, Silvia; Cavatorta, Claudia; Pecori, Emilia; Diletto, B.; Massimino, Maura; Oprandi, Maria Chiara; Poggi, Geraldina; Arrigoni, Filippo; Peruzzo, Denis; Pignoli, E.; Gandola, Lorenza; Cerveri, Pietro; Mainardi, Luca

doi:10.1007/s11517-019-02109-4

Cited by 5 publications

(6 citation statements)

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“…All images used in this study were processed and transported into the same frame of reference by using a homemade multistep registration framework ad hoc developed for this project [36] adopting widely used, cross-platforms, open-source image registration toolkits (ITK) [37] and openMP1. The registration process had to consider possible disease and treatment-related anatomical changes over time and the physiologic anatomical growth of the child from the time of RT to DTI evaluation, during years of follow-up.…”

Section: Data Processingmentioning

confidence: 99%

“…The registration between CT and MR0 was performed using only rotations and translation, since both exams refer to the same time point and complex morphological changes of the head are not expected. The registration between the follow-up MRI/DTI and the MR0 was performed PLOS ONE PLOS ONE using both affine (translation, rotation, scaling and shear) and non-rigid transformations (Bspline), as detailed in [36]. B-spline non-rigid transformation was necessary to account for the substantial anatomical changes related to the normal children's growth.…”

Section: Data Processingmentioning

confidence: 99%

“…B-spline non-rigid transformation was necessary to account for the substantial anatomical changes related to the normal children's growth. The cost function for both types of registration (rigid and non-rigid) was a combination of mutual information and normalized gradient field as detailed in [36]. The patients' brain parcellation was performed using the brain atlas included in the Automatic Anatomical Labeling (AAL) software [38].…”

Section: Data Processingmentioning

confidence: 99%

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Retrospective study of late radiation-induced damages after focal radiotherapy for childhood brain tumors

et al. 2021

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Purpose To study a robust and reproducible procedure to investigate a relation between focal brain radiotherapy (RT) low doses, neurocognitive impairment and late White Matter and Gray Matter alterations, as shown by Diffusion Tensor Imaging (DTI), in children. Methods and materials Forty-five patients (23 males and 22 females, median age at RT 6.2 years, median age at evaluations 11.1 years) who had received focal RT for brain tumors were recruited for DTI exams and neurocognitive tests. Patients’ brains were parceled in 116 regions of interest (ROIs) using an available segmented atlas. After the development of an ad hoc, home-made, multimodal and highly deformable registration framework, we collected mean RT doses and DTI metrics values for each ROI. The pattern of association between cognitive scores or domains and dose or DTI values was assessed in each ROI through both considering and excluding ROIs with mean doses higher than 75% of the prescription. Subsequently, a preliminary threshold value of dose discriminating patients with and without neurocognitive impairment was selected for the most relevant associations. Results The workflow allowed us to identify 10 ROIs where RT dose and DTI metrics were significantly associated with cognitive tests results (p<0.05). In 5/10 ROIs, RT dose and cognitive tests were associated with p<0.01 and preliminary RT threshold dose values, implying a possible cognitive or neuropsychological damage, were calculated. The analysis of domains showed that the most involved one was the “school-related activities”. Conclusion This analysis, despite being conducted on a retrospective cohort of children, shows that the identification of critical brain structures and respective radiation dose thresholds is achievable by combining, with appropriate methodological tools, the large amount of data arising from different sources. This supported the design of a prospective study to gain stronger evidence.

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Section: Data Processingmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

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Retrospective study of late radiation-induced damages after focal radiotherapy for childhood brain tumors

et al. 2021

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“…Longitudinal studies are crucial to assess even subtle changes occurring to muscle tissue in the time and require a robust protocol for image alignment and registration. Most of the specific literature regarding image registration is focused on other areas/organs such as the brain [20,21] and the cardiac parenchyma [22,23] and includes workflows and metrics [24]. However, detailed protocols and pipelines, including even more technical aspects, suited for skeletal muscle images are still missing.…”

Section: Introductionmentioning

confidence: 99%

Multi-Steps Registration Protocol for Multimodal MR Images of Hip Skeletal Muscles in a Longitudinal Study

et al. 2020

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Image registration is crucial in multimodal longitudinal skeletal muscle Magnetic Resonance Imaging (MRI) studies to extract reliable parameters that can be used as indicators for physio/pathological characterization of muscle tissue and for assessing the effectiveness of treatments. This paper aims at proposing a reliable registration protocol and evaluating its accuracy in a longitudinal study. The hips of 6 subjects were scanned, in a multimodal protocol, at 2 different time points by a 3 Tesla scanner; the proposed multi-step registration pipeline is based on rigid and elastic transformations implemented in SimpleITK using a multi-resolution technique. The effects of different image pre-processing (muscle masks, isotropic voxels) and different parameters’ values (learning rates and mesh sizes) were quantitatively assessed using standard accuracy indexes. Rigid registration alone does not provide satisfactory accuracy for inter-sessions alignment and a further elastic step is needed. The use of isotropic voxels, combined with the muscle masking, provides the best result in terms of accuracy. Learning rates can be increased to speed up the process without affecting the final results. The protocol described in this paper, complemented by open-source software, can be a useful guide for researchers that approach for the first time the issues related to the muscle MR image registration.

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“…Second, subject movement between the two repeated images, either voluntary or not, affects the sum and difference images, which could lead to an incorrect estimation of the SNR 7,14 . Nonphysiologic motion between the two replicas could be corrected by image registration, but this would involve additional processing, because a general registration method automatically applicable to any set of MR images does not exist 15,16 . Besides increasing complexity and computation time, incorporating a tailored registration algorithm to the DI method could still yield poor results for regions of the image with low SNR and small anatomical features, which are the most susceptible to motion artifacts.…”

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confidence: 99%

Seeking a Widely Adoptable Practical Standard to Estimate Signal‐to‐Noise Ratio in Magnetic Resonance Imaging for Multiple‐Coil Reconstructions

Montin

Lattanzi

2021

Magnetic Resonance Imaging

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Background Signal‐to‐noise ratio (SNR) is used to evaluate the performance of magnetic resonance (MR) imaging systems. Accurate and consistent estimations are needed to routinely use SNR to assess coils and image reconstruction techniques. Purpose To identify a reliable and practical method for SNR estimation in multiple‐coil reconstructions. Study Type Technical evaluation and comparison. Subjects/Phantom A uniform phantom and four healthy volunteers: 35, 38, 39 y/o males, 25 y/o female. Field Strength/Sequence Two‐dimensional multislice gradient‐echo pulse sequence at 3 T and 7 T. Assessment Reference‐standard SNR was calculated from 100 multiple replicas. Six SNR methods were compared against it: difference image (DI), analytic array combination (AC), pseudo‐multiple‐replica (PMR), generalized pseudo‐replica (GPR), smoothed image subtraction (SIS), and DI with temporal instability correction (TIC). The assessment was repeated for different multiple‐coil reconstructions. Statistical Tests SNR methods were evaluated in terms of relative deviation (RD) and normalized mutual information (NMI) with respect to the reference‐standard, using a linear regression (0.05 significance level) to assess how different factors affect accuracy. Results Average RD (phantom) for DI, AC, PMR, GPR, SIS, and TIC was 7.9%, 6%, 6.7%, 10.1%, 40%, and 14.6%, respectively. RD increased with acceleration. SNR maps with AC were the most similar to the reference standard (NMI = 0.358). Considering all brain regions of interest, average RD for all SNR methods varied 96% among volunteers but remained approximately 10% for AC, PMR, and GPR, whereas it was more than 30% for DI, SIS, and TIC. RD was mainly affected by image reconstruction (beta = 12) for AC and SNR entropy for SIS (beta = 19). Data Conclusion AC provided accurate and robust SNR estimation. PMR and GPR are more generally applicable than AC. DI and TIC should be used only at low acceleration factors, when an additional noise‐only scan cannot be acquired. SIS is a single‐acquisition alternative to DI for generalized autocalibrating partial parallel acquisition (GRAPPA) reconstructions. Evidence Level 1 Technical Efficacy Stage 1

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A multi-metric registration strategy for the alignment of longitudinal brain images in pediatric oncology

Cited by 5 publications

References 24 publications

Retrospective study of late radiation-induced damages after focal radiotherapy for childhood brain tumors

Retrospective study of late radiation-induced damages after focal radiotherapy for childhood brain tumors

Multi-Steps Registration Protocol for Multimodal MR Images of Hip Skeletal Muscles in a Longitudinal Study

Seeking a Widely Adoptable Practical Standard to Estimate Signal‐to‐Noise Ratio in Magnetic Resonance Imaging for Multiple‐Coil Reconstructions

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