Purpose
We investigated the impact on computed tomography (CT) image quality and photon, electron, and proton head‐and‐neck (H&N) radiotherapy (RT) dose calculations of three CT metal artifact ...reduction (MAR) approaches: A CT‐based algorithm (oMAR Philips Healthcare), manual water override, and our recently presented, Magnetic Resonance (MR)‐based kerMAR algorithm. We considered the following three hypotheses: I: Manual water override improves MAR over the CT‐ and MR‐based alternatives; II: The automatic algorithms (oMAR and kerMAR) improve MAR over the uncorrected CT; III: kerMAR improves MAR over oMAR.
Methods
We included a veal shank phantom with/without six metal inserts and nine H&N RT patients with dental implants. We quantified the MAR capabilities by the reduction of outliers in the CT value distribution in regions of interest, and the change in particle range and photon depth at maximum dose.
Results
Water override provided apparent image improvements in the soft tissue region but insignificantly or negatively influenced the dose calculations. We however found significant improvements in image quality and particle range impact, compared to the uncorrected CT, when using oMAR and kerMAR. kerMAR in turn provided superior improvements in terms of high intensity streak suppression compared to oMAR, again with associated impacts on the particle range estimates.
Conclusion
We found no benefits of the water override compared to the rest, and tentatively reject hypothesis I. We however found improvements in the automatic algorithms, and thus support for hypothesis II, and found the MR‐based kerMAR to improve upon oMAR, supporting hypothesis III.
We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using ...Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the extended discriminative -discriminative model to be one of the top ranking methods in the BRATS evaluation.
We present the first deep learning method to segment Multiple Sclerosis lesions and brain structures from MRI scans of any (possibly multimodal) contrast and resolution. Our method only requires ...segmentations to be trained (no images), as it leverages the generative model of Bayesian segmentation to generate synthetic scans with simulated lesions, which are then used to train a CNN. Our method can be retrained to segment at any resolution by adjusting the amount of synthesised partial volume. By construction, the synthetic scans are perfectly aligned with their labels, which enables training with noisy labels obtained with automatic methods. The training data are generated on the fly, and aggressive augmentation (including artefacts) is applied for improved generalisation. We demonstrate our method on two public datasets, comparing it with a state-of-the-art Bayesian approach implemented in FreeSurfer, and dataset specific CNNs trained on real data. The code is available at https://github.com/BBillot/SynthSeg.
Survival prediction models can potentially be used to guide treatment of glioblastoma patients. However, currently available MR imaging biomarkers holding prognostic information are often challenging ...to interpret, have difficulties generalizing across data acquisitions, or are only applicable to pre-operative MR data. In this paper we aim to address these issues by introducing novel imaging features that can be automatically computed from MR images and fed into machine learning models to predict patient survival. The features we propose have a direct anatomical-functional interpretation: They measure the deformation caused by the tumor on the surrounding brain structures, comparing the shape of various structures in the patient's brain to their expected shape in healthy individuals. To obtain the required segmentations, we use an automatic method that is contrast-adaptive and robust to missing modalities, making the features generalizable across scanners and imaging protocols. Since the features we propose do not depend on characteristics of the tumor region itself, they are also applicable to post-operative images, which have been much less studied in the context of survival prediction. Using experiments involving both pre- and post-operative data, we show that the proposed features carry prognostic value in terms of overall- and progression-free survival, over and above that of conventional non-imaging features.
Investigating interindividual variability is a major field of interest in neuroscience. The entorhinal cortex (EC) is essential for memory and affected early in the progression of Alzheimer's disease ...(AD). We combined histology ground‐truth data with ultrahigh‐resolution 7T ex vivo MRI to analyze EC interindividual variability in 3D. Further, we characterized (1) entorhinal shape as a whole, (2) entorhinal subfield range and midpoints, and (3) subfield architectural location and tau burden derived from 3D probability maps. Our results indicated that EC shape varied but was not related to demographic or disease factors at this preclinical stage. The medial intermediate subfield showed the highest degree of location variability in the probability maps. However, individual subfields did not display the same level of variability across dimensions and outcome measure, each providing a different perspective. For example, the olfactory subfield showed low variability in midpoint location in the superior–inferior dimension but high variability in anterior–posterior, and the subfield entorhinal intermediate showed a large variability in volumetric measures but a low variability in location derived from the 3D probability maps. These findings suggest that interindividual variability within the entorhinal subfields requires a 3D approach incorporating multiple outcome measures. This study provides 3D probability maps of the individual entorhinal subfields and respective tau pathology in the preclinical stage (Braak I and II) of AD. These probability maps illustrate the subfield average and may serve as a checkpoint for future modeling.
Based on histologically validated Nissl architecture delineations and tau pathology estimations, Oltmer et al. generated an average entorhinal cortex, provided probability maps, and investigated interindividual variability within the human entorhinal subfields in health and disease.
Accurate and reliable whole-brain segmentation is critical to longitudinal neuroimaging studies. We undertake a comparative analysis of two subcortical segmentation methods, Automatic Segmentation ...(ASEG) and Sequence Adaptive Multimodal Segmentation (SAMSEG), recently provided in the open-source neuroimaging package FreeSurfer 7.1, with regard to reliability, bias, sensitivity to detect longitudinal change, and diagnostic sensitivity to Alzheimer’s disease. First, we assess intra- and inter-scanner reliability for eight bilateral subcortical structures: amygdala, caudate, hippocampus, lateral ventricles, nucleus accumbens, pallidum, putamen and thalamus. For intra-scanner analysis we use a large sample of participants (n = 1629) distributed across the lifespan (age range = 4–93 years) and acquired on a 1.5T Siemens Avanto (n = 774) and a 3T Siemens Skyra (n = 855) scanners. For inter-scanner analysis we use a sample of 24 participants scanned on the day with three models of Siemens scanners: 1.5T Avanto, 3T Skyra and 3T Prisma. Second, we test how each method detects volumetric age change using longitudinal follow up scans (n = 491 for Avanto and n = 245 for Skyra; interscan interval = 1–10 years). Finally, we test sensitivity to clinically relevant change. We compare annual rate of hippocampal atrophy in cognitively normal older adults (n = 20), patients with mild cognitive impairment (n = 20) and Alzheimer’s disease (n = 20). We find that both ASEG and SAMSEG are reliable and lead to the detection of within-person longitudinal change, although with notable differences between age-trajectories for most structures, including hippocampus and amygdala. In summary, SAMSEG yields significantly lower differences between repeated measures for intra- and inter-scanner analysis without compromising sensitivity to changes and demonstrating ability to detect clinically relevant longitudinal changes.
•We add diffusion MRI to Bayesian thalamic nuclei segmentation with structural MRI.•Adding fiber tracts to probabilistic atlases enables orientation modelling.•Thalamus segmentation from joint ...structural and diffusion MRI improves accuracy.•Atlas and companion segmentation code are freely distributed with FreeSurfer.
The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing segmentation of 25 thalamic labels per hemisphere informed by both modalities. We present an improved probabilistic atlas, incorporating thalamic nuclei identified from histology and 45 white matter tracts surrounding the thalamus identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclear groups versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimer’s disease (AUROC 81.98%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer (https://freesurfer.net/fswiki/ThalamicNucleiDTI).
Subfield-specific measurements provide superior information in the early stages of neurodegenerative diseases compared to global hippocampal measurements. The overall goal was to systematically ...compare the performance of five representative manual and automated T1 and T2 based subfield labeling techniques in a sub-set of the ADNI2 population.
The high resolution T2 weighted hippocampal images (T2-HighRes) and the corresponding T1 images from 106 ADNI2 subjects (41 controls, 57 MCI, 8 AD) were processed as follows. A. T1-based: 1. Freesurfer+Large-Diffeomorphic-Metric-Mapping in combination with shape analysis. 2. FreeSurfer 5.1 subfields using in-vivo atlas. B. T2-HighRes: 1. Model-based subfield segmentation using ex-vivo atlas (FreeSurfer 6.0). 2. T2-based automated multi-atlas segmentation combined with similarity-weighted voting (ASHS). 3. Manual subfield parcellation. Multiple regression analyses were used to calculate effect sizes (ES) for group, amyloid positivity in controls, and associations with cognitive/memory performance for each approach.
Subfield volumetry was better than whole hippocampal volumetry for the detection of the mild atrophy differences between controls and MCI (ES: 0.27 vs 0.11). T2-HighRes approaches outperformed T1 approaches for the detection of early stage atrophy (ES: 0.27 vs.0.10), amyloid positivity (ES: 0.11 vs 0.04), and cognitive associations (ES: 0.22 vs 0.19).
T2-HighRes subfield approaches outperformed whole hippocampus and T1 subfield approaches. None of the different T2-HghRes methods tested had a clear advantage over the other methods. Each has strengths and weaknesses that need to be taken into account when deciding which one to use to get the best results from subfield volumetry.
•Comparison of 4 automated and 1 manual subfield labeling technique in common data set.•Subfield labeling approaches perform better than whole hippocampal approaches.•High resolution T2 based approaches perform better than T1 based approaches.•Different high res T2 approaches have different strengths/weaknesses.
...when comparing versions trained on homogeneous and heterogeneous training datasets, it is clear that increasing training data diversity improves performance, as measured by the capability to ...extrapolate to different-looking ischemic brain lesions. The value of brain imaging biomarkers is crucial since early stages of development. ...imaging the neonatal brain with the aim of establishing patterns of (normal or abnormal) brain development is also an important and very active topic of research. ...neuroimaging methods should not only act as tools for new scientific discoveries, but should also provide practical solutions for different neurological conditions in clinical practice. ...this Research Topic clearly illustrates that the field of computational neuroimaging is active and fascinating, with a wide range of novel methodologies aiming at reliability and generalizability through domain adaptation, data augmentation, super-resolution, and quality control.
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