| 1. |
- Groot, Colin, et al.
(författare)
-
Clinical phenotype, atrophy, and small vessel disease in APOEε2 carriers with Alzheimer disease
- 2018
-
Ingår i: Neurology. - 1526-632X. ; 91:20, s. 1851-1859
-
Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: To examine the clinical phenotype, gray matter atrophy patterns, and small vessel disease in patients who developed prodromal or probable Alzheimer disease dementia, despite carrying the protective APOEε2 allele. METHODS: We included 36 β-amyloid-positive (by CSF or PET) APOEε2 carriers (all ε2/ε3) with mild cognitive impairment or dementia due to Alzheimer disease who were matched for age and diagnosis (ratio 1:2) to APOEε3 homozygotes and APOEε4 carriers (70% ε3/ε4 and 30% ε4/ε4). We assessed neuropsychological performance across 4 cognitive domains (memory, attention, executive, and language functions), performed voxelwise and region of interest analyses of gray matter atrophy on T1-weighted MRI, used fluid-attenuated inversion recovery images to automatically quantify white matter hyperintensity volumes, and assessed T2*-weighted images to identify microbleeds. Differences in cognitive domain scores, atrophy, and white matter hyperintensities between ε2 carriers, ε3 homozygotes, and ε4 carriers were assessed using analysis of variance analyses, and Pearson χ2 tests were used to examine differences in prevalence of microbleeds. RESULTS: We found that ε2 carriers performed worse on nonmemory domains compared to both ε3 homozygotes and ε4 carriers but better on memory compared to ε4 carriers. Voxelwise T1-weighted MRI analyses showed asymmetric (left > right) temporoparietal-predominant atrophy with subtly less involvement of medial-temporal structures in ε2 carriers compared to ε4 carriers. Finally, ε2 carriers had larger total white matter hyperintensity volumes compared to ε4 carriers (mean 10.4 vs 7.3 mL) and a higher prevalence of microbleeds compared to ε3 homozygotes (37.5% vs 18.3%). CONCLUSION: APOEε2 carriers who develop Alzheimer disease despite carrying the protective allele display a nonamnestic clinical phenotype with more severe small vessel disease.
|
|
| 2. |
- Hauptmann, Andreas, et al.
(författare)
-
Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography
- 2018
-
Ingår i: IEEE Transactions on Medical Imaging. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0278-0062 .- 1558-254X. ; 37:6, s. 1382-1393
-
Tidskriftsartikel (refereegranskat)abstract
- Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artifacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung computed tomography scans and then applied to in-vivo photoacoustic measurement data.
|
|
| 3. |
|
|
| 4. |
- Svärm, Linus, et al.
(författare)
-
Improving Robustness for Inter-Subject Medical Image Registration Using a Feature-Based Approach
- 2015
-
Ingår i: Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on. ; , s. 824-828
-
Konferensbidrag (refereegranskat)abstract
- We propose new feature-based methods for rigid and affine image registration. These are compared to state-of-the-art intensity-based techniques as well as existing feature-based methods. On challenging datasets of brain MR and whole-body CT images, a significant improvement in terms of speed, robustness to outlier structures and dependence on initialization is shown.
|
|
| 5. |
- Zhuang, Xiahai, et al.
(författare)
-
Evaluation of algorithms for Multi-Modality Whole Heart Segmentation : An open-access grand challenge.
- 2019
-
Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415 .- 1361-8423. ; 58
-
Tidskriftsartikel (refereegranskat)abstract
- Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).
|
|
