Dataset Information

Merged image reconstruction for anomalous systemic arterial supply to a normal lung.

ABSTRACT: We present a rare case of anomalous systemic arterial supply to normal basal segments of the left lower lobe. Plain computed tomography (CT) showed an occupancy lesion in the left lower lobe. Contrast CT and merged three-dimensional (3D) image reconstruction showed that the anomalous systemic artery originated from the descending aorta and substituted the basilar segmental pulmonary artery and the arterial supply to the basilar segment of left lower lobe. We use the merged image reconstruction of 3D CT angiography and bronchography (3D-CTAB) to depict the precise location and stereoscopic shape of this vascular malformation. Therefore, we think that these data add a novel comprehensive perspective on the diagnosis of the feature of malformation and treatment planning for this rare disease.

SUBMITTER: Hu B

PROVIDER: S-EPMC7276187 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Merged image reconstruction for anomalous systemic arterial supply to a normal lung.

Hu Bin B Lan Yunping Y Li Qiang Q Yang Xiaozun X Tian Bo B Qing Haomiao H Zhou Peng P Wang Ting T Yang Xiaojun X

Journal of medical radiation sciences 20200302 2

We present a rare case of anomalous systemic arterial supply to normal basal segments of the left lower lobe. Plain computed tomography (CT) showed an occupancy lesion in the left lower lobe. Contrast CT and merged three-dimensional (3D) image reconstruction showed that the anomalous systemic artery originated from the descending aorta and substituted the basilar segmental pulmonary artery and the arterial supply to the basilar segment of left lower lobe. We use the merged image reconstruction o ...[more]

PMID: 32118356

Similar Datasets

Project description:Brain extraction from 3D medical images is a common pre-processing step. A variety of approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images exhibiting strong pathologies, for example, the presence of a brain tumor or of a traumatic brain injury (TBI), is challenging. In such cases, tissue appearance may substantially deviate from normal tissue appearance and hence violates algorithmic assumptions for standard approaches to brain extraction; consequently, the brain may not be correctly extracted. This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue appearance and pathologies. Specifically, our model uses a three-part image decomposition: (1) normal tissue appearance is captured by principal component analysis (PCA), (2) pathologies are captured via a total variation term, and (3) the skull and surrounding tissue is captured by a sparsity term. Due to its convexity, the resulting decomposition model allows for efficient optimization. Decomposition and image registration steps are alternated to allow statistical modeling of normal tissue appearance in a fixed atlas coordinate system. As a beneficial side effect, the decomposition model allows for the identification of potentially pathological areas and the reconstruction of a quasi-normal image in atlas space. We demonstrate the effectiveness of our approach on four datasets: the publicly available IBSR and LPBA40 datasets which show normal image appearance, the BRATS dataset containing images with brain tumors, and a dataset containing clinical TBI images. We compare the performance with other popular brain extraction models: ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing approaches on all four datasets. Specifically, our model achieves the best median (97.11) and mean (96.88) Dice scores over all datasets. The two best performing competitors, ROBEX and MASS, achieve scores of 96.23/95.62 and 96.67/94.25 respectively. Hence, our approach is an effective method for high quality brain extraction for a wide variety of images.

Dataset Information

Merged image reconstruction for anomalous systemic arterial supply to a normal lung.

Publications

Merged image reconstruction for anomalous systemic arterial supply to a normal lung.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets