Multimodality-based super-resolution reconstruction for routine brain magnetic resonance images / 南方医科大学学报

OBJECTIVE@#To propose a multi-modality-based super-resolution synthesis model for reconstruction of routine brain magnetic resonance images (MRI) with a low resolution and a high thickness into high-resolution images.@*METHODS@#Based on real paired low-high resolution MRI data (2D T1, 2D T2 FLAIR and 3D T1), a structure-constrained image mapping network was used to extract important features from the images with different modalities including the whole T1 and subcortical regions of T2 FLAIR to reconstruct T1 images with higher resolutions. The gray scale intensity and structural similarities between the super-resolution images and high-resolution images were used to enhance the reconstruction performance. We used the anatomical information acquired from segment maps of the super-resolution T1 image and the ground truth by a segmentation tool as a significant constraint for adaptive learning of the intrinsic tissue structure characteristics of the brain to improve the reconstruction performance of the model.@*RESULTS@#Our method showed the performance on the testing dataset than other methods with an average PSNR of 33.11 and SSIM of 0.996. The anatomical structure of the brain including the sulcus, gyrus, and subcortex were all reconstructed clearly using the proposed method, which also greatly enhanced the precision of MSCSR for brain volume measurement.@*CONCLUSION@#The proposed MSCSR model shows excellent performance for reconstructing super-resolution brain MR images based on the information of brain tissue structure and multimodality MR images.

The Abnormalities of Retinal Arteriole in Atherothrombotic Ischemic Stroke Patients Representing the Changes of Cerebral Vasculature Indirectly

PURPOSE: Retinal blood vessels and cerebral small vessels possess similar characteristics anatomically, physiologically and embryologically. We studied the availability of abnormal fundus findings of patients who had an atherothrombotic ischemic stroke and who have the risk factors. METHODS: Fundus photographs and brain images were taken in patients who had a first-ever symptomatic ischemic stroke of large artery atherosclerosis (LAA) or small vessel occlusion (SVO) from March 2004 to February 2005. We analyzed the association between fundus abnormalities and ischemic stroke subtypes. RESULTS: Based on brain MRI and MRA, a total of 47 patients were classified into SVO and LAA groups. The SVO group consisted of 27 patients (mean age: 69.7 years), and the LAA group consisted of 20 patients (mean age: 65.4 years). The control group comprised 15 patients (mean age: 64.9 years). The baseline characteristics were similar among the three groups. The severity of the retinal arteriolar narrowing and sclerosis were associated with hypertension. Compared to the control group, both the SVO and LAA groups showed more severe arteriolar sclerosis, the SVO group showed more severe arteriolar narrowing and the LAA group showed more frequent AV crossing and retinal exudate. CONCLUSIONS: Retinal arteriolar abnormalities such as arteriolar narrowing and sclerosis are more severe in atherothrombotic ischemic stroke patients. Indirectly, retinal microvascular changes may indicate the status of the cerebral vasculature. Thus, analysis of fundus findings is useful for predicting an atherothrombotic ischemic stroke and planning follow-up examinations.

Detection and 3D Reconstruction of Brain from MR Images / 대한의료정보학회지

In this paper, we propose a brain detection algorithm of cross-sectional images through a 3D volume. The proposed brain detection algorithm uses several steps. They are as follows; In the first step, the standard value and downward from input image data are removed. in the second step, the pixels with maximum intensity are removed but undesirable many small areas were appeared as by-products. In order to detect brain, these small areas need to be removed. In the third step, many small areas are removed by masking but some small areas still remained. In the fourth step, they are removed using three-dimensional connectivity. The proposed algorithm was applied to real human MRI data and the brain area was successfully detected.