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Early Diagnosis of B-Acute Lymphoblastic Leukemia in a Child Masquerading as Osteomyelitis

Zelaya, Hector M; Picache, Dyana; Ward, Nicholas D; Yan, Zengmin; Glasser, Chana L
PMID: 37337644
ISSN: 1938-2707
CID: 5542582

Progression of Lyme disease to Bell's Palsy despite treatment with doxycycline

Marcos, Luis A; Yan, Zengmin
PMID: 28705754
ISSN: 1878-3511
CID: 3149462

α-Information-Based Registration of Dynamic Scans for Magnetic Resonance Cystography

Han, Hao; Lin, Qin; Li, Lihong; Duan, Chaijie; Lu, Hongbing; Li, Haifang; Yan, Zengmin; Fitzgerald, John; Liang, Zhengrong
To continue our effort on developing magnetic resonance (MR) cystography, we introduce a novel nonrigid 3-D registration method to compensate for bladder wall motion and deformation in dynamic MR scans, which are impaired by relatively low signal-to-noise ratio in each time frame. The registration method is developed on the similarity measure of α-information, which has the potential of achieving higher registration accuracy than the commonly used mutual information (MI) measure for either monomodality or multimodality image registration. The α-information metric was also demonstrated to be superior to both the mean squares and the cross-correlation metrics in multimodality scenarios. The proposed α-registration method was applied for bladder motion compensation via real patient studies, and its effect to the automatic and accurate segmentation of bladder wall was also evaluated. Compared with the prevailing MI-based image registration approach, the presented α-information-based registration was more effective to capture the bladder wall motion and deformation, which ensured the success of the following bladder wall segmentation to achieve the goal of evaluating the entire bladder wall for detection and diagnosis of abnormality.
PMID: 26087506
ISSN: 2168-2208
CID: 3149452

Bladder wall thickness mapping for magnetic resonance cystography

Zhao, Yang; Liang, Zhengrong; Zhu, Hongbin; Han, Hao; Duan, Chaijie; Yan, Zengmin; Lu, Hongbing; Gu, Xianfeng
Clinical studies have shown evidence that the bladder wall thickness is an effective biomarker for bladder abnormalities. Clinical optical cystoscopy, the current gold standard, cannot show the wall thickness. The use of ultrasound by experts may generate some local thickness information, but the information is limited in field-of-view and is user dependent. Recent advances in magnetic resonance (MR) imaging technologies lead MR-based virtual cystoscopy or MR cystography toward a potential alternative to map the wall thickness for the entire bladder. From a high-resolution structural MR volumetric image of the abdomen, a reasonable segmentation of the inner and outer borders of the bladder wall can be achievable. Starting from here, this paper reviews the limitation of a previous distance field-based approach of measuring the thickness between the two borders and then provides a solution to overcome the limitation by an electric field-based strategy. In addition, this paper further investigates a surface-fitting strategy to minimize the discretization errors on the voxel-like borders and facilitate the thickness mapping on the three-dimensional patient-specific bladder model. The presented thickness calculation and mapping were tested on both phantom and human subject datasets. The results are preliminary but very promising with a noticeable improvement over the previous distance field-based approach.
PMID: 23835844
ISSN: 1361-6560
CID: 3149442