A Target-Orientated Marker Image Binarization Method for Orthopaedic Surgical Navigation System

Camera calibration is the key technique in a C-arm based orthopaedic surgical navigation system. The extraction of marker location information is a necessary step in the calibration process. Ideal marker images should possess uniform background and contain marker shadow only, but in fact marker images always possess nonuniform background and are contaminated by noise and unwanted anatomic information, making the extraction very difficult. A target-orientated marker shadow extraction method was proposed. With this method a proper threshold for marker image binarization can be determined.

Investigation on Positive Correlation of Increased Brain Iron Deposition with Cognitive Impairment in Alzheimer Disease by Using Quantitative MR R2' Mapping

Brain iron deposition has been proposed to play an important role in the pathophysiology of Alzheimer disease（AD）.The aim of this study was to investigate the correlation of brain iron accumulation with the severity of cognitive impairment in patients with AD by using quantitative MR relaxation rate R2＇ measurements.Fifteen patients with AD,15 age-and sex-matched healthy controls,and 30 healthy volunteers underwent 1.5T MR multi-echo T2 mapping and T2＊ mapping for the measurement of transverse relaxation rate R2＇（R2＇=R2＊-R2）.We statistically analyzed the R2＇ and iron concentrations of bilateral hippocampus（HP）,parietal cortex（PC）,frontal white matter（FWM）,putamen（PU）,caudate nucleus（CN）,thalamus（TH）,red nucleus（RN）,substantia nigra（SN）,and dentate nucleus（DN） of the cerebellum for the correlation with the severity of dementia.Two-tailed t-test,Student-Newman-Keuls test（ANOVA） and linear correlation test were used for statistical analysis.In 30 healthy volunteers,the R2＇ values of bilateral SN,RN,PU,CN,globus pallidus（GP）,TH,and FWM were measured.The correlation with the postmortem iron concentration in normal adults was analyzed in order to establish a formula on the relationship between regional R2＇ and brain iron concentration.The iron concentration of regions of interest（ROI） in AD patients and controls was calculated by this formula and its correlation with the severity of AD was analyzed.Regional R2＇ was positively correlated with regional brain iron concentration in normal adults（r=0.977,P0.01）.Iron concentrations in bilateral HP,PC,PU,CN,and DN of patients with AD were significantly higher than those of the controls（P0.05）;Moreover,the brain iron concentrations,especially in parietal cortex and hippocampus at the early stage of AD,were positively correlated with the severity of patients＇ cognitive impairment（P0.05）.The higher the R2＇ and iron concentrations were,the more severe the cognitive impairment was.Regional R2＇ and iron concentration in parietal cortex and hippocampus were positively correlated with the severity of AD patients＇ cognitive impairment,indicating that it may be used as a biomarker to evaluate the progression of AD.

Developing global image feature analysis models to predict cancer risk and prognosis

In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.