This study was conducted to evaluate the differences in the procedural variables between transradial and transfemoral access for coronary angiography, with cardiology fellows as the primary operators.This was a retrospective study of 163 radial and 180 femoral access diagnostic cardiac catheterization procedures, and involved cardiology fellowship trainees as primary operators.The radial approach was associated with significantly higher fluoroscopy time (8.0 ± 6.97 min vs 4.25 ± 3.01 min; P<.001), dose area product (10775 ± 6724 μGy/m² vs 7952 ± 4236 μGy/m²; P<.001), procedure time (38.31 ± 12.25 min vs 27 ± 17.56 min; P<.001), procedure start to vascular access time (8.24 ± 6.31 min vs 5.31 ± 4.59 min; P<.001), and vascular access to procedure end time (30 ± 15.34 min vs 21.2 ± 9.57 min; P<.001). These differences persisted after adjusting for patients with bypass grafts and additional imaging (P<.001). The contrast amount was not significantly different between the two groups (P=.12). Procedure start to vascular access time improved significantly with fellowship training year in both the radial (9.57 ± 6.96 min vs 8.23 ± 6.08 min vs 5.57 ± 4.82 min) and femoral groups (6.17 ± 5.07 min vs 5.47 ± 4.75 min vs 4.01 ± 3.31 min). Fluoroscopy time showed significant difference in only the femoral access group (P=.01). Dose area product did not improve with training in either access group.Radial procedures were associated with higher radiation dose and longer procedure time. Despite decrease in total procedural time for radial cases with the level of training, total radiation dose did not decrease.
Image Fusion is a process of combining the relevant information from a set of images, into a single image, wherein the resultant fused image will be more informative and complete than any of the input images.There are several image fusion algorithms that are being used by the researchers throughout the globe.Each of the image fusion algorithms has their own advantages and disadvantages which are the main area of concern in this paper.The commonly used image fusion algorithms used are pixel by pixel method, discrete wavelet transformation, Haar transformation, PCA base image fusion method, HIS transform base image fusion, etc.In this paper we will discuss the pixel by pixel method and discrete wavelet transformation image fusion methods.A thorough evaluation of all the above mentioned algorithms is conducted with the help of algorithms and mathematical formulas.These algorithms are compared using three quality metrics namely entropy, standard deviation and quality index.The experimental evaluation is conducted using Matlab 7.The reading produced by image quality metrics, based on image quality of the fused images, were used to access the algorithms.
Image Fusion is a process in which combine the relevant or same information from a set of images, into a single image that is more realistic, informative and complete than the previous input images.During the past two decades, many image fusion methods have been proposed and developed.Image Fusion methods are categorized into pixel, feature, and decision levels according to the stage at which image information is integrated.Image fusion algorithms help to achieve benefits like high accuracy and reliability, feature vector with higher dimensionality, faster acquisition of information and cost effective acquisition of information.The proposed technique Modified Haar Wavelet Transform is an enhanced version of Haar Wavelet Transform which can reduce the calculation work and is able to improve the contrast of the image.The main achievement of MHWT is sparse representation and fast transformation.In MHWT at each level, we need to store only half of the original data due to which it becomes more efficient.In this paper we implement Image Fusion MHWT (Modified Haar Wavelet Transformation) and compares its performance with Discrete Wavelet transform (DWT) using performance metrics of standard deviation, entropy and quality index.The modified technique MHWT shows better performance than the earlier methods.A thorough analysis and evaluation of the proposed algorithm is conducted with the help of mathematical formulas.
Abstract The primary objective of the study was to determine whether shear wave elastography can be used to predict the response of neoadjuvant chemotherapy (NACT) in women having invasive breast cancer. A prospective study involving 28 patients having invasive breast cancer and undergoing NACT followed by surgery was done after institutional review board approval. All the patients underwent 2-dimensional B-mode ultrasound and 2-dimensional shear wave elastography before the start of chemotherapy and after 2 cycles of completion of chemotherapy, and mean stiffness was recorded. The patients were segregated to responders and nonresponders based on residual cancer burden scoring. Difference in mean elasticity was compared between the 2 groups. The results showed that the mean stiffness after 2 cycles was significantly different between the responders and nonresponders and so was the change in the mean stiffness after 2 cycles of NACT. Using a cutoff value of 45.5 kPa (20.53%), change in mean elasticity after 2 cycles of NACT, sensitivity of 76.9%, and specificity of 80% with negative predictive value of 80.1 was attained. Responders show greater change in mean stiffness after 2 cycles of NACT as compared with nonresponders on shear wave elastography; thus, it can be used to predict response to NACT after 2 cycles.
3733 Background: Colorectal Cancer (CRC) is a disease of older adults. National awareness of the disease has increased since the adoption of screening colonoscopy for all individuals over the age of 50. How ever some patients are diagnosed at younger age. Our objective was to assess the racial disparity, if any in this age group. Methods: We retrospectively reviewed CRC data from two inner city hospitals in north New Jersey over period of 12 yr at one institution (1992–2004) and 10 years at other (1994–2004). Results: Over all number of patient diagnosed with CRC 1299: 930 (71%) white, 342 (26%) black, and 27 (2%) were other racial background. We identified 124 patients (9.5%) who were less than 50year age: 72 (58%) white and 48(38%) were black. The increase number of cases in the black with CRC in the below 50 age group was statistically significant (p=0.0007 by chi-square). Conclusions: Based on this retrospective analysis large population studies will be needed to determine whether current CRC screening guideline should be modified in Black population. No significant financial relationships to disclose.
With the advent of powerful analysis tools, intelligent medical diagnostics for neurodegenerative disease (NDs) diagnosis are coming close to becoming a reality. In this work, we describe a state-of-the-art machine-learning system with multiclass diagnostic capabilities for the diagnosis of NDs. Our framework for multiclass subject classification comprises feature extraction using principal component analysis, feature selection using Fisher discriminant ratio, and subject classification using least-squares support vector machines. A multisite, multiscanner data set containing 2540 patients clinically diagnosed as Alzheimer Disease (AD), healthy controls (HC), Parkinson disease (PD), mild cognitive impairment (MCI), and scans without evidence of dopaminergic deficit (SWEDD) was obtained from Parkinson's Progression Marker Initiative and Alzheimer's Disease Neuroimaging Initiative. Our work assumes significance since studies have primarily focused on comparing only two subject classes at once, i.e., as binary classes. To profile the diagnostic capabilities for real-time clinical practice, we tested our framework for multiclass disease diagnostic capabilities. The proposed method has been trained and tested on this cohort (2540 subjects), the largest reported so far in the literature. For multiclass diagnosis, our method results in highest reported classification accuracy of 87.89 ± 03.98% with a precision of 82.54 ± 08.85%. Also, we have obtained accuracy of up to 100% for binary class classification of NDs. We believe that this study takes us one step closer to translating machine learning into routine clinical settings as a decision support system for ND diagnosis.
18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is now recognized as a staging investigation for locally advanced breast cancer. This retrospective review of data was performed to correlate the maximum standardized uptake value (SUVmax) of the primary tumor with the molecular subtype of breast cancer.Patients with biopsy-proven, treatment naïve, Stage III breast cancer, for whom 18F-FDG PET/CT data and immunohistochemistry 4 was available were included in the study. Correlations were deduced between the SUVmax of primary tumor to the molecular subtypes.Three hundred and two patients were included in the study. Fifty-two (17.2%) tumors were Luminal A (LA), 131 (43.4%) Luminal B (LB), 42 (13.9%) human epidermal growth factor receptor-2 enriched (HE), and 77 (25.5%) basal-like (BL). SUVmax of the primary tumor differed significantly between LA and other subtypes (SUVmax: LA Median 7.4, LB 11.65, HE 13.5, BL 15.35, P < 0.001). Estrogen receptor (ER) and progesterone receptor (PR) positivity were inversely correlated to the SUVmax of the primary (SUVmax: ER + Median 10.4, ER - 14.2, P < 0.001, PR + 9.65, PR - 13.9, P < 0.001). There was a strong positive correlation observed between Ki67 and SUVmax (Pearson Coefficient 0.408, P < 0.001). A SUVmax value of 9.65 was determined as a cutoff on receiver operating characteristic curve to differentiate between LA and other subtypes with a sensitivity of 92.3% and specificity of 70.6%.SUVmax of primary showed a statistically significant difference between LA subtypes when compared to other subtypes. However, there was overlap of values in each subgroup and thus 18F-FDG PET/CT cannot be used to accurately assess the molecular characteristics of the tumor.
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