Current Medical Imaging - Volume 16, Issue 6, 2020

Background: Using functional neuroimaging techniques to explore the central mechanism of acupoint specificity, the key of acupuncture theory and clinical practice, has attracted increasing attention worldwide. This review aimed to investigate the current status of functional neuroimaging studies on acupoint specificity and explore the potential influencing factors for the expression of acupoint specificity in neuroimaging studies. Methods: PubMed database was searched from January 1st, 1995 to December 31st, 2016 with the language restriction in English. Data including basic information, methodology and study results were extracted and analyzed from the eligible records. Results: Seventy-nine studies were finally enrolled. 65.8% of studies were performed in China, 73.4% of studies were conducted with healthy subjects, 77.2% of studies chose manual acupuncture as the intervention, 86.1% of studies focused on the instant efficacy and 89.9% of studies used functional magnetic resonance imaging as scanning technique. The average sample size was 16 per group. The comparison of verum acupoints and sham acupoints were the main body of acupoint specificity researches. 93.7% of studies obtained the positive results and favored the existence of acupoint specificity. Conclusion: This review affirmed the existence of acupoint specificity and deemed that the acupoint specificity was relative. Multiple factors such as participants, sample size, acupoint combinations, treatment courses, and types of acupoint could influence the expression of acupoint specificity.

Breast cancer is leading cancer among women for the past 60 years. There are no effective mechanisms for completely preventing breast cancer. Rather it can be detected at its earlier stages so that unnecessary biopsy can be reduced. Although there are several imaging modalities available for capturing the abnormalities in breasts, mammography is the most commonly used technique, because of its low cost. Computer-Aided Detection (CAD) system plays a key role in analyzing the mammogram images to diagnose the abnormalities. CAD assists the radiologists for diagnosis. This paper intends to provide an outline of the state-of-the-art machine learning algorithms used in the detection of breast cancer developed in recent years. We begin the review with a concise introduction about the fundamental concepts related to mammograms and CAD systems. We then focus on the techniques used in the diagnosis of breast cancer with mammograms.

Background: Image reconstruction is the mathematical process which converts the signals obtained from the scanning machine into an image. The reconstructed image plays a fundamental role in the planning of surgery and research in the medical field. Discussion: This paper introduces the first comprehensive survey of the literature about medical image reconstruction related to diseases, presenting a categorical study about the techniques and analyzing advantages and disadvantages of each technique. The images obtained by various imaging modalities like MRI, CT, CTA, Stereo radiography and Light field microscopy are included. A comparison on the basis of the reconstruction technique, Imaging Modality and Visualization, Disease, Metrics for 3D reconstruction accuracy, Dataset and Execution time, Evaluation of the technique is also performed. Conclusion: The survey makes an assessment of the suitable reconstruction technique for an organ, draws general conclusions and discusses the future directions.

Background: With the advancement of technology, Computed Tomography (CT) scan imaging can be used to gain deeper insight into the cause of death. Aims: The purpose of this study was to perform a systematic review of the efficacy of Post- Mortem Computed Tomography (PMCT) scan compared with the conventional autopsies gleaned from literature published in English between the year 2009 and 2016. Methodology: A literature search was conducted on three databases, namely PubMed, MEDLINE, and Scopus. A total of 387 articles were retrieved, but only 21 studies were accepted after meeting the review criteria. Data, such as the number of victims, the number of radiologists and forensic pathologists involved, causes of death, and additional and missed diagnoses in PMCT scans were tabulated and analysed by two independent reviewers. Results: Compared with the conventional autopsy, the accuracy of PMCT scans in detecting injuries and causes of death was observed to range between 20% and 80%. The analysis also showed that PMCT had more advantages in detecting fractures, fluid in airways, gas in internal organs, major hemorrhages, fatty liver, stones, and bullet fragments. Despite its benefits, PMCT could also miss certain important lesions in a certain region such as cardiovascular injuries and minor vascular injuries. Conclusion: This systematic review suggests that PMCT can replace most of the conventional autopsies in specific cases and is also a good complementary tool in most cases.

Objective: The aim of our study was to present the prevalence of thyroid nodules we incidentally discovered by routine Magnetic Resonance Imaging (MRI) of the cervical spine, to evaluate their clinical significance, and to discuss the current clinical approach to incidental thyroid nodules. Methods: We retrospectively evaluated the cervical spinal MRI studies of 512 patients. Thyroid glands were evaluated for morphologic and signal characteristics and examined for the presence of nodule(s). The nodules with a maximum diameter of 5 mm or more were taken into analysis. Results: Of 512 MRI studies, 254 revealed incidental thyroid nodule(s) (49.6%). The mean maximum nodule diameter was 7.48±2.92 mm. Thirty-eight of 254 incidental thyroid nodules were radiologically reported, 35 reported nodules were evaluated by US, and 22 were further analyzed by fine needle aspiration cytology. The final diagnosis of 11 aspirated nodules was an adenomatous nodule, whereas 3 were papillary thyroid carcinoma. One of the patients with papillary thyroid carcinoma was a 32-year-old man with a nodule with a maximum diameter of 7 mm. Conclusion: Incidental thyroid nodule is a frequent non-spinal lesion detected by routine cervical spinal MRI. The 3-tiered system which is recommended in the clinical approach to incidental thyroid nodules may miss some clinically significant thyroid nodules. We suggest the criteria of this system to be re-evaluated and modified if necessary. In addition, we would like to emphasize the need for a guideline for radiologists for reporting incidental thyroid nodules on MRI on the basis of a standard clinical approach.

Objective: The study aimed to investigate the relationship between the corpus callosum area (CCa) and the degree of cerebral atrophy in patients with cerebral atrophy. Methods: 119 patients with brain atrophy were grouped according to the degree of brain atrophy. Median sagittal CCa and intracranial area (ICa) were measured, and the ratio of corpus callosum to the intracranial area (CCa-ICa ratio) was calculated. The data were analyzed using ANOVA. Results: CCa significantly reduced in patients with cerebral atrophy, and the degree of cerebral atrophy was found to be positively correlated with the degree of reduction in the CCa. Conclusion: The reduction in the CCa and the CCa-ICa ratio in the median sagittal can be used as a reference indicator for the diagnosis and grading of brain atrophy in clinical practice.

Purpose: Parkinson's disease (PD), which is the second most common neurodegenerative disease following Alzheimer’s disease, can be diagnosed clinically when about 70% of the dopaminergic neurons are lost and symptoms are noticed. Neuroimaging methods such as single photon emission computed tomography have become useful tools in vivo to assess dopamine transporters (DATs) in the striatal region. However, inter- and intra-reader variability of construing the images might result in misdiagnosis. To overcome the challenges posed by classification of the disease, image preparation techniques and a back propagation neural network (BPNN) have been proposed. The aim of this study is to show that the proposed method can be used for the classification of PD with high accuracy. Methods: In this study, we used basic image preparation techniques and a BPNN on DAT imaging datasets from the Parkinson’s Progression Markers Initiative. 1,334 PD and 212 normal control (NC) subjects were included. In the image preparation phase, adaptive histogram equalization was applied to the cropped images, followed by image binarization. Then, the mass-difference method was applied to separate the regions of interest with similar values. Finally, the binarized images were subtracted from the original images, and the average pixel per node approach was applied to the images to minimize the inputs. In the BPNN phase, 400 input neurons and 2 output neurons were used. The dataset was divided into three sets: training, validation, and test. The BPNN was trained several times in order to obtain the optimum values. Results: The use of 40 hidden neurons, a learning rate of 0.00079, and a momentum factor of 0.90 produced superior results and were applied in the final BPNN architecture. The tolerance value used was 0.80. Uniquely, we found the sensitivity, specificity, and accuracy for PD vs. NC classification to be 99.7%, 99.2%, 99.6%, respectively. To the best of our knowledge, this is the highest accuracy value achieved in the existing literature. Our method increases computational speed together with improved performance. Conclusion: We have shown that effective image processing methods and the use of BPNN can successfully be applied to PD datasets to accurately determine any abnormalities in DATs. Using the shallow neural network, this procedure requires less processing time compared to other methods, and its accuracy, sensitivity, and specificity are reliable. However, further studies are needed to establish a prediction method for the preclinical and prodromal stages of the disease.

Background: In the United States, prostate cancer has a relatively large impact on men's health. Magnetic resonance imaging (MRI) is useful for the diagnosis and treatment of prostate cancer. Introduction: The purpose of this study was to develop a quantitative marker for use in prostate cancer magnetization transfer (MT) magnetic resonance imaging (MRI) studies that is independent of radiofrequency (RF) saturation amplitude. Methods: Eighteen patients with biopsy-proven prostate cancer were enrolled in this study. MTMRI images were acquired using four RF saturation amplitudes at 33 frequency offsets. ROIs were delineated for the peripheral zone (PZ), central gland (CG), and tumor. Z-spectral data were collected in each region and fit to a three-parameter equation. The three parameters are: the magnitude of the bulk water pool (Aw), the full width at half maximum of the water pool (Gw), and the magnitude of the bound pool (Ab), while, the slopes from the linear regressions of Gw and Ab on RF saturation amplitude (called kAb and kGw) were used as quantitative markers. Results: A pairwise statistically significant difference was found between the PZ and tumor regions for the two saturation amplitude-independent quantitative markers. No pairwise statistically significant differences were found between the CG and tumor regions for any quantitative markers. Conclusion: The significant differences between the values of the two RF saturation amplitudeindependent quantitative markers in the PZ and tumor regions reveal that these markers may be capable of distinguishing healthy PZ tissue from prostate cancer.

Background: Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. Objectives: The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. Methods: The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Results: Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Conclusion: Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

Background: In this study, a novel and fully automatic skin disease classification approach is proposed using statistical feature extraction and Artificial Neural Network (ANN) based classification using first and second order statistical moments, the entropy of different color channels and texture-based features. Aims: The basic aim of our study is to develop an automated system for skin disease classification that can help a general physician to automatically detect the lesion and classify it to disease types. Method: The performance of the proposed approach is corroborated by extensive experiments performed on a dataset of 588 images containing 6907 lesion regions. Results: The results show that the proposed methodology can be effectively used to construct a skin disease classification system. Conclusion: Our proposed method is designed for a specific skin tone. Future investigation is needed to analyze the impact of different skin tones on the performance of lesions detection and classification system.

Background: Glioma is one of the most common and aggressive primary brain tumors that endanger human health. Tumors segmentation is a key step in assisting the diagnosis and treatment of cancer disease. However, it is a relatively challenging task to precisely segment tumors considering characteristics of brain tumors and the device noise. Recently, with the breakthrough development of deep learning, brain tumor segmentation methods based on fully convolutional neural network (FCN) have illuminated brilliant performance and attracted more and more attention. Methods: In this work, we propose a novel FCN based network called SDResU-Net for brain tumor segmentation, which simultaneously embeds dilated convolution and separable convolution into residual U-Net architecture. SDResU-Net introduces dilated block into a residual U-Net architecture, which largely expends the receptive field and gains better local and global feature descriptions capacity. Meanwhile, to fully utilize the channel and region information of MRI brain images, we separate the internal and inter-slice structures of the improved residual U-Net by employing separable convolution operator. The proposed SDResU-Net captures more pixel-level details and spatial information, which provides a considerable alternative for the automatic and accurate segmentation of brain tumors. Results and Conclusion: The proposed SDResU-Net is extensively evaluated on two public MRI brain image datasets, i.e., BraTS 2017 and BraTS 2018. Compared with its counterparts and stateof- the-arts, SDResU-Net gains superior performance on both datasets, showing its effectiveness. In addition, cross-validation results on two datasets illuminate its satisfying generalization ability.

Background: Metaplastic breast cancer (MC) is a rare disease, thus it is difficult to study its clinical outcomes. Objectives: To investigate whether any clinicopathological or imaging features were associated with clinical outcome in MC. Methods: We retrospectively evaluated the clinicopathological and imaging findings, and the clinical outcomes of seventy-two pathologically confirmed MCs. We then compared these parameters between triple-negative (TNMC) and non-TNMCs (NTNMC). Results: Oval or round shape, and not-circumscribed margin were the most common findings on mammography, ultrasound (US), and magnetic resonance imaging (MRI). It was mostly a mass without calcification on mammography, and revealed complex or hypoechoic echotexture, and posterior acoustic enhancement on US, and rim enhancement, wash-out kinetics, peritumoral edema, and intratumoral necrosis on MRI. Of all 72, 64 were TNMCs, and eight were NTNMCs. Clinicopathological and imaging findings were similar between the two groups, except that MRI showed peritumoral edema more frequently in TNMCs than NTNMCs (p=0.045). There were 21 recurrences and 13 deaths. Multivariable analysis showed that larger tumor size and co-existing DCIS were significantly predictive of Disease free survival (DFS), and larger tumor size and neoadjuvant chemotherapy were significantly predictive of overall survival (OS). Conclusion: MC showed characteristic imaging findings, and some variables associated with survival outcome may help to predict prognosis.

Background: Ultrasound (US) imaging can be a convenient and reliable substitute for magnetic resonance imaging in the investigation or screening of articular cartilage injury. However, US images suffer from two main impediments, i.e., low contrast ratio and presence of speckle noise. Aims: A variation of anisotropic diffusion is proposed that can reduce speckle noise without compromising the image quality of the edges and other important details. Methods: For this technique, four gradient thresholds were adopted instead of one. A new diffusivity function that preserves the edge of the resultant image is also proposed. To automatically terminate the iterative procedures, the Mean Absolute Error as its stopping criterion was implemented. Results: Numerical results obtained by simulations unanimously indicate that the proposed method outperforms conventional speckle reduction techniques. Nevertheless, this preliminary study has been conducted based on a small number of asymptomatic subjects. Conclusion: Future work must investigate the feasibility of this method in a large cohort and its clinical validity through testing subjects with a symptomatic cartilage injury.

Background and Objective: In order to reduce time complexity and to improve the computational efficiency in diagnosing process, automated brain tissue segmentation for magnetic resonance brain images is proposed in this paper. Methods: This method incorporates two processes, the first one is preprocessing and the second one is segmentation of brain tissue using Histogram based Swarm Optimization techniques. The proposed method was investigated with images obtained from twenty volumes and eighteen volumes of T1-Weighted images obtained from Internet Brain Segmentation Repository (IBSR), Alzheimer disease images from Minimum Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) and T2-Weighted real-time images collected from SBC Scan Center Dindigul. Results: The proposed technique was tested with three brain image datasets. Quantitative evaluation was done with Jaccard (JC) and Dice (DC) and also it was compared with existing swarm optimization techniques and other methods like Adaptive Maximum a posteriori probability (AMAP), Biased Maximum a posteriori Probability (BMAP), Maximum a posteriori Probability (MAP), Maximum Likelihood (ML) and Tree structure K-Means (TK-Means). Conclusion: The performance comparative analysis shows that our proposed method Histogram based Darwinian Particle Swarm Optimization (HDPSO) gives better results than other proposed techniques such as Histogram based Particle Swarm Optimization (HPSO), Histogram based Fractional Order Darwinian Particle Swarm Optimization (HFODPSO) and with existing swarm optimization techniques and other techniques like Adaptive Maximum a posteriori Probability (AMAP), Biased Maximum a posteriori Probability (BMAP), Maximum a posteriori Probability (MAP), Maximum Likelihood (ML) and Tree structure K-Means (TK-Means).

Background: Hyperechoic breast lesions are a rare group of breast masses in routine practice. Most of these lesions are benign. However, they rarely may be malignant. Hyperechoic lesions can be evaluated using the same criteria for malignant lesions. Clinical history, mammographic appearance, and certain sonographic features (non-circumscribed margins, irregular shape, presence of hypoechoic areas, nonparallel orientation, and association with microcalcifications can be suggestive of malignancy). In this article, hyperechoic breast lesions with malignant pathology have been presented. Methods: Seven cases during breast ultrasound examination were detected. Results: Four patients had invasive ductal carcinoma, 1 patient had invasive lobular carcinoma, 1 patient had high-grade ductal carcinoma in situ (DCIS), and 1 patient had lymphoma. Ultrasonography of the breast showed a heterogeneous appearance in all the patients, microcalcification in two patients, and an ambiguous contour in one patient. Conclusion: Hyperechoic breast lesions should be evaluated using specific sonographic criteria to prevent misdiagnosis and identify patients who require biopsy and further examination.