Positron Emission Tomography Fusion Research Articles

Medical Image Fusion for Multiple Diseases Features Enhancement

ABSTRACTThroughout the past 20 years, medical imaging has found extensive application in clinical diagnosis. Doctors may find it difficult to diagnose diseases using only one imaging modality. The main objective of multimodal medical image fusion (MMIF) is to improve both the accuracy and quality of clinical assessments by extracting structural and spectral information from source images. This study proposes a novel MMIF method to assist doctors and postoperations such as image segmentation, classification, and further surgical procedures. Initially, the intensity‐hue‐saturation (IHS) model is utilized to decompose the positron emission tomography (PET)/single photon emission computed tomography (SPECT) image, followed by a hue‐angle mapping method to discriminate high‐ and low‐activity regions in the PET images. Then, a proposed structure feature adjustment (SFA) mechanism is used as a fusion strategy for high‐ and low‐activity regions to obtain structural and anatomical details with minimum color distortion. In the second step, a new multi‐discriminator generative adversarial network (MDcGAN) approach is proposed for obtaining the final fused image. The qualitative and quantitative results demonstrate that the proposed method is superior to existing MMIF methods in preserving the structural, anatomical, and functional details of the PET/SPECT images. Through our assessment, involving visual analysis and subsequent verification using statistical metrics, it becomes evident that color changes contribute substantial visual information to the fusion of PET and MR images. The quantitative outcomes demonstrate that, in the majority of cases, the proposed algorithm consistently outperformed other methods. Yet, in a few instances, it achieved the second‐highest results. The validity of the proposed method was confirmed using diverse modalities, encompassing a total of 1012 image pairs.

Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion

Programmed death-ligand 1 (PD-L1) expressions play a crucial role in guiding therapeutic interventions such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional determination of PD-L1 status includes careful surgical or biopsied tumor specimens. These specimens are gathered through invasive procedures, representing a risk of difficulties and potential challenges in getting reliable and representative tissue samples. Using a single center cohort of 189 patients, our objective was to evaluate various fusion methods that used non-invasive computed tomography (CT) and 18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{18}$$\\end{document}F-FDG positron emission tomography (PET) images as inputs to various deep learning models to automatically predict PD-L1 in non-small cell lung cancer (NSCLC). We compared three different architectures (ResNet, DenseNet, and EfficientNet) and considered different input data (CT only, PET only, PET/CT early fusion, PET/CT late fusion without as well as with partially and fully shared weights to determine the best model performance. Models were assessed utilizing areas under the receiver operating characteristic curves (AUCs) considering their 95% confidence intervals (CI). The fusion of PET and CT images as input yielded better performance for PD-L1 classification. The different data fusion schemes systematically outperformed their individual counterparts when used as input of the various deep models. Furthermore, early fusion consistently outperformed late fusion, probably as a result of its capacity to capture more complicated patterns by merging PET and CT derived content at a lower level. When we looked more closely at the effects of weight sharing in late fusion architectures, we discovered that while it might boost model stability, it did not always result in better results. This suggests that although weight sharing could be beneficial when modality parameters are similar, the anatomical and metabolic information provided by CT and PET scans are too dissimilar to consistently lead to improved PD-L1 status predictions.

Retracted: Multimodal Medical Image Fusion of Positron Emission Tomography and Magnetic Resonance Imaging Using Generative Adversarial Networks.

[This retracts the article DOI: 10.1155/2022/6878783.].

PET and MRI image fusion based on a dense convolutional network with dual attention

The fusion techniques of different modalities in medical images, e.g., Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), are increasingly significant in many clinical applications by integrating the complementary information from different medical images. In this paper, we propose a novel fusion model based on a dense convolutional network with dual attention (CSpA-DN) for PET and MRI images. In our framework, an encoder composed of the densely connected neural network is constructed to extract features from source images, and a decoder network is employed to generate the fused image from these features. Simultaneously, a dual-attention module is introduced in the encoder and decoder to further integrate local features along with their global dependencies adaptively. In the dual-attention module, a spatial attention block is leveraged to extract features of each point from encoder network by a weighted sum of feature information at all positions. Meanwhile, the interdependent correlation of all image features is aggregated via a module of channel attention. In addition, we design a specific loss function including image loss, structural loss, gradient loss and perception loss to preserve more structural and detail information and sharpen the edges of targets. Our approach facilitates the fused images to not only preserve abundant functional information from PET images but also retain rich detail structures of MRI images. Experimental results on publicly available datasets illustrate the superiorities of CSpA-DN model compared with state-of-the-art methods according to both qualitative observation and objective assessment.

A cross-attention based image fusion Network for prediction of mild cognitive impairment

Mild cognitive impairment (MCI) is the intermediate stage in the progression of Alzheimer’s disease, where patients exhibit cognitive decline, decreasing ability to perform complex daily tasks and other symptoms that can be mistaken for normal aging, thus missing the optimal time for treatment and leading to further deterioration of the patient’s condition. Structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) are commonly used to examine patients in clinical practice. Currently, the fusion of sMRI and PET based on deep learning are simply extracting features individually and then fusing the extracted features, ignoring the correlation between features of two different modalities. Therefore, this paper proposes an image fusion method based on cross-attention mechanism, which makes it possible to focus not only on the features of its own view but also on the features of the other view during feature extraction to achieve dual-attempt cross-learning. The experimental results from the Alzheimer’s Disease Neuroimaging Initiative dataset show that the images fused based on the method proposed in this paper outperform the original method in three evaluation indexes: peak signal-to-noise ratio, structural similarity and classification accuracy.

Multimodal Medical Image Fusion of Positron Emission Tomography and Magnetic Resonance Imaging Using Generative Adversarial Networks.

Multimodal medical image fusion is a current technique applied in the applications related to medical field to combine images from the same modality or different modalities to improve the visual content of the image to perform further operations like image segmentation. Biomedical research and medical image analysis highly demand medical image fusion to perform higher level of medical analysis. Multimodal medical fusion assists medical practitioners to visualize the internal organs and tissues. Multimodal medical fusion of brain image helps to medical practitioners to simultaneously visualize hard portion like skull and soft portion like tissue. Brain tumor segmentation can be accurately performed by utilizing the image obtained after multimodal medical image fusion. The area of the tumor can be accurately located with the information obtained from both Positron Emission Tomography and Magnetic Resonance Image in a single fused image. This approach increases the accuracy in diagnosing the tumor and reduces the time consumed in diagnosing and locating the tumor. The functional information of the brain is available in the Positron Emission Tomography while the anatomy of the brain tissue is available in the Magnetic Resonance Image. Thus, the spatial characteristics and functional information can be obtained from a single image using a robust multimodal medical image fusion model. The proposed approach uses a generative adversarial network to fuse Positron Emission Tomography and Magnetic Resonance Image into a single image. The results obtained from the proposed approach can be used for further medical analysis to locate the tumor and plan for further surgical procedures. The performance of the GAN based model is evaluated using two metrics, namely, structural similarity index and mutual information. The proposed approach achieved a structural similarity index of 0.8551 and a mutual information of 2.8059.

Multimodal Fusion-Based Deep Learning Network for Effective Diagnosis of Alzheimer’s Disease

Alzheimer’s disease (AD) is a prevalent, irreversible, chronic, and degenerative disorder whose diagnosis at the prodromal stage is critical. Mostly, single modality data, such as magnetic resonance imaging (MRI) or positron emission tomography (PET), are used to make predictions in AD studies. However, the metabolic and structural data fusion can provide a holistic view of AD-staging analysis. To achieve this objective, a novel multimodal fusion-based method is proposed in this article. An optimal fusion of MRI and PET is achieved by harnessing demon algorithm and discrete wavelet transform. Finally, the fused image features are extracted using ResNet-50, and these features are classified using robust energy least square twin support vector machine classifier. Experiments on the AD neuroimaging initiative dataset show descent accuracy of 97%, 94%, and 97.5% for cognitive normal (CN) versus AD, CN versus mild cognitive impairment (MCI), and AD versus MCI, respectively. The proposed model will be beneficial for health professionals in accurately diagnosing AD at an early stage.

Fused PET/CT or CECT in Post-Therapeutic Assessment of Colorectal Cancer: A Study of the Efficacy of the Modality of Choice among Egyptian Patients

Abstract Background: One of the most common cancers, colorectal cancer accounts for several tumor-related mortalities; its high recurrence rates either as a local recurrence of the disease or as a distant metastatic disease (up to 35-40%) have been reported in the treated patients within the first two years following surgery. There has been heated debate over the modality of choice for imaging of the recurrent colorectal cancer. Aim of Study: This study investigates the diagnostic performance of fused Positron Emission Tomography/ Com-puted Tomography (PET/CT) in comparison to Contrast-Enhanced Computed Tomography (CECT) as a follow-up and restaging imaging tool for post-therapeutic colorectal cancers among Egyptian patients. Subjects and Methods: Data were collected from 84 Egyptian patients (26 females and 58 males, age ranges from 35 to 80 years) who were treated from colorectal cancers. They were referred to a private imaging center for evaluation of their disease recurrence by fused PET/CT. Results: Disease recurrence was categorized as operative bed recurrence/residual (incomplete therapeutic response), nodal, and distal metastases. The site of the tumor recurrence was predominantly seen in the rectosigmoid region in 31 patients (36.9%), followed by the ascending colon where it was present in 13 patients (15.4%), then the transverse colon as depicted in 9 patients (10.7%); the descending colon recurrence was noted in 6 patients (7.1%), and the caecal recurrence existed in only one patient (1.3%). With reference to the gold standard (the histopathology reports with a correlation to the clinical and the follow-up examinations for the patients as well as the tumor markers (CEA) levels), the fused PET/CT had sensitivity, specificity, positive predictive value, negative predictive value, and an overall accuracy of 93.33%, 83.33%, 93.33%, 83.33% & 90.48% respectively as compared to CECT (73.33%, 58.33%, 81.48%, 46.67%, 69.05% respectively). Conclusion: Our findings indicate that fused PET/CT is more effective than the CECT regarding the detection of operative bed recurrent disease and incomplete therapeutic responses. PET/CT may also offer a cost-effective whole-body scan for restaging of the recurrent diseases through an accurate detection of the nodal and distant metastases.

Fusion of Brain PET and MRI Images Using Tissue-Aware Conditional Generative Adversarial Network With Joint Loss

Positron emission tomography (PET) has rich pseudo color information that reflects the functional characteristics of tissue, but lacks structural information and its spatial resolution is low. Magnetic resonance imaging (MRI) has high spatial resolution as well as strong structural information of soft tissue, but lacks color information that shows the functional characteristics of tissue. For the purpose of integrating the color information of PET with the anatomical structures of MRI to help doctors diagnose diseases better, a method for fusing brain PET and MRI images using tissue-aware conditional generative adversarial network (TA-cGAN) is proposed. Specifically, the process of fusing brain PET and MRI images is treated as an adversarial machine between retaining the color information of PET and preserving the anatomical information of MRI. More specifically, the fusion of PET and MRI images can be regarded as a min-max optimization problem with respect to the generator and the discriminator, where the generator attempts to minimize the objective function via generating a fused image mainly contains the color information of PET, whereas the discriminator tries to maximize the objective function through urging the fused image to include more structural information of MRI. Both the generator and the discriminator in TA-cGAN are conditioned on the tissue label map generated from MRI image, and are trained alternatively with joint loss. Extensive experiments demonstrate that the proposed method enhances the anatomical details of the fused image while effectively preserving the color information from the PET. In addition, compared with other state-of-the-art methods, the proposed method achieves better fusion effects both in subjectively visual perception and in objectively quantitative assessment.

Efficient graph cut optimization using hybrid kernel functions for segmentation of FDG uptakes in fused PET/CT images

The purpose of this work is to segment the multi region Fluoro Deoxy Glucose radioactivity uptakes from fused Positron Emission Tomography / Computerized Tomography images automatically irrespective of their location in the body. Color image processing is performed to filter and enhance the saturation components of the images. The proposed method of graph cut image partitioning through kernel mapping of the image data is applied for the saturation equalized components of Red, Green, and Blue model images. Energy minimization of the objective function includes the data term minimization within each segmentation region and smoothening the regularization term preserving the boundary regions. Hybrid kernel functions are used for partitioning by graph-cut iterations and computation of region parameters through fixed-point computation. This method combines the performance of global and local kernel functions which makes the segmentation robust and accurate. The performance assessment is carried out for different views of fused Positron Emission Tomography / Computerized Tomography images, and are evaluated qualitatively, quantitatively, and comparatively. This method can be applied for the analysis of certain image features, diagnosis, and display purposes.

P1217Zirconium-89 labelled probe for molecular imaging of inflammation in experimental atherosclerosis

Abstract Background Early detection of inflamed atherosclerotic lesions by molecular imaging might improve risk assessment beyond that of vascular stenosis and plaque morphology imaging, and improve the clinical management of high-risk patients. Purpose To target the key features of unstable atherosclerotic lesions, we studied the feasibility of our radiotracer, based on modified human serum albumin (HSA), to identify inflamed atherosclerotic lesions by in vivo molecular imaging. Methods We applied a maleylated HSA (Mal-HSA) probe, recognised by scavenger receptors on macrophages, in an experimental in vivo imaging study of atherosclerosis. Mal-HSA was coupled with a positron-emittingmetal ion, Zirconium-89 (89Zr). The targeting potential of this probe was evaluated and compared with unspecific 89Zr-HSA and 18F-FDG in a mouse model of atherosclerosis (Apoe−/−, n=22) and compared with wild-type (WT) mice (C57BL/6, n=21) as controls. Radiotracer accumulation in the aortic arch was analysed in vivo by the fusion of positron emission tomography–magnetic resonance imaging (PET-MRI), radiotracer bio-distribution was measured ex vivo by gamma counter, and plaque uptake was evaluated by phosphor imaging (PI) autoradiography (ARG). Results PET-MRI, gamma counter measurements, and PI-ARG showed the accumulation of 89Zr-Mal-HSA in the atherosclerotic lesions of Apoe−/− mice. The maximum standardised uptake value (SUVmax) for 89Zr-Mal-HSA at 16 and 20 weeks were 26% and 20% higher (P<0.05) in Apoe−/− mice than control WT mice, whereas no difference in SUVmax was found for 18F-FDG in the same animals. 89Zr-Mal-HSA uptake in the aorta as evaluated by gamma counter 48 h post-injection was 32% higher (P<0.01) for Apoe−/− mice compared to WT mice, and the aorta-to-blood ratio was 10-fold higher (P<0.001) for 89Zr-Mal-HSA compared with unspecific 89Zr-HSA. HSA probes were mainly distributed to the liver, spleen, kidneys, bone and lymph nodes. The PI-ARG results corroborated the PET and gamma counter measurements, showing higher accumulation of 89Zr-Mal-HSA in the aortas of Apoe−/− mice compared to WT mice; 9.4±1.4 vs 0.8±0.3% (P<0.001). Conclusions The modified HSA-based radiotracer showed in vivo targeting of inflamed atherosclerotic lesions of mouse aorta, which could also be verified ex vivo. 89Zr-Mal-HSA seems to be a promising diagnostic tool for the identification of vascular inflammation. Further methodological studies are needed to verify its applicability for detecting rupture-prone plaques. Acknowledgement/Funding Swedish Research Council (22036); the Swedish HLF (20150423, 20170669); ALF (20150517, 447561, 726481); Söderberg Foundations, VINNOVA and KI

MRI-Driven PET Image Optimization for Neurological Applications.

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are established imaging modalities for the study of neurological disorders, such as epilepsy, dementia, psychiatric disorders and so on. Since these two available modalities vary in imaging principle and physical performance, each technique has its own advantages and disadvantages over the other. To acquire the mutual complementary information and reinforce each other, there is a need for the fusion of PET and MRI. This combined dual-modality (either sequential or simultaneous) could generate preferable soft tissue contrast of brain tissue, flexible acquisition parameters, and minimized exposure to radiation. The most unique superiority of PET/MRI is mainly manifested in MRI-based improvement for the inherent limitations of PET, such as motion artifacts, partial volume effect (PVE) and invasive procedure in quantitative analysis. Head motion during scanning significantly deteriorates the effective resolution of PET image, especially for the dynamic scan with lengthy time. Hybrid PET/MRI device can offer motion correction (MC) for PET data through MRI information acquired simultaneously. Regarding the PVE associated with limited spatial resolution, the process and reconstruction of PET data can be further optimized by using acquired MRI either sequentially or simultaneously. The quantitative analysis of dynamic PET data mainly relies upon an invasive arterial blood sampling procedure to acquire arterial input function (AIF). An image-derived input function (IDIF) method without the need of arterial cannulization, can serve as a potential alternative estimation of AIF. Compared with using PET data only, combining anatomical or functional information from MRI for improving the accuracy in IDIF approach has been demonstrated. Yet, due to the interference and inherent disparity between the two modalities, these methods for optimizing PET image based on MRI still have many technical challenges. This review discussed upon the most recent progress, current challenges and future directions of MRI-driven PET data optimization for neurological applications, with either sequential or simultaneous acquisition approach.

MRI and PET image fusion using the nonparametric density model and the theory of variable-weight

Medical image fusion is important in the field of clinical diagnosis because it can improve the availability of information contained in images. Magnetic Resonance Imaging (MRI) provides excellent anatomical details as well as functional information on regional changes in physiology, hemodynamics, and tissue composition. In contrast, although the spatial resolution of Positron Emission Tomography (PET) provides is lower than that an MRI, PET is capable of depicting the tissue's molecular and pathological activities that are not available from MRI. Fusion of MRI and PET may allow us to combine the advantages of both imaging modalities and achieve more precise localization and characterization of abnormalities. Previous image fusion algorithms, based on the estimation theory, assume that all distortions follow Gaussian distribution and are therefore susceptible to the model mismatch problem. To overcome this mismatch problem, we propose a new image fusion method with multi-resolution and nonparametric density models (MRNDM). The RGB space registered from the source multi-modal medical images is first transformed into a generalized intensity-hue-saturation space (GIHS), and then is decomposed into the low- and high-frequency components using the non-subsampled contourlet transform (NSCT). Two different fusion rules, which are based on the nonparametric density model and the theory of variable-weight, are developed and used to fuse low- and high-frequency coefficients. The fused images are constructed by performing the inverse of the NSCT operation with all composite coefficients. Our experimental results demonstrate that the quality of images fused from PET and MRI brain images using our proposed method MRNDM is higher than that of those fused using six previous fusion methods.

Clinical Value of Fused PET/MRI for Surgical Planning in Patients With Oral/Oropharyngeal Carcinoma.

To evaluate the usefulness of fused positron emission tomography (PET)/magnetic resonance (MR) images for surgical planning in patients with oral/oropharyngeal cancer and suspected mandibular invasion. Individual cohort study. Eleven of 17 patients with suspected mandibular invasion of squamous cell carcinoma of the lower gingiva, oropharynx, and buccal mucosa who underwent 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) and contrast-enhanced MR imaging (MRI) and had fused PET/MR images were enrolled in this study. The area for surgical resection was determined based on the fused images. The usefulness of these images was confirmed by comparing them with the histopathologic findings in the resected tumors. Histopathologic evaluation of the surgical specimens revealed that nine of the 11 patients had invasion into the mandible and/or medial pterygoid muscle. All patients had a negative surgical margin. The sensitivity and specificity for detection of mandibular/medial pterygoid muscle invasion was 100%/40% and 83%/100% by fused PET/MRI, respectively, and 100%/20% and 100%/60% by PET/CT, respectively. Interobserver reproducibility between two radiologists/nuclear medicine physicians and two head and neck surgeons showed that the only statistically significant κ values were for PET/MRI. PET/MRI can be easily understood by head and neck surgeons, who are not diagnostic imaging professionals, and can be used when planning the area to be surgically resected in patients with oral/oropharyngeal cancer and clinically suspected mandibular invasion. Considering the expense of a hybrid PET/MRI system, creation of a fused PET/MR image would provide a reasonable and reliable tool for clinical use in these patients. 2b Laryngoscope, 130:367-374, 2020.

Non‐subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour space

Magnetic resonance imaging (MRI) and positron emission tomography (PET) image fusion is a recent hybrid modality used in several oncology applications. The MRI image shows the brain tissue anatomy and does not contain any functional information, while the PET image indicates the brain function and has a low spatial resolution. A perfect MRI–PET fusion method preserves the functional information of the PET image and adds spatial characteristics of the MRI image with the less possible spatial distortion. In this context, the authors propose an efficient MRI–PET image fusion approach based on non-subsampled shearlet transform (NSST) and simplified pulse-coupled neural network model (S-PCNN). First, the PET image is transformed to YIQ independent components. Then, the source registered MRI image and the Y -component of PET image are decomposed into low-frequency (LF) and high-frequency (HF) subbands using NSST. LF coefficients are fused using weight region standard deviation (SD) and local energy, while HF coefficients are combined based on S-PCCN which is motivated by an adaptive-linking strength coefficient. Finally, inverse NSST and inverse YIQ are applied to get the fused image. Experimental results demonstrate that the proposed method has a better performance than other current approaches in terms of fusion mutual information, entropy, SD, fusion quality, and spatial frequency.

Interregional correlations of glucose metabolism between the basal ganglia and different cortical areas: an ultra-high resolution PET/MRI fusion study using 18F-FDG.

Basal ganglia have complex functional connections with the cerebral cortex and are involved in motor control, executive functions of the forebrain, such as the planning of movement, and cognitive behaviors based on their connections. The aim of this study was to provide detailed functional correlation patterns between the basal ganglia and cerebral cortex by conducting an interregional correlation analysis of the 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) data based on precise structural information. Fifteen participants were scanned with 7-Tesla magnetic resonance imaging (MRI) and high resolution research tomography (HRRT)-PET fusion system using 18F-FDG. For detailed interregional correlation analysis, 24 subregions of the basal ganglia including pre-commissural dorsal caudate, post-commissural caudate, pre-commissural dorsal putamen, post-commissural putamen, internal globus pallidus, and external globus pallidus and 80 cerebral regions were selected as regions of interest on the MRI image and their glucose metabolism were calculated from the PET images. Pearson's product-moment correlation analysis was conducted for the interregional correlation analysis of the basal ganglia. Functional correlation patterns between the basal ganglia and cerebral cortex were not only consistent with the findings of previous studies, but also showed new functional correlation between the dorsal striatum (i.e., caudate nucleus and putamen) and insula. In this study, we established the detailed basal ganglia subregional functional correlation patterns using 18F-FDG PET/MRI fusion imaging. Our methods and results could potentially be an important resource for investigating basal ganglia dysfunction as well as for conducting functional studies in the context of movement and psychiatric disorders.

Fusion of PET and MR Brain Images Based on IHS and Log-Gabor Transforms

In medical imaging, positron emission tomography (PET) shows metabolic changes of an organism in pseudo color and magnetic resonance (MR) imaging presents anatomical structures in gray level. The PET and MR brain medical image fusion produces one composite image rendering the anatomical structures with metabolic changes to help doctors effectively diagnose a possible disease. For this purpose, a new fusion method based on Intensity-Hue-Saturation model and log-Gabor wavelet transform is proposed. First, MR image and the intensity component of PET image are decomposed by log-Gabor wavelet transform with suitable decomposition scale. Then, maximum selection’ fusion rule for the high-frequency sub-band and two-stage fusion rule based on weighted-averaging scheme and visibility measure for the low-frequency sub-band are employed. Finally, a new intensity component, obtained by applying reverse log-Gabor wavelet transform to the fused high-and low-frequency sub-bands, along with original hue and saturation components of PET image are converted to obtain our fused color image. In our fused images, both anatomical structures and color changes are rendered with effectively-reduced color distortion. Experimental results on twelve sets, including normal axial, normal coronal, and Alzheimer’s disease brain images, demonstrate that our fusion method outpaces framelet-transform-based method both visually and quantitatively.

18F-cholin PET MRI (fusion of positron emission tomography and magnetic resonance imaging (PET/MRI) and extensive pelvic lymph node dissection (LND) in high-risk prostate cancer. Role of prostate health index in indication of PET MRI and LND

18F-cholin PET MRI (fusion of positron emission tomography and magnetic resonance imaging (PET/MRI) and extensive pelvic lymph node dissection (LND) in high-risk prostate cancer. Role of prostate health index in indication of PET MRI and LND

PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method

BackgroundThe process of medical image fusion is combining two or more medical images such as Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) and mapping them to a single image as fused image. So purpose of our study is assisting physicians to diagnose and treat the diseases in the least of the time. MethodsWe used Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) as input images, so fused them based on combination of two dimensional Hilbert transform (2-D HT) and Intensity Hue Saturation (IHS) method. Evaluation metrics that we apply are Discrepancy (Dk) as an assessing spectral features and Average Gradient (AGk) as an evaluating spatial features and also Overall Performance (O.P) to verify properly of the proposed method. ResultsIn this paper we used three common evaluation metrics like Average Gradient (AGk) and the lowest Discrepancy (Dk) and Overall Performance (O.P) to evaluate the performance of our method. Simulated and numerical results represent the desired performance of proposed method. ConclusionsSince that the main purpose of medical image fusion is preserving both spatial and spectral features of input images, so based on numerical results of evaluation metrics such as Average Gradient (AGk), Discrepancy (Dk) and Overall Performance (O.P) and also desired simulated results, it can be concluded that our proposed method can preserve both spatial and spectral features of input images.

Rigor and Reproducibility in Analysis of Vascular Calcification.

Clinical and preclinical studies of cardiovascular calcification often require interpretation of images from histopathology, computed tomography, intravascular ultrasound, and positron emission tomography. To avoid potential pitfalls in biological inferences, investigators should know what happens to data in image processing algorithms, the limitations of cross-sectional images in studying mechanostability, and how smoothing algorithms can mask partial-volume artifacts in positron emission tomography. Years ago, a head-on collision sent a motorcyclist to intensive care with multiple limb fractures. A cardiologist was paged to evaluate the unconscious patient for a dangerously low value of cardiac index, suggesting severe cardiac injury. On evaluation, pulse, blood pressure, and cardiac output were normal despite the extremely low cardiac index. How could that be? It turned out that this low value was the result of a common error: failure to scrutinize adjustments in processed data. Cardiac index is a type of processed data, which is derived from cardiac output divided by body surface area, which is, in turn, derived from height and weight. On assessing this adjustment, the cardiologist found that the patient’s weight included the casts on his arms and leg. Although it is obvious that casts do not require perfusion, this is an example of blindly following protocol, in this case resulting in a gross overestimate of weight, overestimate of body surface area, and underestimate of cardiac index. The moral of the story is that blind adherence to adjustment protocols may cause clinical errors. Does this happen in research? Corrections, adjustments, and technical limitations are common in imaging of preclinical and clinical vascular calcification, such as x-ray computed tomography (CT), intravascular ultrasound (IVUS), and, fused positron emission tomography (PET)-CT using the bone-seeking 18F-fluoride PET tracer. There is growing interest in using such imaging methods to infer molecular and cellular mechanisms of vascular calcification. However, colleagues …