X-ray luminescence computed tomography:an emerging molecular imaging modality

X-ray luminescence computed tomography(XLCT) is an emerging hybrid imaging modality that can be used as a new tool in molecular imaging. XLCT has the potential to achieve the high spatial resolution of X-ray imaging and the high sensitivity feature of optical imaging in deep tissues. In XLCT,high energy X-ray photons are used to excite X-ray excitable particles emitting optical photons to be measured for reconstruction of the particle distribution inside tissues. Numerical simulations and experimental studies have been performed and proved that XLCT is suitable for in vivo small animal imaging. The aim of this review paper is to introduce the background of XLCT and to detail the concept of XLCT,including a brief review of tw o types of XLCT imaging system designs. The major challenges in the development of XLCT are also described. Finally,the future applications and development directions of XLCT are discussed.

Relationship between pancreatic morphological changes and diabetes in autoimmune pancreatitis:Multimodal medical imaging assessment has important potential

Autoimmune pancreatitis(AIP)is a special type of chronic pancreatitis with cli-nical symptoms of obstructive jaundice and abdominal discomfort;this condition is caused by autoimmunity and marked by pancreatic fibrosis and dysfunction.Previous studies have revealed a close relationship between early pancreatic atrophy and the incidence rate of diabetes in type 1 AIP patients receiving steroid treatment.Shimada et al performed a long-term follow-up study and reported that the pancreatic volume(PV)of these patients initially exponentially decreased but then slowly decreased,which was considered to be an important factor related to diabetes;moreover,serum IgG4 levels were positively correlated with PV during follow-up.In this letter,regarding the original study presented by Shimada et al,we present our insights and discuss how multimodal medical imaging and arti-ficial intelligence can be used to better assess the relationship between pancreatic morphological changes and diabetes in patients with AIP.

Evaluation of Medical Prescribers’ Theoretical Knowledge on Medical Imaging in the Northern Region of Burkina Faso

Introduction: Medical imaging is a medical specialty that involves producing images of the human body and interpreting them for diagnostic, therapeutic purposes, and for monitoring the progress of pathologies. We aimed to assess the theoretical knowledge of doctors and interns in medical imaging in the northern region of Burkina Faso. Methodology: This was a descriptive cross-sectional survey based on a self-administered questionnaire. Prescribers knowledge was estimated based on scores derived from questionnaire responses. Results: We collected 106 questionnaires out of 163, i.e. a participation rate of 65.03%. The average knowledge score was 81.71% for the contribution of medical imaging to patient management. It was 60.02% for the indications/counter-indications of radiological examinations and 72.56% for the risks associated with exposure to radiation during these examinations. The score was 59.83% for the methods used to select the appropriate radiological examination. As regards the completeness of the clinical and biological information on the forms requesting imaging examinations, the score was 96.65%. Specialist doctors had the highest overall level of knowledge (74.68%). Conclusion: Improved technical facilities, good initial and in-service training, and interdisciplinary collaboration will help to ensure that imaging tests are properly prescribed, leading to better patient care.

Role of molecular imaging in prognosis,diagnosis,and treatment of gastrointestinal cancers:An update on new therapeutic methods

One of the leading causes of cancer-related death is gastrointestinal cancer,which has a significant morbidity and mortality rate.Although preoperative risk assessment is essential for directing patient care,its biological behavior cannot be accurately predicted by conventional imaging investigations.Potential pathophysiological information in anatomical imaging that cannot be visually identified can now be converted into high-dimensional quantitative image features thanks to the developing discipline of molecular imaging.In order to enable molecular tissue profile in vivo,molecular imaging has most recently been utilized to phenotype the expression of single receptors and targets of biological therapy.It is expected that molecular imaging will become increasingly important in the near future,driven by the expanding range of biological therapies for cancer.With this live molecular fingerprinting,molecular imaging can be utilized to drive expression-tailored customized therapy.The technical aspects of molecular imaging are first briefly discussed in this review,followed by an examination of the most recent research on the diagnosis,prognosis,and potential future clinical methods of molecular imaging for GI tract malignancies.

Construction and Identification of the Adenoviral Vector with Dual Reporter Gene for Multimodality Molecular Imaging

Summary： In this study, the recombinant adenovirus （Ad） vector containing dual reporter gene [i.e. human transferrin receptor gene （TFRC） and firefly luciferase reporter gene] was constructed to provide a novel experimental tool for magnetic resonance （MR） and bioluminescence dual-modality molecular imaging. The cDNA of TFRC was amplified by polymerase chain reaction （PCR） and cloned into the multiple cloning site of pShuttle-CMV-CMV-Luciferase vector. After identification by Sfi I digestion and sequencing, pShuttle-TFRC-Luciferase vector and the adenoviral backbone vector （pAdeno） were subjected to homologous recombination. The correct recombinant plasmid was then transfected into 293 packaging cells to produce adenoviral particles and confirmed by PCR. After infection of human colo- rectal cancer LOVO cells with Ad-TFRC-Luciferase, the expressions of transferrin receptor （TfR） and luciferase protein were detected respectively by Western blotting and bioluminescence imaging in vitro. The results showed that TFRC gene was successfully inserted into the adenoviral shuttle vector carrying luciferase gene. DNA sequence analysis indicated that the TFRC gene sequence in the shuttle plasmid was exactly the same as that reported in GenBank. The recombinant plasmid was identified correct by restriction digestion. Ad-TFRC-Luciferase recombinant adenovirns was constructed successfully, and the virus titer was 1.6x10^10 pfu/mL. Forty-eight h after dual reporter gene transfection, the expressions of TfR and luciferase protein were increased significantly （P〈0.01）. It was concluded that the recombinant adenovirus vector with dual reporter gene was successfully established, which may be used for in vivo tracing target cells in multimodality imaging.

Optical Molecular Imaging Frontiers in Oncology: The Pursuit of Accuracy and Sensitivity

Cutting-edge technologies in optical molecular imaging have ushered in new frontiers in cancer research, clinical translation, and medical practice, as evidenced by recent advances in optical multimodality imaging, Cerenkov luminescence imaging(CLI), and optical imageguided surgeries. New abilities allow in vivo cancer imaging with sensitivity and accuracy that are unprecedented in conventional imaging approaches. The visualization of cellular and molecular behaviors and events within tumors in living subjects is improving our deeper understanding of tumors at a systems level. These advances are being rapidly used to acquire tumor-to-tumor molecular heterogeneity, both dynamically and quantitatively, as well as to achieve more effective therapeutic interventions with the assistance of real-time imaging. In the era of molecular imaging, optical technologies hold great promise to facilitate the development of highly sensitive cancer diagnoses as well as personalized patient treatment—one of the ultimate goals of precision medicine.

The Artificial Intelligence-Enabled Medical Imaging:Today and Its Future

Medical imaging is now being reshaped by artificial intelligence (AI) and progressing rapidly toward future.In this article,we review the recent progress of AI-enabled medical imaging.Firstly,we briefly review the background about AI in its way of evolution.Then,we discuss the recent successes of AI in different medical imaging tasks,especially in image segmentation,registration,detection and recognition.Also,we illustrate several representative applications of AI-enabled medical imaging to show its advantage in real scenario,which includes lung nodule in chest CT,neuroimaging,mammography,and etc.Finally,we report the way of human-machine interaction.We believe that,in the future,AI will not only change the traditional way of medical imaging,but also improve the clinical routines of medical care and enable many aspects of the medical society.

Importance of Features Selection,Attributes Selection,Challenges and Future Directions for Medical Imaging Data:A Review

In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of data.These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality.On the other hand,this growth also made it difficult to comprehend and utilize data for various purposes.The results of imaging data can become biased because of extraneous features present in larger datasets.Feature selection gives a chance to decrease the number of components in such large datasets.Through selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization precision.The correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast control.This paper presents a review of feature selection techniques and attributes selection measures for medical imaging.This review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining algorithms.The review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced data.Moreover,this review provides the importance of feature selection for correct classification of medical infections.In the end,critical analysis and future directions are provided.

Diffusion magnetic resonance imaging: A molecular imaging tool caught between hope, hype and the real world of “personalized oncology”

"Personalized oncology" is a multi-disciplinary science, which requires inputs from various streams for optimal patient management. Humongous progress in the treatment modalities available and the increasing need to provide functional information in addition to the morphological data; has led to leaping progress in the field of imaging. Magnetic resonance imaging has undergone tremendous progress with various newer MR techniques providing vital functional information and is becoming the cornerstone of "radiomics/radiogenomics". Diffusionweighted imaging is one such technique which capitalizes on the tendency of water protons to diffuse randomly in a given system. This technique has revolutionized oncological imaging, by giving vital qualitative and quantitative information regarding tumor biology which helps in detection, characterization and post treatment surveillance of the lesions and challenging the notion that "one size fits all". It has been applied at various sites with different clinical experience. We hereby present a brief review of this novel functional imaging tool, with its application in "personalized oncology".

Tumor Molecular Imaging with Nanoparticles

Molecular imaging(MI)can provide not only structural images using traditional imaging techniques but also functional and molecular information using many newly emerging imaging techniques.Over the past decade,the utilization of nanotechnology in MI has exhibited many significant advantages and provided new opportunities for the imaging of living subjects.It is expected that multimodality nanoparticles(NPs)can lead to precise assessment of tumor biology and the tumor microenvironment.This review addresses topics related to engineered NPs and summarizes the recent applications of these nanoconstructs in cancer optical imaging,ultrasound,photoacoustic imaging,magnetic resonance imaging(MRI),and radionuclide imaging.Key challenges involved in the translation of NPs to the clinic are discussed.

Preclinical and clinical applications of specific molecular imaging for HER2-positive breast cancer

Precision medicine and personalized therapy are receiving increased attention, and molecular-subtype classification has become crucial in planning therapeutic schedules in clinical practice for patients with breast cancer. Human epidermal growth factor receptor 2(HER2) is associated with high-grade breast tumors, high rates of lymph-node involvement, high risk of recurrence, and high resistance to general chemotherapy. Analysis of HER2 expression is highly important for doctors to identify patients who can benefit from trastuzumab therapy and monitor the response and efficacy of treatment. In recent years, significant efforts have been devoted to achieving specific and noninvasive HER2-positive breast cancer imaging in vivo. In this work, we reviewed existing literature on HER2 imaging in the past decade and summarized the studies from different points of view, such as imaging modalities and HER2-specific probes. We aimed to improve the understanding on the translational process in molecular imaging for HER2 breast cancer.

Deep Learning for Image Segmentation: A Focus on Medical Imaging

Image segmentation is crucial for various research areas. Manycomputer vision applications depend on segmenting images to understandthe scene, such as autonomous driving, surveillance systems, robotics, andmedical imaging. With the recent advances in deep learning (DL) and itsconfounding results in image segmentation, more attention has been drawnto its use in medical image segmentation. This article introduces a surveyof the state-of-the-art deep convolution neural network (CNN) models andmechanisms utilized in image segmentation. First, segmentation models arecategorized based on their model architecture and primary working principle.Then, CNN categories are described, and various models are discussed withineach category. Compared with other existing surveys, several applicationswith multiple architectural adaptations are discussed within each category.A comparative summary is included to give the reader insights into utilizedarchitectures in different applications and datasets. This study focuses onmedical image segmentation applications, where the most widely used architecturesare illustrated, and other promising models are suggested that haveproven their success in different domains. Finally, the present work discussescurrent limitations and solutions along with future trends in the field.

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers

Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply artificial intelligence(AI)techniques to multiple clinical scenarios of ovarian cancer,especially in the field of medical imaging.AI-assisted imaging studies have involved computer tomography(CT),ultrasonography(US),and magnetic resonance imaging(MRI).In this review,we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer,and bring up the advances in terms of four clinical aspects,including medical diagnosis,pathological classification,targeted biopsy guidance,and prognosis prediction.Meanwhile,current status and existing issues of the researches on AI application in ovarian cancer are discussed.

Recent progress in the molecular imaging of therapeutic monoclonal antibodies

Therapeutic monoclonal antibodies have become one of the central components of the healthcare system and continuous efforts are made to bring innovative antibody therapeutics to patients in need.It is equally critical to acquire sufficient knowledge of their molecular structure and biological functions to ensure the efficacy and safety by incorporating new detection approaches since new challenges like individual differences and resistance are presented.Conventional techniques for determining antibody disposition including plasma drug concentration measurements using LC-MS or ELISA,and tissue distribution using immunohistochemistry and immunofluorescence are now complemented with molecular imaging modalities like positron emission tomography and near-infrared fluorescence imaging to obtain more dynamic information,while methods for characterization of antibody’s interaction with the target antigen as well as visualization of its cellular and intercellular behavior are still under development.Recent progress in detecting therapeutic antibodies,in particular,the development of methods suitable for illustrating the molecular dynamics,is described here.

Noninvasive in vivo cell tracking using molecular imaging:A useful tool for developing mesenchymal stem cell-based cancer treatment

Mounting evidence has emphasized the potential of cell therapies in treating various diseases by restoring damaged tissues or replacing defective cells in the body.Cell therapies have become a strong therapeutic modality by applying noninvasive in vivo molecular imaging for examining complex cellular processes,understanding pathophysiological mechanisms of diseases,and evaluating the kinetics/dynamics of cell therapies.In particular,mesenchymal stem cells(MSCs)have shown promise in recent years as drug carriers for cancer treatment.They can also be labeled with different probes and tracked in vivo to assess the in vivo effect of administered cells,and to optimize therapy.The exact role of MSCs in oncologic diseases is not clear as MSCs have been shown to be involved in tumor progression and inhibition,and the exact interactions between MSCs and specific cancer microenvironments are not clear.In this review,a multitude of labeling approaches,imaging modalities,and the merits/demerits of each strategy are outlined.In addition,specific examples of the use of MSCs and in vivo imaging in cancer therapy are provided.Finally,present limitations and future outlooks in terms of the translation of different imaging approaches in clinics are discussed.

DEVELOPMENT OF NEEDLE-BASED MICROENDOSCOPY FOR FLUORESCENCE MOLECULAR IMAGING OF BREAST TUMOR MODELS

Fluorescence molecular imaging enables the visualization of basic molecular processes such as gene expression,enzyme activity,and disease-specific molecular interactions in vivo using targeted contrast agents,and therefore,is being developed for early detection and in situ characterization of breast cancers.Recent advances in developing near-infrared fluorescent imaging contrast agents have enabled the specific labeling of human breast cancer cells in mouse model systems.In synergy with contrast agent development,this paper describes a needle-based fluorescence molecular imaging device that has the strong potential to be translated into clinical breast biopsy procedures.This microendoscopy probe is based on a gradient-index(GRIN)lens interfaced with a laser scanning microscope.Specifications of the imaging performance,including the field-of-view,transverse resolution,and focus tracking characteristics were calibrated.Orthotopic MDA-MB-231 breast cancer xenografts stably expressing the tdTomato red fluorescent protein(RFP)were used to detect the tumor cells in this tumor model as a proof of principle study.With further development,this technology,in conjunction with the development of clinically applicable,injectable fluorescent molecular imaging agents,promises to perform fluorescence molecular imaging of breast cancers in vivo for breast biopsy guidance.

Role of molecular imaging in the management of patients affected by inflammatory bowel disease:State-of-the-art

AIM To present the current state-of-the art of molecular imaging in the management of patients affected by inflammatory bowel disease(IBD).METHODS A systematic review of the literature was performed in order to find important original articles on the role of molecular imaging in the management of patients affected by IBD. The search was updated until February 2016 and limited to articles in English.RESULTS Fifty-five original articles were included in this review, highlighting the role of single photon emission tomography and positron emission tomography. CONCLUSION To date, molecular imaging represents a useful tool to detect active disease in IBD. However, the available data need to be validated in prospective multicenter studies on larger patient samples.

Photoacoustic molecular imaging with functional nanoparticles

Photoacoustic imaging(PAI)breaks through the optical di®usion limit by making use of the PA e®ect.By converting incident photons into ultrasonic waves,PAI combines high contrast of optical imaging and high spatial resolution in depth tissue of ultrasound imaging in a single imaging modality.This imaging modality has now shown potential for molecular imaging,which enables visualization of biological processes with systemically introduced functional nanoparticles.In the current review,the potentials of di®erent optical nanoprobes as PAI contrast agents were elucidated and discussed.

MINIMIZING EXCITATION LIGHT LEAKAGE AND MAXIMIZING MEASUREMENT SENSITIVITY FOR MOLECULAR IMAGING WITH NEAR-INFRARED FLUORESCENCE

Near-infraredfluorescence(NIRF)imaging involves the separation of weakfluorescence signals from backscattered excitation light.The measurement sensitivity of current NIRF imaging systems is limited by the excitation light leakage through rejectionfilters.In this contribution,the authors demonstrate that the excitation light leakage can be suppressed upon using appropriatefilter combination and permutations.The excitation light leakage and measurement sensitivity were assessed and compared in this study by computing the transmission ratios of excitation to emission light collected and the signal-to-noise ratios in well-controlled phantom studies with differentfilter combinations and permutations.Using appropriatefilter combinations and permutations,we observe as much as two orders of magnitude reduction in the transmission ratio and higher signal-to-noise ratio.

Spectral selective fluorescence molecular imaging with volume holographic imaging system

A compact volume holographic imaging(VHI)method that can detect fluorescence objects located in diffusive medium in spectral selective imaging manner is presented.The enlargement of lateralfield of view of the VHI system is realized by using broadband illumination and demagnification optics.Each target spectrum of°uorescence emitting from a di®usive medium is probed by tuning the inclination angle of the transmission volume holographic grating(VHG).With the use of the single transmission VHG,fluorescence images with different spectrum are obtained sequentially and precise three-dimensional(3D)information of deep fluorescent objects located in a diffusive medium can be reconstructed from these images.The results of phantom experiments demonstrate that two fluorescent objects with a sub-millimeter distance can be resolved by spectral selective imaging.