Medical Imaging Applications Research Articles

All-optical Compton scattering at shallow interaction angles

All-optical Compton sources combine laser-wakefield accelerators and intense scattering pulses to generate ultrashort bursts of backscattered radiation. The scattering pulse plays the role of a small-period undulator ( ${\sim }1\,\mathrm {\mu }{\rm m}$ ) in which relativistic electrons oscillate and emit X-ray radiation. To date, most of the working laser-plasma accelerators operate preferably at energies of a few hundreds of megaelectronvolts and the Compton sources developed so far produce radiation in the range from hundreds of kiloelectronvolts to a few megaelectronvolts. However, for such applications as medical imaging and tomography the relevant energy range is 10–100 keV. In this article, we discuss different scattering geometries for the generation of X-rays in this range. Through numerical simulations, we study the influence of electron beam parameters on the backscattered photons. We find that the spectral bandwidth remains constant for beams of the same emittance regardless of the scattering geometry. A shallow interaction angle of $30^{\circ }$ or less seems particularly promising for imaging applications given parameters of existing laser-plasma accelerators. Finally, we discuss the influence of the radiation properties for potential applications in medical imaging and non-destructive testing.

Deep Learning for Pneumonia Classification in Chest Radiography Images using Wavelet Transform

Chronic respiratory diseases constitute a prognostic severity factor for some respiratory illnesses. A case in point is pneumonia, a lung infection, whose effective management requires highly accurate diagnosis and precise treatment. Categorizing pneumonia as positive or negative does go through a process of classifying chest radiography images. This task plays a crucial role in medical diagnostics as it facilitates the detection of pneumonia and helps in making timely treatment decisions. Deep learning has shown remarkable effectiveness in various medical imaging applications, including the recognition and categorization of pneumonia in chest radiography images. The main aim of this research is to compare the efficacy of two convolutional neural network models for classifying pneumonia in chest radiography images. The first model was directly trained on the original images, achieving a training accuracy of 0.9266, whereas the second model was trained on images transformed using wavelets and achieved a training accuracy of 0.94. The second model demonstrated significantly superior results in terms of accuracy, sensitivity, and specificity.

Patch-wise 3D segmentation quality assessment combining reconstruction and regression networks.

General deep-learning (DL)-based semantic segmentation methods with expert level accuracy may fail in 3D medical image segmentation due to complex tissue structures, lack of large datasets with ground truth, etc. For expeditious diagnosis, there is a compelling need to predict segmentation quality without ground truth. In some medical imaging applications, maintaining the quality of segmentation is crucial to the localized regions where disease is prevalent rather than just globally maintaining high-average segmentation quality. We propose a DL framework to identify regions of segmentation inaccuracies by combining a 3D generative adversarial network (GAN) and a convolutional regression network. Our approach is methodologically based on the learned ability to reconstruct the original images identifying the regions of location-specific segmentation failures, in which the reconstruction does not match the underlying original image. We use conditional GAN to reconstruct input images masked by the segmentation results. The regression network is trained to predict the patch-wise Dice similarity coefficient (DSC), conditioned on the segmentation results. The method relies directly on the extracted segmentation related features and does not need to use ground truth during the inference phase to identify erroneous regions in the computed segmentation. We evaluated the proposed method on two public datasets: osteoarthritis initiative 4D (3D + time) knee MRI (knee-MR) and 3D non-small cell lung cancer CT (lung-CT). For the patch-wise DSC prediction, we observed the mean absolute errors of 0.01 and 0.04 with the independent standard for the knee-MR and lung-CT data, respectively. This method shows promising results in localizing the erroneous segmentation regions that may aid the downstream analysis of disease diagnosis and prognosis prediction.

Chip-integrated metasurface full-Stokes polarimetric imaging sensor

Polarimetric imaging has a wide range of applications for uncovering features invisible to human eyes and conventional imaging sensors. Chip-integrated, fast, cost-effective, and accurate full-Stokes polarimetric imaging sensors are highly desirable in many applications, which, however, remain elusive due to fundamental material limitations. Here we present a chip-integrated Metasurface-based Full-Stokes Polarimetric Imaging sensor (MetaPolarIm) realized by integrating an ultrathin (~600 nm) metasurface polarization filter array (MPFA) onto a visible imaging sensor with CMOS compatible fabrication processes. The MPFA is featured with broadband dielectric-metal hybrid chiral metasurfaces and double-layer nanograting polarizers. This chip-integrated polarimetric imaging sensor enables single-shot full-Stokes imaging (speed limited by the CMOS imager) with the most compact form factor, records high measurement accuracy, dual-color operation (green and red) and a field of view up to 40 degrees. MetaPolarIm holds great promise to enable transformative applications in autonomous vision, industry inspection, space exploration, medical imaging and diagnosis.

Generative AI for medical imaging analysis and applications

Generative AI plays a pivotal role in medical imaging analysis, enabling precise diagnosis, treatment planning and disease monitoring. Techniques like generative adversarial networks (GANs) and variational autoencoders (VAEs) enhance medical imaging by generating synthetic images, improving reconstruction, segmentation and facilitating disease diagnosis and treatment planning. Nonetheless, ethical, legal and regulatory concerns arise regarding patient privacy, data protection and fairness. This paper offers an overview of generative AI in medical imaging analysis, highlighting applications, challenges and case studies. It compares results with traditional methods and examines potential implications on healthcare policies. The paper concludes with recommendations for responsible implementation and suggests future research and development directions.

MM-GLCM-CNN: A multi-scale and multi-level based GLCM-CNN for polyp classification.

Distinguishing malignant from benign lesions has significant clinical impacts on both early detection and optimal management of those early detections. Convolutional neural network (CNN) has shown great potential in medical imaging applications due to its powerful feature learning capability. However, it is very challenging to obtain pathological ground truth, addition to collected in vivo medical images, to construct objective training labels for feature learning, leading to the difficulty of performing lesion diagnosis. This is contrary to the requirement that CNN algorithms need a large number of datasets for the training. To explore the ability to learn features from small pathologically-proven datasets for differentiation of malignant from benign polyps, we propose a Multi-scale and Multi-level based Gray-level Co-occurrence Matrix CNN (MM-GLCM-CNN). Specifically, instead of inputting the lesions' medical images, the GLCM, which characterizes the lesion heterogeneity in terms of image texture characteristics, is fed into the MM-GLCN-CNN model for the training. This aims to improve feature extraction by introducing multi-scale and multi-level analysis into the construction of lesion texture characteristic descriptors (LTCDs). To learn and fuse multiple sets of LTCDs from small datasets for lesion diagnosis, we further propose an adaptive multi-input CNN learning framework. Furthermore, an Adaptive Weight Network is used to highlight important information and suppress redundant information after the fusion of the LTCDs. We evaluated the performance of MM-GLCM-CNN by the area under the receiver operating characteristic curve (AUC) merit on small private lesion datasets of colon polyps. The AUC score reaches 93.99% with a gain of 1.49% over current state-of-the-art lesion classification methods on the same dataset. This gain indicates the importance of incorporating lesion characteristic heterogeneity for the prediction of lesion malignancy using small pathologically-proven datasets.

0D Hybrid Cuprous Halide as an Efficient Light Emitter and X‐Ray Scintillator

AbstractDespite the extraordinary X‐ray scintillation performance of three‐dimensional (3D) lead perovskite nanocrystals (PNCs), the serious biotoxicity of Pb2+ and luminescent instability in water remain insurmountable obstacles for their applications in medical imaging. To address these drawbacks, herein, the study demonstrates one new lead‐free zero‐dimensional (0D) hybrid cuprous halide of single‐crystalline [BzTPP]2Cu2I4 (BzTPP = Benzyltriphenylphosphonium) as a satisfactory X‐ray scintillator. [BzTPP]2Cu2I4 displays broadband yellowish‐green light emission with a high photoluminescent quantum yield (PLQY) of 44.2% and large Stokes shift of 167 nm upon UV light excitation. High PLQY and negligible self‐absorption enable [BzTPP]2Cu2I4 to display impressive scintillation performance excited by X‐ray with a light yield of 27 706 photons MeV‐1 and low detection limit of 0.352 µGy s−1, surpassing typical 3D PNCs. More importantly, [BzTPP]2Cu2I4 represents extraordinary structural and luminescent stability in water for at least one month. The excellent and stable radioluminescence performance as well as solution growth method ensure a [BzTPP]2Cu2I4‐based screen for fine‐resolution X‐ray imaging with potential in radiography and inspection. This work highlights the multiple merits of low‐toxicity and cost, high light yield, and long‐term water‐stability of 0D hybrid cuprous halides as highly desirable X‐ray scintillators.

Classification of Alzheimer's disease stages from magnetic resonance images using deep learning.

Alzheimer's disease (AD) is a progressive type of dementia characterized by loss of memory and other cognitive abilities, including speech. Since AD is a progressive disease, detection in the early stages is essential for the appropriate care of the patient throughout its development, going from asymptomatic to a stage known as mild cognitive impairment (MCI), and then progressing to dementia and severe dementia; is worth mentioning that everyone suffers from cognitive impairment to some degree as we age, but the relevant task here is to identify which people are most likely to develop AD. Along with cognitive tests, evaluation of the brain morphology is the primary tool for AD diagnosis, where atrophy and loss of volume of the frontotemporal lobe are common features in patients who suffer from the disease. Regarding medical imaging techniques, magnetic resonance imaging (MRI) scans are one of the methods used by specialists to assess brain morphology. Recently, with the rise of deep learning (DL) and its successful implementation in medical imaging applications, it is of growing interest in the research community to develop computer-aided diagnosis systems that can help physicians to detect this disease, especially in the early stages where macroscopic changes are not so easily identified. This article presents a DL-based approach to classifying MRI scans in the different stages of AD, using a curated set of images from Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies databases. Our methodology involves image pre-processing using FreeSurfer, spatial data-augmentation operations, such as rotation, flip, and random zoom during training, and state-of-the-art 3D convolutional neural networks such as EfficientNet, DenseNet, and a custom siamese network, as well as the relatively new approach of vision transformer architecture. With this approach, the best detection percentage among all four architectures was around 89% for AD vs. Control, 80% for Late MCI vs. Control, 66% for MCI vs. Control, and 67% for Early MCI vs. Control.

Automated chronic wounds medical assessment and tracking framework based on deep learning

Chronic wounds are a latent health problem worldwide, due to high incidence of diseases such as diabetes and Hansen. Typically, wound evolution is tracked by medical staff through visual inspection, which becomes problematic for patients in rural areas with poor transportation and medical infrastructure. Alternatively, the design of software platforms for medical imaging applications has been increasingly prioritized. This work presents a framework for chronic wound tracking based on deep learning, which works on RGB images captured with smartphones, avoiding bulky and complicated acquisition setups. The framework integrates mainstream algorithms for medical image processing, including wound detection, segmentation, as well as quantitative analysis of area and perimeter. Additionally, a new chronic wounds dataset from leprosy patients is provided to the scientific community. Conducted experiments demonstrate the validity and accuracy of the proposed framework, with up to 84.5% in precision.

The synthesis of bottlebrushes with improved longer side chains by combining grafting through and grafting from methods

Bottlebrushes have intensive applications in photonics, surfactants, supersoft elastomers, medical imaging, drug delivery and nanomaterials owing to their unique morphology and properties. While the traditional grafting from method required low monomer conversion to avoid gelation, and resulted shorter side chain length. Compared with the traditional grafting from method, the bottlebrushes with improved longer side chains were successfully prepared by combining grafting through and grafting from methods. In this approach, RAFT and ROMP polymerizations were used in sequence to synthesize a series of main chains: including PNB-PMA7, PNB-PMA13 and PNB-PMA25. RAFT polymerization was then used to prepare bottlebrushes with different kinds of main chains under the same conditions. In the meantime, homo-polymerization of PMA was appeared inevitably, which was related to the polymerization time. Owing to the greater molecular weight of PNB-PMA13 and PNB-PMA25, the polymerized solutions transformed into gel in shorter time, and resulted to the lower proportion of homo-polymerization (<11%). Compared with PNB-SCS as main chain, the side chain’s degree of polymerization increased from 41 to 80 when PNB-PMA13 was used as main chain.

A robust MRI-based brain tumor classification via a hybrid deep learning technique

The brain is the most vital component of the neurological system. Therefore, brain tumor classification is a very challenging task in the field of medical image analysis. There has been a qualitative leap in the field of artificial intelligence, deep learning, and their medical imaging applications in the last decade. The importance of this remarkable development has emerged in the field of biomedical engineering due to the sensitivity and seriousness of the issues related to it. The use of deep learning in the field of detecting and classifying tumors in general and brain tumors in particular using magnetic resonance imaging (MRI) is a crucial factor in the accuracy and speed of diagnosis. This is due to its great ability to deal with huge amounts of data and avoid errors resulting from human intervention. The aim of this research is to develop an efficient automated approach for classifying brain tumors to assist radiologists instead of consuming time looking at several images for a precise diagnosis. The proposed approach is based on 3064 T1-weighted contrast-enhanced brain MR images (T1W-CE MRI) from 233 patients. In this study, the proposed system is based on the results of five different models to use the combined potential of multiple models, trying to achieve promising results. The proposed system has led to a significant improvement in the results, with an overall accuracy of 99.31%.

Deep Transfer Learning with Enhanced Feature Fusion for Detection of Abnormalities in X-ray Images.

Medical image classification poses significant challenges in real-world scenarios. One major obstacle is the scarcity of labelled training data, which hampers the performance of image-classification algorithms and generalisation. Gathering sufficient labelled data is often difficult and time-consuming in the medical domain, but deep learning (DL) has shown remarkable performance, although it typically requires a large amount of labelled data to achieve optimal results. Transfer learning (TL) has played a pivotal role in reducing the time, cost, and need for a large number of labelled images. This paper presents a novel TL approach that aims to overcome the limitations and disadvantages of TL that are characteristic of an ImageNet dataset, which belongs to a different domain. Our proposed TL approach involves training DL models on numerous medical images that are similar to the target dataset. These models were then fine-tuned using a small set of annotated medical images to leverage the knowledge gained from the pre-training phase. We specifically focused on medical X-ray imaging scenarios that involve the humerus and wrist from the musculoskeletal radiographs (MURA) dataset. Both of these tasks face significant challenges regarding accurate classification. The models trained with the proposed TL were used to extract features and were subsequently fused to train several machine learning (ML) classifiers. We combined these diverse features to represent various relevant characteristics in a comprehensive way. Through extensive evaluation, our proposed TL and feature-fusion approach using ML classifiers achieved remarkable results. For the classification of the humerus, we achieved an accuracy of 87.85%, an F1-score of 87.63%, and a Cohen's Kappa coefficient of 75.69%. For wrist classification, our approach achieved an accuracy of 85.58%, an F1-score of 82.70%, and a Cohen's Kappa coefficient of 70.46%. The results demonstrated that the models trained using our proposed TL approach outperformed those trained with ImageNet TL. We employed visualisation techniques to further validate these findings, including a gradient-based class activation heat map (Grad-CAM) and locally interpretable model-independent explanations (LIME). These visualisation tools provided additional evidence to support the superior accuracy of models trained with our proposed TL approach compared to those trained with ImageNet TL. Furthermore, our proposed TL approach exhibited greater robustness in various experiments compared to ImageNet TL. Importantly, the proposed TL approach and the feature-fusion technique are not limited to specific tasks. They can be applied to various medical image applications, thus extending their utility and potential impact. To demonstrate the concept of reusability, a computed tomography (CT) case was adopted. The results obtained from the proposed method showed improvements.

A novel hybrid heuristic adopted ensemble of deep learning models for COVID-19 detection framework using CT and X-Ray images

ABSTRACT In 2019, Corona Virus Disease (COVID)-19 has created an important impact on people’s health and economy because of its rapid spreading. Therefore, the earlier detection and diagnosis for handling the further spread is regarded as a significant factor. Computed Tomography (CT) images of the lungs are utilised for the detection process of COVID-19 infection. Chest X-Ray (CXR) and CT serves as the major source for the identification of COVID-19. Some of the classifiers have shown promising outcomes as well as better performance over various medical imaging applications and computer vision processes. Due COVID pandemic, researchers have utilised standard techniques for accurately identifying the coronavirus infection in lung images. Here, a new COVID-19 detection approach has been implemented with the adoption of ensemble classifiers to recognise the occurrence of COVID-19 infection at the starting phase. The required CT and the CXR images are garnered from publicly presented online sources. Meanwhile, the acquired images undergo the process of pre-processing using filtering methods and histogram equalisation techniques in order to ignore the unnecessary artifacts are presented in the images. Then, the detection of COVID-19 is made possible through Ensemble Classifiers (EC) such as Multi-scale Convolutional Neural Networks (CNN), Residual Attention Networks (RAN), Visual Geometry Group (VGG), and Long Short Term Memory (LSTM), where the variables within the machine learning strategies are tuned by Modified Mexican Axolotl Shuffled Frog-Leaping Optimization (MMASFLO). The efficacy of the newly developed ensemble classifier-based COVID-19 detection approaches will be tested through various benchmark datasets, and is tested with several existing approaches and conventional COVID-19 detection approaches.

Enhanced medicinal applications of Co-doped Zn0.5Ni0.5Fe2-xO4 for (X = 0.00 and 0.0250) soft ferrites: A structural analysis

In this experimental research paper, we investigate the potential enhancement of Co-doped Zn0.5Ni0.5Fe2-xCoxO4 for (x = 0.0, and 0.0250) ferrites, synthesis by green synthesis method for applications in medicine. The structural analysis of the synthesized material is a crucial step in understanding its suitability for medical applications. X-ray Diffraction (XRD) is employed to elucidate the crystallographic structure of the Co-doped ZnNiFe2O4 ferrites. The results demonstrate that the doping process has a significant influence on the material’s crystal structure, which may impact its potential in various biomedical applications. The Co-doped ZnNiFe2O4 spinel ferrite materials become more suitable for medical applications as the decrease in X-ray density and simultaneous increase in bulk density can facilitate better tissue penetration and biocompatibility, making them ideal for non-invasive medical imaging and therapeutic applications, while minimizing potential health risks.

Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation.

Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high pixel-wise accuracy, complete topology structure and robustness to various contrast variations are critical and challenging, and most existing methods focus only on achieving one or two of these aspects. In this paper, we present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach. Specifically, we compute a multiscale affinity field for each pixel, capturing its semantic relationships with neighboring pixels in the predicted mask image. This field represents the local geometry of vessel segments of different sizes, allowing us to learn spatial- and scale-aware adaptive weights to strengthen vessel features. We evaluate our AFN on four different types of vascular datasets: X-ray angiography coronary vessel dataset (XCAD), portal vein dataset (PV), digital subtraction angiography cerebrovascular vessel dataset (DSA) and retinal vessel dataset (DRIVE). Extensive experimental results demonstrate that our AFN outperforms the state-of-the-art methods in terms of both higher accuracy and topological metrics, while also being more robust to various contrast changes. The source code of this work is available at https://github.com/TY-Shi/AFN.

Compositionally disordered tungstate scintillation materials

The scintillation properties of compositionally disordered self-activated scintillation materials: (Pb, Ca)WO4, (Pb, Sr)WO4 and, (Pb, Ba)WO4 are described for a fist time. New family of the scintillation materials has a density more than 7 g/cm3, an effective charge Zeff >70. The scintillation kinetics occurs faster and the light yield (LY) is close to the LY of Bi4Ge3O12 (BGO). New materials have a good prospect for application in nuclear instrumentation and medical imaging devises.

Few-Shot Classification of Autism Spectrum Disorder using Site-Agnostic Meta-Learning and Brain MRI.

For machine learning applications in medical imaging, the availability of training data is often limited, which hampers the design of radiological classifiers for subtle conditions such as autism spectrum disorder (ASD). Transfer learning is one method to counter this problem of low training data regimes. Here we explore the use of meta-learning for very low data regimes in the context of having prior data from multiple sites - an approach we term site-agnostic meta-learning. Inspired by the effectiveness of meta-learning for optimizing a model across multiple tasks, here we propose a framework to adapt it to learn across multiple sites. We tested our meta-learning model for classifying ASD versus typically developing controls in 2,201 T1-weighted (T1-w) MRI scans collected from 38 imaging sites as part of Autism Brain Imaging Data Exchange (ABIDE) [age: 5.2 -64.0 years]. The method was trained to find a good initialization state for our model that can quickly adapt to data from new unseen sites by fine-tuning on the limited data that is available. The proposed method achieved an area under the receiver operating characteristic curve (ROC-AUC)=0.857 on 370 scans from 7 unseen sites in ABIDE using a few-shot setting of 2-way 20-shot i.e., 20 training samples per site. Our results outperformed a transfer learning baseline by generalizing across a wider range of sites as well as other related prior work. We also tested our model in a zero-shot setting on an independent test site without any additional fine-tuning. Our experiments show the promise of the proposed site-agnostic meta-learning framework for challenging neuroimaging tasks involving multi-site heterogeneity with limited availability of training data.Clinical Relevance- We propose a learning framework that accommodates multi-site heterogeneity and limited data to assist in challenging neuroimaging tasks.

Determinants of physicians’ intention to use AI-assisted diagnosis: An integrated readiness perspective

Artificial intelligence (AI) in medical imaging applications improves the effectiveness and efficiency of disease diagnosis in medical image analysis through neural networks, machine learning tools, and big data. However, previous studies have only explained the impact of AI medical applications on the healthcare process, providing clinical diagnoses, and recommending treatments; therefore, they are insufficient to explain physician AI-assisted diagnosis adoption behavior. The present study has proposed a theoretical model to explain physicians' AI-assisted diagnosis adoption behavior. The resulting 237 valid questionnaires constituted a response rate of 72.48%. The results indicated that functional value, social value, emotional value, and epistemic value had positive effects on personal involvement. The results also showed that functional value, emotional value, personal involvement, optimism, and innovativeness had a positive influence on physicians’ intentions to adopt AI-assisted diagnosis. The results also found that perceived unregulated standards and perceived mistrust have negative effects on behavior intentions. The results provide unique insights that will not only assist hospital administrators in developing an appropriate AI-assisted diagnosis implementation strategy but also enable software developers, medical imaging manufacturers, and government agencies to develop and appropriate their own marketing and administrative strategies for the future.

A Survey on Artificial Intelligence in Pulmonary Imaging.

Over the last decade, deep learning (DL) has contributed a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This paper presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.

Contextual information extraction in brain tumour segmentation

AbstractAutomatic brain tumour segmentation in MRI scans aims to separate the brain tumour's endoscopic core, edema, non‐enhancing tumour core, peritumoral edema, and enhancing tumour core from three‐dimensional MR voxels. Due to the wide range of brain tumour intensity, shape, location, and size, it is challenging to segment these regions automatically. UNet is the prime three‐dimensional CNN network performance source for medical imaging applications like brain tumour segmentation. This research proposes a context aware 3D ARDUNet (Attentional Residual Dropout UNet) network, a modified version of UNet to take advantage of the ResNet and soft attention. A novel residual dropout block (RDB) is implemented in the analytical encoder path to replace traditional UNet convolutional blocks to extract more contextual information. A unique Attentional Residual Dropout Block (ARDB) in the decoder path utilizes skip connections and attention gates to retrieve local and global contextual information. The attention gate enabled the Network to focus on the relevant part of the input image and suppress irrelevant details. Finally, the proposed Network assessed BRATS2018, BRATS2019, and BRATS2020 to some best‐in‐class segmentation approaches. The proposed Network achieved dice scores of 0.90, 0.92, and 0.93 for the whole tumour. On BRATS2018, BRATS2019, and BRATS2020, tumour core is 0.90, 0.92, 0.93, and enhancing tumour is 0.92, 0.93, 0.94.