Tomography Magnetic Resonance Imaging Research Articles

Super resolution using sparse sampling at portable ultra-low field MR

Ultra-low field (ULF) magnetic resonance imaging (MRI) holds the potential to make MRI more accessible, given its cost-effectiveness, reduced power requirements, and portability. However, signal-to-noise ratio (SNR) drops with field strength, necessitating imaging with lower resolution and longer scan times. This study introduces a novel Fourier-based Super Resolution (FouSR) approach, designed to enhance the resolution of ULF MRI images with minimal increase in total scan time. FouSR combines spatial frequencies from two orthogonal ULF images of anisotropic resolution to create an isotropic T2-weighted fluid-attenuated inversion recovery (FLAIR) image. We hypothesized that FouSR could effectively recover information from under-sampled slice directions, thereby improving the delineation of multiple sclerosis (MS) lesions and other significant anatomical features. Importantly, the FouSR algorithm can be implemented on the scanner with changes to the k-space trajectory. Paired ULF (Hyperfine SWOOP, 0.064 tesla) and high field (Siemens, Skyra, 3 Tesla) FLAIR scans were collected on the same day from a phantom and a cohort of 10 participants with MS or suspected MS (6 female; mean ± SD age: 44.1 ± 4.1). ULF scans were acquired along both coronal and axial planes, featuring an in-plane resolution of 1.7 mm × 1.7 mm with a slice thickness of 5 mm. FouSR was evaluated against registered ULF coronal and axial scans, their average (ULF average) and a gold standard SR (ANTs SR). FouSR exhibited higher SNR (47.96 ± 12.6) compared to ULF coronal (36.7 ± 12.2) and higher lesion conspicuity (0.12 ± 0.06) compared to ULF axial (0.13 ± 0.07) but did not exhibit any significant differences contrast-to-noise-ratio (CNR) compared to other methods in patient scans. However, FouSR demonstrated superior image sharpness (0.025 ± 0.0040) compared to all other techniques (ULF coronal 0.021 ± 0.0037, q = 5.9, p-adj. = 0.011; ULF axial 0.018 ± 0.0026, q = 11.1, p-adj. = 0.0001; ULF average 0.019 ± 0.0034, q = 24.2, p-adj. < 0.0001) and higher lesion sharpness (−0.97 ± 0.31) when compared to the ULF average (−1.02 ± 0.37, t(543) = −10.174, p = <0.0001). Average blinded qualitative assessment by three experienced MS neurologists showed no significant difference in WML and sulci or gyri visualization between FouSR and other methods. FouSR can, in principle, be implemented on the scanner to produce clinically useful FLAIR images at higher resolution on the fly, providing a valuable tool for visualizing lesions and other anatomical structures in MS.

Synthesis, MTT assay, 99m-Technetium radiolabeling, biodistribution evaluation of radiotracer and in vitro magnetic resonance imaging study of P,N-doped graphene quantum dots as a new multipurpose imaging nano-agent

Abstract In this study, a new nano-structure, N,P-doped graphene quantum dots (N,P-GQDs), were synthesized as multipurpose imaging agent for performing scintigraphy and magnetic resonance imaging (MRI). Some standard characterization methods were used to identify the nano-structure. In vitro cytotoxicity evaluation using MTT assay revealed that N,P-GQDs nanoparticles had no significant cytotoxicity after 24 and 48 h against normal (MCF-10A) and cancerous (MCF 7) human breast cell line in concentration up to 200 μg/mL. The N,P-GQDs were radiolabeled with Technetium-99m as 99mTc-(N,P-GQDs) and the radiochemical purity was assayed by ITLC concluding RCP ≥ 95 %. The passing of 99mTc-(N,P-GQDs) through 0.1 µm filter demonstrated that 70.8 % of particles were <0.1 µm. In order to perform scintigraphy, the 99mTc-(N,P-GQDs) were injected to female healthy Wistar rats. The results showed that the radio-complex was captured and eliminated just by kidneys. Moreover, in vitro T1-weighted phantom MRI imaging showed that the N,P-GQDs have proper relaxivity in comparison to Dotarem® as a clinically available contrast agent. The results showed that the N,P-GQDs have potential to be considered as a novel and encouraging agent for both molecular MRI and nuclear medicine imagings.

CNN-based glioma detection in MRI: A deep learning approach.

More than a million people are affected by brain tumors each year; high-grade gliomas (HGGs) and low-grade gliomas (LGGs) present serious diagnostic and treatment hurdles, resulting in shortened life expectancies. Glioma segmentation is still a significant difficulty in clinical settings, despite improvements in Magnetic Resonance Imaging (MRI) and diagnostic tools. Convolutional neural networks (CNNs) have seen recent advancements that offer promise for increasing segmentation accuracy, addressing the pressing need for improved diagnostic and therapeutic approaches. The study intended to develop an automated glioma segmentation algorithm using CNN to accurately identify tumor components in MRI images. The goal was to match the accuracy of experienced radiologists with commercial instruments, hence improving diagnostic precision and quantification. 285 MRI scans of high-grade gliomas (HGGs) and low-grade gliomas (LGGs) were analyzed in the study. T1-weighted sequences were utilised for segmentation both pre-and post-contrast agent administration, along with T2-weighted sequences (with and without Fluid Attenuation by Inversion Recovery [FAIRE]). The segmentation performance was assessed with a U-Net network, renowned for its efficacy in medical image segmentation. DICE coefficients were computed for the tumour core with contrast enhancement, the entire tumour, and the tumour nucleus without contrast enhancement. The U-Net network produced DICE values of 0.7331 for the tumour core with contrast enhancement, 0.8624 for the total tumour, and 0.7267 for the tumour nucleus without contrast enhancement. The results align with previous studies, demonstrating segmentation accuracy on par with professional radiologists and commercially accessible segmentation tools. The study developed a CNN-based automated segmentation system for gliomas, achieving high accuracy in recognising glioma components in MRI images. The results confirm the ability of CNNs to enhance the accuracy of brain tumour diagnoses, suggesting a promising avenue for future research in medical imaging and diagnostics. This advancement is expected to improve diagnostic processes for clinicians and patients by providing more precise and quantitative results.

Ensemble Learning-based Brain Stroke Prediction Model Using Magnetic Resonance Imaging

Brain stroke (BS) imposes a substantial burden on healthcare systems due to the long-term care and high expenditure. Earlier detection and intervention can reduce the impact of BS. Magnetic resonance imaging (MRI) is commonly applied for BS detection. Deep learning techniques can employ MRI images to identify the BS risks in the initial stages. This study developed a BS detection model using an ensemble learning approach that combines the predictions of the base models. A MobileNet V3 model backbone was used to extract the intricate patterns of BS from MRI images. LightGBM and CatBoost models were used as base models to predict BS using the extracted features. In addition, the random forest model was used to integrate the predictions of base models to identify BS. The proposed model was generalized on a public MRI dataset that covers 2888 clinical MRI images. The experimental outcomes showed the effectiveness of the suggested BS detection model. The proposed model has obtained an accuracy of 98.7%, an area under the receiver operating characteristic score of 0.95, and an area under the precision–recall curve of 0.92. The recommended model is believed to be deployed in real-time healthcare settings to assist radiologists and clinicians in making effective decisions.

Multi-Modal in Vitro Experiments Mimicking the Flow Through a Mitral Heart Valve Phantom.

Fluid-structure interaction (FSI) models are more commonly applied in medical research as computational power is increasing. However, understanding the accuracy of FSI models is crucial, especially in the context of heart valve disease in patient-specific models. Therefore, this study aimed to create a multi-modal benchmarking data set for cardiac-inspired FSI models, based on clinically important parameters, such as the pressure, velocity, and valve opening, with an in vitro phantom setup. An in vitro setup was developed with a 3D-printed phantom mimicking the left heart, including a deforming mitral valve. A range of pulsatile flows were created with a computer-controlled motor-and-pump setup. Catheter pressure measurements, magnetic resonance imaging (MRI), and echocardiography (Echo) imaging were used to measure pressure and velocity in the domain. Furthermore, the valve opening was quantified based on cine MRI and Echo images. The experimental setup, with 0.5% cycle-to-cycle variation, was successfully built and six different flow cases were investigated. Higher velocity through the mitral valve was observed for increased cardiac output. The pressure difference across the valve also followed this trend. The flow in the phantom was qualitatively assessed by the velocity profile in the ventricle and by streamlines obtained from 4D phase-contrast MRI. A multi-modal set of data for validation of FSI models has been created, based on parameters relevant for diagnosis of heart valve disease. All data is publicly available for future development of computational heart valve models.

Squeeze-and-excitation-attention-based mobile vision transformer for grading recognition of bladder prolapse in pelvic MRI images.

Bladder prolapse is a common clinical disorder of pelvic floor dysfunction in women, and early diagnosis and treatment can help them recover. Pelvic magnetic resonance imaging (MRI) is one of the most important methods used by physicians to diagnose bladder prolapse; however, it is highly subjective and largely dependent on the clinical experience of physicians. The application of computer-aided diagnostic techniques to achieve a graded diagnosis of bladder prolapse can help improve its accuracy and shorten the learning curve. The purpose of this study is to combine convolutional neural network (CNN) and vision transformer (ViT) for grading bladder prolapse in place of traditional neural networks, and to incorporate attention mechanisms into mobile vision transformer (MobileViT) for assisting in the grading of bladder prolapse. This study focuses on the grading of bladder prolapse in pelvic organs using a combination of a CNN and a ViT. First, this study used MobileNetV2 to extract the local features of the images. Next, a ViT was used to extract the global features by modeling the non-local dependencies at a distance. Finally, a channel attention module (i.e., squeeze-and-excitation network) was used to improve the feature extraction network and enhance its feature representation capability. The final grading of the degree of bladder prolapse was thus achieved. Using pelvic MRI images provided by a Huzhou Maternal and Child Health Care Hospital, this study used the proposed method to grade patients with bladder prolapse. The accuracy, Kappa value, sensitivity, specificity, precision, and area under the curve of our method were 86.34%, 78.27%, 83.75%, 95.43%, 85.70%, and 95.05%, respectively. In comparison with other CNN models, the proposed method performed better. Thus, the model based on attention mechanisms exhibits better classification performance than existing methods for grading bladder prolapse in pelvic organs, and it can effectively assist physicians in achieving a more accurate bladder prolapse diagnosis.

Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

AbstractThe early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model’s ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model’s decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes.

3D printing of ferromagnetic passive shims for field shaping in magnetic resonance imaging

Magnetic Resonance Imaging (MRI) often encounters image quality degradation due to magnetic field inhomogeneities. Conventional passive shimming techniques involve the manual placement of discrete magnetic materials, imposing limitations on correcting complex inhomogeneities. To overcome this, we propose a novel 3D printing method utilizing binder jetting technology to enable precise deposition of a continuous range of concentrations of ferromagnetic ink. This approach grants complete control of the magnitude of the magnetic moment within the passive shim enabling tailored corrections of B0 field inhomogeneities. By optimizing the magnetic field distribution using linear programming and an in-house written Computer-Aided Design (CAD) generation software, we printed shims with promising results in generating low spherical harmonic corrections. Experimental evaluations demonstrate feasibility of these 3D printed passive shims to induce target magnetic fields corresponding to second-order spherical harmonic, as evidenced by acquired B0 maps. The electrically insulating properties of the printed shims eliminate the risk of eddy currents and heating, thus ensuring safety. The dimensional fabrication accuracy of the printed shims surpasses previous methods, enabling more precise and localized correction of subject-specific inhomogeneities. The findings highlight the potential of binder-jetted 3D printed passive shims in MRI shimming as a versatile and efficient solution for fabricating passive shims, with the potential to enhance the quality of MRI imaging while also being applicable to other types of Magnetic Resonance systems.

Brain Tumor Prediction and Segmentation with Morphological Region-based Active Contour Model and Refinement using Boltzmann Monte Carlo Method in MRI Images

Objectives: The primary goal of the research work is to accurately detect the precise location of the brain tumor in the radiological Magnetic Resonance Imaging (MRI) images of human brain using segmentation method. Methods: In this research work, we introduce mainly the Morphological Region-based Active Contour model and Boltzmann Monte Carlo method (MACB model), involving a comprehensive three-step methodology for the segmentation of the brain, MRI images in order to detect brain tumor. The initial step involves pre-processing which includes Gaussian filtering for noise reduction and Contrast Limited Adaptive Histogram Equalization (CLAHE) technique to enhance image features. In the second step, we identify tumor-related clusters using morphological operations and delineate the tumor regions using Active Contour (Snake) model to get a segmented image. In the final step, the Boltzmann Monte Carlo method is used to refine the edges of the segmented image. To evaluate the effectiveness of this approach, the 2D brain tumor datasets, available in the public domain, are used. The first dataset is taken from Kaggle website and has 3064 MRI human brain images and its respective ground truth images which is used for segmentation. The second dataset is used for visualization of segmented tumor, available in the same Kaggle website. Findings: The Performance metrics for finding similarity between the segmented images generated using the proposed MACB model and the ground truth images, available in the first dataset, exhibit higher values. That is, the proposed method has achieved higher values of Dice Similarity Coefficient (DSC): 93.26%, Jaccard Co-efficient: 86.44%, Sensitivity: 97.27%, Specificity: 99.43% and Pixel accuracy: 98.95%. Novelty: In this research work, MACB model is proposed for the detection, segmentation, and refinement process of brain tumor by incorporating Boltzmann Monte Carlo method with Morphological Region-Based Active Contour model. This novel approach has resulted in enhanced precision and efficiency in the brain tumor segmentation process. Keywords: Brain Tumor Segmentation, Morphological Operation, Active Contour, Boltzmann Monte Carlo Method, Magnetic Resonance Imaging

CVG-Net: novel transfer learning based deep features for diagnosis of brain tumors using MRI scans

Brain tumors present a significant medical challenge, demanding accurate and timely diagnosis for effective treatment planning. These tumors disrupt normal brain functions in various ways, giving rise to a broad spectrum of physical, cognitive, and emotional challenges. The daily increase in mortality rates attributed to brain tumors underscores the urgency of this issue. In recent years, advanced medical imaging techniques, particularly magnetic resonance imaging (MRI), have emerged as indispensable tools for diagnosing brain tumors. Brain MRI scans provide high-resolution, non-invasive visualization of brain structures, facilitating the precise detection of abnormalities such as tumors. This study aims to propose an effective neural network approach for the timely diagnosis of brain tumors. Our experiments utilized a multi-class MRI image dataset comprising 21,672 images related to glioma tumors, meningioma tumors, and pituitary tumors. We introduced a novel neural network-based feature engineering approach, combining 2D convolutional neural network (2DCNN) and VGG16. The resulting 2DCNN-VGG16 network (CVG-Net) extracted spatial features from MRI images using 2DCNN and VGG16 without human intervention. The newly created hybrid feature set is then input into machine learning models to diagnose brain tumors. We have balanced the multi-class MRI image features data using the Synthetic Minority Over-sampling Technique (SMOTE) approach. Extensive research experiments demonstrate that utilizing the proposed CVG-Net, the k-neighbors classifier outperformed state-of-the-art studies with a k-fold accuracy performance score of 0.96. We also applied hyperparameter tuning to enhance performance for multi-class brain tumor diagnosis. Our novel proposed approach has the potential to revolutionize early brain tumor diagnosis, providing medical professionals with a cost-effective and timely diagnostic mechanism.

MRI of pelvic endometriosis: evaluation of the mr#Enzian classification and the importance of adenomyosis subtypes.

This study aimed to investigate the utility of the #Enzian classification in magnetic resonance imaging (MRI) for endometriosis assessment, focusing on inter-reader agreement, diagnostic accuracy, and the correlation of adenomyosis with deep endometriosis (DE). This IRB- approved retrospective single-center study included 412 women who underwent MRI evaluation for endometriosis between February 2017 and June 2022. Two experienced radiologists independently analyzed MRI images using the #Enzian classification and assessed the type of adenomyosis, if any. The surgical #Enzian classification served as the gold standard for evaluating preoperative MRI results of 45 patients. Statistical analysis was performed to assess inter-reader agreement and diagnostic accuracy. Inter-reader agreement was substantial to excellent (Cohen's kappa 0.75-0.96) for most compartments except peritoneal involvement (0.39). The preoperative MRI showed mostly substantial to excellent accuracy (0.84-0.98), sensitivity (0.62-1.00), specificity (0.87-1.00), positive (0.58-1.00) and negative predictive values (0.86-1.00) for most compartments, except for peritoneal lesions (0.36, 0.17, 1.00, 1.00, 0.26 respectively). A trend with a higher prevalence of concordant DE in women with MR features of external adenomyosis compared to those with internal adenomyosis was visible (p = 0.067). The mr#Enzian showed mostly high inter-reader agreement and good diagnostic accuracy for various endometriosis compartments. MRI's role is particularly significant in the context of the current paradigm shift towards medical endometriosis treatment. The inclusion of information about the type of adenomyosis in the mr#Enzian classification could enhance diagnostic accuracy and inform treatment planning.

A Hybrid Technique to Predict Brain Tumour using MRI Image

Currently, the radiologist can more accurately identify brain tumours through the development of Computer-Assisted Diagnosis (CAD), Machine Learning and Deep Learning. Recently, Deep Learning (DL) strategies have gained traction as a means to rapidly and accurately construct automated systems for diagnosing and segmenting the image. The standard approach to this issue is to create a custom feature for classification. Most neurological diseases originate from abnormal growth of brain cells, which can compromise brain architecture and even lead to malignant brain tumours. Brain tumour detection and classification algorithms that are both quick and accurate have been the subject of extensive study. This facilitates the straight forward diagnosis of brain tumours using Magnetic Resonance Image (MRI) images. Through Deep Learning (DL) model the diagnosis of brain malignancies in MRI images using Convolutional Neural Network (CNN) is possible by training the data. So, in this paper the brain tumouris predicted byproposing a Hybridfeature extraction technique i.e., tuned CNN model with ResNet150 and U-net.

Magnetic Resonance Images Segmentation of Multifidus based on Dense-unet and Superpixel.

Lumbar disc herniation (LDH) is a common clinical condition causing lower back and leg pain. Accurate segmentation of the lumbar discs is crucial for assessing and diagnosing LDH. Magnetic resonance imaging (MRI) can reveal the condition of articular cartilage. However, manual segmentation of MRI images is burdensome for physicians and needs to be more efficient. In this study, we propose a method that combines UNet and superpixel segmentation to address the problem of loss of detailed information in the feature extraction phase, leading to poor segmentation results at object edges. The aim is to provide a reproducible solution for diagnosing patients with lumbar disc herniation. We suggest using the network structure of UNet. Firstly, dense blocks are inserted into the UNet network, and training is performed using the Swish activation function. The Dense-UNet model extracts semantic features from the images and obtains rough semantic segmentation results. Then, an adaptive-scale superpixel segmentation algorithm is applied to segment the input images into superpixel images. Finally, high-level abstract semantic features are fused with the detailed information of the superpixels to obtain edge-optimized semantic segmentation results. Evaluation of a private dataset of multifidus muscles in magnetic resonance images demonstrates that compared to other segmentation algorithms, this algorithm exhibits better semantic segmentation performance in detailed areas such as object edges. Compared to UNet, it achieves a 9.5% improvement in the Dice Similarity Coefficient (DSC) and an 11.3% improvement in the Jaccard Index (JAC). The experimental results indicate that this algorithm improves segmentation performance while reducing computational complexity.

Radiological Imaging of the Lacrimal Gland in Sjogren’s Syndrome: A Systematic Review and Meta-Analysis

Purpose To critically appraise the evidence on the ability of the lacrimal gland ultrasonography (USG) or magnetic resonance imaging (MRI) to differentiate between Sjogren’s syndrome and non-Sjogren’s syndrome/healthy controls. Methods A systematic review and meta-analysis (based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines) of online literature search was performed using PubMed, Scopus, and Cochrane databases. Cohort studies comparing the imaging features of the lacrimal glands of Sjogren’s syndrome with a control group were included. Quantitative synthesis was performed using the RevMan (Version 5.4.1). Results Six studies used USG as an imaging technique, and three used MRI for the lacrimal gland imaging. The lacrimal gland affected with Sjogren’s syndrome shows glandular heterogeneity on USG and MRI. Heterogeneity on USG had 6.18 times higher odds of the lacrimal gland being involved with Sjogren’s syndrome (95% CI, 3.31–11.55). Gland hyperechogenicity cannot reliably differentiate the glandular involvement in Sjogren’s syndrome. There is insufficient data for analysis on the gland size, hypoechoic areas, fibrous bands, and increased lacrimal artery resistance in Sjogren’s syndrome patients. Of the three MRI-based studies, reduced apparent diffusion coefficient and heterogeneity were the characteristics of Sjogren’s syndrome. Clinical parameters such as dry eye symptomatology and Schirmer values had variable associations with USG or MRI parameters. Ultrasonography parameters were no different between dry eye versus no dry eye in Sjogren’s syndrome patients, whereas small-sized glands had low Schirmer on MRI-based studies. Conclusion Glandular heterogeneity on USG is significantly associated with lacrimal gland involvement in Sjogren’s syndrome patients. However, the role of radiology in predicting lacrimal gland involvement is unclear as the evidence is insufficient and heterogeneous.

DEPLOYMENT OF ENHANCED DEEP LEARNING MODEL WITH THE BEST ESTIMATORS ON OPTIMIZERS AND ACTIVATION FUNCTIONS FOR HEALTHCARE IN WEB APPLICATION

Breast cancer and brain tumor stand as leading global causes of mortality. Brain tumor uses Magnetic Resonance Imaging (MRI) which offers superior clarity in visualizing brain structures compared to other imaging modalities, while Breast cancer uses ultrasonography (US) serves as a common tool for detecting breast cancer despite its inherent limitations in image quality. Motion artifacts frequently hinder MRI scans, necessitating skilled radiologists for accurate interpretation. Computer-aided diagnosis (CAD) systems driven by artificial intelligence, present a promising solution by consistently assisting radiologists in analyzing US images. Convolutional neural networks (CNNs) leverage various optimizers like Adam and Stochastic Gradient Descent (SGD), RMSprop, Adagrad, and Adadelta as well as activation functions including PReLu, LeakyReLu, Elu, and ReLu for their construction and training. The comparative analysis highlights the importance of optimizers and activation functions in deep learning algorithms for predicting brain tumors and breast cancer. The Adam optimizer combined with the ReLU activation function achieved an accuracy of 85% for breast cancer prediction, while RMSprop combined with ReLU activation function achieved a higher accuracy of 93% for brain tumor classification. From this research, considerable deep learning configurations are identified for both breast cancer and brain tumor prediction, facilitating more precise and efficient diagnoses. The comparative analysis provides valuable insights for those involved in medical imaging applications. Furthermore, The CNN model is deployed in web interface using flask framework to streamline the integration of these models into healthcare systems. This interface simplifies the input of medical data including image data and provides real-time predictions. KEYWORDS—Convolutional neural networks, Comparative Analysis, Activation functions, Optimizers, Ultrasound images, MRI images, Breast cancer, Brain tumor, medical imaging applications, FLASK.

Value of magnetic resonance imaging radiomics features in predicting histologic grade of invasive ductal carcinoma of the breast.

Breast cancer has the second highest mortality rate of all cancers and occurs mainly in women. To investigate the relationship between magnetic resonance imaging (MRI) radiomics features and histological grade of invasive ductal carcinoma (IDC) of the breast and to evaluate its diagnostic efficacy. The two conventional MRI quantitative indicators, i.e. the apparent diffusion coefficient (ADC) and the initial enhancement rate, were collected from 112 patients with breast cancer. The breast cancer lesions were manually segmented in dynamic contrast-enhanced MRI (DCE-MRI) and ADC images, the differences in radiomics features between Grades I, II and III IDCs were compared and the diagnostic efficacy was evaluated. The ADC values (0.77 ± 0.22 vs 0.91 ± 0.22 vs 0.92 ± 0.20, F= 4.204, p< 0.01), as well as the B_sum_variance (188.51 ± 67.803 vs 265.37 ± 77.86 vs 263.74 ± 82.58, F= 6.040, p< 0.01), L_energy (0.03 ± 0.02 vs 0.13 ± 0.11 vs 0.12 ± 0.14, F= 7.118, p< 0.01) and L_sum_average (0.78 ± 0.32 vs 16.34 ± 4.23 vs 015.45 ± 3.74, F= 21.860, p< 0.001) values of patients with Grade III IDC were significantly lower than those of patients with Grades I and II IDC. The B_uniform (0.15 ± 0.12 vs 0.11 ± 0.04 vs 0.12 ± 0.03, F= 3.797, p< 0.01) and L_SRE (0.85 ± 0.07 vs 0.78 ± 0.03 vs 0.79 ± 0.32, F= 3.024, p< 0.01) values of patients with Grade III IDC were significantly higher than those of patients with Grades I and II IDC. All differences were statistically significant (p< 0.05). The ADC radiomics signature model had a higher area-under-the-curve value in identifying different grades of IDC than the ADC value model and the DCE radiomics signature model (0.869 vs 0.711 vs 0.682). The accuracy (0.812 vs 0.647 vs 0.710), specificity (0.731 vs 0.435 vs 0.342), positive predictive value (0.815 vs 0.663 vs 0.669) and negative predictive value (0.753 vs 0.570 vs 0.718) of the ADC radiomics signature model were all significantly better than the ADC value model and the DCE radiomics signature model. ADC values and breast MRI radiomics signatures are significant in identifying the histological grades of IDC, with the ADC radiomics signatures having greater value.

Motion Accuracy of Pneumatic Stepper Motor-Driven Robotic System Developed for MRI-Guided High-Intensity Focused Ultrasound Treatment of Prostate Disease

The latest advancement in high-intensity focused ultrasound (HIFU) treatment technology integrates magnetic resonance imaging (MRI) guidance for precise treatment of prostate disease. As conventional electromagnetic motors are not applicable for utilization within MRI scanners, we have developed a prototype robotic system driven by pneumatic stepper motors to control the movement of the HIFU transducer within an intrarectal probe during MRI-guided HIFU treatment procedures. These pneumatic stepper motors were constructed entirely from MRI-compatible plastic materials. Assessment of the robotic system’s MRI compatibility was conducted utilizing a 3.0T MRI scanner, revealing no discernible MRI image distortion with a minor decrease in the signal-to-noise ratio (2.8%) during the motor operation. The robotic system enabled the transducer to move inside the probe with two degrees of freedom, allowing both linear and rotational motion. The positional accuracy of the transducer movement was assessed, yielding ±0.20 and ±0.22 mm accuracies in the forward and backward linear movements, respectively, and ±0.79° and ±0.74° accuracies in the clockwise and counterclockwise rotational motions, respectively. Emulation of authentic HIFU procedures involved creating a two-dimensional array of thermal lesions in a tissue-mimicking phantom, achieving positional accuracy within ±1 mm for the generated HIFU focal spots. The prototype robotic system incorporating pneumatic stepper motors fabricated entirely from MRI-compatible plastic materials has demonstrated the requisite positional accuracy necessary for effective HIFU treatment of prostate disease, indicating substantial promise for future clinical application.

Use of MRI Radiomics Models in Evaluating the Low HER2 Expression in Breast Cancer.

To investigate the magnetic resonance imaging (MRI) radiomics models in evaluating the human epidermal growth factor receptor 2(HER2) expression in breast cancer. <P> Materials and Methods: The MRI data of 161 patients with invasive ductal carcinoma (non-special type) of breast cancer were retrospectively collected, and the MRI radiomics models were established based on the MRI imaging features of the fat suppression T2 weighted image (T2WI) sequence, dynamic contrast-enhanced (DCE)-T1WIsequence and joint sequences. The T-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used for feature dimensionality reduction and screening, respectively, and the random forest (RF) algorithm was used to construct the classification model. <P> Results: The model established by the LASSO-RF algorithm was used in the ROC curve analysis. In predicting the low expression state of HER2 in breast cancer, the radiomics models of the fat suppression T2WI sequence, DCE-T1WI sequence, and the combination of the two sequences showed better predictive efficiency. In the receiver operating characteristic (ROC) curve analysis for the verification set of low, negative, and positive HER2 expression, the area under the ROC curve (AUC) value was 0.81, 0.72, and 0.62 for the DCE-T1WI sequence model, 0.79, 0.65 and 0.77 for the T2WI sequence model, and 0.84, 0.73 and 0.66 for the joint sequence model, respectively. The joint sequence model had the highest AUC value. <P> Conclusions: The MRI radiomics models can be used to effectively predict the HER2 expression in breast cancer and provide a non-invasive and early assistant method for clinicians to formulate individualized and accurate treatment plans.

An improved attention module based on nnU-Net for segmenting primary central nervous system lymphoma (PCNSL) in MRI images1.

Accurate volumetric segmentation of primary central nervous system lymphoma (PCNSL) is essential for assessing and monitoring the tumor before radiotherapy and the treatment planning. The tedious manual segmentation leads to interindividual and intraindividual differences, while existing automatic segmentation methods cause under-segmentation of PCNSL due to the complex and multifaceted nature of the tumor. To address the challenges of small size, diffused distribution, poor inter-layer continuity on the same axis, and tendency for over-segmentation in brain MRI PCNSL segmentation, we propose an improved attention module based on nnUNet for automated segmentation. We collected 114 T1 MRI images of patients in the Huashan Hospital, Shanghai. Then randomly split the total of 114 cases into 5 distinct training and test sets for a 5-fold cross-validation. To efficiently and accurately delineate the PCNSL, we proposed an improved attention module based on nnU-Net with 3D convolutions, batch normalization, and residual attention (res-attention) to learn the tumor region information. Additionally, multi-scale dilated convolution kernels with different dilation rates were integrated to broaden the receptive field. We further used attentional feature fusion with 3D convolutions (AFF3D) to fuse the feature maps generated by multi-scale dilated convolution kernels to reduce under-segmentation. Compared to existing methods, our attention module improves the ability to distinguish diffuse and edge enhanced types of tumors; and the broadened receptive field captures tumor features of various scales and shapes more effectively, achieving a 0.9349 Dice Similarity Coefficient (DSC). Quantitative results demonstrate the effectiveness of the proposed method in segmenting the PCNSL. To our knowledge, this is the first study to introduce attention modules into deep learning for segmenting PCNSL based on brain magnetic resonance imaging (MRI), promoting the localization of PCNSL before radiotherapy.

Improved Hybrid K-Nearest Neighbours Techniques in Segmentation of Low-Grade Tumor and Cerebrospinal Fluid

In image segmentation, identifying information or object detection in medical im-ages is crucial, particularly information that is harder to spot in magnetic resonance imaging (MRI) of low-grade tumors or cerebrospinal fluid (CSF). To address the aforementioned problems associated with missing data in MRI images and the low quality of MRI images that required longer processing times, this research is to seg-ment brain tumors or detect CSF in four-dimensional MRI images. A new hybrid k-nearest neighbors (k-NN) framework is also proposed, which consists of three tech-niques: correlation matrices of discrete Fourier transform (CM-DFT), Laplace Eigen maps of locally preserving projection (LELPP), and a hybrid GrabCut hidden Markov model of k-mean clustering (GCHMkC). The combination of the Hidden Markov Model (HMM) and the k-mean clustering technique is known as the Hidden Markov Model of the k-mean clustering method (HMMkC). To begin with, the Graph Cut and Support Vector Machine (GCSVM) and the GCHMkC approach are combined. The method increased the quality of the images suggested by the methodology, achieving an accuracy of 99.83%, a sensitivity of 99.99%, a specificity of 99.8%, and a computa-tional execution time of 14.9 seconds. Second, a technique called CM-DFT is sug-gested to improve MRI images while resolving the issue of missing imputation data. The accuracy of the MRI image datasets was improved to 99.84%, the time-lag in the hybrid k-NN algorithm was reduced to 99%, the missing data ratio was reduced to 0.9%, 10%, and 12%, and the correctness of the imputed data was improved to 1.533 seconds with computational execution. Thirdly, the nonlinear data is reduced and unnecessary features are eliminated using the Laplace Eigen maps of locally pre-serving projection (LELPP) approach. The hybrid k-NN algorithm used by the tech-nique yields results with 99% accuracy and an execution duration of 2.42 seconds.