T2-weighted Magnetic Resonance Imaging Images Research Articles

An Implementation of Effective CNN Model for AD Detection

This paper focuses on detecting Alzheimer’s Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images are taken for preprocessing from ADNI. The Dataset images are taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3D MRI scans into 2D image slices shows the optimization method in the training process while achieving 96% and 94% accuracy in VGG 16 and ResNet 18 respectively. This study aims to classify AD from brain 3D MRI images and obtain better results.

Photoelectric active copper/iron porphyrin-based metal–organic framework embedded with ferric oxide nanocrystals for photoelectrochemical aptasensing miRNA-141 and in vivo tumor magnetic resonance imaging

A heterostructure of bimetallic copper/ferric metal–organic framework embedded with ferric oxide (denoted as Fe2O3@CuFe-MOF) nanocrystals was simultaneously employed as the bioplatform for constructing the photoelectrochemical (PEC) aptasensor to detect miRNA-141 and an excellent contrast agent for in vivo diagnosis cancer via magnetic resonance imaging (MRI). Fe2O3@CuFe-MOF was synthesized by linking 5,10,15,20-tetra(4-pyridyl)porphyrin (TPyP) with Cu2+ and Fe2+ ions, while partial Fe2+ ions were oxidized to Fe2O3 nanocrystals and embraced within CuFe-MOF (denoted as Fe2O3@CuFe-TPyP-MOF). Comparing with Cu-TPyP-MOF and Fe2O3@Fe-TPyP-MOF, Fe2O3@CuFe-TPyP-MOF exhibited enhanced separation ability of photocarriers, promoted electron transfer, larger specific surface area, and richer functionality, rendering boosted photoelectric conversion efficiency and superior PEC performance. The Fe2O3@CuFe-TPyP-MOF-based PEC aptasensor thus illustrated an ultralow limit of detection (0.8 fM) toward miRNA-141 within a wide range from 1.0 fM to 1.0 nM, accompanying with other outperformed aptasensing ability. In addition, due to the paramagnetic property of Cu2+ and Fe2+ ions, Fe2O3@CuFe-TPyP-MOF also afforded the promoted T1-weighted MRI imaging ability, showing the potential for the diagnosis of prostate tumor in vivo. This work provides a robust photoelectronic active MOF-based heterostructure for the sensitive detection and early cancer diagnosis.

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.

Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine Learning.

Regarding the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the isocitrate dehydrogenase (IDH) mutation status is one of the most important factors for CNS tumor classification. The aim of our study is to analyze which of the commonly used magnetic resonance imaging (MRI) sequences is best suited to obtain this information non-invasively using radiomics-based machine learning models. We developed machine learning models based on different MRI sequences and determined which of the MRI sequences analyzed yields the highest discriminatory power in predicting the IDH mutation status. In our retrospective IRB-approved study, we used the MRI images of 106 patients with histologically confirmed gliomas. The MRI images were acquired using the T1 sequence with and without administration of a contrast agent, the T2 sequence, and the Fluid-Attenuated Inversion Recovery (FLAIR) sequence. To objectively compare performance in predicting the IDH mutation status as a function of the MRI sequence used, we included only patients in our study cohort for whom MRI images of all four sequences were available. Seventy-one of the patients had an IDH mutation, and the remaining 35 patients did not have an IDH mutation (IDH wild-type). For each of the four MRI sequences used, 107 radiomic features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects associated with the data partitioning. Feature preselection and subsequent model development were performed using Random Forest, Lasso regression, LDA, and Naïve Bayes. The performance of all models was determined with independent test data. Among the different approaches we examined, the T1-weighted contrast-enhanced sequence was found to be the most suitable for predicting IDH mutations status using radiomics-based machine learning models. Using contrast-enhanced T1-weighted MRI images, our seven-feature model developed with Lasso regression achieved a mean area under the curve (AUC) of 0.846, a mean accuracy of 0.792, a mean sensitivity of 0.847, and a mean specificity of 0.681. The administration of contrast agents resulted in a significant increase in the achieved discriminatory power. Our analyses show that for the prediction of the IDH mutation status using radiomics-based machine learning models, among the MRI images acquired with the commonly used MRI sequences, the contrast-enhanced T1-weighted images are the most suitable.

Clinical analysis of uterine parameters evaluated by preoperative magnetic resonance imaging in patients treated by hysteroscopic approach with previous cesarean scar defect-related abnormal uterine bleeding: a retrospective cohort study.

As the cesarean delivery rate continues to rise globally, the treatment of previous cesarean scar defects (PCSD) remains challenging. This study aimed to analyze the variables that may influence the clinical cure rate of patients with PCSD-related abnormal uterine bleeding (AUB) as determined by preoperative magnetic resonance imaging (MRI) following hysteroscopic therapy. Women who underwent hysteroscopic surgery for PCSD-related AUB at the Gynecology Department of Third Xiangya Hospital of Central South University from 2018 to 2022 were recruited to this retrospective cohort investigation. A total of 147 patients were enrolled in this study and underwent follow-up over 6 months. The significance of clinical characteristics linked to the clinical cure rate of AUB was examined by logistic regression. There were 64 clinically cured (43.5%) and 83 non-clinically cured (56.5%) patients in the study. There were no significant differences in the age, menstrual duration, gravidity, parity, number of cesarean sections, time since the previous cesarean section, uterus position, width, depth, and thickness of the remaining muscle layer of the defect by MRI T2-weighted images (T2WI) before hysteroscopic surgery between the 2 groups. MRI T2WI of the myometrial thickness adjacent to the defect [P=0.038, odds ratio (OR) =2.095, 95% confidence interval (CI): 1.047-4.261] and the distance from the defect to the external cervical os (P=0.021, OR =2.254, 95% CI: 1.136-4.540) before hysteroscopic surgery are risk factors for the clinical cure rate. The myometrial thickness adjacent to the defect and the distance from the defect to the external cervical os in preoperative MRI are risk factors for clinical cure rate in patients with PCSD-related AUB after hysteroscopic treatment, which is helpful for evaluating the prognosis of disease.

Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement in MRI Imaging.

Background: The development of advanced computational models for medical imaging is crucial for improving diagnostic accuracy in healthcare. This paper introduces a novel approach for virtual contrast enhancement (VCE) in magnetic resonance imaging (MRI), particularly focusing on nasopharyngeal cancer (NPC). Methods: The proposed model, Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement (PGMGVCE), makes use of pixelwise gradient methods with Generative Adversarial Networks (GANs) to enhance T1-weighted (T1-w) and T2-weighted (T2-w) MRI images. This approach combines the benefits of both modalities to simulate the effects of gadolinium-based contrast agents, thereby reducing associated risks. Various modifications of PGMGVCE, including changing hyperparameters, using normalization methods (z-score, Sigmoid and Tanh) and training the model with T1-w or T2-w images only, were tested to optimize the model's performance. Results: PGMGVCE demonstrated a similar accuracy to the existing model in terms of mean absolute error (MAE) (8.56 ± 0.45 for Li's model; 8.72 ± 0.48 for PGMGVCE), mean square error (MSE) (12.43 ± 0.67 for Li's model; 12.81 ± 0.73 for PGMGVCE) and structural similarity index (SSIM) (0.71 ± 0.08 for Li's model; 0.73 ± 0.12 for PGMGVCE). However, it showed improvements in texture representation, as indicated by total mean square variation per mean intensity (TMSVPMI) (0.124 ± 0.022 for ground truth; 0.079 ± 0.024 for Li's model; 0.120 ± 0.027 for PGMGVCE), total absolute variation per mean intensity (TAVPMI) (0.159 ± 0.031 for ground truth; 0.100 ± 0.032 for Li's model; 0.153 ± 0.029 for PGMGVCE), Tenengrad function per mean intensity (TFPMI) (1.222 ± 0.241 for ground truth; 0.981 ± 0.213 for Li's model; 1.194 ± 0.223 for PGMGVCE) and variance function per mean intensity (VFPMI) (0.0811 ± 0.005 for ground truth; 0.0667 ± 0.006 for Li's model; 0.0761 ± 0.006 for PGMGVCE). Conclusions: PGMGVCE presents an innovative and safe approach to VCE in MRI, demonstrating the power of deep learning in enhancing medical imaging. This model paves the way for more accurate and risk-free diagnostic tools in medical imaging.

MRI-based prostate cancer classification using 3D efficient capsule network.

Prostate cancer (PCa) is the most common cancer in men and the second leading cause of male cancer-related death. Gleason score (GS) is the primary driver of PCa risk-stratification and medical decision-making, but can only be assessed at present via biopsy under anesthesia. Magnetic resonance imaging (MRI) is a promising non-invasive method to further characterize PCa, providing additional anatomical and functional information. Meanwhile, the diagnostic power of MRI is limited by qualitative or, at best, semi-quantitative interpretation criteria, leading to inter-reader variability. Computer-aided diagnosis employing quantitative MRI analysis has yielded promising results in non-invasive prediction of GS. However, convolutional neural networks (CNNs) do not implicitly impose a frame of reference to the objects. Thus, CNNs do not encode the positional information properly, limiting method robustness against simple image variations such as flipping, scaling, or rotation. Capsule network (CapsNet) has been proposed to address this limitation and achieves promising results in this domain. In this study, we develop a 3D Efficient CapsNet to stratify GS-derived PCa risk using T2-weighted (T2W) MRI images. In our method, we used 3D CNN modules to extract spatial features and primary capsule layers to encode vector features. We then propose to integrate fully-connected capsule layers (FC Caps) to create a deeper hierarchy for PCa grading prediction. FC Caps comprises a secondary capsule layer which routes active primary capsules and a final capsule layer which outputs PCa risk. To account for data imbalance, we propose a novel dynamic weighted margin loss. We evaluate our method on a public PCa T2W MRI dataset from the Cancer Imaging Archive containing data from 976 patients. Two groups of experiments were performed: (1) we first identified high-risk disease by classifying low + medium risk versus high risk; (2) we then stratified disease in one-versus-one fashion: low versus high risk, medium versus high risk, and low versus medium risk. Five-fold cross validation was performed. Our model achieved an area under receiver operating characteristic curve (AUC) of 0.83 and 0.64 F1-score for low versus high grade, 0.79 AUC and 0.75 F1-score for low + medium versus high grade, 0.75 AUC and 0.69 F1-score for medium versus high grade and 0.59 AUC and 0.57 F1-score for low versus medium grade. Our method outperformed state-of-the-art radiomics-based classification and deep learning methods with the highest metrics for each experiment. Our divide-and-conquer strategy achieved weighted Cohen's Kappa score of 0.41, suggesting moderate agreement with ground truth PCa risks. In this study, we proposed a novel 3D Efficient CapsNet for PCa risk stratification and demonstrated its feasibility. This developed tool provided a non-invasive approach to assess PCa risk from T2W MR images, which might have potential to personalize the treatment of PCa and reduce the number of unnecessary biopsies.

High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling

Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion diagnosis, prognosis, and delineation. However, gradient power and hardware limitations prohibit recording thin slices or sub-1 mm resolution. Furthermore, long scan time is not clinically acceptable. Conventional high-resolution images generated using statistical or analytical methods include the limitation of capturing complex, high-dimensional image data with intricate patterns and structures. This study aims to harness cutting-edge diffusion probabilistic deep learning techniques to create a framework for generating high-resolution MRI from low-resolution counterparts, improving the uncertainty of denoising diffusion probabilistic models (DDPM). Approach. DDPM includes two processes. The forward process employs a Markov chain to systematically introduce Gaussian noise to low-resolution MRI images. In the reverse process, a U-Net model is trained to denoise the forward process images and produce high-resolution images conditioned on the features of their low-resolution counterparts. The proposed framework was demonstrated using T2-weighted MRI images from institutional prostate patients and brain patients collected in the Brain Tumor Segmentation Challenge 2020 (BraTS2020). Main results. For the prostate dataset, the bicubic interpolation model (Bicubic), conditional generative-adversarial network (CGAN), and our proposed DDPM framework improved the noise quality measure from low-resolution images by 4.4%, 5.7%, and 12.8%, respectively. Our method enhanced the signal-to-noise ratios by 11.7%, surpassing Bicubic (9.8%) and CGAN (8.1%). In the BraTS2020 dataset, the proposed framework and Bicubic enhanced peak signal-to-noise ratio from resolution-degraded images by 9.1% and 5.8%. The multi-scale structural similarity indexes were 0.970 ± 0.019, 0.968 ± 0.022, and 0.967 ± 0.023 for the proposed method, CGAN, and Bicubic, respectively. Significance. This study explores a deep learning-based diffusion probabilistic framework for improving MR image resolution. Such a framework can be used to improve clinical workflow by obtaining high-resolution images without penalty of the long scan time. Future investigation will likely focus on prospectively testing the efficacy of this framework with different clinical indications.

The Success of Deep Learning Modalities in Evaluating Modic Changes

BackgroundModic changes are pathologies that are common in the population and cause low back pain. The aim of the study is to analyze the modic changes detected in magnetic resonance imaging (MRI) using deep learning modalities. MethodsThe sagittal T1, sagittal and axial T2-weighted lumbar MRI images of 307 patients, of which 125 were female and 182 were male, aged 19-86 years, who underwent MRI examination between 2016-2021 were analyzed. Modic changes (MC) were categorized and marked according to signal changes. Our study consists of two independent stages: classification and segmentation. The categorized data was first classified using convolutional neural network (CNN) architectures such as DenseNet-121, DenseNet-169, and VGG-19. In the next stage, masks were removed by segmentation using U-Net, which is the CNN architecture, with image processing programs on the marked images. ResultsDuring the classification stage, the success rates for modic type 1, type 2, and type 3 changes were 98%, 96%, 100% in DenseNet-121, 100%, 94%, 100% in DenseNet-169, and 98%, 92%, 97% in VGG-19, respectively. At the segmentation phase, the success rate was 71% with the U-Net architecture. ConclusionEvaluation of MRI findings of MC in the etiology of lower back pain with deep learning architectures can significantly reduce the workload of the radiologist by providing ease of diagnosis.

Association between paraspinal muscle fat infiltration and regional kyphosis angle in thoracolumbar fracture patients: a retrospective study

This study aims to evaluate the impact of percutaneous pedicle screw fixation (PPSF) and open pedicle screw fixation (OPSF) on the postoperative paraspinal muscle fat infiltration (FI) rate in patients with thoracolumbar fractures through magnetic resonance imaging (MRI), and explore the association between paraspinal muscle FI rate and regional kyphosis angle. We retrospectively analyzed clinical data from 35 patients who underwent either PPSF or OPSF for thoracolumbar fractures, examining data at preoperative, 1-month postoperative, and 9-months postoperative time points, which included Visual Analog Scale (VAS), Oswestry Disability Index (ODI), and regional kyphosis angle. We obtained preoperative and 9-month postoperative paraspinal muscle FI rates using T2-weighted MRI images and ImageJ software. We analyzed the correlation of FI rates with VAS, ODI, as well as the correction loss percentage of regional kyphosis angle. The analysis revealed a positive correlation between postoperative FI rate increase and correction loss percentage of regional kyphosis angle (r = 0.696, p < 0.001). The increase in paraspinal muscle FI rate was positively correlated with 9-month postoperative ODI (r = 0.763, p < 0.001). These findings indicate that an increase in postoperative paraspinal muscle FI rate may result in more significant correction loss of regional kyphosis angle and can lead to increased functional impairment in patients.

Classification of orbital tumors using convolutional neural networks

AbstractOrbital tumors are the most common eye tumors that affect people all over the world. Early detection prevents the progression to other regions of the eye and the body. Also, early identification and treatment could reduce mortality. A computer-assisted diagnosis (CAD) system to help physicians diagnose tumors is in great demand in ophthalmology. In recent years, deep learning has demonstrated promising outcomes in computer vision systems. This work proposes a CAD system for detecting various forms of orbital tumors using convolutional neural networks. The system has three stages: preprocessing, data augmentation and classification. The proposed system was evaluated on two datasets of magnetic resonance imaging (MRI) images containing 1404 MRI T1-weighted images and 1560 MRI T2-weighted images. The results have shown that the system is capable of detecting and classifying the tumor in each image type, and the recognition rate for the T1-weighted image is 98% and for the T2-weighted image is 97%.

More rapid progression of brain atrophy in patients on peritoneal dialysis compared with hemodialysis: The VCOHP Study.

We previously reported that brain atrophy was more severe and progressed more rapidly in patients with end-stage kidney disease on peritoneal dialysis (PD) than those with non-dialysis-dependent chronic kidney disease. However, it remains unknown whether there is a difference between patients on PD and hemodialysis (HD). In total, 73 PD and 34 HD patients who underwent brain magnetic resonance imaging (MRI) were recruited for a cross-sectional analysis. Among them, 42 PD and 25 HD patients who underwent a second brain MRI after 2 years were recruited for a longitudinal analysis. T1-weighted MRI images were analyzed. Total gray matter volume (GMV), total white matter volume, and cerebrospinal fluid volume were segmented, and each volume was quantified using statistical parametric mapping software. The ratio of GMV (GMR) was calculated by dividing GMV by intracranial volume, to adjust for variations in head size. We compared GMR between PD and HD patients in the cross-sectional analysis and the annual change in GMR(AC-GMR) in the longitudinal analysis. In the cross-sectional analysis, age- and sex-adjusted GMR was significantly lower in PD than HD patients [least square mean (LSM): 39.2% vs. 40.0%, P = 0.018]. AC-GMRwas significantly greater in PD than HD patients and this difference remained significant even after adjustment for potential confounding factors (LSM: -0.68 vs. -0.28 percentage-points/year, P = 0.011). In conclusion, the present study demonstrated amore rapid progression of brain atrophy in PD patients compared with HD patients. We demonstrated that decline in GMR progressed significantly more rapidly in PD than HD patients independent of potential confounding factors. GMR gray matter volume ratio, HD hemodialysis, PD peritoneal dialysis.

PDFF-CNN: An attention-guided dynamic multi-orientation feature fusion method for gestational age prediction on imbalanced fetal brain MRI dataset.

Fetal brain magnetic resonance imaging (MRI)-based gestational age prediction has been widely used to characterize normal fetal brain development and diagnose congenital brainmalformations. The uncertainty of fetal position and external interference leads to variable localization and direction of the fetal brain. In addition, pregnant women typically concentrate on receiving MRI scans during the fetal anomaly scanning week, leading to an imbalanced distribution of fetal brain MRI data. The above-mentioned problems pose great challenges for deep learning-based fetal brain MRI gestational ageprediction. In this study, a pyramid squeeze attention (PSA)-guided dynamic feature fusion CNN (PDFF-CNN) is proposed to robustly predict gestational ages from fetal brain MRI images on an imbalanced dataset. PDFF-CNN contains four components: transformation module, feature extraction module, dynamic feature fusion module, and balanced mean square error (MSE) loss. The transformation and feature extraction modules are employed by using the PSA to learn multiscale and multi-orientation feature representations in a parallel weight-sharing Siamese network. The dynamic feature fusion module automatically learns the weights of feature vectors generated in the feature extraction module to dynamically fuse multiscale and multi-orientation brain sulci and gyri features. Considering the fact of the imbalanced dataset, the balanced MSE loss is used to mitigate the negative impact of imbalanced data distribution on gestational age predictionperformance. Evaluated on an imbalanced fetal brain MRI dataset of 1327 routine clinical T2-weighted MRI images from 157 subjects, PDFF-CNN achieved promising gestational age prediction performance with an overall mean absolute error of 0.848 weeks and an of 0.904. Furthermore, the attention activation maps of PDFF-CNN were derived, which revealed regional features that contributed to gestational age prediction at each gestationalstage. These results suggest that the proposed PDFF-CNN might have broad clinical applicability in guiding treatment interventions and delivery planning, which has the potential to be helpful with prenatal diagnosis.

Thresholding approaches for estimating paraspinal muscle fat infiltration using T1- and T2-weighted MRI: Comparative analysis using water-fat MRI.

Paraspinal muscle fat infiltration is associated with spinal degeneration and low back pain, however, quantifying muscle fat using clinical magnetic resonance imaging (MRI) techniques continues to be a challenge. Advanced MRI techniques, including chemical-shift encoding (CSE) based water-fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice. To facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1- and T2-weighted images, with measurements from water-fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K-mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients (r), mean absolute errors, and mean bias errors were calculated for FF estimates from T1- and T2-weighted MRI with water-fat MRI for the lumbar multifidus (MF), erector spinae (ES), quadratus lumborum (QL), and psoas (PS), and for all muscles combined. We found that for all muscles combined, FF measurements from T1- and T2-weighted images were strongly positively correlated with measurements from the water-fat images for all thresholding techniques (r = 0.70-0.86, p < 0.0001) and that variations in inter-muscle correlation strength were much greater than variations in inter-method correlation strength. We conclude that muscle FF can be quantified using thresholded T1- and T2-weighted MRI images with relatively low bias and absolute error in relation to water-fat MRI, particularly in the MF and ES, and the choice of thresholding technique should depend on the muscle and clinical MRI sequence of interest.

MRI motion artifact reduction using a conditional diffusion probabilistic model (MAR-CDPM).

High-resolution magnetic resonance imaging (MRI) with excellent soft-tissue contrast is a valuable tool utilized for diagnosis and prognosis. However, MRI sequences with long acquisition time are susceptible to motion artifacts, which can adversely affect the accuracy of post-processingalgorithms. This study proposes a novel retrospective motion correction method named "motion artifact reduction using conditional diffusion probabilistic model" (MAR-CDPM). The MAR-CDPM aimed to remove motion artifacts from multicenter three-dimensional contrast-enhanced T1 magnetization-prepared rapid acquisition gradient echo (3D ceT1 MPRAGE) brain dataset with different brain tumor types. This study employed two publicly accessible MRI datasets: one containing 3D ceT1 MPRAGE and 2D T2-fluid attenuated inversion recovery (FLAIR) images from 230 patients with diverse brain tumors, and the other comprising 3D T1-weighted (T1W) MRI images of 148 healthy volunteers, which included real motion artifacts. The former was used to train and evaluate the model using the in silico data, and the latter was used to evaluate the model performance to remove real motion artifacts. A motion simulation was performed in k-space domain to generate an in silico dataset with minor, moderate, and heavy distortion levels. The diffusion process of the MAR-CDPM was then implemented in k-space to convert structure data into Gaussian noise by gradually increasing motion artifact levels. A conditional network with a Unet backbone was trained to reverse the diffusion process to convert the distorted images to structured data. The MAR-CDPM was trained in two scenarios: one conditioning on the time step of the diffusion process, and the other conditioning on both and T2-FLAIR images. The MAR-CDPM was quantitatively and qualitatively compared with supervised Unet, Unet conditioned on T2-FLAIR, CycleGAN, Pix2pix, and Pix2pix conditioned on T2-FLAIR models. To quantify the spatial distortions and the level of remaining motion artifacts after applying the models, quantitative metrics were reported including normalized mean squared error (NMSE), structural similarity index (SSIM), multiscale structural similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multiscale gradient magnitude similarity deviation (MS-GMSD). Tukey's Honestly Significant Difference multiple comparison test was employed to quantify the difference between the models where p-value was considered statisticallysignificant. Qualitatively, MAR-CDPM outperformed these methods in preserving soft-tissue contrast and different brain regions. It also successfully preserved tumor boundaries for heavy motion artifacts, like the supervised method. Our MAR-CDPM recovered motion-free in silico images with the highest PSNR and VIF for all distortion levels where the differences were statistically significant (p-values ). In addition, our method conditioned on t and T2-FLAIR outperformed (p-values ) other methods to remove motion artifacts from the in silico dataset in terms of NMSE, MS-SSIM, SSIM, and MS-GMSD. Moreover, our method conditioned on only t outperformed generative models (p-values ) and had comparable performances compared with the supervised model (p-values ) to remove real motionartifacts. The MAR-CDPM could successfully remove motion artifacts from 3D ceT1 MPRAGE. It is particularly beneficial for elderly who may experience involuntary movements during high-resolution MRI imaging with long acquisitiontimes.

A case report of huge pancreas mucinous cystic neoplasm during pregnancy: How doctors think.

Pancreas mucinous cystic neoplasm (PMCN) is uncommon, and its occurrence during pregnancy is rare. The management of PMCN during pregnancy, including diagnosis and surgical timing, is a great challenge. A nontender epigastric mass of the upper abdomen was detected by palpation in a 35-year-old woman, gravida 2, para 1, during the 36th week of gestation. She was referred to our institution for further evaluation. Magnetic resonance imaging (MRI) showed a multilocular cystic mass in the body and tail of the pancreas (16.7/12.1/17.6 cm), well-circumscribed with a hyper signal on T2-weighted MRI images. The diagnosis of a pancreatic cyst, probable mucinous, was established. The patient was informed of the possibilities of malignancy, rapid growth, and rupture of the tumor. After a laparotomy and cesarean section, a large cystic tumor was discovered adherent to the pancreas, spleen, mesocolon, and retroperitoneum. The spleen was preserved since there was no evidence of invasion. According to macroscopic examinations, the tumor measured 18 cm was filled with a dark yellow-brownish mucinous fluid and did not appear to communicate with the pancreatic ducts. After six months of follow-up, there were no signs of recurrence in the patient. PMCN may need to be surgically resected in cases characterized by malignancy risk during pregnancy. As female sex hormones may influence the behavior of PMCN during pregnancy, surgical timing should be determined based on the stage of pregnancy, malignancy status, and condition of the mother and fetus.

Deep Learning Framework for Liver Segmentation from T1-Weighted MRI Images.

The human liver exhibits variable characteristics and anatomical information, which is often ambiguous in radiological images. Machine learning can be of great assistance in automatically segmenting the liver in radiological images, which can be further processed for computer-aided diagnosis. Magnetic resonance imaging (MRI) is preferred by clinicians for liver pathology diagnosis over volumetric abdominal computerized tomography (CT) scans, due to their superior representation of soft tissues. The convenience of Hounsfield unit (HoU) based preprocessing in CT scans is not available in MRI, making automatic segmentation challenging for MR images. This study investigates multiple state-of-the-art segmentation networks for liver segmentation from volumetric MRI images. Here, T1-weighted (in-phase) scans are investigated using expert-labeled liver masks from a public dataset of 20 patients (647 MR slices) from the Combined Healthy Abdominal Organ Segmentation grant challenge (CHAOS). The reason for using T1-weighted images is that it demonstrates brighter fat content, thus providing enhanced images for the segmentation task. Twenty-four different state-of-the-art segmentation networks with varying depths of dense, residual, and inception encoder and decoder backbones were investigated for the task. A novel cascaded network is proposed to segment axial liver slices. The proposed framework outperforms existing approaches reported in the literature for the liver segmentation task (on the same test set) with a dice similarity coefficient (DSC) score and intersect over union (IoU) of 95.15% and 92.10%, respectively.

The advancement of MRI in differentiating Modic type I degenerative changes from early spinal infections.

MRI is the most sensitive and specific imaging method for the detection of advanced spinal infections. However, the differential diagnosis of early spinal infection and Modic Type I degenerative changes based on conventional MRI is difficult clinically, as they both may mimic each other by showing hypointensity on T1 weighted images and hyperintensity on T2 weighted spine MRI images. This review summarizes recent advancements in MRI, which may be useful in discriminating degenerative Modic Type I endplate changes from early spinal infection, and evaluates the diagnostic accuracy and limitations of MRI. We aim to provide indications for early differential diagnosis to help initiate appropriate treatment in a timely manner so that associated complications can be avoided.

Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm

Introduction and importanceAccurate segmentation of meningiomas from contrast-enhanced T1-weighted (CE T1-w) magnetic resonance imaging (MRI) is crucial for diagnosis and treatment planning. Manual segmentation is time-consuming and prone to variability. To evaluate an automated segmentation approach for meningiomas using marker-controlled watershed segmentation (MCWS) and fuzzy c-means (FCM) algorithms. Case presentation and methodsCE T1-w MRI of 3 female patients (aged 59, 44, 67 years) with right frontal meningiomas were analyzed. Images were converted to grayscale and preprocessed with Otsu's thresholding and FCM clustering. MCWS segmentation was performed. Segmentation accuracy was assessed by comparing automated segmentations to manual delineations. Clinical discussionThe approach successfully segmented meningiomas in all cases. Mean sensitivity was 0.8822, indicating accurate identification of tumors. Mean Dice similarity coefficient between Otsu's and FCM1 was 0.6599, suggesting good overlap between segmentation methods. ConclusionThe MCWS and FCM approach enables accurate automated segmentation of meningiomas from CE T1-w MRI. With further validation on larger datasets, this could provide an efficient tool to assist in delineating meningioma boundaries for clinical management.

Prediction of high-grade soft-tissue sarcoma using a combined intratumoural and peritumoural MRI-based radiomics nomogram

To develop an intratumoural and peritumoural magnetic resonance imaging (MRI)-based radiomics nomogram for predicting tumour grade to improve clinical treatment and long-term prognosis. MRI (3 T) features and T2-weighted imaging with fat-saturation (T2WI-FS)-based radiomics features of 57 patients with soft-tissue sarcoma (STS) were analysed retrospectively. Tumour size, ratio of width and length, relative depth to the peripheral fascia, peritumoural oedema, heterogeneity on T2WI, necrosis signal, enhancement model, and peritumoural enhancement were obtained. Independent risk factors were screened to construct an MRI feature nomogram. Radiomics features were obtained from intratumoural and peritumoural images on T2WI-FS. The optimal radiomics model was selected by the four-step dimensionality reduction method of minimum and maximum normalisation, optimal feature selection, selection based on support vector machine with L1-norm regularisation model, and iterative feature selection. MRI features and optimal radiomics features were used to construct a radiomics nomogram. The MRI feature nomogram model, the radiomics model, and the radiomics nomogram model were assessed by receiver operating characteristic (ROC) curves and calibration curves of the training and validation sets. Heterogeneity on T2WI and peritumoural enhancement were independent risk factors for predicting high-grade STS. The areas under the curves of the training set and verification set of the three models were as follows: MRI feature nomogram, 0.86 and 0.83, respectively; intratumoural and peritumoural combined radiomics model, 0.99 and 0.86, respectively; and radiomics nomogram model, 0.98 and 0.96, respectively. The radiomics nomogram model based on MRI features and combined intratumoural and peritumoural radiomic features was best able to predict high-grade STS.