Magnetic Resonance Imaging Sequences Research Articles

Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images.

Adult-type diffuse gliomas are among the central nervous system's most aggressive malignant primary neoplasms. Despite advancements in systemic therapies and technological improvements in radiation oncology treatment delivery, the survival outcome for these patients remains poor. Fast and accurate assessment of tumor response to oncologic treatments is crucial, as it can enable the early detection of recurrent or refractory gliomas, thereby allowing timely intervention with life-prolonging salvage therapies. Radiomics is a developing field with great potential to improve medical image interpretation. This study aims to apply a radiomics-based predictive model for classifying response to radiotherapy within the first 3 months post-treatment. Ninety-five patients were selected from the Burdenko Glioblastoma Progression Dataset. Tumor regions were delineated in the axial plane on contrast-enhanced T1(CE T1W) and T2 fluid-attenuated inversion recovery (T2_FLAIR) magnetic resonance imaging (MRI). Hand-crafted radiomic (HCR) features, including first- and second-order features, were extracted using PyRadiomics (3.7.6) in Python (3.10). Then, recursive feature elimination with a random forest (RF) classifier was applied for feature dimensionality reduction. RF and support vector machine (SVM) classifiers were built to predict treatment outcomes using the selected features. Leave-one-out cross-validation was employed to tune hyperparameters and evaluate the models. For each segmented target, 186 HCR features were extracted from the MRI sequence. Using the top-ranked radiomic features from a combination of CE T1W and T2_FLAIR, an optimized classifier achieved the highest averaged area under the curve (AUC) of 0.829±0.075 using the RF classifier. The HCR features of CE T1W produced the worst outcomes among all models (0.603±0.024 and 0.615±0.075 for RF and SVM classifiers, respectively). We developed and evaluated a radiomics-based predictive model for early tumor response to radiotherapy, demonstrating excellent performance supported by high AUC values. This model, harnessing radiomic features from multi-modal MRI, showed superior predictive performance compared to single-modal MRI approaches. These results underscore the potential of radiomics in clinical decision support for this disease process.

Comparison of Imaging Characteristics in Pediatric Patients With Trochlear Versus Medial Femoral Condyle Osteochondritis Dissecans.

Although osteochondritis dissecans (OCD) lesions are well-described in the femoral condyles and have been associated with varus limb alignment, there is limited data on OCD lesions in the trochlea. To compare the baseline imaging characteristics in pediatric patients with trochlear OCD with those with medial femoral condyle (MFC) OCD to understand whether measures of coronal plane alignment predispose to OCD development by anatomic location. Cross-sectional study; Level of evidence, 3. This study retrospectively reviewed all pediatric patients (age ≤18 years) diagnosed with isolated trochlear OCD at a tertiary-care medical center from January 2016 to May 2023; all included patients had weight-bearing hip-to-ankle alignment radiographs. Hip-knee-ankle angle (HKA), mechanical lateral distal femoral angle (mLDFA), medial proximal tibial angle (MPTA), mechanical axis deviation (MAD), Caton-Deschamps Index (CDI), patellar tilt, and sulcus angle were measured on initial/preoperative anteroposterior and lateral knee radiographs. Tibial tubercle-trochlear groove (TT-TG) distance and OCD lesion size were measured on initial/preoperative magnetic resonance imaging sequences. Patients were 1 to 2 matched based on age (±2 years) and sex to a cohort with isolated MFC OCD. A total of 18 extremities in 16 patients were included in the trochlear OCD cohort and matched to 36 extremities in the MFC OCD cohort. The mean age at the first clinical visit for all patients was 14.8 ± 1.5 years and did not differ significantly between the two groups (P = .40). The extremities with trochlear OCD had significantly less varus HKA (1°± 2° vs -1°± 2°; P = .004) and MAD (4 ± 8 vs -3 ± 8 mm; P = .004) compared with the MFC cohort as well as lower mLDFA (86°± 2° vs 88°± 2°; P = .004). There were no differences in MPTA, CDI, patellar tilt, sulcus angle, TT-TG distance, or OCD lesion size between groups. Pediatric patients with trochlear OCD had statistically less varus coronal plane alignment compared with age- and sex-matched patients with MFC OCD, with the latter exhibiting more significant varus based on the HKA and MAD.

Accelerating Brain MR Imaging With Multisequence and Convolutional Neural Networks.

Magnetic resonance imaging (MRI) refers to one of the critical image modalities for diagnosis, whereas its long acquisition time limits its application. In this study, the aim was to investigate whether deep learning-based techniques are capable of using the common information in different MRI sequences to reduce the scan time of the most time-consuming sequences while maintaining the image quality. Fully sampled T1-FLAIR, T2-FLAIR, and T2WI brain MRI raw data originated from 217 patients and 105 healthy subjects. The T1-FLAIR and T2-FLAIR sequences were subsampled using Cartesian masks based on four different acceleration factors. The fully sampled T1/T2-FLAIR images were predicted from undersampled T1/T2-FLAIR images and T2WI images through deep learning-based reconstruction. They were qualitatively assessed by two senior radiologists in accordance with the diagnosis decision and a four-point scale image quality score. Furthermore, the images were quantitatively assessed based on regional signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs). The chi-square test was performed, where p<0.05 indicated a difference with statistical significance. The diagnosis decisions from two senior radiologists remained unchanged in accordance with the accelerated and fully sampled images. There were no significant differences in the regional SNRs and CNRs of most assessed regions (p>0.05) between the accelerated and fully sampled images. Moreover, no significant difference was identified in the image quality assessed by two senior radiologists (p>0.05). Deep learning-based image reconstruction is capable of significantly expediting the brain MR imaging process and producing acceptable image quality without affecting diagnosis decisions.

MFFE CT-like MRI Sequences for the Assessment of Vertebral Fractures.

The aim of this study was to evaluate the diagnostic performance, image quality, and inter- and intra-observer agreement of the 3D T1 multi-echo fast field echo (mFFE) sequence in cervico-thoraco-lumbar vertebral fractures compared with conventional computed tomography (CT) as the gold standard. We conducted a prospective single-centre study including 29 patients who underwent spinal magnetic resonance imaging (MRI) at the surgeon's request, in addition to CT for vertebral fracture assessment and classification. A 3D T1 mFFE sequence was added to the standard MRI protocol. Consecutively, two readers analyzed the 3D mFFE sequence alone, the 3D mFFE sequence with the entire MRI protocol, including the STIR and T1 sequences, and, finally, the CT images in random order and 1 month apart. A standardized assessment was performed to determine the presence or absence of a fracture, its location, its classification according to the Genant and AO classifications for traumatic and osteoporotic fractures, respectively, the loss of height of the anterior and posterior walls of the vertebral body, and the presence of concomitant disco-ligamentous lesions. Contingency tables, intraclass correlation coefficients, and Cohen's kappa tests were used for statistical analysis. A total of 25 fractures were recorded (48% cervical, 20% thoracic, and 32% lumbar), of which 52% were classified A, according to the AO classification system. The quality of the 3D mFFE image was good or excellent in 72% of cases. Inter-observer agreement was near perfect (0.81-1) for vertebral body height and for AO and Genant classifications for all modalities. Intra-observer agreement was strong-to-near perfect between CT and the 3D mFFE sequence. Regarding the diagnostic performance of the 3D mFFE sequence, the sensitivity was 0.9200 and 0.9600, the specificity was 0.9843 and 0.9895, and the accuracy was 0.9861 and 0.9769 for Readers 1 and 2, respectively. In addition, up to 40% of intervertebral disc lesions and 33% of ligamentous lesions were detected by the 3D mFFE sequence compared to CT, allowing four AO type A fractures to be reclassified as type B. The 3D mFFE sequence allows accurate diagnosis of vertebral fractures, with superiority over CT in detecting disco-ligamentous lesions and a more precise classification of fractures, which can prompt clinicians to adapt their management despite an image quality that still requires improvement in some cases. Vertebral fractures and disco-ligamentous lesions can be assessed using CT-like MRI sequences, with 3D T1 mFFE being superior to CT for the detection of disco-ligamentous lesions. CT-like images using the 3D T1 mFFE sequence improve the diagnostic accuracy of bone structures in MRI.

Magnetic Resonance Myocardial Imaging in Patients With Implantable Cardiac Devices: Challenges, Techniques, and Clinical Applications.

Cardiovascular magnetic resonance imaging (MRI) in patients with cardiac implants, such as pacemakers and defibrillators, has gained importance in recent years with the development of modern cardiac implantable electronic devices. The increasing clinical need to perform MRI examinations in patients with cardiac implants has driven the development of new advanced MRI sequences to mitigate image artifacts associated with cardiac implants. More specifically, advances in imaging techniques, such as wideband late gadolinium enhancement imaging, wideband T1 mapping, and wideband perfusion, have been designed to improve image quality and examinations in patients with cardiac implants, enabling a comprehensive and more reliable diagnosis, which was previously unattainable in these patients. This review article explores recent developments and applications of wideband techniques in the field of cardiovascular MRI, offering insights into their transformative potential. Clinical applications of wideband cardiovascular MRI are highlighted, particularly in assessing myocardial viability, guiding ventricular tachycardia ablation, and characterizing myocardial tissue.

Magnetic resonance imaging quantitative assessment of corticospinal tract damage in basal ganglia infarction.

To retrospectively explore the role of magnetic resonance imaging (MRI) diffusion tensor imaging (DTI) in analyzing the corticospinal tract injury in acute cerebral anterior circulation infarction in the basal ganglia region and the correlation between DTI parameters and neurological function scores, patients with acute cerebral infarction and stroke had undergone plain MRI and DTI sequence scanning were enrolled. Diffusion tensor tractography was used to perform 3-dimensional reconstruction of bilateral corticospinal tracts (CST). The image data were processed to obtain fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values, and the correlation between the DTI parameters and neurological function scores of National Institutes of Health Stroke Scale (NIHSS) was evaluated. A total of thirty-two patients with acute ischemic cerebral infarction were retrospectively enrolled, including 19 males and 13 females with a mean age 63.22 ± 7.78 years. The CST score was 0 in 2 (6.25%) patients, 1 in 9 (28.13%), 2 in 9 (28.13%), 3 in 7 (21.88%), and 4 in 5 (15.63%). The FA value significantly (P = .01) decreased (0.52 ± 0.03 vs 0.62 ± 0.04) on the ischemic side compared with that on the normal side. A significant (P < .05) difference was detected in the number of nerve fibers (223.3 ± 110.0 vs 246.7 ± 104.4) rather than in the ADC values (0.86 ± 0.06 vs 0.84 ± 0.06) between the ischemic and healthy sides. The FA and ADC values were not significantly (P > .05) different according to the CST scores 0, 1, 2, 3, or 4 between the ischemic and healthy sides. There was a significant (P < .05) negative correlation between the FA value on the infarcted side and the NIHSS score. In conclusion, with the DTI technology, varying degrees of damage to the corticospinal tract at the infarcted side can be detected and important clinical information can be provided for the diagnosis and prognosis of acute cerebral infarction by evaluating the degree of corticospinal tract injury.

Magnetic Resonance Imaging Parameters in the Subacute Phase after Traumatic Cervical Spinal Cord Injury: A Prospective, Observational Longitudinal Study. Part 1: Conventional Imaging Characteristics.

Magnetic resonance imaging (MRI) remains the gold standard for evaluating spinal cord tissue damage after spinal cord injury (SCI). Several MRI findings may have some prognostic potential, but their evolution over time, especially from the subacute to the chronic phase has not been studied extensively. We performed a prospective observational longitudinal study exploring the evolution of MRI parameters from the subacute to chronic phase after human traumatic cervical SCI. The study, conducted between 2016 and 2021, involved standardized neurological examinations and MRI scans 1 month, 3 months, and 1 year after SCI. The study cohort comprises 52 patients with cervical SCI. Patients were classified into AIS grades (American Spinal Injury Association Impairment Scale), and neurological recovery was assessed using the Integrated Neurological Change Score. The MRI protocol included various routine sequences, allowing the evaluation of established parameters such as intramedullary hemorrhage, lesion dimensions, maximum spinal cord compression, and various grading scales. The persistence of intramedullary hemorrhage one month after injury was associated with worse lower extremity motor scores and pinprick values after 3 months, and also in the chronic phase. In addition, dorsal column T2-weighted hyperintensities detected 3 months post-injury and in the chronic phase were related to lower pinprick sensory scores. The basic score and Sagittal Grade at 1 month were predictive for motor function 3 months after SCI and for neurological recovery between 1 and 3 months after injury. The study contributes valuable insights into the utility of routine MRI sequences for evaluating traumatic cervical SCI during the subacute to chronic phase. The identified MRI parameters and scores offer prognostic information and could support clinical decision-making.

Unmet Needs in Spondyloarthritis: Imaging in Axial Spondyloarthritis.

Imaging biomarkers in axial spondyloarthritis (axSpA) are currently the most specific biomarkers for the diagnosis of this condition. Despite advances in imaging, from plain radiographs-which detect only damage-to magnetic resonance imaging (MRI)-which identifies disease activity and structural change-there are still many challenges that remain. Imaging in sacroiliitis is characterized by active and structural changes. Current classification criteria stress the importance of bone marrow edema (BME); however, BME can occur in various diseases, mechanical conditions, and healthy individuals. Thus, the identification of structural lesions such as erosion, subchondral fat, backfill, and ankylosis is important to distinguish from mimics on differential diagnosis. Various imaging modalities are available to examine structural lesions, but computed tomography (CT) is considered the current reference standard. Nonetheless, recent advances in MRI allow for direct bone imaging and the reconstruction of CT-like images that can provide similar information. Therefore, the ability of MRI to detect and measure structural lesions is strengthened. Here, we present an overview of the spectrum of current and cutting-edge techniques for SpA imaging in clinical practice; namely, we discuss the advantages, disadvantages, and usefulness of imaging in SpA through radiography, low-dose and dual-energy CT, and MRI. Cutting-edge MRI sequences including volumetric interpolated breath-hold examination, ultrashort echo time, zero echo time, and deep learning-based synthetic CT that creates CT-like images without ionizing radiation, are discussed. Imaging techniques allow for quantification of inflammatory and structural lesions, which is important in the assessment of treatment response and disease progression. Radiographic damage is poorly sensitive to change. Artificial intelligence has already revolutionized radiology practice, including protocolization, image quality, and image interpretation.

Relationship between the Articular Cartilage Thickness of the Patella and Trochlea in Trochlear Dysplasia Compared to Normal Knees

Abstract Introduction: Anterior knee pain is not an uncommon problem. A plethora of pathologies can cause anterior knee pain. Patellofemoral maltracking (trochlear dysplasia) is not an uncommon cause of anterior knee pain in adolescents and young adults. Aim: We hypothesize that there is an alteration of articular cartilage of the patella and trochlear in trochlear dysplasia. Materials and Methods: Magnetic resonance images of 86 knee joints were reviewed independently by two musculoskeletal radiologists. Standard magnetic resonance imaging (MRI) sequences were used in this study. Based on the Dejour classification for trochlear dysplasia, the patients were divided into normal, Type A, B, C, and D. The articular cartilage thickness of the patella and trochlea was measured and compared between the groups. Results: Out of the 86 MRI scans of the knees which were reviewed, 32 patients were male and 54 were female. The mean patient age was 21.9 years (range = 10–40). Sixty-six knee joints had trochlear dysplasia, with 15 patients classified into Dejour Type A, 13 in Type B, 31 in Type C, and 7 in Type D. There was no significant difference in the articular cartilage thickness of the lateral facet of the patella between all groups (ANOVA F = 1.43, P = 0.23). Conclusion: There was no significant difference in the articular cartilage thickness in the lateral facet of the patella and trochlea in patients with normal knee joints compared to those with trochlear dysplasia of Type A–D.

Concentric-object and equiangular-object methods to perform standardized regional analysis in renal mpMRI.

Renal multiparametric magnetic resonance imaging (mpMRI) sequences, including T1-T2 mapping, Blood oxygenation level-dependent (BOLD), Renal blood flow (RBF), and Apparent Diffusion Coefficient (ADC) from diffusion-weighted imaging (DWI), provide insights into kidney function. However, consensus on selecting regions of interest (ROIs) is lacking. This study aims to describe and compare the Concentric Objects (CO) and Equiangular Objects (EO) methods for standardized ROI selection and assess their efficacy in capturing regional variations in renal MRI parameters. Twelve healthy volunteers underwent mpMRI renal scans. ROIs were selected manually and by applying the CO and EO algorithms to each mpMRI sequence. The methods were tested across various subregion configurations. Regional differences in renal MRI parameters were evaluated. CO and EO methods demonstrated statistically significant differences in mpMRI parameters across renal regions. ASL-RBF, BOLD-MRI, and T2-map results indicated substantial variations from the lower to upper kidney areas. This study implemented CO and EO algorithms in renal mpMRI, showing their potential for evaluating cortico-medullary and cranio-caudal profiles. The findings validate the CO method for BOLD and ADC measurements and presented ASL-RBF and T1-T2 map profiles. The EO method's utility needs further validation with renal patients.

Differentiation of Benign and Malignant Neck Neoplastic Lesions Using Diffusion-Weighted Magnetic Resonance Imaging.

The most difficult diagnostic challenge in neck imaging is the differentiation between benign and malignant neoplasms. The purpose of this work was to study the role of the ADC (apparent diffusion coefficient) value in discriminating benign from malignant neck neoplastic lesions. The study was conducted on 53 patients with different neck pathologies (35 malignant and 18 benign/inflammatory). In all of the subjects, conventional MRI (magnetic resonance imaging) sequences were performed apart from DWI (diffusion-weighted imaging). The mean ADC values in the benign and malignant groups were compared using the Mann-Whitney test. The ADCs of malignant lesions (mean 0.86 ± 0.28) were significantly lower than the benign lesions (mean 1.43 ± 0.57), and the mean ADC values of the inflammatory lesions (1.19 ± 0.75) were significantly lower than those of the benign lesions. The cutoff value of 1.1 mm2/s effectively differentiated benign and malignant lesions with a 97.14% sensitivity, a 77.78% specificity, and an 86.2% accuracy. There were also statistically significant differences between the ADC values of different malignant tumors of the neck (p, 0.001). NHL (0.59 ± 0.09) revealed significantly lower ADC values than SCC (0.93 ± 0.15). An ADC cutoff point of 0.7 mm2/s was the best for differentiating NHL (non-Hodgkin lymphoma) from SCC (squamous cell carcinoma); it provided a diagnostic ability of 100.0% sensitivity and 89.47% specificity. ADC mapping may be an effective MRI tool for the differentiation of benign and inflammatory lesions from malignant tumors in the neck.

Functional Localization of the Human Auditory and Visual Thalamus Using a Thalamic Localizer Functional Magnetic Resonance Imaging Task.

Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due in part to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmark their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.

P10.22.B PREDICTING INTRAOPERATIVE 5-ALA-INDUCED TUMOR FLUORESCENCE VIA MRI AND DEEP LEARNING IN GLIOMAS WITH RADIOGRAPHIC LOWER-GRADE CHARACTERISTICS

Abstract BACKGROUND Lower-grade gliomas typically exhibit 5-aminolevulinic acid (5-ALA)-induced fluorescence in only 20-30% of cases, a rate that can be increased by doubling the administered dose of 5-ALA. Fluorescence can depict anaplastic foci, which can be precisely resected to avoid undergrading. We aimed to analyze whether a deep learning model can predict intraoperative fluorescence based on preoperative magnetic resonance imaging (MRI). Material and Methods: The MRI images of gliomas lacking high-grade characteristics (necrosis, extended contrast-enhancement, a.o.) consisted of T1, T1-post gadolinium, and FLAIR sequences. The preprocessed MRIs were fed into an encoder-decoder convolutional neural network (U-Net), pre-trained for tumor segmentation using those three MRI sequences. We used the outputs of the bottleneck layer of the U-Net in the Variational Autoencoder (VAE) as features for classification. We identified and utilized the most effective features in a Random Forest classifier using the principal component analysis (PCA) and the partial least square discriminant analysis (PLS-DA) algorithms. We evaluated the performance of the classifier using a 10-fold cross-validation procedure. RESULTS We evaluated a cohort of 163 glioma patients categorized as fluorescent (n=83) or non-fluorescent (n=80). Our proposed approach’s performance was evaluated using metrics such as mean balanced accuracy, mean sensitivity, and mean specificity. The optimal results were obtained by employing top-performing features selected by PCA, resulting in a mean balanced accuracy of 80% and mean sensitivity and specificity of 84% and 76%, respectively. CONCLUSION Our findings highlight the potential of a U-Net model, coupled with a random forest classifier, for intraoperative fluorescence prediction. We achieved good accuracy using advanced techniques such as deep learning-based tumor segmentation and Variational Autoencoder for radiomics feature extraction. While the model can still be improved, it has the potential for evaluating when to administer 5-ALA to gliomas lacking typical high-grade radiographic features.

A controlled study of MR DWI, FS-PDW, and CE-T1W imaging for the evaluation of the internal opening of anal fistulas.

Anal fistula (AF) is an abnormal tunnel under the skin connecting the anal canal in the colon to the skin of buttocks. Fat-suppressed (FS) proton density-weighted (PDW) imaging is mainly used for the diagnosis of diseases involving bones and joints. Until now, its value in the diagnosis of anal fistula has been rarely reported. To compare three magnetic resonance imaging (MRI) sequences - diffusion-weighted imaging (DWI), FS-PDW), and contrast-enhanced (CE) T1-weighted (T1W) imaging - for the diagnostic value of the internal opening of AF. MRI scans of 132 patients suspected of having AF between December 2021 and April 2023 were retrospectively analyzed. In total, 65 patients who underwent preoperative MRI and were treated surgically were included. The lesion conspicuity and accuracy for featuring AF were calculated by evaluating the three imaging datasets DWI, FS-PDW, and CE-T1W imaging, with surgical findings serving as the reference standard for the presence of fistulas. The statistical analysis included the application of the chi-square test and Kruskal-Wallis test. In 65 patients with AF, 87 internal openings of AF were confirmed. In terms of the diagnostic accuracy of the internal openings, both FS-PDW and CE-T1W imaging sequences were significantly better than DWI sequences, and the difference was statistically significant (P < 0.05). The FS-PDW imaging sequence showed comparable diagnostic performance of the internal opening of AF to CE-T1W imaging, which can provide an important diagnostic basis for clinical procedures.

Differentiation between high-grade gliomas and solitary brain metastases based on multidiffusion MRI model quantitative analysis.

Differentiating high-grade gliomas (HGGs) from solitary brain metastases (SBMs) using conventional magnetic resonance imaging (MRI) remains challenging due to their similar imaging features. This study aimed to evaluate the diagnostic performance of advanced diffusion models, such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator magnetic resonance imaging (MAP-MRI), incomparison to traditional techniques like diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI) for distinguishing HGGs from SBMs. In total, 17 patients with HGGs and 26 patients with SBMs were prospectively recruited based on the established inclusion and exclusion criteria. Structural MRI sequences and diffusion spectrum imaging (DSI) were utilized to assess quantitative parameter models, including NODDI, MAP-MRI, DWI, DTI, and DKI. Quantitative parameters were measured for both the tumor parenchymal area and the peritumoral edema area. The quantitative parameters of the two patient groups were compared using either the independent Student's t-test or the Mann-Whitney U test. The effectiveness of each model was evaluated using receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Finally, the DeLong test was employed to compare the diagnostic performance of each model through pairwise comparisons of ROC curves. Isotropic volume fraction (Viso) based on NODDI; mean squared displacement (MSD) and the return to plane probabilities (RTPP) based on MAP-MRI; radial diffusivity (RDk) and mean diffusivity (MDk) based on DKI; and axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) based on DTI of the peritumoral edema tumor were significantly different between HGGs and SBMs (p < 0.05). The optimal single discriminant parameters for each model are NODDI_Viso, MAP-MRI_MSD, DKI_MDk, and DTI_AD. Among these, the AUC of Viso (0.809) exceeds that of MSD (0.733), MDk (0.718), and AD (0.779). The combined model, which incorporates DTI_AD, DKI_RD, and NODDI_Viso, demonstrated superior diagnostic performance (0.897). Advanced diffusion MRI quantitative parameters derived from NODDI, such as Viso, have the potential to enhance the differentiation between HGGs and SBMs. The integrated utilization of these models is anticipated to enhance diagnostic accuracy and refine MRI protocols for brain tumor assessment.

Building a pelvic organ prolapse diagnostic model using vision transformer on multi-sequence MRI.

Although the uterus, bladder, and rectum are distinct organs, their muscular fasciae are often interconnected. Clinical experience suggests that they may share common risk factors and associations. When one organ experiences prolapse, it can potentially affect the neighboring organs. However, the current assessment of disease severity still relies on manual measurements, which can yield varying results depending on the physician, thereby leading to diagnostic inaccuracies. This study aims to develop a multilabel grading model based on deep learning to classify the degree of prolapse of three organs in the female pelvis using stress magnetic resonance imaging (MRI) and provide interpretable result analysis. We utilized sagittal MRI sequences taken at rest and during maximum Valsalva maneuver from 662 subjects. The training set included 464 subjects, the validation set included 98 subjects, and the test set included 100 subjects (training set n=464, validation set n=98, test set n=100). We designed a feature extraction module specifically for pelvic floor MRI using the vision transformer architecture and employed label masking training strategy and pre-training methods to enhance model convergence. The grading results were evaluated using Precision, Kappa, Recall, and Area Under the Curve (AUC). To validate the effectiveness of the model, the designed model was compared with classic grading methods. Finally, we provided interpretability charts illustrating the model's operational principles on the grading task. In terms of POP grading detection, the model achieved an average Precision, Kappa coefficient, Recall, and AUC of 0.86, 0.77, 0.76, and 0.86, respectively. Compared to existing studies, our model achieved the highest performance metrics. The average time taken to diagnose a patient was 0.38s. The proposed model achieved detection accuracy that is comparable to or even exceeds that of physicians, demonstrating the effectiveness of the vision transformer architecture and label masking training strategy for assisting in the grading of POP under static and maximum Valsalva conditions. This offers a promising option for computer-aided diagnosis and treatment planning of POP.

Automated multiple sclerosis progression rate computation of a patient from 2D FLAIR images with Rayleigh-Weibull-Fuzzy imaging and augmented morphing method

Multiple sclerosis (MS) is a neurological demyelinating disorder affecting brain and spinal cord by attacking the myelin sheaths of nerves. Estimation of the volumetric changes in MS lesions is a challenging and specialized task which is executed and judged by medical experts. The change in the volume of the lesions provides crucial information on MS progression or regression by comparing the magnetic resonance images (MRI) taken in successive scans. However, visual comparison of the images, even with an expert eye, would not always lead to a conclusive decision nor a consensus on progression or regression. Therefore, we present an automated expert system for estimating MS progression rate by automatic lesion segmentation and volume estimation using two-dimensional MRIs, which is also adaptable to various parameters, slice thickness and increment. A clinical dataset is specially formed for this research which contains three sets of 135 MR images of an MS patient generated within approximately 23- and 6-month periods consecutively with identical device parameters. The lesions are segmented by a novel Rayleigh-Weibull-Fuzzy (RWF) imaging method based on the Nakagami distribution and specialized fuzzy 2-means. Subsequently, the segmentation module is trained to fit the ground truths images created by experts to achieve the highest dice score possible for a total number of 56 images containing lesions, which is found as 93.76 %. Afterwards, several imaginary image sequences are generated by augmented linear and nonlinear morphing for re-segmentation of imaginary lesions by RWF. Finally, we estimated the volumetric change between the first two MRI sequences to adjust the morphing module and to predict the progression rate of the lesions in time. The framework automatically selected the highest accuracy, which is 99.9 % in the training session and estimated the progression rate in the testing phase with 99.69 % accuracy, which are not achievable without augmented morphing methodology. For the first time in the literature, an automated framework could estimate the MS progression rate from the raw MR images, which is also the main innovation of this paper and the outputs would be beneficial for the experts working on this field.

Advancements in sarcopenia diagnosis: from imaging techniques to non-radiation assessments.

Sarcopenia is a prevalent condition with significant clinical implications, and it is expected to escalate globally, demanding for effective diagnostic strategies, possibly at an early stage of the disease. Imaging techniques play a pivotal role in comprehensively evaluating sarcopenia, offering insights into both muscle quantity and quality. Among all the imaging techniques currently used for the diagnosis and follow up of sarcopenia, it is possible to distinguish two classes: Rx based techniques, using ionizing radiations, and non-invasive techniques, which are based on the use of safe and low risk diagnostic procedures. Dual-energy x-ray Absorptiometry and Computed Tomography, while widely utilized, entail radiation exposure concerns. Ultrasound imaging offers portability, real-time imaging, and absence of ionizing radiation, making it a promising tool Magnetic Resonance Imaging, particularly T1-weighted and Dixon sequences, provides cross- sectional and high-resolution images and fat-water separation capabilities, facilitating precise sarcopenia quantification. Bioelectrical Impedance Analysis (BIA), a non-invasive technique, estimates body composition, including muscle mass, albeit influenced by hydration status. Standardized protocols, such as those proposed by the Sarcopenia through Ultrasound (SARCUS) Working Group, are imperative for ensuring consistency across assessments. Future research should focus on refining these techniques and harnessing the potential of radiomics and artificial intelligence to enhance diagnostic accuracy and prognostic capabilities in sarcopenia.

Simulation of Dendrite Growth with a Diffusion-Limited Aggregation Model Validated by MRI of a Lithium Symmetric Cell during Charging

Lithium metal batteries offer high energy density but are challenged by dendrite growth, which can lead to short circuits and battery failure. Multiple models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. This study presents a simple model to simulate macroscale dendrite growth on lithium metal electrodes. The model uses a 3D single-particle Diffusion-Limited Aggregation (DLA) algorithm with an electric field bias to simulate dendrite growth. The electric field bias was introduced into the model with an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. Before performing the simulation with the proposed model, the dendrite growth in a lithium symmetric cell during charging was measured by sequential 3D magnetic resonance imaging (MRI). These data were then used to validate the simulation, as the dendrite structure in each measured MRI time frame was used a starting point for a new simulation, the results of which were then validated with the measured dendrite structure of the next time frame. The best agreement between the simulated and measured dendrite structures using the overlap and displacement of deposition sites metrics was obtained at the biasing factor c = 0.7. This agreement was also good in terms with the fractal dimension of the dendrite structures. The proposed method offers a simple, accurate, and scalable framework for predicting dendrite growth over long deposition periods, making it a valuable tool for studying dendrite suppression under real-world battery charging conditions.

Accuracy of Diagnosing Optic Neuritis Using DANTE T1-SPACE Imaging.

To evaluate the use of delay alternating with nutation for tailored excitation-prepared T1-weighted turbo spin echo (DANTE T1-SPACE) imaging for diagnosing optic neuritis and to analyze its correlation with clinical findings before and after treatment. Patients diagnosed with optic neuritis or non-arteritic anterior ischemic optic neuropathy (NA-AION) were evaluated at the Ophthalmology Department of Kyoto University Hospital. All patients underwent magnetic resonance (MR) studies before treatment initiation and ophthalmic examinations before and after treatment. Three ophthalmologists independently reviewed the MR scans for abnormalities. The magnetic resonance imaging (MRI) assessments included post-contrast DANTE T1-SPACE, post-contrast volumetric interpolated breath-hold examination (VIBE), and short T1 inversion recovery (STIR) scans. The presence of abnormalities in each sequence was determined. Of 36 eyes from 30 patients, 21 eyes from 17 patients were diagnosed with optic neuritis, and 15 eyes from 13 patients were diagnosed with NA-AION. DANTE T1-SPACE sequences showed better sensitivity for detecting optic neuritis than STIR sequences (100% vs 67%, p = 0.009). VIBE images did not confirm enhancement of lesions in some cases with optic neuritis. No differences were observed among the sequences for NA-AION. Lesion length evaluated by DANTE T1-SPACE sequences was associated with circumpapillary retinal nerve fiber layer thickness at the initial visit, eye pain, and the time interval from symptom onset to MRI scan. Contrast-enhanced DANTE T1-SPACE was better than other sequences of MRI for diagnosing optic neuritis.