Recovery Sequences Research Articles

Inflammatory Knee Synovitis: Evaluation of an Accelerated FLAIR Sequence Compared With Standard Contrast-Enhanced Imaging.

The aim of this study was to assess the diagnostic value and accuracy of a deep learning (DL)-accelerated fluid attenuated inversion recovery (FLAIR) sequence with fat saturation (FS) in patients with inflammatory synovitis of the knee. Patients with suspected knee synovitis were retrospectively included between January and September 2023. All patients underwent a 3 T knee magnetic resonance imaging including a DL-accelerated noncontrast FLAIR FS sequence (acquisition time: 1 minute 38 seconds) and a contrast-enhanced (CE) T1-weighted FS sequence (acquisition time: 4 minutes 50 seconds), which served as reference standard. All knees were scored by 2 radiologists using the semiquantitative modified knee synovitis score, effusion synovitis score, and Hoffa inflammation score. Diagnostic confidence, image quality, and image artifacts were rated on separate Likert scales. Wilcoxon signed rank test was used to compare the semiquantitative scores. Interreader and intrareader reproducibility were calculated using Cohen κ. Fifty-five patients (mean age, 52 ± 17 years; 28 females) were included in the study. Twenty-seven patients (49%) had mild to moderate synovitis (synovitis score 6-13), and 17 patients (31%) had severe synovitis (synovitis score >14). No signs of synovitis were detected in 11 patients (20%) (synovitis score <5). Semiquantitative assessment of the whole knee synovitis score showed no significant difference between the DL-accelerated FLAIR sequence and the CE T1-weighted sequence (mean FLAIR score: 10.69 ± 8.83, T1 turbo spin-echo FS: 10.74 ± 10.32; P = 0.521). Both interreader and intrareader reproducibility were excellent (range Cohen κ [0.82-0.96]). Assessment of inflammatory knee synovitis using a DL-accelerated noncontrast FLAIR FS sequence was feasible and equivalent to CE T1-weighted FS imaging.

Association Between Myocardial Oxygenation and Fibrosis in Duchenne Muscular Dystrophy: Analysis by Rest Oxygenation-Sensitive Magnetic Resonance Imaging.

Myocardial hypoxia has been demonstrated in many cardiomyopathies and is related to development of myocardial fibrosis. However, myocardial hypoxia and its association with myocardial fibrosis are understudied in Duchenne muscular dystrophy (DMD)-associated cardiomyopathy. To evaluate myocardial hypoxia by oxygenation-sensitive (OS) cardiac magnetic resonance imaging, and further explore its association with fibrosis. Prospective. Ninety-one DMD boys (8.78 ± 2.32) and 30 healthy boys (9.07 ± 2.30). 3 T, Balanced steady-state free procession, Modified Look-Locker inversion recovery sequence and Single-shot phase-sensitive inversion recovery sequence. Cardiac MRI data, including left ventricular functional, segmental native T1, and oxygenation signal-intensity (SI) according to AHA 17-segment model, were acquired. Patients were divided into LGE+ and LGE- groups. In patients with LGE, all segments were further classified as positive or negative segments by segmentally presence/absence of LGE. Variables were compared using Student's t, Wilcoxon, Kruskal-Wallis test and one-way analysis of variance. Bivariate Pearson or Spearman correlation were calculated to determine association between oxygenation SI and native T1. Variables with P < 0.10 in the univariable analysis were included in multivariable model. Receiver operating characteristic analysis was used to assess the performance of OS in diagnosing myocardial hypoxia. The myocardial oxygenation SI of DMD was significantly decreased in all segments compared with normal controls, and more obvious in the LGE+ segments (0.46 ± 0.03 vs. 0.52 ± 0.03). For patients with and without LGE, myocardial oxygenation SI were significantly negatively correlated with native T1 in all segments (r = -0.23 to -0.42). The inferolateral oxygenation SI was a significant independent associator of LGE presence (adjusted OR = 0.900). Myocardial hypoxia evaluated by the OS-Cardiac-MRI indeed occurs in DMD and associate with myocardial fibrosis, which might be used as a biomarker in assessing myocardial damage in DMD. 1 TECHNICAL EFFICACY: Stage 1.

Management of Spontaneous Spinal CSF Leaks Using Noninvasive Dynamic MR Neurography: A Case Series

AbstractSpontaneous spinal cerebrospinal fluid (CSF) leak presents with orthostatic headache and may lead to formation of subdural collection. Invasive magnetic resonance imaging or computed tomography myelography is routinely used to detect these leaks. Noninvasive dynamic magnetic resonance neurography can also detect these leaks and confirm its resolution post treatment. It includes three-dimensional NerveView, a high-resolution short-tau inversion recovery sequence for better visualization of spinal nerves and leak sites. This article showcases the diagnostic and therapeutic journey of three patients with spontaneous spinal CSF leak all of whom were diagnosed with dynamic magnetic resonance neurography and treated successfully with autologous targeted epidural blood patch. In one case, epidural blood patch was repeated after 2 months for recurrence of the leak.

In-phase and opposed-phase Dixon chemical shift imaging for the assessment of skeletal marrow lesions: comparison of measurements from longitudinal sequences to those from axial sequences.

In-phase and opposed-phase chemical shift imaging (CSI) is a useful technique for assessing skeletal lesions. This study determined the frequency of significant differences in measurements obtained from longitudinal (coronal or sagittal) sequences to those obtained from axial sequences. Chemical shift imaging was undertaken in 96 consecutive patients referred from the Musculoskeletal Sarcoma and Spinal Oncology services for assessment of possible bone tumours as part of a standard tumour protocol, which included turbo spin echo and inversion recovery sequences. For spinal lesions, CSI was obtained in the sagittal and axial planes, while for all other sites, it was obtained in the coronal and axial planes. The study included 49 (51.0%) males and 47 (49.0%) females with mean age 42.4 years (range 2-91 years). In 4 cases, 2 individual lesions were assessed, making a total of 100 lesions. Based on typical imaging features (n = 57) or histology (n = 43), 22 lesions (22%) were classified as non-neoplastic, 44 (44%) as benign neoplasms, 6 (6%) as intermediate-grade neoplasms, and 28 (28%) as malignant neoplasms. A significant discrepancy, wherein a lesion was classified as fat-containing (% SI drop >20%-25%) in the longitudinal plane, while in the axial plane it was classified as fat-replacing (% SI drop <20%-25%), or vice versa, occurred in 9%-14% of cases. However, this discrepancy had no appreciable effect on overall diagnostic accuracy, which was calculated at 79% for the longitudinal plane and 75%-80% for the axial plane. Significant differences in CSI measurements occur in 9%-14% of cases based on imaging plane, but with no significant effect on diagnostic accuracy. Radiologists should be aware that CSI measurements in different planes appear to have significant differences in up to 14% of lesions. However, diagnostic accuracy does not seem to be significantly affected.

Features of cardiovascular magnetic resonance native T1 mapping in maintenance hemodialysis patients and their related factors

Purpose Increased myocardial T1 values on cardiovascular MRI (CMRI) have been shown to be a surrogate marker for myocardial fibrosis. The use of CMRI in patients on hemodialysis (HD) remains limited. This research aimed to explore the characteristics of native T1 values in HD patients and identify factors related to T1 values. Methods A total of thirty-two patients on HD and fourteen healthy controls were included in this study. All participants underwent CMRI. Using modified Look-Locker inversion recovery (MOLLI) sequence, native T1 mapping was achieved. Native CMRI T1 values were compared between the two groups. In order to analyze the relationship between T1 values and clinical parameters, correlation analysis was performed in patients on HD. Results Patients on HD exhibited elevated global native T1 values compared to control subjects. In the HD group, the global native T1 value correlated positively with intact parathyroid hormone (iPTH) (r = 0.418, p = 0.017) and negatively with triglycerides (r= −0.366, p = 0.039). Moreover, the global native T1 value exhibited a positive correlation with the left ventricular end-diastolic volume indexed to body surface area (BSA; r = 0.528, p = 0.014), left ventricular end-systolic volume indexed to BSA (r = 0.506, p = 0.019), and left ventricular mass indexed to BSA (r = 0.600, p = 0.005). A negative correlation was observed between the global native T1 value and ejection fraction (r = 0.-0.551, p = 0.010). Conclusion The global native T1 value was prolonged in HD patients compared with controls. In the HD group, the global T1 value correlated strongly with iPTH, triglycerides, and cardiac structural and functional parameters.

FLAIR signal intensity ratio predicts small subcortical infarct early neurologic deterioration: a cross-sectional study.

Prior studies have used the fluid-attenuated inversion recovery sequence signal intensity ratio (FLAIR-SIR) to predict those with an incomplete infarct that may safely receive acute thrombolytics. Clinical early neurologic deterioration (END) of small subcortical infarcts (SSIs) is suspected to occur due to delayed infarct completion. We aimed to understand if a lower FLAIR-SIR, suggestive of an incomplete infarct, would have a higher likelihood of SSI-related END. A cross-sectional retrospective study was performed of those with an acute SSI (anterior or posterior circulation) without significant parent vessel steno-occlusive disease. END was defined as a new or worsened disabling neurologic deficit during the index hospitalization. Standard-of-care brain MRIs were reviewed from the hospitalization, and a FLAIR-SIR cutoff of ≤ 1.15 was used based on prior studies. Adjusted logistic regression models were used for analysis. We identified 252 patients meeting inclusion criteria: median (IQR) age 68 (12) years, 38.5% (97/252) female, and 11% (28/252) with END. Tobacco use was more common in those without END (32%) compared with END (55%, p = 0.03). In adjusted analyses, a FLAIR-SIR cutoff of ≤ 1.15 yielded an odds ratio of 2.8 (95% CI 1.23-6.13, p = 0.012) of early neurological deterioration. Those with a FLAIR-SIR ≤ 1.15 are nearly threefold more likely to develop SSI-related END.

Implications of Inflammatory Processes on a Developing Central Nervous System in Childhood-Onset Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that is increasingly affecting pediatric and adult populations. Neuropsychiatric manifestations (ie, cognitive dysfunction and mood disorders) appear to occur with greater severity and poorer prognosis in childhood-onset SLE (cSLE) versus adult-onset SLE, negatively impacting school function, self-management, and psychosocial health, as well as lifelong health-related quality of life. In this review, we describe pathogenic mechanisms active in cSLE, such as maladaptive inflammatory processes and ischemia, which are hypothesized to underpin central phenotypes in patients with cSLE, and the role of alterations in protective central nervous system (CNS) barriers (ie, the blood-brain barrier) are also discussed. Recent findings derived from novel neuroimaging approaches are highlighted because the methods employed in these studies hold potential for identifying CNS abnormalities that would otherwise remain undetected with conventional multiple resonance imaging studies (eg, T2-weighted or fluid-attenuated inversion recovery sequences). Furthermore, we propose that a more robust presentation of neuropsychiatric symptoms in cSLE is in part due to the harmful impact of a chronic inflammatory insult on a developing CNS. Although the immature status of the CNS may leave patients with cSLE more vulnerable to harboring neuropsychiatric manifestations, the same property may represent a greater urgency to reverse the maladaptive effects associated with a proneuroinflammatory state, provided that effective diagnostic tools and treatment strategies are available. Finally, considering the crosstalk among the CNS and other organ systems affected in cSLE, we postulate that a finer understanding of this interconnectivity and its role in the clinical presentation in cSLE is warranted.

Can Short Tau Inversion Recovery (STIR) Imaging Be Used as a Stand-Alone Sequence To Assess a Perianal Fistulous Tract on MRI? A Retrospective Cohort Study Comparing STIR and T1-Post Contrast Imaging.

Perianal fistulas demand precise preoperative assessment for optimal surgical outcomes. MRI, using Short Tau Inversion Recovery (STIR) and T1-post contrast sequences, plays a crucial role in this evaluation.This retrospective cohort study compared STIR imaging's diagnostic efficacy with T1-post contrast sequences in identifying perianal fistulous tracts. The study investigated whether STIR imaging could serve as the sole diagnostic sequence, simplifying clinical practice. In a tertiary care hospital, 100 patients underwent pelvic MRI for suspected perianal fistulas. Radiologists independently evaluated STIR and T1-post contrast sequences for internal openings, tract extent, distinction, abscess presence, and tract type. Sensitivity, specificity, area under the curve (AUC), and Cohen's kappa analysis were used for diagnostic assessment. STIR imaging showed notable sensitivity (79.8-97.9%) and specificity (100%) for identifying internal openings and tracts. Combined with T1-post contrast, diagnostic accuracy improved significantly, with near-perfect AUC values. Kappa values indicated moderate to substantial agreement between radiological assessments and clinical diagnosis. The combined sequences achieved 100% sensitivity and specificity for tract visualization. STIR imaging presents promise as a singular diagnostic tool for perianal fistulas, especially when combined with T1-post contrast sequences. While offering potential clinical diagnosis simplifications, further studies are warranted to validate its utility and ensure comprehensive diagnostic accuracy.

Characterization of bone marrow lesions in axial spondyloarthritis using quantitative T1 mapping MRI.

Conventional magnetic resonance imaging (MRI) uses T1-weighted and short-tau inversion recovery (STIR) sequences to characterize bone marrow in axial spondyloarthritis. However, quantification is restricted to estimating the extent of lesions because signal intensities are highly variable both within individuals and across patients and MRI scanners. This study evaluates the performance of quantitative T1 mapping for distinguishing different types of bone marrow lesions of the sacroiliac joints. In this prospective study, 62 patients underwent computed tomography (CT) and MRI of the sacroiliac joints including T1, STIR, and T1 mapping. Bone marrow lesions were characterized by three readers and assigned to one of four groups: sclerosis, osteitis, fat lesions, and mixed marrow lesions. Relaxation times on T1 maps were compared using generalized estimating equations and receiver operating characteristics (ROC) analysis. A total of 119 lesions were selected (sclerosis: 38, osteitis: 27, fat lesions: 40; mixed lesions: 14). T1 maps showed highly significant differences between the lesions with the lowest values for sclerosis (1516±220 ms), followed by osteitis (1909±75 ms), and fat lesions (2391±200 ms); p<0.001. T1 mapping differentiated lesions with areas under the ROC curve of 99% (sclerosis vs. osteitis) and 100% (other comparisons). T1 mapping allows accurate characterization of sclerosis, osteitis, and fat lesions at the sacroiliac joint but only for homogeneous, non-mixed lesions. Thus, further sequence development is needed before implementation in clinical routine.

7 T Lesion-Attenuated Magnetization-Prepared Gradient Echo Acquisition for Detection of Posterior Fossa Demyelinating Lesions in Multiple Sclerosis.

Detection of infratentorial demyelinating lesions in multiple sclerosis (MS) presents a challenge in magnetic resonance imaging (MRI), a difficulty that is further heightened in 7 T MRI. This study aimed to assess the efficacy of a novel MRI approach, lesion-attenuated magnetization-prepared gradient echo acquisition (LAMA), for detecting demyelinating lesions within the posterior fossa and upper cervical spine on 7 T MRI and contrast its performance with conventional double-inversion recovery (DIR) and T2-weighted turbo spin echo sequences. We conducted a retrospective cross-sectional study in 42 patients with a confirmed diagnosis of MS. All patients had 7 T MRI that incorporated LAMA, 3D DIR, and 2D T2-weighted turbo spin echo sequences. Three readers assessed lesion count in the brainstem, cerebellum, and upper cervical spinal cord using both DIR and T2-weighted images in one session. In a separate session, LAMA was analyzed alone. Contrast-to-noise ratio was also compared between LAMA and the conventional sequences. Lesion counts between methods were assessed using nonparametric Wilcoxon signed rank test. Interrater agreement in lesion detection was estimated by intraclass correlation coefficients. LAMA identified a significantly greater number of lesions than DIR + T2 (mean 6.4 vs 3.0; P < 0.001). LAMA also exhibited better interrater agreement (intraclass correlation coefficient [95% confidence interval], 0.75 [0.41-0.88] vs 0.61 [0.35-0.78]). The contrast-to-noise ratio for LAMA (3.7 ± 0.9) significantly exceeded that of DIR (1.94 ± 0.7) and T2 (1.2 ± 0.7) (all P 's < 0.001). In cases with no lesions detected using DIR + T2, at least 1 lesion was identified in 83.3% with LAMA. Across all analyzed brain regions, LAMA consistently detected more lesions than DIR + T2. LAMA significantly improves the detection of infratentorial demyelinating lesions in MS patients compared with traditional methods. Integrating LAMA with standard magnetization-prepared 2 rapid acquisition gradient echo acquisition provides a valuable tool for accurately characterizing the extent of MS disease.

A deep neural network for MRI spinal inflammation in axial spondyloarthritis.

To develop a deep neural network for the detection of inflammatory spine in short tau inversion recovery (STIR) sequence of magnetic resonance imaging (MRI) on patients with axial spondyloarthritis (axSpA). A total 330 patients with axSpA were recruited. STIR MRI of the whole spine and clinical data were obtained. Regions of interests (ROIs) were drawn outlining the active inflammatory lesion consisting of bone marrow edema (BME). Spinal inflammation was defined by the presence of an active inflammatory lesion on the STIR sequence. The 'fake-color' images were constructed. Images from 270 and 60 patients were randomly separated into the training/validation and testing sets, respectively. Deep neural network was developed using attention UNet. The neural network performance was compared to the image interpretation by a radiologist blinded to the ground truth. Active inflammatory lesions were identified in 2891 MR images and were absent in 14,590 MR images. The sensitivity and specificity of the derived deep neural network were 0.80 ± 0.03 and 0.88 ± 0.02, respectively. The Dice coefficient of the true positive lesions was 0.55 ± 0.02. The area under the curve of the receiver operating characteristic (AUC-ROC) curve of the deep neural network was 0.87 ± 0.02. The performance of the developed deep neural network was comparable to the interpretation of a radiologist with similar sensitivity and specificity. The developed deep neural network showed similar sensitivity and specificity to a radiologist with four years of experience. The results indicated that the network can provide a reliable and straightforward way of interpreting spinal MRI. The use of this deep neural network has the potential to expand the use of spinal MRI in managing axSpA.

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.

To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epidermal growth factor receptor (EGFR), EGFR exon 19 deletion (19Del), and EGFR exon 21-point mutation (21L858R) simultaneously. A total of 399 patients with proven brain metastases of non-small cell lung cancer (NSCLC) were retrospectively enrolled and divided into training (n = 306) and testing (n = 93) cohorts separately based on two timepoints. All patients underwent 3.0-T brain MRI including T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and contrast-enhanced T1-weighted sequences. Radiomics features were extracted from each lesion based on four sequences. An algorithm combining radiomics approach with graph convolutional networks architecture (Radio-GCN) was designed for the prediction of EGFR mutation status and subtype. The area under the curve (AUC) at receiver operating characteristic analysis was used to evaluate the predication capabilities of each model. We extracted 1,290 radiomics features from each MRI sequence. The AUCs of the Radio-GCN model for identifying EGFR 19Del, 21L858R, and WT for the lesion-wise analysis were 0.996 ± 0.004, 0.971 ± 0.013, and 1.000 ± 0.000 on the independent testing cohort separately. It also yielded AUCs of 1.000 ± 0.000, 0.991 ± 0.009, and 1.000 ± 0.000for predicting EGFR mutations respectively for the patient-wise analysis. The κ coefficients were 0.735 and 0.812, respectively. The constructed Radio-GCN model is a new potential tool to predict the EGFR mutation status and subtype in NSCLC patients with brain metastases. The study demonstrated that a deep learning approach based on multisequence MRI can help to predict the EGFR mutation status in NSCLC patients with brain metastases, which is beneficial to guide a personalized treatment. • This is the first study to predict the EGFR mutation subtype simultaneously. • The Radio-GCN model holds the potential to be used as a diagnostic tool. • This study provides an imaging surrogate for identifying the EGFR mutation subtype.

Deep Learning Algorithm of the SPARCC Scoring System in SI Joint MRI.

The Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system is a sacroiliitis grading system. To develop a deep learning-based pipeline for grading sacroiliitis using the SPARCC scoring system. Prospective. The study included 389 participants (42.2-year-old, 44.6% female, 317/35/37 for training/validation/testing). A pretrained algorithm was used to differentiate image with/without sacroiliitis. 3-T, short tau inversion recovery (STIR) sequence, fast spine echo. The regions of interest as ground truth for models' training were identified by a rheumatologist (HYC, 10-year-experience) and a radiologist (KHL, 6-year-experience) using the Assessment of Spondyloarthritis International Society definition of MRI sacroiliitis independently. Another radiologist (YYL, 4.5-year-experience) solved the discrepancies. The bone marrow edema (BME) and sacroiliac region models were for segmentation. Frangi-filter detected vessels used as intense reference. Deep learning pipeline scored using SPARCC scoring system evaluating presence and features of BMEs. A rheumatologist (SCWC, 6-year-experience) and a radiologist (VWHL, 14-year-experience) scored using the SPARCC scoring system once. The radiologist (YYL) scored twice with 5-day interval. Independent samples t-tests and Chi-squared tests were used. Interobserver and intraobserver reliability by intraclass correlation coefficient (ICC) and Pearson coefficient evaluated consistency between readers and the deep learning pipeline. We evaluated the performance using sensitivity, accuracy, positive predictive value, and Dice coefficient. A P-value <0.05 was considered statistically significant. The ICC and the Pearson coefficient between the SPARCC scores from three readers and the deep learning pipeline were 0.83 and 0.86, respectively. The sensitivity in identifying BME and accuracy of identifying SI joints and blood vessels was 0.83, 0.90, and 0.88, respectively. The dice coefficients were 0.82 (sacrum) and 0.80 (ilium). The high consistency with human readers indicated that deep learning pipeline may provide a SPARCC-informed deep learning approach for scoring of STIR images in spondyloarthritis. 1 TECHNICAL EFFICACY: Stage 2.

Juvenile Dermatomyositis Outcome Prediction Using Magnetic Resonance Imaging STIR Sequences

Background – Juvenile Dermatomyositis can have a unpredictable course after diagnosis, with no tools to prognosticate near-term outcomes. We explored a Short Tau Inversion Recovery (STIR) sequence Magnetic Resonance Imaging (MRI) protocol as a non-invasive baseline investigation to assess the present outcome in a cohort of JDM patients. Objectives – To determine the difference in baseline STIR-MRI parameters between children with JDM, who are currently in remission with medication and children in sustained remission off medications. Methods – We retrospectively reviewed the baseline MRI findings of 28 consecutive patients, evaluated and followed up for at least one year, by a senior paediatric rheumatologist. A radiologist reviewed the images of each child’s MRI thigh STIR Sequences, scored on four domains - STIR hyper-intensities, fatty infiltration of muscle, presence of skin and subcutaneous tissue involvement and muscle atrophy. We compared the median (inter quartile range) scores between children in remission with and without medication. Results – Median baseline age was 7 (7.5) years. At baseline, they reported a median CMAS of 17 (21), with STIR signal intensity of 2 (2), fatty infiltration grade of 0 (1). Twelve (42.9%) and four children (14.3%) showed evidence of skin and subcutaneous involvement and muscle atrophy, respectively. After being followed up for a median of 6 (6) years, eight children (28.6%) were in remission off medication. There was evidence that fatty infiltration differed between children in remission with medication vs off medication (p = 0.043). MRI Fatty infiltration grades predicted delayed treatment initiation ([Formula: see text] 6 months since symptom onset) in children, fairly accurately (AUC 0.731 ± 0.11, p = 0.036). Conclusions – Our study indicates that fatty infiltration in baseline STIR-MRI protocol may suggest a chronic disease course. Fatty infiltration, also suggests a history of inadequate/delayed diagnosis and treatment since symptom onset. Thus, early MRI may prognosticate the disease course in children with JDM. Further research is needed to exclude potential effect modifiers and confounders in the analysis.

MRI Data Analysis in Malformations of Cortical Development.

Brain magnetic resonance imaging (MRI) is a noninvasive imaging modality that utilizes powerful magnets and radio waves to generate detailed images of the brain, making it a valuable tool for investigating malformations of cortical development (MCD). Various MRI techniques, including 3D T1-weighted, multiplanar thin-sliced T2-weighted, and 3D fluid-attenuated inversion recovery (FLAIR) sequences, can provide high-resolution images with excellent spatial and contrast resolution, allowing for a detailed visualization of cortical anatomy and abnormalities. Almost all MCD can be detected and characterized using MRI. Advanced techniques, such as arterial spin labeling MR perfusion, diffusion tensor imaging (DTI), and functional MRI (fMRI), may be used to improve the detection rate of these malformations and to plan surgery in case of drug-resistant epilepsy. However, there are also limitations related to high cost, relatively low availability, need for sedation or anesthesia, and limited sensitivity for detecting subtle focal cortical malformations. Despite these limitations, brain MRI plays a crucial role in the investigation of MCD, providing valuable information for diagnosis, treatment planning, and patient management.

Predicting the apolipoprotein E ε4 allele carrier status based on gray matter volumes and cognitive function.

Apolipoprotein E (ApoE) ε4 carriers have a higher risk of developing Alzheimer's disease (AD) and show brain atrophy and cognitive decline even before diagnosis. To predict ApoE ε4 status using gray matter volume (GMV) obtained from magnetic resonance imaging images and demographic data with machine learning (ML) methods. We recruited 74 participants (25 probable AD, 24 amnestic mild cognitive impairment, and 25 cognitively normalolder people) with known ApoE genotype (22 ApoE ε4 carriers and 52 noncarriers) and scanned them with three-dimensional (3D) T1-weighted (T1W) and 3D double inversion recovery (DIR) sequences. We extracted GMV from regions of interestrelated to AD pathology and used them as features along with age and mini-mental state examination (MMSE) scores to train different ML models. We performed both receiver operating characteristiccurve analysis and the prediction analysis of the ApoE ε4 carrier with different ML models. The best model of ML analyses was a cubic support vector machine (SVM3) that used age, the MMSE score, and DIR GMVs at the amygdala, hippocampus, and precuneus as features (AUC=.88). This model outperformed models using T1W GMV or demographic data alone. Our results suggest that brain atrophy with DIR GMV and cognitive decline with aging can be useful biomarkers for predicting ApoE ε4 status and identifying individuals at risk of AD progression.

Evolution of Clinical, Electroencephalographic, and Radiological Changes in Sporadic Creutzfeldt–Jakob Disease

Abstract Creutzfeldt–Jakob disease (CJD) is a rare, progressive, and fatal disease. Sporadic CJD (sCJD) accounts for 85% of the CJD cases. It is characterized by rapidly progressive dementia, myoclonic jerks, and pyramidal, extrapyramidal, visual, and psychiatric symptoms. Electroencephalography (EEG) and magnetic resonance imaging (MRI) of the brain are integral to the diagnosis of the disease and show serial changes as the disease progresses. MRI brain in sCJD may be initially normal and later evolve to high-intensity signal changes in cortical, striatal, and thalamic regions on diffusion-weighted sequences and fluid-attenuated inversion recovery sequences. EEG shows evolution from the early, nonspecific slowing to the characteristic periodic sharp wave complexes (PSWC) in middle and late stages. We, hereby, report the case of a 68-year-old male who presented with rapidly progressive dementia, psychiatric symptoms, and myoclonus, whose MRI brain was initially normal and later showed diffusion restriction involving bilateral caudate, putamen, and bilateral cingulate gyrus and EEG showed evolution from diffuse slowing to typical PSWC.

The added value of double inversion recovery (DIR) sequence compared to fluid attenuation inversion recovery (FLAIR) in identifying multiple sclerosis lesions and different cortical subtypes

Background. Diabetic here is a rising concern about multiple sclerosis (MS) lesions that occur in the cortical gray matter (GM), owing to its direct relationship with the disability and cognition dysfunction that occur during the MS course. Two magnetic resonance imaging (MRI) sequences that aid in the precise identification of these lesions are the Double Inversion Recovery (DIR) and Fluid Attenuation Inversion Recovery (FLAIR) sequences. Objective. To study the importance of the DIR for brain lesion detection and the various cortical subtypes distinction in MS-diagnosed patients and its correlations to the level of patient disability by comparing it to FLAIR. Patients and Methods: Thirty MS patients participated in this retrospective cross-sectional study, who had been previously diagnosed with MS and were routinely followed up at the Radiology Department at Ain-Shams University. Results. Comparing the DIR sequence to FLAIR, the overall MS lesion detection enhanced by 8% (p&lt;0.001). Regarding the detection of cortical lesion subtypes (I–IV), it was discovered that the DIR sequence revealed significantly more lesions than FLAIR (p&lt;0.001). The cortical lesions detected by DIR or FLAIR were found to have positive, highly significant correlations with both patients’ disability and the MS duration. Conclusion. For lesion identification, DIR is more effective than FLAIR, particularly for cortical and juxtacortical lesions in MS patients.

Value of the short time inversion recovery sequence of magnetic resonance imaging in the staging of Klippel-Trenaunay syndrome complicated with lymphedema

ObjectiveCurrently, the focus on limb lymphedema (LE) is on classification and staging. However, few scholars have conducted staging for Klippel-Trenaunay syndrome complicated LE (KTS-LE). This study aimed to investigate the value of the short time inversion recovery sequence of magnetic resonance imaging (MRI) in the staging of KTS-LE. MethodsForty-six patients who were diagnosed with KTS-LE were recruited for this retrospective study from July 2011 to November 2022. Referring to the clinical staging standard of lower extremity LE of the International Society of Lymphology in 2020, all patients were divided into three groups: stages I, II, and III. The MRI indicators of the three groups were recorded and statistically compared: LE range (unilateral bilateral, lower limbs, only thighs, only calves and ankles), abnormal parts (skin thickening, abnormal subcutaneous fat signal, abnormal muscle signal, muscle hypertrophy or contraction, abnormal bone signal, hyperostosis), and subcutaneous soft tissue signs (parallel line sign, grid sign, band sign, honeycomb sign, lymph lake sign, crescent sign, and nebula sign). ResultsThere was a significant difference in the honeycomb sign among the three periods (P = .028). There was a significant difference between stage II and stage I disease (P < .05). There was a significant difference between stage II and stage III disease (P < .05). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the honeycomb sign in diagnosing KTS-LE of stage II were 87.5%, 63.2%, 33.3%, 96.0%, and 67.4%, respectively. In contrast, the other signs were not statistically significant among the three periods. ConclusionsThe short time inversion recovery sequence of MRI is of great value in KTS-LE. The honeycomb sign is an important imaging indicator for the diagnosis of stage II disease. It is necessary to evaluate the severity of edema with MRI for KTS-LE, which is very important for therapeutic options.

Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI.

The purpose of this study is to analyze the texture characteristics of chronic non-bacterial osteomyelitis (CNO) bone lesions, identified as areas of altered signal intensity on short tau inversion recovery (STIR) sequences, and to distinguish them from bone marrow growth-related changes through Machine Learning (ML) and Deep Learning (DL) analysis. We included a group of 66 patients with confirmed diagnosis of CNO and a group of 28 patients with suspected extra-skeletal systemic disease. All examinations were performed on a 1.5 T MRI scanner. Using the opensource 3D Slicer software version 4.10.2, the ROIs on CNO lesions and on the red bone marrow were sampled. Texture analysis (TA) was carried out using Pyradiomics. We applied an optimization search grid algorithm on nine classic ML classifiers and a Deep Learning (DL) Neural Network (NN). The model's performance was evaluated using Accuracy (ACC), AUC-ROC curves, F1-score, Positive Predictive Value (PPV), Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE). Furthermore, we used Shapley additive explanations to gain insight into the behavior of the prediction model. Most predictive characteristics were selected by Boruta algorithm for each combination of ROI sequences for the characterization and classification of the two types of signal hyperintensity. The overall best classification result was obtained by the NN with ACC = 0.91, AUC = 0.93 with 95% CI 0.91-0.94, F1-score = 0.94 and PPV = 93.8%. Between classic ML methods, ensemble learners showed high model performance; specifically, the best-performing classifier was the Stack (ST) with ACC = 0.85, AUC = 0.81 with 95% CI 0.8-0.84, F1-score = 0.9, PPV = 90%. Our results show the potential of ML methods in discerning edema-like lesions, in particular by distinguishing CNO lesions from hematopoietic bone marrow changes in a pediatric population. The Neural Network showed the overall best results, while a Stacking classifier, based on Gradient Boosting and Random Forest as principal estimators and Logistic Regressor as final estimator, achieved the best results between the other ML methods.