MRI study on the influence of lumbosacral vertebral body and disc factors on lumbar lordosis in children.

PURPOSE: To evaluate the influence of vertebral and disc wedging on the contribution of lumbar lordosis and the change of disc thickness before and after walking based on MRI. METHODS: Cross-sectional study. A total of 96 normally developing children, aged 5.7 ± 3.0 years old, 55 boys and 41 girls. They were divided into 3 groups: Pre-walking group, Walking group, and Post-walking group. PARAMETERS: lumbar lordosis Angle (LLA), the sum of the lumbar disc wedge Angle (∑D), the sum of the lumbar vertebral body wedge Angle (∑B), disc height (DH). RESULTS: (1) LLA, ∑D, ∑B, and DHL1-S1 were 33.2 ± 8.7°, 14.1 ± 8.6°, 11.9 ± 8.6°, and 6.9 ± 1.2 mm, 7.6 ± 1.4 mm, 8.2 ± 1.6 mm, 8.9 ± 1.7 mm, 8.5 ± 1.8 mm. (2) The difference in LLA values between the Pre-walking and the Post-walking group was statistically significant. DH were significantly different among the three groups. (3) In the Post-walking group, LLA value of girls was significantly higher than that of boys, and DHL3 - 4 and DHL4 - 5 values of girls were significantly lower than that of boys. (4) Age had a low positive correlation with LLA and ∑D and a moderate to strong positive correlation with DH; LLA showed a moderate positive correlation with ∑D, and a low positive correlation with ∑B and DH. CONCLUSION: Age and walking activity are the influencing factors of lumbar lordosis and disc thickening. Walking activity can significantly increase lumbar lordosis, and age is the main factor promoting lumbar disc thickening. DHL4-5 was the thickest lumbar intervertebral disc with the fastest intergroup thickening. Disc wedging contributes more to lumbar lordosis than vertebral wedging.

Prediction of microvascular invasion in hepatocellular carcinoma patients with MRI radiomics based on susceptibility weighted imaging and T2-weighted imaging.

BACKGROUND: The accurate identification of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is of great clinical importance. PURPOSE: To develop a radiomics nomogram based on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) for predicting MVI in early-stage (Barcelona Clinic Liver Cancer stages 0 and A) HCC patients. MATERIALS AND METHODS: A prospective cohort of 189 participants with HCC was included for model training and testing, and an additional 34 participants were enrolled for external validation. ITK-SNAP was used to manually segment the tumour, and PyRadiomics was used to extract radiomic features from the SWI and T2W images. Variance filtering, student's t test, least absolute shrinkage and selection operator regression and random forest (RF) were applied to select meaningful features. Four machine learning classifiers, including K-nearest neighbour, RF, logistic regression and support vector machine-based models, were established. Independent clinical and radiological risk factors were also determined to establish a clinical model. The best radiomics and clinical models were further evaluated in the validation set. In addition, a nomogram was constructed from the radiomic model and independent clinical factors. Diagnostic efficacy was evaluated by receiver operating characteristic curve analysis with fivefold cross-validation. RESULTS: AFP levels greater than 400 ng/mL [odds ratio (OR) 2.50; 95% confidence interval (CI) 1.239-5.047], tumour diameter greater than 5 cm (OR 2.39; 95% CI 1.178-4.839), and absence of pseudocapsule (OR 2.053; 95% CI 1.007-4.202) were found to be independent risk factors for MVI. The areas under the curve (AUCs) of the best radiomic model were 1.000 and 0.882 in the training and testing cohorts, respectively, while those of the clinical model were 0.688 and 0.6691. In the validation set, the radiomic model achieved better diagnostic performance (AUC = 0.888) than the clinical model (AUC = 0.602). The combination of clinical factors and the radiomic model yielded a nomogram with the best diagnostic performance (AUC = 0.948). CONCLUSION: SWI and T2WI-derived radiomic features are valuable for noninvasively and accurately identifying MVI in early-stage HCC. Furthermore, the integration of radiomics and clinical factors yielded a predictive nomogram with satisfactory diagnostic performance and potential clinical benefits.

Acceleration of uterine 3D T2-weighted imaging by compressed SENSE-a multicentre study.

OBJECTIVES: To find the optimal acceleration factor (AF) of the compressed SENSE (CS) technique for uterine isotropic high-resolution 3D T2-weighted imaging (3D-ISO-T2WI). METHODS: A total of 91 female volunteers from the First Affiliated Hospital of Dalian Medical University, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, and The Fourth Hospital of Harbin were recruited. A total of 44 volunteers received uterus sagittal 3D-ISO-T2WI scans on 3.0T MRI device with different CS AFs (including SENSE3, CS3, CS4, CS5, CS6, and CS7), 51 received 3D-ISO-T2WI scans with different degrees of fat suppression (none, light, moderate, and severe), while 4 volunteers received both series of scans. Image quality was subjectively evaluated with a 3-point scoring system. Junction zone signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and myometrial SNR were also calculated. Intraclass correlation coefficients were used to analyse the consistency of the measurement results by 2 observers. Analysis of variance test or Friedman rank sum test was used to compare the differences in subjective scores, SNR, and CNR under different AFs/different degrees of fat suppression. RESULTS: Images by AFs of CS3, CS4, and CS5 had the highest SNR and CNR. Among them, CS5 had the shortest scan time. CS5 also had one of the highest subjective scores. There was no significant difference in SNR and CNR among images acquired with different degrees of fat suppression. Also, images with moderate fat suppression had the highest subjective scores. CONCLUSION: The CS5 combined with moderate fat suppression is recommended for routine female pelvic 3D-ISO-T2WI scan. ADVANCES IN KNOWLEDGE: The CS5 has the highest image quality and has the shortest scan time, which is the best AF. Moderate fat suppression has the highest subjective scores. The CS5 and moderate fat suppression are the best combination for a female pelvic 3D-ISO-T2WI scan.

Deep learning-accelerated T2WI: image quality, efficiency, and staging performance against BLADE T2WI for gastric cancer.

PURPOSE: The purpose of our study is to investigate image quality, efficiency, and diagnostic performance of a deep learning-accelerated single-shot breath-hold (DLSB) against BLADE for T2-weighted MR imaging (T2WI) for gastric cancer (GC). METHODS: 112 patients with GCs undergoing gastric MRI were prospectively enrolled between Aug 2022 and Dec 2022. Axial DLSB-T2WI and BLADE-T2WI of stomach were scanned with same spatial resolution. Three radiologists independently evaluated the image qualities using a 5-scale Likert scales (IQS) in terms of lesion delineation, gastric wall boundary conspicuity, and overall image quality. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated in measurable lesions. T staging was conducted based on the results of both sequences for GC patients with gastrectomy. Pairwise comparisons between DLSB-T2WI and BLADE-T2WI were performed using the Wilcoxon signed-rank test, paired t-test, and chi-squared test. Kendall's W, Fleiss' Kappa, and intraclass correlation coefficient values were used to determine inter-reader reliability. RESULTS: Against BLADE, DLSB reduced total acquisition time of T2WI from 495 min (mean 4:42 per patient) to 33.6 min (18 s per patient), with better overall image quality that produced 9.43-fold, 8.00-fold, and 18.31-fold IQS upgrading against BALDE, respectively, in three readers. In 69 measurable lesions, DLSB-T2WI had higher mean SNR and higher CNR than BLADE-T2WI. Among 71 patients with gastrectomy, DLSB-T2WI resulted in comparable accuracy to BLADE-T2WI in staging GCs (P > 0.05). CONCLUSIONS: DLSB-T2WI demonstrated shorter acquisition time, better image quality, and comparable staging accuracy, which could be an alternative to BLADE-T2WI for gastric cancer imaging.

Intratumoral and Peritumoral Edema Radiomics Based on Fat-Suppressed T2- Weighted Imaging for Preoperative Prediction of Triple-Negative Breast Cancer.

AIM: Our aim was to explore the feasibility of using radiomics data derived from intratumoral and peritumoral edema on fat-suppressed T2-weighted imaging (T2 FS) to distinguish triple-negative breast cancer (TNBC) from non-triple-negative breast cancer (non-TNBC). METHODS: This retrospective study enrolled 174 breast cancer patients. According to the MRI examination time, patients before 2021 were divided into training (n = 119) or internal test (n = 30) cohorts at a ratio of 8:2. Patients from 2022 were included in the external test cohort (n = 25). Four regions of interest for each lesion were defined: intratumoral regions, peritumoral edema regions, regions with a combination of intratumoral and peritumoral edema, and regions with a combination of intratumoral and 5-mm peritumoral. Four radiomic signatures were built using the least absolute shrinkage and selection operator (LASSO) method after selecting features. Furthermore, a radio mic-radiological model was constructed using a combination of intratumoral and peritumoral edema regions along with clinical-radiologic features. Area under the receiver operating characteristic curve (AUC) calculations, decision curve analysis, and calibration curve analysis were performed to assess the performance of each model. RESULTS: The radiomic-radiological model showed the highest AUC values of 0.906 (0.788-1.000) and 82.5 (0.622-0.947) in both the internal and external test sets, respectively. The radiology-radiomic model exhibited excellent predictive performance, as evidenced by the calibration curves and decision curve analysis. CONCLUSION: The ensemble model based on T2 FS-based radiomic features of intratumoral and peritumoral edema, along with radiological factors, performed better in distinguishing TNBC from non-TNBC than a single model. We explored the possibility of developing explainable models to support the clinical decision-making process.

T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma.

BACKGROUND: The study on predicting the differentiation grade of colorectal cancer (CRC) based on magnetic resonance imaging (MRI) has not been reported yet. Developing a non-invasive model to predict the differentiation grade of CRC is of great value. AIM: To develop and validate machine learning-based models for predicting the differentiation grade of CRC based on T2-weighted images (T2WI). METHODS: We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023. Patients were randomly assigned to a training cohort (n = 220) or a validation cohort (n = 95) at a 7:3 ratio. Lesions were delineated layer by layer on high-resolution T2WI. Least absolute shrinkage and selection operator regression was applied to screen for radiomic features. Radiomics and clinical models were constructed using the multilayer perceptron (MLP) algorithm. These radiomic features and clinically relevant variables (selected based on a significance level of P < 0.05 in the training set) were used to construct radiomics-clinical models. The performance of the three models (clinical, radiomic, and radiomic-clinical model) were evaluated using the area under the curve (AUC), calibration curve and decision curve analysis (DCA). RESULTS: After feature selection, eight radiomic features were retained from the initial 1781 features to construct the radiomic model. Eight different classifiers, including logistic regression, support vector machine, k-nearest neighbours, random forest, extreme trees, extreme gradient boosting, light gradient boosting machine, and MLP, were used to construct the model, with MLP demonstrating the best diagnostic performance. The AUC of the radiomic-clinical model was 0.862 (95%CI: 0.796-0.927) in the training cohort and 0.761 (95%CI: 0.635-0.887) in the validation cohort. The AUC for the radiomic model was 0.796 (95%CI: 0.723-0.869) in the training cohort and 0.735 (95%CI: 0.604-0.866) in the validation cohort. The clinical model achieved an AUC of 0.751 (95%CI: 0.661-0.842) in the training cohort and 0.676 (95%CI: 0.525-0.827) in the validation cohort. All three models demonstrated good accuracy. In the training cohort, the AUC of the radiomic-clinical model was significantly greater than that of the clinical model (P = 0.005) and the radiomic model (P = 0.016). DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process. CONCLUSION: In this study, we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC. This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.

Value of radiomics-based automatic grading of muscle edema in polymyositis/dermatomyositis based on MRI fat-suppressed T2-weighted images.

BACKGROUND: The precise and objective assessment of thigh muscle edema is pivotal in diagnosing and monitoring the treatment of dermatomyositis (DM) and polymyositis (PM). PURPOSE: Radiomic features are extracted from fat-suppressed (FS) T2-weighted (T2W) magnetic resonance imaging (MRI) of thigh muscles to enable automatic grading of muscle edema in cases of polymyositis and dermatomyositis. MATERIAL AND METHODS: A total of 241 MR images were analyzed and classified into five levels using the Stramare criteria. The correlation between muscle edema grading and T2-mapping values was assessed using Spearman's correlation. The dataset was divided into a 7:3 ratio of training (168 samples) and testing (73 samples). Thigh muscle boundaries in FS T2W images were manually delineated with 3D-Slicer. Radiomics features were extracted using Python 3.7, applying Z-score normalization, Pearson correlation analysis, and recursive feature elimination for reduction. A Naive Bayes classifier was trained, and diagnostic performance was evaluated using receiver operating characteristic (ROC) curves and comparing sensitivity and specificity with senior doctors. RESULTS: A total of 1198 radiomics parameters were extracted and reduced to 18 features for Naive Bayes modeling. In the testing set, the model achieved an area under the ROC curve of 0.97, sensitivity of 0.85, specificity of 0.98, and accuracy of 0.91. The Naive Bayes classifier demonstrated grading performance comparable to senior doctors. A significant correlation (r = 0.82, P <0.05) was observed between Stramare edema grading and T2-mapping values. CONCLUSION: The Naive Bayes model, utilizing radiomics features extracted from thigh FS T2W images, accurately assesses the severity of muscle edema in cases of PM/DM.

Comparing the Efficacy of a Combination of Diffusion-Weighted Imaging and T2-STIR (Short Tau Inversion Recovery) Imaging With Contrast-Enhanced MRI in the Evaluation of Perianal Fistula.

BACKGROUND: Perianal fistula is clinically diagnosed and commonly characterized using magnetic resonance imaging (MRI). Diffusion-weighted imaging (DWI) and T2-weighted imaging are emerging techniques that can obviate the need for contrast injection in cases where contrast administration is not feasible or contraindicated. The main objective of our study was to compare the efficacy of the combination of DWI and T2 STIR (short tau inversion recovery) imaging with contrast-enhanced MRI for the diagnosis and characterization of perianal fistula. METHODS: Sixty-nine patients with clinical perianal fistula with at least one external opening were evaluated with DWI, T2 STIR, and contrast MRI. A comparative cross-sectional study was conducted in the Department of Radiodiagnosis and Imaging, All India Institute of Medical Sciences, Bhopal, India. The chi-square test was done to find the association between categorical variables. The Kappa test was done to estimate the agreement between two different tests in measuring the outcome. The validity of tests was measured using sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. RESULTS: The combination of DWI and T2 STIR is equivalent to contrast-enhanced MRI in the evaluation of primary and complicated perianal fistula. The combination of DWI and T2 STIR is superior to DWI alone in the classification and characterization of perianal fistula. However, DWI is superior to T2 STIR in differentiating perianal inflammation with abscess from perianal inflammation without abscess and can be used as an alternative to post-contrast fat-suppressed T1-WI in the detection of perianal abscesses and disease activity. CONCLUSION: DWI can be used as an adjunct to T2 STIR, and the combination of DWI and T2 STIR can replace the post-contrast fat-suppressed T1 MRI sequence in the classification and characterization of perianal fistula.

Infratentorial superficial siderosis: report of six cases and review of the literature.

Objectives: To investigate the etiology, clinical manifestations, imaging features, and treatment of patients with infratentorial superficial siderosis (iSS), enhance clinicians' comprehension of this rare disease, and conduct oral deferiprone intervention and subsequent monitoring. Methods: Six patients diagnosed with iSS based on magnetic resonance imaging (MRI) and susceptibility weighted imaging (SWI) were enrolled from 2021 to 2023 at the First Affiliated Hospital of Fujian Medical University. Their clinical datas were summarized, and the etiology and imaging characteristics were analyzed. Follow-up was conducted through telephone or outpatient visits. Results: Among the 6 patients, there were 3 males and 3 females. The onset age ranged from 35 to 71 years, with an average onset age of 53 years. The clinical symptoms mainly included acoustic disturbances (6/6), gait imbalance (6/6), dysolfactory (6/6), cognitive impairment (2/6), epilepsy (2/6), and pyramidal tract sign (2/6). Evidence of superficial siderosis was observed on MRI across the cortex, brainstem, cerebellum, and spinal cord in all patients. T2-space sequence MRI revealed two instances of dural tear. During the follow-up period ranging from 1 month to 3 years, three patients who received oral deferiprone treatment showed improvement, whereas the remaining three patients who declined deferiprone treatment demonstrated progression. Conclusion: The primary clinical manifestations of iSS include bilateral sensorineural hearing disturbances, progressive cerebellar ataxia, and spinal cord lesions. The key diagnostic criteria involve the presence of linear hypointensity on T2-WI in the surface region of the nervous system. Dural tear caused by various factors is considered to be the most common cause of iSS, and its treatment mainly involves surgical intervention for hemorrhagic primary diseases as well as pharmacotherapy with deferiprone.

Corrigendum: Infratentorial superficial siderosis: report of six cases and review of the literature.

[This corrects the article DOI: 10.3389/fnins.2024.1373358.].

Feasibility of the application of deep learning-reconstructed ultra-fast respiratory-triggered T2-weighted imaging at 3 T in liver imaging.

OBJECTIVE: The evaluate the feasibility of a novel deep learning-reconstructed ultra-fast respiratory-triggered T2WI sequence (DL-RT-T2WI) In liver imaging, compared with respiratory-triggered Arms-T2WI (Arms-RT-T2WI) and respiratory-triggered FSE-T2WI (FSE-RT-T2WI) sequences. METHODS: 71 patients with liver lesions underwent 3-T MRI and were prospectively enrolled. Two readers independently analyzed images acquired with DL-RT-T2WI, Arms-RT-T2WI, and FSE-RT-T2WI. The qualitative evaluation indicators, including overall image quality (OIQ), sharpness, noise, artifacts, lesion detectability (LC), lesion characterization (LD), cardiacmotion-related signal loss (CSL), and diagnostic confidence (DC), were evaluated in two readers, and further statistically compared using paired Wilcoxon rank-sum test among three sequences. RESULTS: 176 lesions were detected in DL-RT-T2W and Arms-RT-T2WI, and 175 were detected in FSE-RT-T2WI. The acquisition time of DL-RT-T2WI was improved by 4.8-7.9 folds compared to the other two sequences. The OIQ was scored highest for DL-RT-T2WI (R1, 4.61 ± 0.52 and R2, 4.62 ± 0.49), was significantly superior to Arms-RT-T2WI (R1, 4.30 ± 0.66 and R2, 4.34 ± 0.69) and FSE-RT-T2WI (R1, 3.65 ± 1.08 and R2, 3.75 ± 1.01). Artifacts and sharpness scored highest for DL-RT-T2WI, followed by Arms-RT-T2WI, and were lowest for FSE-RT-T2WI in both two readers. Noise and CSL for DL-RT-T2WI scored similar to Arms-RT-T2WI (P > 0.05) and were significantly superior to FSE-RT-T2WI (P < 0.001). Both LD and LC for DL-RT-T2WI were significantly superior to Arms-RT-T2WI and FSE-RT-T2WI in two readers (P < 0.001). DC for DL-RT-T2WI scored best, significantly superior to Arms-RT-T2WI (P < 0.010) and FSE-RT-T2WI (P < 0.001). CONCLUSIONS: The novel ultra-fast DL-RT-T2WI is feasible for liver imaging and lesion characterization and diagnosis, not only offers a significant improvement in acquisition time but also outperforms Arms-RT-T2WI and FSE-RT-T2WI concerning image quality and DC.

Development and validation of a radiomics model based on T2-weighted imaging for predicting the efficacy of high intensity focused ultrasound ablation in uterine fibroids.

Background: The heterogeneity of uterine fibroids in magnetic resonance imaging (MRI) is complex for a subjective visual evaluation, therefore it is difficult for an accurate prediction of the efficacy of high intensity focused ultrasound (HIFU) ablation in fibroids before the treatment. The purpose of this study was to set up a radiomics model based on MRI T2-weighted imaging (T2WI) for predicting the efficacy of HIFU ablation in uterine fibroids, and it would be used in preoperative screening of the fibroids for achieving high non-perfused volume ratio (NPVR). Methods: A total of 178 patients with uterine fibroids were consecutively enrolled and treated with ultrasound-guided HIFU under conscious sedation between February 2017 and December 2021. Among them, 96 patients with 108 uterine fibroids with high ablation efficacy (NPVR ≥80%, h_NPVR) and 82 patients with 92 fibroids with lower ablation efficacy (NPVR <80%, l_NPVR) were retrospectively analyzed. The transverse T2WI images of fibroids were selected, and the fibroids were delineated slice by slice using ITK-SNAP software. The radiomics analysis was performed to find the imaging biomarker for the construction of a predicting model for the evaluation of the ablation efficacy, including the feature extraction, feature selection and model construction. The prediction model was built by logistic regression and assessed by receiver operating characteristic (ROC) curve, and the prediction efficiency of the two models was compared by Delong test. The ratio of the training set to the testing set was 8:2. Results: The logistic regression model showed that the mean area under the curve (AUC) of the training set was 0.817 [95% confidence interval (CI): 0.755-0.882], and the testing set was 0.805 (95% CI: 0.670-0.941), respectively, which indicated a strong classification ability. The Delong test showed that there was no significant difference in the area under the ROC curve between the training set and testing set (P>0.05). Conclusions: The radiomics model based on T2WI is feasible and effective for predicting the efficacy of HIFU ablation in treatment of uterine fibroids.

Brush Sign on pre-treatment imaging is associated with good functional outcome in stroke patients treated with mechanical thrombectomy: A prospective monocentric study.

BACKGROUND: The Brush Sign (BrS) is a radiological biomarker (MRI) showing signal decrease of subependymal and deep medullary veins on paramagnetic-sensitive magnetic resonance sequences. Previous studies have shown controversial results regarding the prognostic value of BrS. We aimed to assess whether BrS on T2*-weighted sequences could predict functional prognosis in patients treated with mechanical thrombectomy (MT). METHODS: We included all consecutive patients with large artery occlusion related stroke in anterior circulation treated with MT between February 2020 and August 2022 at Reims University Hospital. Multivariable logistic regression models were used to investigate factors associated with BrS and its impact on outcomes. RESULTS: Of the 327 included patients, 124 (37,9%) had a BrS on baseline MRI. Mean age was 72 ± 16 years and 184 (56,2 %) were female. In univariate analysis, BrS was associated with a younger age (67 vs 74; p<0.001), a higher NIHSS score (16(10-20) vs 13(8-19); p = 0.047) history of diabetes (15.3% vs 26.1 %; p = 0.022) and a shorter onset to MRI time (145.5 (111.3-188.5) vs 162 (126-220) p = 0.008). In multivariate analyses, patients with a BrS were younger (OR:0.970 (0.951 - 0.989)), tend to have a higher NIHSS score at baseline (OR:1.046 (1.000 - 1.094) and were less likely to have diabetes (OR: 0.433; 0.214-0.879). The presence of BrS was independently associated with functional independence (OR: 2.234(1.158-4,505) at 3 months but not with mortality nor with symptomatic intracerebral hemorrhage. CONCLUSION: BrS on pre-treatment imaging could be considered as a biomarker of physiological adaptation to cerebral ischemia, allowing prolonged viability of brain tissue and might participate in the therapeutic decision.

Expanding Ventricular Diverticulum Overlying the Cerebral Hemisphere through an Open-Lip Schizencephalic Cleft: A Report of Two Pediatric Cases.

INTRODUCTION: Open-lip-type schizencephaly is characterized by trans-cerebral clefts filled with cerebrospinal fluid (CSF) between the subarachnoid space at the hemisphere surface and the lateral ventricles. Disorders related to CSF retention, including hydrocephalus and arachnoid cysts, have reportedly been associated with open-lip schizencephaly and have induced intracranial hypertension in some cases. However, detailed neuroimaging and surgical treatment findings have rarely been described. CASE PRESENTATION: We report 2 cases of open-lip schizencephaly with an expanding CSF-filled cavity overlying the ipsilateral cerebral hemisphere that manifested as signs of intracranial hypertension. Detailed three-dimensional heavily T2-weighted imaging revealed thin borders between the CSF-filled cavity and the subarachnoid space, but no separating structures between the cavity and the lateral ventricle, suggesting that the cavity was directly connected to the lateral ventricle through the schizencephalic cleft but not to the subarachnoid space. Neuroendoscopic observation in case 1 confirmed this finding. Endoscopic fenestration of the cavity to the prepontine cistern was ineffective in case 1. Shunting between the lateral ventricle (case 1) or CSF-filled cavity (case 2) and the peritoneal cavity slightly decreased the size of the CSF-filled cavity. DISCUSSION: We speculate that the thin borders along the margin of the CSF-filled cavity are membranes that previously covered the schizencephalic cleft and are now pushed peripherally. In addition, we believe that the cavity is a ventricular diverticulum protruding through the cleft and that shunting operation is effective against such expanding cavity. Detailed magnetic resonance imaging can be useful for evaluating patients with schizencephaly associated with CSF retention disorders.

An applicability study of rapid artificial intelligence-assisted compressed sensing (ACS) in anal fistula magnetic resonance imaging.

Objective: To evaluate the applicability of artificial intelligence-assisted compressed sensing (ACS) to anal fistula magnetic resonance imaging (MRI). Methods: 51 patients were included in this study and underwent T2-weighted sequence of MRI examinations both with ACS and without ACS technology in a 3.0 T MR scanner. Subjective image quality scores, and objective image quality-related metrics including scanning time, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), were evaluated and statistically compared between the images collected with and without ACS. Results: No significant difference in the subjective image quality of lesion conspicuity was observed between the two groups. However, ACS MRI decreased the acquisition time with regard to control group (74.00 s vs. 156.00 s). Besides, SNR of perianal and muscle in the ACS group was significantly higher than that of the control group (164.07 ± 33.35 vs 130.81 ± 29.10, p < 0.001; 109.87 ± 22.01 vs 87.61 ± 17.95, p < 0.001; respectively). The CNR was significantly higher in the ACS group than in the control group (54.02 ± 23.98 vs 43.20 ± 21.00; p < 0.001). Moreover, the accuracy rate of the ACS groups in evaluating the direction and internal opening of the fistula was 88.89 %, exactly the same as that of the control group. Conclusion: We demonstrated the applicability of using ACS to accelerate MR of anal fistulas with improved SNR and CNR. Meanwhile, the accuracy rates of the ACS group and the control were equivalent in evaluating the direction and internal opening of the fistula, based on the results of surgical exploration.

Interobserver Agreement in Automatic Segmentation Annotation of Prostate Magnetic Resonance Imaging.

We aimed to compare the performance and interobserver agreement of radiologists manually segmenting images or those assisted by automatic segmentation. We further aimed to reduce interobserver variability and improve the consistency of radiomics features. This retrospective study included 327 patients diagnosed with prostate cancer from September 2016 to June 2018; images from 228 patients were used for automatic segmentation construction, and images from the remaining 99 were used for testing. First, four radiologists with varying experience levels retrospectively segmented 99 axial prostate images manually using T2-weighted fat-suppressed magnetic resonance imaging. Automatic segmentation was performed after 2 weeks. The Pyradiomics software package v3.1.0 was used to extract the texture features. The Dice coefficient and intraclass correlation coefficient (ICC) were used to evaluate segmentation performance and the interobserver consistency of prostate radiomics. The Wilcoxon rank sum test was used to compare the paired samples, with the significance level set at p < 0.05. The Dice coefficient was used to accurately measure the spatial overlap of manually delineated images. In all the 99 prostate segmentation result columns, the manual and automatic segmentation results of the senior group were significantly better than those of the junior group (p < 0.05). Automatic segmentation was more consistent than manual segmentation (p < 0.05), and the average ICC reached >0.85. The automatic segmentation annotation performance of junior radiologists was similar to that of senior radiologists performing manual segmentation. The ICC of radiomics features increased to excellent consistency (0.925 [0.888~0.950]). Automatic segmentation annotation provided better results than manual segmentation by radiologists. Our findings indicate that automatic segmentation annotation helps reduce variability in the perception and interpretation between radiologists with different experience levels and ensures the stability of radiomics features.

Impact of Deep-Learning Based Reconstruction on Single-Breath-Hold, Single-Shot Fast Spin-Echo in MR Enterography for Crohn's Disease.

Purpose: To assess the quality of four images obtained using single-breath-hold (SBH), single-shot fast spin-echo (SSFSE) and multiple-breath-hold (MBH) SSFSE with and without deep-learning based reconstruction (DLR) in patients with Crohn's disease. Materials and Methods: This study included 61 patients who underwent MR enterography (MRE) for Crohn's disease. The following images were compared: SBH-SSFSE with (SBH-DLR) and without (SBH-conventional reconstruction [CR]) DLR and MBH-SSFSE with (MBH-DLR) and without (MBH-CR) DLR. Two radiologists independently reviewed the overall image quality, artifacts, sharpness, and motion-related signal loss using a 5-point scale. Three inflammatory parameters were evaluated in the ileum, the terminal ileum, and the colon. Moreover, the presence of a spatial misalignment was evaluated. Signal-to-noise ratio (SNR) was calculated at two locations for each sequence. Results: DLR significantly improved the image quality, artifacts, and sharpness of the SBH images. No significant differences in scores between MBH-CR and SBH-DLR were detected. SBH-DLR had the highest SNR (p < 0.001). The inter-reader agreement for inflammatory parameters was good to excellent (κ = 0.76-0.95) and the inter-sequence agreement was nearly perfect (κ = 0.92-0.94). Misalignment artifacts were observed more frequently in the MBH images than in the SBH images (p < 0.001). Conclusion: SBH-DLR demonstrated equivalent quality and performance compared to MBH-CR. Furthermore, it can be acquired in less than half the time, without multiple BHs and reduce slice misalignments.

Magnetic resonance imaging based deep-learning model: a rapid, high-performance, automated tool for testicular volume measurements.

Background: Testicular volume (TV) is an essential parameter for monitoring testicular functions and pathologies. Nevertheless, current measurement tools, including orchidometers and ultrasonography, encounter challenges in obtaining accurate and personalized TV measurements. Purpose: Based on magnetic resonance imaging (MRI), this study aimed to establish a deep learning model and evaluate its efficacy in segmenting the testes and measuring TV. Materials and methods: The study cohort consisted of retrospectively collected patient data (N = 200) and a prospectively collected dataset comprising 10 healthy volunteers. The retrospective dataset was divided into training and independent validation sets, with an 8:2 random distribution. Each of the 10 healthy volunteers underwent 5 scans (forming the testing dataset) to evaluate the measurement reproducibility. A ResUNet algorithm was applied to segment the testes. Volume of each testis was calculated by multiplying the voxel volume by the number of voxels. Manually determined masks by experts were used as ground truth to assess the performance of the deep learning model. Results: The deep learning model achieved a mean Dice score of 0.926 ± 0.034 (0.921 ± 0.026 for the left testis and 0.926 ± 0.034 for the right testis) in the validation cohort and a mean Dice score of 0.922 ± 0.02 (0.931 ± 0.019 for the left testis and 0.932 ± 0.022 for the right testis) in the testing cohort. There was strong correlation between the manual and automated TV (R2 ranging from 0.974 to 0.987 in the validation cohort; R2 ranging from 0.936 to 0.973 in the testing cohort). The volume differences between the manual and automated measurements were 0.838 ± 0.991 (0.209 ± 0.665 for LTV and 0.630 ± 0.728 for RTV) in the validation cohort and 0.815 ± 0.824 (0.303 ± 0.664 for LTV and 0.511 ± 0.444 for RTV) in the testing cohort. Additionally, the deep-learning model exhibited excellent reproducibility (intraclass correlation >0.9) in determining TV. Conclusion: The MRI-based deep learning model is an accurate and reliable tool for measuring TV.

Volumetric T2 -weighted spin echo imaging with improved SNR using localized quadratic encoding and a spiral readout trajectory.

PURPOSE: To demonstrate T2 -weighted (single-echo) spin-echo (SE) imaging with near-optimal acquisition efficiency by applying SNR-efficient RF slice encoding and spiral readout. METHODS: A quadratic-phase (frequency swept) excitation RF pulse replaced the conventional excitation in T2 -weighted SE sequence to excite a thick slab that is internally spatially encoded by a variable phase along the slice direction. Highly overlapping slabs centered at every desired slice location were acquired in multiple passes, such that the entire imaging volume was excited by contiguous slabs in any given pass. Following 90° excitation, each slab was refocused with a conventional 180° RF to produce a SE signal, followed by a spiral in-out readout. A noise-insensitive reconstruction removed the quadratic phase in the spatial frequency domain, yielding desired slice resolution and improved SNR. RESULTS: Increasing the RF frequency sweep (hence, excitation width) allowed more frequent encoding of each slice over the multiple passes, improving final image SNR, until crosstalk ensued at excessive slab widths compared to their center-to-center spacing. With an optimized slab width, the proposed technique used all passes to acquire every prescribed slice, with substantially improved SNR over conventional SE or 2D-turbo-spin-echo (TSE) scans. Quantitative SNR measurements indicated similar SNR as 3D-TSE, but radiologist scoring favored 3D-TSE, mainly because of spiral-related artifacts and possibly because of regularized reconstructions in 3D-TSE. CONCLUSION: Using SNR-efficient slice excitation scheme and spiral readout helped eliminate SNR and temporal inefficiencies in conventional T2 -weighted imaging, yielding SNR independent of TR or number of passes.

Assessment of deep learning-based reconstruction on T2-weighted and diffusion-weighted prostate MRI image quality.

PURPOSE: To evaluate the impact of a commercially available deep learning-based reconstruction (DLR) algorithm with varying combinations of DLR noise reduction settings and imaging parameters on quantitative and qualitative image quality, PI-RADS classification and examination time in prostate T2-weighted (T2WI) and diffusion-weighted (DWI) imaging. METHOD: Forty patients were included. Standard-of-care (SoC) prostate MRI sequences including T2WI and DWI were reconstructed without and with different DLR de-noising levels (low, medium, high). In addition, faster T2WI(Fast) and DWI(Fast) sequences, and a higher resolution T2WI(HR) sequence were evaluated. Quantitative analysis included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) values. Two radiologists performed qualitative analysis, independently evaluating imaging datasets using 5-point scoring scales for image quality and artifacts. PI-RADS category assignment was also performed by the more experienced radiologist. RESULTS: All DLR levels resulted in significantly higher SNR and CNR compared to the DLR(off) acquisitions. DLR allowed the acquisition time to be reduced by 33% for T2WI(Fast) and 49% for DWI(Fast) compared to SoC, without affecting image quality, whilst T2WI(HR) with DLR allowed for a 73% increase in spatial resolution in the phase encode direction compared to SoC. The inter-reader agreement for image quality and artifact scores was substantial for all subjective measurements on T2WI and DWI. The T2WI(Fast) protocol with DLR(medium) and DWI(Fast) with DLR(low) received the highest qualitative quality score. CONCLUSION: DLR can reduce T2WI and DWI acquisition time and increase SNR and CNR without compromising image quality or altering PI-RADS classification.