T2-weighted Research Articles

Diagnostic performance of multishot echo-planar imaging (RESOLVE) and non-echo-planar imaging (HASTE) diffusion-weighted imaging in cholesteatoma with an emphasis on signal intensity ratio measurement

To evaluate the diagnostic efficacy of multishot echo-planar imaging (EPI) [RESOLVE (RS)] and non-EPI (HASTE) diffusion-weighted imaging (DWI) in detecting cholesteatoma (CHO), and to explore the role of signal intensity (SI) ratio measurements in addressing diagnostic challenges. We analyzed RS-EPI and non-EPI DWI images from 154 patients who had undergone microscopic middle ear surgery, with pathological confirmation of their diagnoses. Two radiologists, referred to as Reader A and Reader B, independently reviewed the images without prior knowledge of the outcomes. Their evaluation focused on lesion location, T1-weighted (T1W) signal characteristics, and contrast enhancement in temporal bone magnetic resonance imaging. Key parameters included lesion hyperintensity, size, SI, SI ratio, and susceptibility artifact scores across both imaging modalities. Of the patients, 62.3% (96/154) were diagnosed with CHO, whereas 37.7% (58/154) were found to have non-CHO conditions. In RS-EPI DWI, Reader A achieved 89.6% sensitivity, 79.3% specificity, 87.8% positive predictive value (PPV), and 82.1% negative predictive value (NPV). Non-EPI DWI presented similar results with sensitivities of 89.6%, specificities of 86.2%, PPVs of 91.5%, and NPVs of 83.3%. Reader B’s results for RS-EPI DWI were 82.3% sensitivity, 84.5% specificity, 89.8% PPV, and 74.2% NPV, whereas, for non-EPI DWI, they were 86.5% sensitivity, 89.7% specificity, 93.3% PPV, and 80% NPV. The interobserver agreement was excellent (RS-EPI, κ: 0.84; non-EPI, κ: 0.91). The SI ratio measurements were consistently higher in non-EPI DWI (Reader A: 2.51, Reader B: 2.46) for the CHO group compared with RS-EPI. The SI ratio cut-off (>1.98) effectively differentiated hyperintense lesions between CHO and non-CHO groups, demonstrating 82.9% sensitivity and 100% specificity, with an area under the curve of 0.901 (95% confidence interval: 0.815–0.956; P < 0.001). Susceptibility artifact scores averaged 1.18 ± 0.7 (Reader A) and 1.04 ± 0.41 (Reader B) in RS-EPI, with non-EPI DWI recording a mean score of 0. Both RS-EPI and non-EPI DWI exhibited high diagnostic accuracy for CHO. While RS-EPI DWI cannot replace non-EPI DWI, their combined use improves sensitivity. SI ratio measurement in non-EPI DWI was particularly beneficial in complex diagnostic scenarios. This study refines CHO diagnostic protocols by showcasing the diagnostic capabilities of both RS-EPI and non-EPI DWI and highlighting the utility of SI measurements as a diagnostic tool. These findings may reduce false positives and aid in more accurate treatment planning, offering substantial insights for clinicians in managing CHO.

Visual assessment of cerebrospinal fluid flow dynamics using 3D T2-weighted SPACE sequence-based classification system.

Flow-related signal void artifacts can be visualized on the T2-weighted (T2W) three-dimensional sampling perfection with application-optimized contrast (3D-SPACE) sequence. Flow void artifacts in the cerebral aqueduct and the fourth ventricle can provide information about cerebrospinal fluid (CSF) flow dynamics. In this study, we aimed to test the performance of the T2W 3D-SPACE sequence in assessing the CSF flow in the aqueduct and/or fourth ventricle. A total of 137 patients (age range = 3-89 years) who underwent CSF flow study were included. The amount of signal loss on T2W 3D-SPACE due to flow in the aqueduct and fourth ventricle was assessed and graded using a 4-point scale of 0 (absence of flow void) to 3 (signal void filling the aqueduct and entire fourth ventricle). A correlation was then sought between the quantitative values obtained by phase-contrast magnetic resonance imaging (PC-MRI) and the amount of signal void in the 3D-SPACE sequence. At the aqueduct level, there was a statistically significant difference in the forward flow velocity and the flow volume among different grades (all P < 0.001). In the grade 3 group, CSF peak systolic flow velocity and mean flow volume were found to be significantly higher than in the other grades (P < 0.001). The mean aqueduct area in the grade 0 group was found to be significantly different from that in the other classes (P < 0.001). The amount of signal loss in the fourth ventricle observed on T2W 3D-SPACE is correlated with the peak systolic velocity and flow volume measured quantitatively in PC-MRI.

Differentiation of MS lesions through analysis of microvascular distribution

Abstract Conventional MRI is crucial for diagnosing multiple sclerosis (MS) but lacks precision, leading to the clinico-radiological paradox and misdiagnosis risk, especially when confronted with unspecific lesions not related to MS. Advancements in perfusion-weighted imaging (PWI) with an algorithm designed for diseases with anticipated contrast agent extravasation offer insight into microvascular impairment and flow heterogeneity. Our study aimed to assess these factors in MS patients and their association with clinically relevant white matter injury and disease course. We evaluated 60 adults with white matter lesions (WML), including 50 diagnosed with MS or MS syndromes and 10 non-diseased symptomatic controls (SC) with unspecific WML. MRI included conventional three-dimensional (3D) T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), 3D magnetization-prepared 2 rapid acquisition gradient-echo (MP2RAGE), post-contrast 3D T1-weighted (T1) images and Dynamic Susceptibility Contrast (DSC) PWI at 3T. WML masks of “unspecific T2-FLAIR lesions”, “MS T2-FLAIR lesions”, and “MS T1-lesions” were manually outlined and validated by a neuroradiologist. DSC-derived parameters were analyzed in WML masks and healthy-appearing tissue. MS T2-FLAIR lesions showed increased flow heterogeneity and vasodilation compared to unspecific T2-FLAIR lesions in SC, as well as compared to unspecific T2-FLAIR lesions within the MS group. MS T1-lesions exhibited more homogenized flow. Our findings suggest that DSC-PWI combined with lesion delineation, can provide clinically relevant differentiation of MS lesions from unspecific WML, highlighting potential microvascular pathology previously overlooked in MS.

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.

Micro-enema immediately prior to prostate MRI: effects on rectal gas, image quality and PI-QUAL score.

Our aim was to determine whether the administration of a micro-enema immediately prior to prostate MRI is associated with a reduction in rectal gas, gas related artifacts and an improvement in image quality and PI-QUAL score. This retrospective analysis enrolled 171 patients who underwent multiparametric 3T prostate MRI at our institution between January 2021 and September 2022. 86 patients received a micro-enema, and a further 85 patients did not. Two fellowship trained abdominal radiologists were blinded and independently reviewed each prostate MRI, recording scores on a dedicated scoring sheet. The quality of T2 weighted (T2W), diffusion weighted (DWI), and dynamic contrast enhancement (DCE) images were assessed according to standardised scales supported in the literature. In addition, gas related artifacts and rectal gas level were examined. An independent-samples Mann-Whitney U and t-test were performed, comparing both the median and mean score between micro-enema and no micro-enema groups for each reader. Spearman's correlation was used to determine the strength of relationship between variables. A quadratic weighted Cohen's Kappa and percent agreement were used to assess inter-observer agreement. Image quality was improved in those who received the micro-enema compared to those who did not according to the visual grading scale on the DWI sequence for both readers (reader 1: median 4 vs. 4, p < 0.001, mean 4.27 vs. 3.92, p < 0.001; reader 2: median 5 vs. 4, p < 0.001, mean 4.74 vs. 4.14, p < 0.001). PI-QUAL score was significantly improved in the micro-enema group for reader 2 only (reader 1: median 4 vs. 4, p = 0.25, mean 3.99 vs. 4.08, p = 0.21; reader 2: median 5 vs. 5, p = 0.01, mean 4.95 vs. 4.78, p = 0.01). Visual grading score for both the T2W and DCE images was not significantly different. Rectal gas level was lower in patients who received the micro-enema for both readers (reader 1: median 2 vs. 4, p < 0.001, mean 2.12 vs. 3.60, p < 0.001; reader 2: median 1 vs. 2, p < 0.001, mean 1.37 vs. 2.48, p < 0.001), correlating with a lower score for gas-related artifacts (reader 1: median 1 vs. 2, p < 0.001, mean 1.50 vs. 1.92, p < 0.001; reader 2: median 1 vs. 1, p < 0.001, mean 1.16 vs. 1.71, p < 0.001) in this group. Correlation between rectal gas level and gas-related artifacts on DWI regardless of whether a micro-enema was given was strong (rs = 0.71, p < 0.001). Correlation was moderate to strong between rectal gas level and image quality on DWI (rs = -0.63, p < 0.001). There was only 1 (1.2%) borderline diagnostic or non-diagnostic DWI sequence in those who received the micro-enema, compared to 9 (10.6%) in those who did not (p = 0.009). Interobserver agreement was moderate for image quality on DWI, gas related artifacts and rectal gas level (weighted kappa values of 0.52, 0.49 and 0.53 respectively). The administration of a micro-enema immediately prior to prostate MRI is associated with a significant improvement in image quality on the DWI sequence compared to no bowel preparation. This is mediated through a reduction in rectal gas and gas related artifacts. Improvements in PI-QUAL score was mixed between readers.

Magnetic resonance metrics for identification of cuprizone-induced demyelination in the mouse model of neurodegeneration: a review.

Neurodegenerative disorders, including Multiple Sclerosis (MS), are heterogenous disorders which affect the myelin sheath of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) provides a non-invasive method for studying, diagnosing, and monitoring disease progression. As an emerging research area, many studies have attempted to connect MR metrics to underlying pathophysiological presentations of heterogenous neurodegeneration. Most commonly, small animal models are used, including Experimental Autoimmune Encephalomyelitis (EAE), Theiler's Murine Encephalomyelitis (TMEV), and toxin models including cuprizone (CPZ), lysolecithin, and ethidium bromide (EtBr). A contrast and comparison of these models is presented, with focus on the cuprizone model, followed by a review of literature studying neurodegeneration using MRI and the cuprizone model. Conventional MRI methods including T1 Weighted (T1W) and T2 Weighted (T2W) Imaging are mentioned. Quantitative MRI methods which are sensitive to diffusion, magnetization transfer, susceptibility, relaxation, and chemical composition are discussed in relation to studying the CPZ model. Overall, additional studies are needed to improve both the sensitivity and specificity of MRI metrics for underlying pathophysiology of neurodegeneration and the relationships in attempts to clear the clinico-radiological paradox. We therefore propose a multiparametric approach for the investigation of MR metrics for underlying pathophysiology.

Effects of Mixed Metal Exposure on MRI Metrics in Basal Ganglia.

Welding fumes contain various metals. Past studies, however, mainly focused on Manganese (Mn)-related neurotoxicity. This study investigated welding-related mixed metal exposure effects on MRI metrics in the basal ganglia (BG) and their dose-response relationship. Subjects with (N = 23) and without (N = 24) a welding exposure history were examined. Metal exposure was estimated with exposure history questionnaire and whole blood metal levels. T1 (weighted-intensity and relaxation time; estimates of brain Mn accumulation), diffusion tensor imaging [Axial (AD), mean (MD), radial diffusivity (RD), and fractional anisotropy (FA); estimates of microstructural differences] metrics in BG [caudate nucleus, putamen, and globus pallidus (GP)] and voxel-based morphometry (for volume) were examined and related with metal exposure measures. Compared to controls, welders showed higher GP R1 (1/T1; p = 0.034) but no differences in blood metal and T1-weighted (T1W) values in any ROIs (p's > 0.120). They also had higher AD and MD values in the GP (p's < 0.033) but lower FA values in the putamen (p = 0.039) with no morphologic differences. In welders, higher blood Mn and Vanadium (V) levels predicted higher BG R1 and T1W values (p's < 0.015). There also were significant overall metal mixture effects on GP T1W and R1 values. Moreover, GP AD and MD values showed non-linear associations with BG T1W values: They increased with increasing T1W values only above certain threshold of T1 values. The current findings suggest that Mn and V individually but also metal mixtures jointly predict GP T1 signals that may in turn contribute to altered DTI metrics in the BG after certain exposure threshold levels.

Study on the Cordyceps militaris Cultivation in Differential Media and Substrates Formulation

The study was study on the Cordyceps militaris Cultivation in Differential Media in plant diseases and weeds laboratory, Faculty of Agriculture, National University of Laos. The experiment was conducted for six months. This is of study have two parts, part one to testing fibers of growing Cordyceps militaris in Differential Media and Substrates Formulation. The experiment was set up as Completely Randomized Design (CRD) layout was used with four replications and three treatments as they are: T1 PDA (control), T2 supplements one + Silk worm pupae, T3 supplements two + egg. To testing fibers of growing Cordyceps militaris in three kind differentials of twenty days had different significant (P&lt;0.05), T2 have medium testing fibers of growing Cordyceps militaris with amount of 8.71 cm, by T3 is 8.32 cm, the lowest by T1 8.06 cm, and to notice T2 have dark orange in color colony, by T3 and T1 light color yellow. Part two to testing substrates and supplements Cordyceps militaris in three kind differentials to yield. The experiment was arranged in a Completely Randomized Design (CRD) layout was used with four replications and five treatments as they are: T1 Rice bean + potato dextrose broth PDB (control), T2 Rice rations + potato dextrose broth (PDB), T3 Rice bean + liquid broth Silk worm pupae 30 gram, T4 Rice bean + liquid broth Silk worm pupae 60 gram, T5 Rice rations + liquid broth Silk worm pupae 60 gram. The study was use twenty-four. The results found that there are different significant (P&lt;0.05), T4 fibers of growing Cordyceps militaris by 5.93 days,by T5, T3and T2 is 6.25, 8.00 and 10.18 days respectively. By T1 is 10.00 days. The developmenting of fibers to substitute flower mushroom in 5 treatments. The results found that there are different significant (P&lt;0.05), T5. The developmenting of fibers to substitute flower mushroom 22.18 days, by T2, T3 and T4 is 28.56, 25.06 and 21.81 days respectively. T1 is 29.31 days. However, weight flower mushroom T5 weight 27.25 gram/glass, by T4, T3 and T1 weight 26.88, 23.44 and 18.13 gram/glass respectively. By T2 weight 17.81 gram/glass.

Preoperative MRI-based radiomic nomogram for distinguishing solitary fibrous tumor from angiomatous meningioma: a multicenter study.

This study evaluates the efficacy of radiomics-based machine learning methodologies in differentiating solitary fibrous tumor (SFT) from angiomatous meningioma (AM). A retrospective analysis was conducted on 171 pathologically confirmed cases (94 SFT and 77 AM) spanning from January 2009 to September 2020 across four institutions. The study comprised a training set (n=137) and a validation set (n=34). All patients underwent contrast-enhanced T1-weighted (CE-T1WI) and T2-weighted(T2WI) MRI scans, from which 1166 radiomics features were extracted. Subsequently, seventeen features were selected through minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was employed to assess the independence of these features as predictors. A clinical model, established via both univariate and multivariate logistic regression based on MRI morphological features, was integrated with the optimal radiomics model to formulate a radiomics nomogram. The performance of the models was assessed utilizing the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV). The radiomics nomogram demonstrated exceptional discriminative performance in the validation set, achieving an AUC of 0.989. This outperformance was evident when compared to both the radiomics algorithm (AUC= 0.968) and the clinical model (AUC = 0.911) in the same validation sets. Notably, the radiomics nomogram exhibited impressive values for ACC, SEN, and SPE at 97.1%, 93.3%, and 100%, respectively, in the validation set. The machine learning-based radiomic nomogram proves to be highly effective in distinguishing between SFT and AM.

Investigation of ComBat Harmonization on Radiomic and Deep Features from Multi-Center Abdominal MRI Data.

ComBat harmonization has been developed to remove non-biological variations for data in multi-center research applying artificial intelligence (AI). We investigated the effectiveness of ComBat harmonization on radiomic and deep features extracted from large, multi-center abdominal MRI data. A retrospective study was conducted on T2-weighted (T2W) abdominal MRI data retrieved from individual patients with suspected or known chronic liver disease at three study sites. MRI data were acquired using systems from three manufacturers and two field strengths. Radiomic features and deep features were extracted using the PyRadiomics pipeline and a Swin Transformer. ComBat was used to harmonize radiomic and deep features across different manufacturers and field strengths. Student's t-test, ANOVA test, and Cohen's F score were applied to assess the difference in individual features before and after ComBat harmonization. Between two field strengths, 76.7%, 52.9%, and 26.7% of radiomic features, and 89.0%, 56.5%, and 0.1% of deep features from three manufacturers were significantly different. Among the three manufacturers, 90.1% and 75.0% of radiomic features and 89.3% and 84.1% of deep features from two field strengths were significantly different. After ComBat harmonization, there were no significant differences in radiomic and deep features among manufacturers or field strengths based on t-tests or ANOVA tests. Reduced Cohen's F scores were consistently observed after ComBat harmonization. ComBat harmonization effectively harmonizes radiomic and deep features by removing the non-biological variations due to system manufacturers and/or field strengths in large multi-center clinical abdominal MRI datasets.

Quantifying the reproducibility and longitudinal repeatability of radiomics features in magnetic resonance Image-Guide accelerator Imaging: A phantom study

ObjectiveThis study aimed to quantitatively evaluate the inter-platform reproducibility and longitudinal acquisition repeatability of MRI radiomics features in Fluid-Attenuated Inversion Recovery (FLAIR), T2-weighted (T2W), and T1-weighted (T1W) sequences on MR-Linac systems using an American College of Radiology (ACR) phantom. Materials and MethodsThis study used two MR-Linac systems (A and B) in different cancer centers. The ACR phantom was scanned on system A daily for 30 consecutive days to evaluate longitudinal repeatability. Additionally, retest data were collected after repositioning the phantom. Inter-platform reproducibility was assessed by conducting scans under identical conditions using system B. Regions of interest were delineated on the T1W sequence from system A and mapped to other sequences via rigid registration. Intra-observer and inter-observer comparisons were conducted. Repeatability and reproducibility were assessed using the intraclass correlation coefficient (ICC) and coefficient of variation (CV). Robust radiomics features were identified based on ICC>0.9 and CV<10 %. ResultsAnalysis showed that a higher proportion of radiomics features derived from longitudinal FLAIR sequence (51.65 %) met robustness criteria compared to T2W (48.35 %) and T1W (43.96 %). Additionally, more inter-platform features from the FLAIR sequence (62.64 %) were robust compared to T2W (42.86 %) and T1W (39.56 %). Test-retest and intra-observer repeatability were excellent across all sequences, with a median ICC of 0.99 and CV<5%. However, inter-observer reproducibility was inferior, especially for the T1W sequence. ConclusionsDifferent sequences show variations in repeatability and reproducibility. The FLAIR sequence demonstrated advantages in both longitudinal repeatability and inter-platform reproducibility. Caution is warranted when interpreting data, particularly in longitudinal or multiplatform radiomics studies.

Evaluating Prostate Cancer: The Diagnostic Impact of MRI and Its Relationship With Transrectal Ultrasound (TRUS)-Guided Biopsy.

Background Prostate disorders, including benign enlargement and malignancy, are commonly evaluated through imaging techniques. Historically, transrectal ultrasound (TRUS) has been used for prostate imaging and biopsy. However, multiparametric MRI (mpMRI), which integrates structural and functional imaging methods, offers enhanced diagnostic capabilities. This study evaluates the effectiveness of mpMRI, including its grading via Prostate Imaging - Reporting and Data System (PI-RADS) or Likert scoring, in distinguishing between benign and malignant prostatic conditionsand compares these findings with TRUS outcomes. Methodology This prospective study enrolled 30 male patients aged 45 to 75 years (mean age 60 years), selected based on prostatic abnormalities, elevated prostate-specific antigen (PSA) levels (>4 ng/dL), or palpable nodules detected via digital rectal examination. MRI, including PI-RADS or Likert scoring, was utilized to assess prostatic lesions, and results were compared with histopathological data obtained from TRUS-guided biopsies. Results Among the 30 patients, common symptoms included urinary retention (60%) and painful urination (53.3%). Malignant tumors were diagnosed in 12 patients (40%). MRI identified eight cases with enlarged transitional zones and irregular signals in peripheral zones (benign prostatic hyperplasia with tumor)and four cases with irregular signals in both zones (sarcoma). Concordance between MRI T2-weighted (T2W) observations and biopsy results showed 60% malignancy detection. Sensitivity assessments revealed MRI detected 15 true-positives (50%), TRUS detected six true positives (20%), and multivoxel spectroscopic analysis (MVS) identified 14 true-positives (46.7%). PI-RADS or Likert scoring of mpMRI was correlated with TRUS outcomes, highlighting its enhanced diagnostic accuracy compared to TRUS alone. Conclusion While TRUS remains a standard diagnostic tool, it is limited by significant sampling errors and complications. The integration of mpMRI, with its grading system, significantly improves diagnostic accuracy and treatment planning. Although mpMRI alone has limitations, its combination with contrast-enhanced MRI, diffusion-weighted imaging, and MR spectroscopy offers a comprehensive approach to enhanced prostate cancer detection.

Assessment of multi-modal magnetic resonance imaging for glioma based on a deep learning reconstruction approach with the denoising method.

Deep learning reconstruction (DLR) with denoising has been reported as potentially improving the image quality of magnetic resonance imaging (MRI). Multi-modal MRI is a critical non-invasive method for tumor detection, surgery planning, and prognosis assessment; however, the DLR on multi-modal glioma imaging has not been assessed. To assess multi-modal MRI for glioma based on the DLR method. We assessed multi-modal images of 107 glioma patients (49 preoperative and 58 postoperative). All the images were reconstructed with both DLR and conventional reconstruction methods, encompassing T1-weighted (T1W), contrast-enhanced T1W (CE-T1), T2-weighted (T2W), and T2 fluid-attenuated inversion recovery (T2-FLAIR). The image quality was evaluated using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness. Visual assessment and diagnostic assessment were performed blindly by neuroradiologists. In contrast with conventionally reconstructed images, (residual) tumor SNR for all modalities and tumor to white/gray matter CNR from DLR images were higher in T1W, T2W, and T2-FLAIR sequences. The visual assessment of DLR images demonstrated the superior visualization of tumor in T2W, edema in T2-FLAIR, enhanced tumor and necrosis part in CE-T1, and fewer artifacts in all modalities. Improved diagnostic efficiency and confidence were observed for preoperative cases with DLR images. DLR of multi-modal MRI reconstruction prototype for glioma has demonstrated significant improvements in image quality. Moreover, it increased diagnostic efficiency and confidence of glioma.

Assessment of Tubo-Ovarian Abscess Using Diffusion-Weighted Magnetic Resonance Imaging

Aim: To evaluate systematically the magnetic resonance imaging (MRI) features of tubo-ovarian abscess (TOA), the efficacy of diffusion-weighted imaging (DWI) in the assessment of TOA, and the differentiation of TOA from ovarian masses. Material and methods: PubMed/MEDLINE, Web of Science, and ResearchGate were searched with keywords: Tubo-ovarian abscess and Diffusion-weighted magnetic resonance imaging. No restrictions regarding the language or publication year were applied. Inclusion criteria regarded research papers evaluating the role of MRI and DWI in the assessment of TOA, and the differentiation of TOA from ovarian tumours. Studies appearing to meet inclusion criteria were reviewed in full. Results: A total of 14 studies were included. TOA is usually presented as a multilocular, cystic, pelvic mass with a heterogeneously high signal on T2-weighted (T2W) and low signal on T1-weighted (T1W) images with the “penumbra sign” on T1W images. Following contrast administration, septal and thick wall rim enhancement could be visualized. TOA usually demonstrated hyperintensity on DWI and hypointensity on ADC maps with lower ADC values of cystic, and higher ADC values of solid components compared to ovarian malignancy. TOA was smaller in size, and invasion into adjacent organs and tubal dilatation were more frequent in TOA than in ovarian neoplasms. In comparison to other methods, DWI possessed the highest sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in differentiating TOA from adnexal tumours. Conclusions: The addition of DWI with ADC values improves the detection, characterization, and overall diagnostic accuracy of TOA and its distinction from ovarian malignancy. The combination of DWI with MRI in the assessment of TOA is obligatory.

Ghost Sign on Diffusion-Weighted Imaging Generated Apparent Diffusion Coefficient Map: Additional MRI Diagnostic Marker for Extremity Osteomyelitis

Objective The aim of this study was to determine the sensitivity and specificity and inter-reader reliability of previously known “ghost sign” and “penumbra sign” on T1-weighted (T1W) imaging and “ghost sign” on apparent diffusion coefficient (ADC) map in osteomyelitis (OM) of the extremities. Materials and Methods In this cross-sectional retrospective study, two fellowship-trained musculoskeletal readers blinded to final diagnosis of OM versus no OM were asked to report the penumbra sign and ghost sign on T1W images and ghost sign on ADC map, as well as diagnosis of OM. Cohen's kappa was used. Diagnostic performance measures including sensitivity, specificity, and accuracy were calculated. Results A sample of 178 magnetic resonance imaging (MRI) scans of pathology-proven cases were included in this study, with 41 being positive for OM and 137 being negative for OM. There was a fair inter-reader agreement for imaging signs, and moderate agreement of 0.60 for OM. The sensitivities of the penumbra sign on T1W imaging, ghost sign on T1W imaging, and ghost sign on ADC map for OM are 3.7, 9.8, and 19.5%, respectively, while their respective specificities are 98.9, 97.8, and 94.5%, respectively. All three imaging signs showed a similar (good) accuracy of 76 to 78%. Conclusion The ghost sign on ADC can be used as an additional marker for OM and is a similarly highly specific but a more sensitive sign for OM than the conventionally used penumbra sign and ghost sign on T1W imaging. Key Points

Multimodal Fusion for Enhanced Semantic Segmentation in Brain Tumor Imaging: Integrating Deep Learning and Guided Filtering Via Advanced 3D Semantic Segmentation Architectures

ABSTRACTBrain tumor segmentation is paramount in medical diagnostics. This study presents a multistage segmentation model consisting of two main steps. First, the fusion of magnetic resonance imaging (MRI) modalities creates new and more effective tumor imaging modalities. Second, the semantic segmentation of the original and fused modalities, utilizing various modified architectures of the U‐Net model. In the first step, a residual network with multi‐scale backbone architecture (Res2Net) and guided filter are employed for pixel‐by‐pixel image fusion tasks without requiring any training or learning process. This method captures both detailed and base elements from the multimodal images to produce better and more informative fused images that significantly enhance the segmentation process. Many fusion scenarios were performed and analyzed, revealing that the best fusion results are attained when combining T2‐weighted (T2) with fluid‐attenuated inversion recovery (FLAIR) and T1‐weighted contrast‐enhanced (T1CE) with FLAIR modalities. In the second step, several models, including the U‐Net and its many modifications (adding attention layers, residual connections, and depthwise separable connections), are trained using both the original and fused modalities. Further, a “Model Selection‐based” fusion of these individual models is also considered for more enhancement. In the preprocessing step, the images are resized by cropping them to decrease the pixel count and minimize background interference. Experiments utilizing the brain tumor segmentation (BraTS) 2020 dataset were performed to verify the efficiency and accuracy of the proposed methodology. The “Model Selection‐based” fusion model achieved an average Dice score of 88.4%, an individual score of 91.1% for the whole tumor (WT) class, an average sensitivity score of 86.26%, and a specificity score of 91.7%. These results prove the robustness and high performance of the proposed methodology compared to other state‐of‐the‐art methods.

Reproducibility for carotid wall segmentation using T1-weighted DANTE-SPACE sequence on high-resolution 3-T carotid MRI.

T1-weighted (T1W) magnetic resonance imaging (MRI) using the delay alternating with nutation for excitation-sampling perfection with application-optimized contrasts using different flip angle evolution (DANTE-SPACE) is the preferred imaging technique for evaluation of the vessel wall. To evaluate the intra- and inter-rater reproducibility of carotid wall segmentation on T1W DANTE-SPACE in patients with symptomatic (acute stroke or transient ischemic attack) internal carotid artery (ICA) stenosis. This prospective study included 25 patients with acute (≤3 months) stroke or transient ischemic attack and 50%-99% stenosis of the ICA. All patients underwent 3.0-T high-resolution carotid MRI. Two radiologists independently performed the manual segmentation of the vessel wall and inner lumen of the bilateral carotid artery on DANTE-SPACE. The intraclass correlation coefficient (ICC), Dice similarity coefficient (DSC), and Hausdorff distance (HD) were calculated. The ICCs for intra-rater reproducibility of carotid wall volume, inner lumen volume, and normalized wall index were 0.965, 0.990, and 0.962, respectively. The ICCs for inter-rater reproducibility of carotid wall volume, inner lumen, and normalized wall index were 0.856, 0.981, and 0.904. DSC and HD for intra- and inter-rater reproducibility of carotid wall segmentation were as follows: 0.873 and 0.809 (DSC); and 0.079 and 0.118 (HD), respectively. For evaluation of reproducibility only in the carotid artery with symptomatic stenosis, the ICCs for intra- and inter-rater reproducibility indicated all perfect agreement. T1W DANTE-SPACE is a reproducible sequence for evaluation of the carotid wall using carotid MRI in patients with symptomatic ICA stenosis.

T1-weighted Motion Mitigation in Abdominal MRI: Technical Principles, Clinical Applications, Current Limitations, and Future Prospects.

T1-weighted (T1W) pulse sequences are an indispensable component of clinical protocols in abdominal MRI but usually require multiple breath holds (BHs) during the examination, which not all patients can sustain. Patient motion can affect the quality of T1W imaging so that key diagnostic information, such as intrinsic signal intensity and contrast enhancement image patterns, cannot be determined. Patient motion also has a negative impact on examination efficiency, as multiple acquisition attempts prolong the duration of the examination and often remain noncontributory. Techniques for mitigation of motion-related artifacts at T1W imaging include multiple arterial acquisitions within one BH; free breathing with respiratory gating or respiratory triggering; and radial imaging acquisition techniques, such as golden-angle radial k-space acquisition (stack-of-stars). While each of these techniques has inherent strengths and limitations, the selection of a specific motion-mitigation technique is based on several factors, including the clinical task under investigation, downstream technical ramifications, patient condition, and user preference. The authors review the technical principles of free-breathing motion mitigation techniques in abdominal MRI with T1W sequences, offer an overview of the established clinical applications, and outline the existing limitations of these techniques. In addition, practical guidance for abdominal MRI protocol strategies commonly encountered in clinical scenarios involving patients with limited BH abilities is rendered. Future prospects of free-breathing T1W imaging in abdominal MRI are also discussed. ©RSNA, 2024 See the invited commentary by Fraum and An in this issue.

Integrating a deep neural network and Transformer architecture for the automatic segmentation and survival prediction in cervical cancer.

Automated tumor segmentation and survival prediction are critical to clinical diagnosis and treatment. This study aimed to develop deep-learning models for automatic tumor segmentation and survival prediction in magnetic resonance imaging (MRI) of cervical cancer (CC) by combining deep neural networks and Transformer architecture. This study included 406 patients with CC, each with comprehensive clinical information and MRI scans. We randomly divided patients into training, validation, and independent test cohorts in a 6:2:2 ratio. During the model training, we employed two architecture types: one being a hybrid model combining convolutional neural network (CNN) and ransformer (CoTr) and one of pure CNNs. For survival prediction, the hybrid model combined tumor image features extracted by segmentation models with clinical information. The performance of the segmentation models was evaluated using the Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95). The performance of the survival models was assessed using the concordance index. The CoTr model performed well in both contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) imaging segmentation tasks, with average DSCs of 0.827 and 0.820, respectively, which outperformed other the CNN models such as U-Net (DSC: 0.807 and 0.808), attention U-Net (DSC: 0.814 and 0.811), and V-Net (DSC: 0.805 and 0.807). For survival prediction, the proposed deep-learning model significantly outperformed traditional methods, yielding a concordance index of 0.732. Moreover, it effectively divided patients into low-risk and high-risk groups for disease progression (P<0.001). Combining Transformer architecture with a CNN can improve MRI tumor segmentation, and this deep-learning model excelled in the survival prediction of patients with CC as compared to traditional methods.

Artificial intelligence improves the diagnosis of human leukocyte antigen (HLA)-B27-negative axial spondyloarthritis based on multi-sequence magnetic resonance imaging and clinical features.

Axial spondyloarthritis (axSpA) is frequently diagnosed late, particularly in human leukocyte antigen (HLA)-B27-negative patients, resulting in a missed opportunity for optimal treatment. This study aimed to develop an artificial intelligence (AI) tool, termed NegSpA-AI, using sacroiliac joint (SIJ) magnetic resonance imaging (MRI) and clinical SpA features to improve the diagnosis of axSpA in HLA-B27-negative patients. We retrospectively included 454 HLA-B27-negative patients with rheumatologist-diagnosed axSpA or other diseases (non-axSpA) from the Third Affiliated Hospital of Southern Medical University and Nanhai Hospital between January 2010 and August 2021. They were divided into a training set (n=328) for 5-fold cross-validation, an internal test set (n=72), and an independent external test set (n=54). To construct a prospective test set, we further enrolled 87 patients between September 2021 and August 2023 from the Third Affiliated Hospital of Southern Medical University. MRI techniques employed included T1-weighted (T1W), T2-weighted (T2W), and fat-suppressed (FS) sequences. We developed NegSpA-AI using a deep learning (DL) network to differentiate between axSpA and non-axSpA at admission. Furthermore, we conducted a reader study involving 4 radiologists and 2 rheumatologists to evaluate and compare the performance of independent and AI-assisted clinicians. NegSpA-AI demonstrated superior performance compared to the independent junior rheumatologist (≤5 years of experience), achieving areas under the curve (AUCs) of 0.878 [95% confidence interval (CI): 0.786-0.971], 0.870 (95% CI: 0.771-0.970), and 0.815 (95% CI: 0.714-0.915) on the internal, external, and prospective test sets, respectively. The assistance of NegSpA-AI promoted discriminating accuracy, sensitivity, and specificity of independent junior radiologists by 7.4-11.5%, 1.0-13.3%, and 7.4-20.6% across the 3 test sets (all P<0.05). On the prospective test set, AI assistance also improved the diagnostic accuracy, sensitivity, and specificity of independent junior rheumatologists by 7.7%, 7.7%, and 6.9%, respectively (all P<0.01). The proposed NegSpA-AI effectively improves radiologists' interpretations of SIJ MRI and rheumatologists' diagnoses of HLA-B27-negative axSpA.