Dynamic Contrast-enhanced Research Articles

Identifying major depressive disorder based on cerebral blood flow and brain structure: An explainable multimodal learning study.

Magnetic resonance imaging (MRI) offers non-invasive assessments of brain structure and function for analyzing brain disorders. With the increasing accumulation of multimodal MRI data in recent years, integrating information from various modalities has become an effective strategy for improving the detection of brain disorders. This study focuses on identifying major depressive disorder (MDD) by using arterial spin labeling (ASL) perfusion MRI in conjunction with structural MRI data. We collected ASL and structural MRI data from 260 participants, including 169 MDD patients and 91 healthy controls. We developed an explainable fusion method to identify MDD, utilizing cerebral blood flow (CBF) data from ASL perfusion MRI and brain tissue volumes from structural MRI. The fusion model, which integrates multimodal data, demonstrated superior predictive performance for MDD. By combining MRI regional volumes with CBF data, we achieved more effective results than using each modality independently. Additionally, we analyzed feature importance and interactions to explain the fusion model. We identified fourteen important features, comprising eight regional volumes and six regional CBF measures, that played a crucial role in the identification of MDD. Furthermore, we found three feature interactions among the important features and seven interactions between structural and functional features, which were particularly prominent in the model. The results of this study suggest that the fusion learning approach, which integrates ASL and structural MRI data, is effective in detecting MDD. Moreover, the study demonstrates that the model explanation method can reveal key features that influence the decisions of models, as well as potential interactions among these key features or between functional and structural features in identifying MDD.

The Brainstem Score on Diffusion-weighted Imaging before Mechanical Thrombectomy in Acute Basilar Artery Occlusion is a Reliable Predictor for Prognosis: A Comparative Study with Critical Area Perfusion Score on Perfusion MRI

The Brainstem Score on Diffusion-weighted Imaging before Mechanical Thrombectomy in Acute Basilar Artery Occlusion is a Reliable Predictor for Prognosis: A Comparative Study with Critical Area Perfusion Score on Perfusion MRI

Image-guided patient-specific prediction of interstitial fluid flow and drug transport in solid tumors.

Tumor fluid dynamics and drug delivery simulations in solid tumors are highly relevant topics in clinical oncology. The current study introduces a novel method combining computational fluid dynamics (CFD) modeling, quantitative magnetic resonance imaging (MRI; including dynamic contrast-enhanced (DCE) MRI and diffusion-weighted (DW) MRI), and a novel ex-vivo protocol to generate patient-specific models of solid tumors in four patients with peritoneal metastases. DCE-MRI data were analyzed using the extended Tofts model to estimate the spatial distribution of tumor capillary permeability using the Ktrans parameter. DW-MRI data analysis provided a 3D representation of drug diffusivity, and DW-MRI coupled to an ex-vivo measurement protocol informed the spatial heterogeneity of the hydraulic conductivity of tumor tissue. The patient-specific data were subsequently incorporated into a computational fluid dynamics (CFD) model to simulate individualized tumor perfusion and drug transport maps. The results on interstitial fluid flow demonstrated noticeable heterogeneity of interstitial fluid pressure and velocity within the tumor, along with heterogeneous drug penetration profiles among different tumors, even with a similar drug administration regimen.

Abstract WMP21: Ten Year Increases in White Matter Hyperintensity Volume Correlates with the Severity of Ongoing Blood Brain Barrier Permeability when Measured with Dynamic Susceptibility Contrast MRI

Abstract WMP21: Ten Year Increases in White Matter Hyperintensity Volume Correlates with the Severity of Ongoing Blood Brain Barrier Permeability when Measured with Dynamic Susceptibility Contrast MRI

The kinetic parameters of dynamic contrast-enhanced MRI with ultrafast imaging in breast cancer patients receiving neoadjuvant chemotherapy: Prediction of pathologic complete response and correlation with histologic microvessel density.

Early prediction of pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients can help forecast prognosis and guide decisions on adjuvant therapy. This study aimed to determine whether the kinetic parameters of dynamic contrast-enhanced MRI (DCE-MRI) with ultrafast imaging can predict pCR following NAC in breast cancer patients and whether these parameters are correlated with histologic microvessel density (MVD). In this retrospective study, 61 breast cancer patients who underwent NAC and surgery between August 2020 and 2022 were analyzed. Ultrafast and conventional DCE-MRI features, along with pathologic results, were compared between the pCR and non-pCR groups. Regression analysis was conducted to identify predictive factors for pCR. Additionally, MRI kinetic parameters were correlated with histologic MVD. Of the 61 patients, 17 (27.9%) achieved pCR. The pCR group exhibited a larger delayed washout component (P = .002) and a smaller angiovolume (P = .02) compared to the non-pCR group; however, these factors lost significance when accounting for tumor size, lymph node status, and molecular subtypes. In a subgroup analysis based on molecular subtype, a low initial enhancement value (≤362.5%) and angiovolume (≤10.3 cc) predicted pCR in human epidermal growth factor receptor 2-enriched breast cancer, with an area under the curve of 0.833. The maximum slope on ultrafast MRI was higher in the high MVD group compared to the low MVD group (P = .049). Human epidermal growth factor receptor 2-enriched breast cancer with low vascularity on DCE-MRI is more likely to achieve pCR, although MRI kinetic parameters were not independent predictors of pCR in all breast cancer subtypes. The maximum slope on ultrafast MRI was the only kinetic parameter that correlated with histologic MVD. Larger studies focused on molecular subtypes are warranted.

Large blood vessel segmentation in quantitative DCE-MRI of brain tumors: A Swin UNETR approach.

Brain tumor growth is associated with angiogenesis, wherein the density of newly developed blood vessels indicates tumor progression and correlates with the tumor grade. Quantitative dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) has shown potential in brain tumor grading and treatment response assessment. Segmentation of large-blood-vessels is crucial for automatic and accurate tumor grading using quantitative DCE-MRI. Traditional manual and semi-manual rule-based large-blood-vessel segmentation methods are time-intensive and prone to errors. This study proposes a novel deep learning-based technique for automatic large-blood-vessel segmentation using Swin UNETR architectures and comparing it with U-Net and Attention U-Net architectures. The study employed MRI data from 187 brain tumor patients, with training, validation, and testing datasets sourced from two centers, two vendors, and two field-strength magnetic resonance scanners. To test the generalizability of the developed model, testing was also carried out on different brain tumor types, including lymphoma and metastasis. Performance evaluation demonstrated that Swin UNETR outperformed other models in segmenting large-blood-vessel regions (achieving Dice scores of 0.979, and 0.973 on training and validation sets, respectively, with test set performance ranging from 0.835 to 0.982). Moreover, most quantitative parameters showed significant differences (p < 0.05) between with and without large-blood-vessel. After large-blood-vessel removal, using both ground truth and predicted masks, the values of parameters in non-vascular tumoral regions were statistically similar (p > 0.05). The proposed approach has potential applications in improving the accuracy of automatic grading of tumors as well as in treatment planning.

Radiomics prediction of MGMT promoter methylation in adult diffuse gliomas: a combination of structural MRI, DCE, and DTI

PurposeTo assess the predictive value of radiomics features extracted from structural MRI, dynamic contrast enhanced (DCE), and diffusion tensor imaging (DTI) in detecting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in patients with diffuse gliomas.MethodsRetrospective MRI data of 110 patients were enrolled in this study. The training dataset included 88 patients (mean age 52.84 ± 14.71, 47 females). The test dataset included 22 patients (mean age 50.64 ± 12.58, 12 females). A total of 2,782 radiomic features were extracted from structural MRI, DCE, and DTI within two region of interests (ROIs). Feature section was conducted using Pearson correlation and least absolute shrinkage and selection operator. Principal component analysis was utilized for dimensionality reduction. Support vector machine was employed for model construction. Two radiologists with 1 year and 5 years of experience evaluated the MGMT status in the test dataset as a comparison with the models. The chi-square test and independent samples t-test were used for assessing the statistical differences in patients’ clinical characteristics.ResultsOn the training dataset, the model structural MRI + DCE achieved the highest AUC of 0.906. On the test dataset, the model structural MRI + DCE + DTI achieved the highest AUC of 0.868, outperforming two radiologists.ConclusionThe radiomics models have obtained promising performance in predicting MGMT promoter methylation status. Adding DCE and DTI features can provide extra information to structural MRI in detecting MGMT promoter methylation.

Preoperative MRI to Predict Upstaging of DCIS to Invasive Cancer at Surgery.

Ductal carcinoma in situ (DCIS) is overtreated, in part because of inability to predict which DCIS cases diagnosed at core needle biopsy (CNB) will be upstaged at excision. This study aimed to determine whether quantitative magnetic resonance imaging (MRI) features can identify DCIS at risk of upstaging to invasive cancer. This prospective observational clinical trial analyzed women with a diagnosis of DCIS on CNB. All the participantsunderwent preoperative 3T MRI. Quantitative MRI features from routine dynamic contrast-enhanced (DCE) MR images (e.g., peak percent enhancement [PE]) and from advanced high temporal-resolution DCE MR images (e.g., Ktrans) were measured. Clinical, pathologic, and mammographic features were reviewed. Associations with upstaging were summarized using the area under the receiver operating characteristic curve (AUC). Of 58 DCIS lesions at CNB, 15 (26%) were upstaged to invasive cancer at surgery. Of the 58 lesions, 46 (79%) enhanced on MRI, although enhancement alone was not significantly associated with upstaging (p = 0.71). Among the DCIS lesions that enhanced, higher PE was most strongly associated with upstaging (AUC, 0.81; adjusted p = 0.009) and outperformed MRI features acquired via high temporal resolution DCE-MRI (AUC, 0.50-0.73). Lesion span on MRI was not significantly associated with upstaging risk (AUC, 0.55; adjusted p = 0.61), nor were any clinical, pathologic, or mammographic features (p > 0.24). Quantitative features acquired from routine clinical breast MRI and advanced DCE-MRI demonstrated good performance in identifying which DCIS lesions were upstaged to invasive cancer at excision. These features may prove valuable for appropriate selection of active surveillance in future DCIS de-escalation trials.

P-03 NORMALIZATION OF THE DEXAMETHASONE SUPPRESSION TEST FOLLOWING PSYCHIATRIC TREATMENT IN A PATIENT WITH PSYCHOTIC DEPRESSION: A CASE REPORT

Abstract Introduction Cushing's syndrome (CS) is characterized by hypercortisolism and is typically diagnosed through clinical and laboratory evaluations. However, severe depression can lead to misleading results. In this case, a patient with severe depressive symptoms initially showed abnormal dexamethasone suppression test (DST) results, which normalized following psychiatric treatment. This case highlights the impact of psychiatric conditions on CS assessment and demonstrates how psychiatric intervention can influence test outcomes. Clinical Case A 67-year-old male patient with a known diagnosis of psychotic depression was referred to our outpatient clinic for hormonal evaluation after a left adrenal mass was detected. Physical examination revealed no signs of hypercortisolism. The patient's blood pressure was 100/70 mmHg, weight 50 kg, height 168 cm, and BMI 17 kg/m². Initial tests revealed that the nadir cortisol level after a 1 mg DST was not suppressed, with a value of 16 µg/dL. Additional tests for hypercortisolism were ordered. The patient’s 24-hour urinary free cortisol (UFC) was 97 µg/dL (normal: &amp;lt;130 µg/dL), and late-night salivary cortisol was 0.64 ng/mL (normal: &amp;lt;2.74 ng/mL), both within normal ranges. An ACTH level of 121 pg/mL was noted, leading to a planned evaluation for ACTH-dependent hypercortisolism. The laboratory results are presented in Table 1. The patient underwent dynamic contrast-enhanced 3-Tesla MRI to evaluate ACTH-dependent hypercortisolism. The sella MRI did not reveal any adenoma or other pathology. Due to the patient's agitation, bilateral inferior petrosal sinus sampling could not be performed. Given the absence of clinical signs of hypercortisolism, the patient’s psychiatric treatment was adjusted, and a re-evaluation from an endocrinological perspective was planned. Following nine sessions of inpatient electroconvulsive therapy, during which the patient was initiated on olanzapine 20 mg once daily and venlafaxine 150 mg once daily, he was discharged and continued these medications as part of his outpatient treatment. The patient was subsequently reassessed in the endocrinology clinic. With the adjusted psychiatric treatment, the patient’s nadir cortisol level after 1 mg DST decreased to 0.78 µg/dL. 24-hour UFC and late-night salivary cortisol levels were within normal ranges. It was concluded that the untreated psychiatric disorder had affected the DST results. The patient was placed under regular follow-up by the psychiatric unit. Conclusion This case underscores the potential impact of untreated psychiatric disorders on diagnostic tests for CS, such as the DST. The normalization of test results following effective psychiatric treatment highlights the importance of considering psychiatric conditions in the comprehensive laboratory evaluation of patients, particularly those without clinical signs of hypercortisolism.Table 1:The laboratory test resultsACTH=Adrenocorticotropic hormone; DST=Dexamethasone suppression test; HbA1c=Hemoglobin A1C; UFC=Urinary free cortisol

Dynamic Contrast-enhanced MRI Processing Comparison for Distinguishing True Progression From Pseudoprogression in High-grade Glioma.

Treatment-related changes may occur due to radiation and temozolomide in glioblastoma and can mimic tumor progression on conventional MRI. DCE-MRI enables quantification of the extent of blood-brain barrier (BBB) disruption, providing information about areas of suspicious postcontrast T1 enhancement. We compared DCE-MRI processing methods for distinguishing true disease progression from pseudoprogression in high-grade gliomas (HGGs). We identified 110 patients with HGG treated with surgery and chemoradiation who underwent DCE-MRI to further interrogate areas of new/increasing enhancement. All patients had confirmatory surgery/biopsy with pathology-confirmed progression or pseudoprogression. Scans were performed at 3T and analyzed using nordicICE. The MCA, SSS, and Parker models are three standardized processing methodologies used to create ktrans maps, a parameter that quantifies BBB permeability. Three equal regions of interest were placed at sites of peak contrast enhancement within each lesion. Data from each method was processed for mean and maximum ktrans. We conducted several rounds of analysis and finalized a strategy on penalized support vector machines based on engineered features with bootstrap sampling. The Parker method was significant for ktrans maximum in the combined pathology and clinical as well as the pathology-only data sets. MCA and SSS did not perform well under the SVM classifier for pathology only. For clinical follow-up subjects, the Parker method yielded statistically significant results for maximum and mean ktrans. The Parker method was effective in distinguishing PD and PsP for pathology and clinical data sets. MCA and SSS techniques were effective for the clinical data set.

Conditional diffusion-generated super-resolution for myocardial perfusion MRI

IntroductionMyocardial perfusion MRI is important for diagnosing coronary artery disease, but current clinical methods face challenges in balancing spatial resolution, temporal resolution, and slice coverage. Achieving broader slice coverage and higher temporal resolution is essential for accurately detecting abnormalities across different slice locations but remains difficult due to constraints in acquisition speed and heart rate variability. While techniques like parallel imaging and compressed sensing have significantly advanced perfusion imaging, they still suffer from noise amplification, residual artifacts, and potential temporal blurring due to the rapid transit of dynamic contrast vs. the temporal constraints of the reconstruction.MethodsThis study introduces a conditional diffusion-based generative model for myocardial perfusion MRI super resolution, addressing the trade-offs between spatiotemporal resolution and slice coverage. We adapted Denoising Diffusion Probabilistic Models (DDPM) to enhance low-resolution perfusion images into high-resolution outputs without requiring temporal regularization. The forward diffusion process introduces Gaussian noise incrementally, while the reverse process employs a U-Net architecture to progressively denoise the images, conditioned on the low-resolution input image.ResultsWe trained and validated the model on a retrospective dataset of dynamic contrast-enhanced (DCE) perfusion MRI, consisting of both stress and rest images from 47 patients with heart disease. Our results showed significant image quality improvements, with a 5.1% reduction in nRMSE, a 1.1% increase in PSNR, and a 2.2% boost in SSIM compared to GAN-based super-resolution method (P &amp;lt; 0.05 for all metrics using paired t-test) in retrospective study. For the 9 prospective subjects, we achieved a total nominal acceleration of 8.5-fold across 5–6 slices through a combination of low-resolution acquisition and GRAPPA. PerfGen outperformed GAN-based approach in sharpness (4.36 ± 0.38 vs. 4.89 ± 0.22) and overall image quality (4.14 ± 0.28 vs. 4.89 ± 0.22), as assessed by two experts in a blinded evaluation (P &amp;lt; 0.05) in prospective study.DiscussionThis work demonstrates the capability of diffusion-based generative models in generating high-resolution myocardial perfusion MRI from conditional low-resolution images. This approach has shown the potentials to accelerate myocardial perfusion MRI while enhancing slice coverage and temporal resolution, offering a promising alternative to existing methods.

"Synthetic" DSC Perfusion MRI with Adjustable Acquisition Parameters in Brain Tumors Using Dynamic Spin-and-Gradient-Echo Echoplanar Imaging.

Normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) computed from dynamic susceptibility contrast (DSC) perfusion imaging are useful biomarkers for differential diagnosis and treatment response assessment in brain tumors. However, their measurements are dependent on DSC acquisition factors, and CBV-optimized protocols technically differ from PSR-optimized protocols. This study aimed to generate "synthetic" DSC data with adjustable synthetic acquisition parameters using dual-echo gradient-echo (GE) DSC datasets extracted from dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI). Synthetic DSC was aimed at: 1) simultaneously create nrCBV and PSR maps using optimal sequence parameters, 2) compare DSC datasets with heterogeneous external cohorts, and 3) assess the impact of acquisition factors on DSC metrics. Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection and included in this cross-sectional study. Multiple synthetic DSC curves with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo GE data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation. Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection, while PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman's ρ = 0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p<0.0001), supporting a role of synthetic DSC for multicenter comparisons. Acquisition factors highly impacted PSR, and nrCBV without leakage correction also showed parameter-dependence, although less pronounced. However, this dependence was remarkably mitigated by post-hoc leakage correction. Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with one acquisition and a single contrast injection, facilitating the use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion metrics across heterogeneous multicenter datasets, as it facilitates post-hoc harmonization.

Assessing blood flow in uterine fibroids using intravoxel incoherent motion imaging compared with dynamic contrast-enhanced MRI

To assess the utility of IVIM parameters in evaluating uterine fibroid blood flow compared to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived blood flow. Sixteen premenopausal women with uterine fibroids were enrolled in this prospective study. Pelvic MRI scans were obtained for each subject, both with and without continuous intravenous infusion of oxytocin, known to decrease significantly uterine fibroid blood flow, to assess the changes in blood flow of uterine fibroids. IVIM and DCE analyses were conducted using separate dedicated software. The bi-exponential IVIM model was used to estimate perfusion fraction (f), pseudo-diffusion coefficient (D*), and diffusion coefficient (D). DCE blood flow values were derived via T1 perfusion deconvolution arithmetic, utilizing a first pass of an AIF curve. The correlation between the parameters were analyzed by Spearman’s rank correlation analysis due to small sample size. Means of the parameters were compared with a nonparametric Wilcoxon sign rank method for each pair. Statistically significant positive correlations were found between perfusion fraction values and DCE blood flow values with oxytocin (Spearson’s ρ = 0.78, p = 0.0004), and between fD* values and DCE blood flow values with oxytocin (Spearson’s ρ = 0.64, p = 0.0071). Significant differences in blood flow were detected across most IVIM parameters: f, D*, and fD* (p < 0.001, p = 0.0027, and p = 0.0002, respectively) when comparing the values without oxytocin and with oxytocin. IVIM imaging shows promise for assessing blood flow in uterine fibroids.

Evaluating Suspicious Breast Lesions: A Head-to-Head Comparison of Contrast-Enhanced Mammography and Breast MRI

Background: Magnetic resonance imaging (MRI) is the most sensitive technique for the detection of breast cancer in contemporary medical practice. Contrast-enhanced mammography (CEM), a novel technology, provides a contrast-enhanced imaging alternative to breast MRI. Objectives: to assess the diagnostic efficacy of CEM in the characterization of suspicious breast lesions, with histopathological results serving as the gold standard. Compare CEM with dynamic contrast enhanced MRI (DCE-MRI). Patients and Methods: 30 individuals with suspicious lesions identified using conventional mammography or ultrasonography underwent CEM and DCE-MRI examinations after receiving ethical approval. Proficient radiologists evaluated all discernible lesions using the Breast Imaging Reporting and Data System (BI-RADS) classifications (categories 1–6). Histopathological data were compared to the morphological descriptions of each lesion that were received from each modality. Results: 30 lesions were identified by the combination of breast MRI and CEM in the 30 patients who were enrolled in CEM/MRI investigations. Histopathology verified that 15 of the 30 lesions were malignant and 15 were benign. Twelve of the fifteen malignant lesions were invasive cancers, while three were in situ cancers. Both breast MRI and CEM demonstrated a sensitivity of 93.33%. The specificity of breast MRI was 93.33%, while CEM had a specificity of 80.00%. The breast MRI achieved an accuracy of 93.33%, while the CEM achieved an accuracy of 86.67%. Conclusions: Contrast-enhanced mammography (CEM) has the potential to replace dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a problem-solving instrument for the characterization of indeterminate breast lesions, particularly in situations where MRI is contraindicated or not promptly accessible. CEM provides a viable alternative for evaluating ambiguous lesions, as it utilizes similar principles of contrast enhancement and tumor vascularity. However, it has the additional benefit of being more broadly available, less expensive, and better tolerated by specific patient populations.

Role of MRI in evaluation of residual tumor burden following neoadjuvant therapy in patients with breast cancer

Objectives To correlate the accuracy of MRI with pathology in assessing the response of neoadjuvant chemotherapy (NACT) in patients with breast cancer and assessment of factors affecting the accuracy of MRI. Materials and Methods Twenty-five patients (with 33 tumors) having biopsy-proven breast cancer were included to undergo dynamic contrast-enhanced MRI, mammography, and ultrasound prior to NACT and after completion of NACT before undergoing surgery. Tumor morphological features and receptor subtypes were compared between complete and non-complete responders, and the accuracy of MRI in estimating residual disease was assessed with respect to histopathology. The performance of MRI was also compared with ultrasound and mammography, wherever feasible. Results The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of MRI for predicting pathological complete response (pCR) were 100%, 70%, 83.3%, 100%, and 88%, respectively, which were significantly better (p = 0.02) in the triple-negative subtype. Size measured in preoperative MRI had a significant positive correlation with pathological size (r = 0.76, p &lt; 0.001) with the lowest mean size difference in triple negative subtype and in tumors showing a concentric pattern of shrinkage. Among the baseline morphological features on MRI, significant difference was seen in the shape (p = 0.02) and enhancement (p = 0.036) of the tumors between complete and non-complete responders. Also, MRI had the highest overall accuracy in predicting pCR and residual tumor size as compared to mammography and ultrasound. Conclusion MRI is a sensitive modality for predicting pCR and residual tumor size with better accuracy for triple-negative tumors as compared to other subtypes.

Noninvasive in vivo assessment of placental insufficiency using pCASL: a prospective case–control study

BackgroundArterial spin labeling is a noninvasive imaging modality that does not use contrast for evaluation of placental perfusion in the placental pathological conditions. Our study enrolled 30 pregnant females with different risk factors for developing IUGR and other 30 females with the same age group having normal pregnancy. The two groups were evaluated by free breathing arterial spin labeling magnetic resonance imaging. Image findings of pseudo-continuous arterial spin labeling were compared to color duplex Doppler ultrasound of umbilical and middle cerebral arteries.AimThe aim of the present study was to evaluate the role of pseudo-continuous arterial spin labeling in measurement of placental perfusion with quantitative analysis of regional placental blood flow.MethodsThis prospective case–control study enrolled 30 pregnant females who had different causes of placental insufficiency, and another healthy group of matched ages, all were evaluated by 2D and color duplex Doppler ultrasound, conventional non-contrast magnetic resonance imaging and MRI perfusion using arterial spin labeling technique. The study was carried out from October 2021 to October 2023.ResultsSignificant relation was found between placental perfusion values and umbilical and middle cerebral artery resistive indices as indicator for placental insufficiency with sensitivity and specificity 77.8%, 80%, respectively, for umbilical artery resistive index and 90%, 83.3%, respectively, for middle cerebral artery resistive index. While the diagnostic value of ASL perfusion was as follows, it had sensitivity = 90%, specificity = 83.3%, PPV = 84.4%, NPV = 89.3% and accuracy = 86.7%.ConclusionsASL-MRI is an excellent noninvasive in vivo MRI technique for evaluation of placental perfusion and quantitative analysis of regional placental blood flow in cases of placental insufficiency. This study supported the value of the new noninvasive MRI techniques in the elaboration of placental pathology.

Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumor

Abstract Best current practice in the analysis of dynamic contrast enhanced (DCE)-MRI is to employ a voxel-by-voxel model selection from a hierarchy of nested models. This nested model selection (NMS) assumes that the observed time-trace of contrast-agent (CA) concentration within a voxel, corresponds to a singular physiologically nested model. However, admixtures of different models may exist within a voxel’s CA time-trace. This study introduces an unsupervised feature engineering technique (Kohonen-Self-Organizing-Map (K-SOM)) to estimate the voxel-wise probability of each nested model. Sixty-six immune-compromised-RNU rats were implanted with human U-251 N cancer cells, and DCE-MRI data were acquired from all the rat brains. The time-trace of change in the longitudinal-relaxivity (ΔR1) for all animals’ brain voxels was calculated. DCE-MRI pharmacokinetic (PK) analysis was performed using NMS to estimate three model regions: Model-1: normal vasculature without leakage, Model-2: tumor tissues with leakage without back-flux to the vasculature, Model-3: tumor vessels with leakage and back-flux. Approximately two hundred thirty thousand (229,314) normalized ΔR1 profiles of animals’ brain voxels along with their NMS results were used to build a K-SOM (topology-size: 8 × 8, with competitive-learning algorithm) and probability map of each model. K-fold nested-cross-validation (NCV, k = 10) was used to evaluate the performance of the K-SOM probabilistic-NMS (PNMS) technique against the NMS technique. The K-SOM PNMS’s estimation for the leaky tumor regions were strongly similar (Dice-Similarity-Coefficient, DSC = 0.774 [CI: 0.731–0.823], and 0.866 [CI: 0.828–0.912] for Models 2 and 3, respectively) to their respective NMS regions. The mean-percent-differences (MPDs, NCV, k = 10) for the estimated permeability parameters by the two techniques were: -28%, + 18%, and + 24%, for vp, Ktrans, and ve, respectively. The KSOM-PNMS technique produced microvasculature parameters and NMS regions less impacted by the arterial-input-function dispersion effect. This study introduces an unsupervised model-averaging technique (K-SOM) to estimate the contribution of different nested-models in PK analysis and provides a faster estimate of permeability parameters.

Imaging of Peripheral Intraneural Tumors: A Comprehensive Review for Radiologists.

Intraneural tumors (INTs) pose a diagnostic challenge, owing to their varied origins within nerve fascicles and their wide spectrum, which includes both benign and malignant forms. Accurate diagnosis and management of these tumors depends upon the skills of the radiologist in identifying key imaging features and correlating them with the patient's clinical symptoms and examination findings. This comprehensive review systematically analyzes the various imaging features in the diagnosis of intraneural tumors, ranging from basic MR to advanced MR imaging techniques such as MR neurography (MRN), diffusion tensor imaging (DTI), and dynamic contrast-enhanced (DCE) MRI. The article emphasizes the differentiation of benign from malignant lesions using characteristic MRI features, such as the "target sign" and "split-fat sign" for tumor characterization. The role of advanced multiparametric MRI in improving biopsy planning, guiding surgical mapping, and enhancing post-treatment monitoring is also highlighted. The review also underlines the importance of common diagnostic pitfalls and highlights the need for a multi-disciplinary approach to achieve an accurate diagnosis, appropriate treatment strategy, and post-therapy surveillance planning. In this review, we illustrate the main imaging findings of intraneural tumors, focusing on specific MR imaging features that are crucial for an accurate diagnosis and the differentiation between benign and malignant lesions.

Predictive Value of Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging and Diffusion-Weighted Imaging Findings for Sentinel Lymph Node Metastasis in Early-Stage Invasive Breast Cancer.

This retrospective study aimed to evaluate the predictive value of the preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) findings of mass lesions for predicting sentinel lymph node (SLN) metastasis in early breast cancer. A total of 310 patients with suspicious mass lesions detected in preoperative MRI who subsequently underwent surgery and SLN biopsy (SLNB) between September 2015 and September 2022 were analyzed. The relationship between DCE-MRI and DWI findings and SLNB positivity was analyzed. SLNB was positive for SLN metastasis in 108 of 310 lesions. Younger age (p = 0.001) and larger lesion size (p < 0.001) were found to be associated with SLNB positivity. Findings associated with SLN metastasis included peritumoral edema in 53%, adjacent vessel sign (AVS) in 81%, and increased whole-breast vascularity (WBV) in 58% of patients with positive SLNB (p < 0.001). The SLNB positivity rate was higher in mass lesions with DCE-MRI findings of heterogenous enhancement pattern (p = 0.003), medium or rapid initial phase enhancement (p = 0.001), and washout delayed phase kinetic curve (p = 0.001). It was found that lower tumoral apparent diffusion coefficient (ADC) values (p = 0.003) and higher peritumoral/tumoral ADC ratios (p = 0.018) increased the probability of encountering SLN metastasis. Patient age, presence of peritumoral edema, presence of AVS, increased WBV, and initial phase kinetic curve of the lesions on MRI were found to be associated with SLN metastasis. We found that younger age and MR findings obtained from the perilesional area of breast cancer may be helpful in the preoperative prediction of SLN metastasis.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging Findings and the Relevance of Histopathological Findings in Intraocular Solitary Fibrous Tumors

Dynamic Contrast-Enhanced Magnetic Resonance Imaging Findings and the Relevance of Histopathological Findings in Intraocular Solitary Fibrous Tumors