Contrast-enhanced T1 Research Articles

Development of an Artificial Intelligence System for Distinguishing Malignant from Benign Soft Tissue Tumors Using Contrast-Enhanced MR Images.

The integration of artificial intelligence (AI) into orthopedics has enhanced the diagnosis of various conditions; however, its use in diagnosing soft-tissue tumors remains limited owing to its complexity. This study aimed to develop and assess an AI-driven diagnostic support system for magnetic resonance imaging (MRI)-based soft-tissue tumor diagnosis, potentially improving accuracy and aiding radiologists and orthopedic surgeons. Experienced orthopedic oncologists and radiologists annotated 720 images from 77 cases (41 benign and 36 malignant soft-tissue tumors). Eleven tumor subtypes were identified and classified into benign and malignant groups based on histological diagnosis. Utilizing the standard machine learning classifier pipeline, we examined and down-selected imaging protocols and their predominant radiomic features within the tumor's three-dimensional region to differentiate between benign and malignant tumors. Among the scan protocols, contrast-enhanced T1 weighted fat-suppressed images showed the most accurate classification based on radiomics features. We focused on the two-dimensional features from the largest tumor boundary surface and its neighboring slices, leveraging texture-based radiomic and deep convolutional neural network features from a pretrained VGG19 model. The test data comprised 44 contrast-enhanced images (22 benign and 22 malignant soft-tissue tumors) containing six malignant and five benign subtypes distinct from the training data. We compared expert and non-expert human performances against AI by assessing malignancy detection and the time required for classification. The AI model showed comparable accuracy (AUC 0.91) to that of radiologists (AUC 0.83) and orthopedic surgeons (AUC 0.73). Notably, the AI model processed data approximately 400 times faster than its human counterparts, showcasing its capacity to significantly boost diagnostic efficiency. We developed an AI-driven diagnostic support system for MRI-based soft-tissue tumor diagnosis. While additional refinement is necessary for clinical applications, our system has exhibited promising potential in differentiating between benign and malignant soft-tissue tumors based on MRI.

MRI-Based Machine Learning for Prediction of Clinical Outcomes in Primary Central Nervous System Lymphoma.

A portion of individuals diagnosed with primary central nervous system lymphomas (PCNSL) may experience early relapse or refractory (R/R) disease following treatment. This research explored the potential of MRI-based radiomics in forecasting R/R cases in PCNSL. Forty-six patients with pathologically confirmed PCNSL diagnosed between January 2008 and December 2020 were included in this study. Only patients who underwent pretreatment brain MRIs and complete postoperative follow-up MRIs were included. Pretreatment contrast-enhanced T1WI, T2WI, and T2 FLAIR imaging were analyzed. A total of 107 radiomic features, including 14 shape-based, 18 first-order statistical, and 75 texture features, were extracted from each sequence. Predictive models were then built using five different machine learning algorithms to predict R/R in PCNSL. Of the included 46 PCNSL patients, 20 (20/46, 43.5%) patients were found to have R/R. In the R/R group, the median scores in predictive models such as support vector machine, k-nearest neighbors, linear discriminant analysis, naïve Bayes, and decision trees were significantly higher, while the apparent diffusion coefficient values were notably lower compared to those without R/R (p < 0.05). The support vector machine model exhibited the highest performance, achieving an overall prediction accuracy of 83%, a precision rate of 80%, and an AUC of 0.78. Additionally, when analyzing tumor progression, patients with elevated support vector machine and naïve Bayes scores demonstrated a significantly reduced progression-free survival (p < 0.05). These findings suggest that preoperative MRI-based radiomics may provide critical insights for treatment strategies in PCNSL.

Shine and darkle the blood vessels: Multiparameter hypersensitive MR angiography for diagnosis of panvascular diseases.

Magnetic resonance angiography (MRA) is pivotal for diagnosing panvascular diseases. However, single-modality MRA falls short in diagnosing diverse vascular abnormalities. Thus, contrast agents combining T1 and T2 effects are sought for multiparameter MRA with clinical promise, yet achieving a balance in T1 and T2 contrast enhancement effects remains a scientific challenge. Herein, we developed a hypersensitive multiparameter MRA strategy using dual-modality NaGdF4 nanoparticles. Because of the longer tumbling time (τR), NaGdF4 nanoparticles can improve the longitudinal relaxivity (r1), brightening vessels in T1-weighted sequences. Simultaneously, the regular arrangement of Gd3+ in the crystal induces magnetic anisotropy, creating local static magnetic field heterogeneity and generating negative signals in T2-weighted sequences. Consequently, the efficacy of NaGdF4-enhanced high-resolution multiparameter MRA has been validated in diagnosing ischemic stroke and Alzheimer's disease in rodent models. In addition, the dual-contrast imaging has been realized on swine with a clinical 3.0-T magnetic resonance imaging scanner, highly emphasizing the clinical translation prospect.

A DTI-based radiomics model for predicting epidermal growth factor receptor (EGFR) amplification in adult IDH1-wild glioblastomas.

Molecular alteration events are common in glioblastomas, the isocitrate dehydrogenase (IDH)-wild of which have had poor survival results so far. The progress of radiomics-based model provides novel sights for its preoperatively noninvasive prediction. To develop a radiomics-based model for predicting epidermal growth factor receptor (EGFR) amplification status in IDH1-wild glioblastomas of adults by pretreatment diffusion tensor imaging (DTI). A total of 124 patients with diagnosed glioblastomas were retrospectively collected. Six conventional magnetic resonance imaging (MRI) features of all the tumors were evaluated visually. Patients were divided into the training (n = 87) and the test set (n = 37) with a ratio of 7:3. Radiomics features were extracted from two regions of the glioblastomas, which were the total tumor (ROI_1) and the solid portion of tumor (ROI_2). The radiomics features extracted from the DTI and T1-contrast-enhanced (T1C) images were selected using the least absolute shrinkage and selection operator (LASSO) regression algorithm. Logistic regression analysis was conducted to develop models for EGFR amplification prediction in the training set. The radiomics model based on ROI_1 demonstrated favorable discrimination in both the training (area under the curve [AUC] = 0.86) and the test set (AUC = 0.82) (P < 0.05). Combining the radiomics features and the conventional feature tumor location, no significant improvement of AUCs was achieved (AUC = 0.86 and 0.81). The radiomics model derived from pretreatment DTI may have potential in differentiating the EGFR mutation status in glioblastomas.

Deep Learning Virtual Contrast-Enhanced T1 Mapping for Contrast-Free Myocardial Extracellular Volume Assessment.

The acquisition of contrast-enhanced T1 maps to calculate extracellular volume (ECV) requires contrast agent administration and is time consuming. This study investigates generative adversarial networks for contrast-free, virtual extracellular volume (vECV) by generating virtual contrast-enhanced T1 maps. This retrospective study includes 2518 registered native and contrast-enhanced T1 maps from 1000 patients who underwent cardiovascular magnetic resonance at 1.5 Tesla. Recent hematocrit values of 123 patients (hold-out test) and 96 patients from a different institution (external evaluation) allowed for calculation of conventional ECV. A generative adversarial network was trained to generate virtual contrast-enhanced T1 maps from native T1 maps for vECV creation. Mean and SD of the difference per patient (ΔECV) were calculated and compared by permutation of the 2-sided t test with 10 000 resamples. For ECV and vECV, differences in area under the receiver operating characteristic curve (AUC) for discriminating hold-out test patients with normal cardiovascular magnetic resonance versus myocarditis or amyloidosis were tested with Delong's test. ECV and vECV showed a high agreement in patients with myocarditis (ΔECV: hold-out test, 2.0%±1.5%; external evaluation, 1.9%±1.7%) and normal cardiovascular magnetic resonance (ΔECV: hold-out test, 1.9%±1.4%; external evaluation, 1.5%±1.2%), but variations in amyloidosis were higher (ΔECV: hold-out test, 6.2%±6.0%; external evaluation, 15.5%±6.4%). In the hold-out test, ECV and vECV had a comparable AUC for the diagnosis of myocarditis (ECV AUC, 0.77 versus vECV AUC, 0.76; P=0.76) and amyloidosis (ECV AUC, 0.99 versus vECV AUC, 0.96; P=0.52). Generation of vECV on the basis of native T1 maps is feasible. Multicenter training data are required to further enhance generalizability of vECV in amyloidosis.

UTILITY OF CONTRAST-ENHANCED T2-FLAIR FOR IMAGING OF BRAIN METASTASESUSING A HALF-DOSE HIGH-RELAXIVITY CONTRAST AGENT IN COMPARISON WITH ROUTINE DOSE CONTRAST-ENHANCED BRAIN IMAGING

Aim &amp; Objectives: This study aims to assess the efficacy of delayed contrast-enhanced T2 fluid-attenuated inversion recovery (CE-T2 FLAIR) by using half dose high relaxivity contrast agent to assess the degree of enhancement among the lesions and to detect additional metastatic brain lesions usually smaller lesions(&lt;5mm), which are significantly missed on routine-dose CE-T1 weighted imaging (CE-T1WI). Material &amp; Methods:The main source of data for this study was radiologically diagnosed lung cancer patients who were clinically suspected to have brain metastasis referred from various departments of Silchar Medical College and Hospital. The studywas carried out for a period of 1 year from March 1, 2023, to February 29, 2024, A total of 40 patients with brain metastases were scanned using a SIEMENS TIM AVANTO 1.5T scanner, routine MR pulse sequences pre- and post-contrast administration were acquired. Results: Out of 40 patients, males were more affected (57.5 %) than females (42.5 %). The highest number of cases were found between the 61-70 years age group with the lung (31 cases) being the most common primary malignancy causing brain metastases. A total of 90 lesions were detected among 40 patients which were grouped into A (25 lesions), B (36 lesions), and C (29 lesions) groups. A total of 6 lesions were missed on Routine dose contrast-enhanced brain imaging of which 2 lesions belong to group B (Rim enhancing lesions &gt;5mm) and 4 lesions belong to group C (Lesions &lt;5mm), however, these lesions were picked up on delayed half dose contrast-enhanced FLAIR images. The contrast ratio (CR) on 3 consecutive half dose CE-T2 FLAIR ranged between 59.09%-76.80%, suggesting that delay in imaging post-contrast administration resulted in a significant increase in the contrast ratio (CR) among metastatic lesions. Conclusions: CE-T1-weighted sequences and CE-T2 FLAIR sequences complement each other effectively in evaluating brain metastases. Bigger and homogenously enhancing lesions are best seen on CE-T1-weighted sequences. Smaller lesions and lesions showing rim enhancement are best visualized on delayed (5 min) CE-T2 fluid-attenuated inversion recovery (FLAIR) sequences with half the dose ofcontrast agent (gadobenatedimeglumine) compared to routine dose CE-T1-weighted sequences. This approach serves as a cost-saving measure for both patients and the healthcare system.

Magnetic resonance imaging signal changes at the intramuscular injection site in dogs: Comparison of medetomidine and saline.

Intramuscular administration is a commonly used method for delivering sedatives and anesthetics in veterinary medicine. Previous studies have reported inflammation at the intramuscular injection site in laboratory animals and observed signal changes on MRI following intramuscular injections in humans. We hypothesized that following intramuscular injection, the site would exhibit T2 hyperintensity and contrast enhancement on MRI. To investigate this, this prospective study evaluated the pattern of signal changes and grade of T2 signal intensity and contrast enhancement over time after the intramuscular injection of medetomidine at a premedication dosage, comparing it to saline. MRI scans were performed immediately postinjection into the biceps femoris and quadriceps femoris muscles, as well as at 2, 8, 24, and 72h, and 7 days postinjection. A semiquantitative scale was utilized to grade signal intensity and contrast enhancement. Both medetomidine and saline injections showed T2 hyperintensity immediately after injection and contrast enhancement from 2h postinjection, manifesting as flame-shaped. These signal changes decreased up to 24h postinjection (p<.05). The signal changes induced by medetomidine showed higher T2 hyperintense change and stronger contrast enhancement compared with saline at most time points, with the signal changes persisting for a longer duration (p<.05). These findings suggest that intramuscular administration of medetomidine induces a more severe tissue reaction compared with saline, and the results are expected to aid in the differentiation of various muscle diseases that present with similar MRI findings.

Barriers of the CNS transfer rate dynamics in patients with vascular cognitive impairment and dementia.

Advances in in vivo MRI techniques enable cerebral barrier transfer rates (K trans ) measurement in patients with vascular cognitive impairment and dementia (VCID). However, a consensus has not been reached on the dynamic contribution and importance of cerebral barrier abnormalities to the differential diagnosis of dementia subtypes. Our goal was to investigate the dynamics of blood-brain barrier (BBB) and blood-CSF barrier (BCSFB) K trans in patients with VCID longitudinally and determine the effect of aging. We studied subjects at two time points over two years; they were 65.5 years of age (SD = 15.94, M/F = 24/14) at the first visit. We studied 38 patients, 18 of whom had two visits. We calculated the BBB and BCSFB K trans with dynamic contrast-enhanced T1 MR, and we used 1H-MR spectroscopy to measure N-acetylaspartate (NAA) levels in the white matter as a marker of injury. In addition, we measured CSF levels of active-matrix metalloproteinase-3 (MMP3) as an inflammatory biomarker to aid in patient clustering. Longitudinal BBB measurements revealed variable dynamic behavior: after two years, the BBB K trans increased in 55% of patients and decreased in the remaining 45% unpredictably. We did not find a significant linear model of BBB K trans versus age for VCID. For healthy controls, the model was K trans = 0.0014 + 0.0002 × age, which was significant (p = 0.046). VCID patients showed a reduction in BCSFB K trans compared to healthy controls (p = 0.01). Combining NAA, CSF MMP3, and K trans in a clustering analysis separated patients into groups. These results suggest that BBB K trans in VCID is dynamic and BCSFB K trans reduced by age. By combining inflammatory biomarkers with BBB K trans data, it is possible to separate VCID patients into distinct groups with different underlying pathologies.

Determinants of long-term survival in patients with IDH-mutant gliomas.

Survival times of patients with IDH-mutant gliomas are variable and can extend to decades. Many studies provide progression-free rather than overall survival times and prognostic factors remain ill-defined. Here we explored characteristics of short- and long-term survivors within a cohort of patients with extended follow-up. This single-center, case-control study included 86 patients diagnosed between 1998 and 2023 who either died within 6 years after diagnosis or survived at least 15 years. Patient characteristics and prognostic factors were stratified by short- (< 6 years) versus long-term (≥ 15 years) survival. Forty-seven patients (55%) diagnosed with astrocytoma and 39 patients (45%) with oligodendroglioma were included retrospectively. Median follow-up of the survivors was 16.6 years (range 15-28.9). Thirty-four deaths (40%) had been reported at database closure. Long-term survival was associated with CNS WHO grade 2 (p < 0.01), smaller tumor volumes (p = 0.01), lack of contrast enhancement (p < 0.01), wait-and-scan strategies (p < 0.01) and female sex (p = 0.04). In multivariate analyses for oligodendroglioma, larger T2 tumor volumes were associated with shorter survival (HR 1.02; 95% CI 1.01-1.05; p = 0.04). In patients with astrocytoma, lack of contrast enhancement (HR 0.38; 95% CI 0.15-0.94; p = 0.04) and wait-and-scan strategies (HR 5.75; 95% CI 1.66-26.61; p = 0.01) were associated with longer survival. Large T2 tumor volume and contrast enhancement may be important risk factors for shorter survival, while age might be of lesser importance. Wait-and-scan strategies may yield excellent long-term survival in some patients with astrocytoma.

The many faces of myxopapillary ependynomas.

Myxopapillary ependymomas (MPE), classified as grade 2 tumors by the WHO, are rare spinal neoplasms. Despite their slow growth and generally benign nature, MPE have a high recurrence rate and potential for cerebrospinal fluid dissemination. This study aims to identify the MRI characteristics and pathological patterns of MPE and investigate potential correlations between the MRI characteristics and specific histopathological patterns. We assessed 13 patients (7 men; mean age, 45.1 years) with pathologically proven MPE. MRI images were reviewed for tumor location, size, T1 and T2 signal characteristics, contrast enhancement, hemosiderin cap presence, vertebral scalloping, drop metastasis, and prominent intradural flow voids. Four histopathological patterns (microcystic, solid, hemorrhagic, and high hyalin content) were defined and segmented, with surface areas measured and percentages calculated relative to the total tissue surface. Most tumors were in the lumbar region (84.61%), with MRI revealing typical features such as T2 hyperintensity (100%) and contrast enhancement (92.3%). A rare non-enhancing MPE was noted. Large tumors exhibited a microcystic pathology pattern, with two cases with this pattern showing drop metastasis on MRI. Smaller tumors typically presented a solid pathology pattern with homogenous MRI signals. This study underscores the diverse MRI presentations of MPE and suggests a potential link between microcystic patterns in pathology and large MPE with drop metastasis.ABBREVIATIONS: MPE= myxopapillary ependymoma.

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

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

Generating Contrast-Enhanced Liver MRI Images from Native Sequences

Abstract Generating synthetic contrast-enhanced liver MRI scans from native MRI images can serve to mitigate the issue of sparse contrast-enhanced image datasets while concurrently circumventing the time-consuming and costly process of administering contrast agents during image acquisition. In this study, we conducted three experiments using paired image-to-image translation techniques. Native T1 sequences showing the abdominal liver region served as the input, while contrast-enhanced T1 sequences were the target. The data preprocessing methods and image boundaries were varied for the individual experiments in addition to the implementation of a 5-fold cross-validation for the top-performing approach. Focusing on liver regions achieved the best results with a mean absolute error (MAE) of 0.0837 ± 0.0068, a mean squared error (MSE) of 0.0128 ± 0.0023 and a peak signal-to-noise ratio (PSNR) of 18.99 ± 0.81 dB. Our findings serve as a proof-of-concept, demonstrating the feasibility of generating contrast-enhanced MRI images. However, the current state necessitates further enhancements for effectively addressing the challenge posed by limited dataset sizes with difficult anatomical circumstances as well as MR imaging-related heterogenous tissue contrasts.

Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning.

This study aimed to utilize MR radiomics-based machine learning classifiers on a large-sample, multicenter dataset to develop an optimal model for predicting malignant sinonasal tumors and tumor-like lesions. This study included 1711 adult patients (875 benign and 836 malignant) with sinonasal tumors or tumor-like lesions from three institutions. Patients from institution 1 (n = 1367) constituted both the training and validation cohorts, while those from institution 2 and 3 (n = 158/186) made up the test cohorts. Manual segmentation of the region of interest of the tumor was performed on T1WI, T2WI, and contrast-enhanced T1WI (CE-T1WI). Data normalization, dimensional reductions, feature selection, and classifications were performed using ten machine-learning classifiers. Four fusion models, namely T1WI + T2WI, T1WI + CE-T1WI, T2WI + CE-T1WI, and T1WI + T2WI + CE-T1WI, were constructed using the top ten features with the highest contribution in feature selection in the optimal models of T1WI, T2WI, and CE-T1WI. The Delong test compared areas under the curve (AUC) between models. The AUCs of training/validation/test1/test2 datasets for T1WI, T2WI, and CE-T1WI were 0.900/0.842/0.872/0.839, 0.876/0.789/0.842/0.863, and 0.899/0.824/0.831/0.707, respectively. The fusion model from T1WI + T2WI + CE-T1WI had the highest AUC. The AUCs of training/validation/test1/test2 datasets were 0.947/0.849/0.871/0.887. The T1WI + T2WI + CE-T1WI model demonstrated a significantly higher AUC than the T2WI + CE-T1WI model in both cohorts (p < 0.05) and outperformed the T2WI model in test 1 (p = 0.008) and the T1WI model in test 2 (p = 0.006). This fusion model based on radiomics from T1WI + T2WI + CE-T1WI images and machine learning can improve the power in predicting malignant sinonasal tumors with high accuracy, resilience, and robustness. Our study proposes a radiomics-based machine learning fusion model from T1- and T2-weighted images and contrast-enhanced T1-weighted images, which can non-invasively identify the nature of sinonasal tumors and improve the performance in predicting malignant sinonasal tumors. Differentiating benign and malignant sinonasal tumors is difficult due to similar clinical presentations. A radiomics model from T1 + T2 + contrast-enhanced T1 images can identify the nature of sinonasal tumors. This model can help distinguish benign and malignant sinonasal tumors.

A 3D Convolutional Neural Network Based on Non-enhanced Brain CT to Identify Patients with Brain Metastases.

Dedicated brain imaging for cancer patients is seldom recommended in the absence of symptoms. There is increasing availability of non-enhanced CT (NE-CT) of the brain, mainly owing to a wider utilization of Positron Emission Tomography-CT (PET-CT) in cancer staging. Brain metastases (BM) are often hard to diagnose on NE-CT. This work aims to develop a 3D Convolutional Neural Network (3D-CNN) based on brain NE-CT to distinguish patients with and without BM. We retrospectively included NE-CT scans for 100 patients with single or multiple BM and 100 patients without brain imaging abnormalities. Patients whose largest lesion was < 5mm were excluded. The largest tumor was manually segmented on a matched contrast-enhanced T1 weighted Magnetic Resonance Imaging (MRI), and shape radiomics were extracted to determine the size and volume of the lesion. The brain was automatically segmented, and masked images were normalized and resampled. The dataset was split into training (70%) and validation (30%) sets. Multiple versions of a 3D-CNN were developed, and the best model was selected based on accuracy (ACC) on the validation set. The median largest tumor Maximum-3D-Diameter was 2.29cm, and its median volume was 2.81cc. Solitary BM were found in 27% of the patients, while 49% had > 5 BMs. The best model consisted of 4 convolutional layers with 3D average pooling layers, dropout layers of 50%, and a sigmoid activation function. Mean validation ACC was 0.983 (SD: 0.020) and mean area under receiver-operating characteristic curve was 0.983 (SD: 0.023). Sensitivity was 0.983 (SD: 0.020). We developed an accurate 3D-CNN based on brain NE-CT to differentiate between patients with and without BM. The model merits further external validation.

Citrate stabilized maghemite hydrosol with controllable MRI contrast: Key role of nanoparticle size

In this study, we have synthetized a series of citric acid stabilized superparamagnetic iron oxide nanoparticles (CA-SPIONs) with different core sizes in an automated chemical reactor with high repeatability of the nanoparticle size and chemical composition. The prepared CA-SPIONs are highly crystalline spherical-shaped particles with the diameters of 3.5 ± 0.7, 6 ± 1, 9 ± 1, and 12 ± 2 nm. The valent state of iron oxide was determined by a combination of X-ray photoelectron spectroscopy, UV–Vis spectroscopy, and Mössbauer studies, which confirmed predominantly maghemite formation. Under normal conditions, these nanoparticles exhibit no coercive force and no hysteresis, while saturation magnetization increases from 2 to 61 emu/g along with the increasing core size. Both longitudinal (r1) and transverse (r2) relaxivities of maghemite hydrosols with different nanoparticle sizes were measured and compared with the same data for the commercial Gd-complex (Gadovist). Magnetic circular dichroism spectroscopy indicated that aggregation occurs in magnetic field, but 9 nm samples slightly aggregate in the fields above 1.0 T, whereas 3.5 nm colloids are stable and do not exhibit aggregation behavior even at 1.5 T. The obtained series were examined in phantom test in clinical 1.5 T MRI scanner, which showed that increasing the particle core size resulted in an enhanced T2 contrast, while T1 contrast declined. Finally, the smallest CA-SPION colloid nanoparticles with the size of 3.5 nm exhibited significant T1 contrast enhancement, comparable with the commercial Gd-complex in water and human plasma as well. The maghemite hydrosol formed by nanoparticles with 3.5 nm size thus has a promising future as a T1 MRI contrast agent.

Unique brain injury patterns after proton vs photon radiotherapy for WHO grade 2-3 gliomas.

Central nervous system (CNS) injury following brain-directed radiotherapy remains a major challenge. Proton radiotherapy (PRT) minimizes radiation to healthy brain, potentially limiting sequelae. We characterized CNS radiotoxicity, including radiation-induced leukoencephalopathy (RIL), brain tissue necrosis (TN), and cerebral microbleeds (CMB), in glioma patients treated with PRT or photons (XRT). Thirty-four patients (19 male; median age 39.6 years) with WHO grade 2-3 gliomas treated with partial cranial radiotherapy (XRT [n = 17] vs PRT[n = 17]) were identified and matched by demographic/clinical criteria. Radiotoxicity was assessed longitudinally for 3 years post-radiotherapy via serial analysis of T2/FLAIR- (for RIL), contrast-enhanced T1- (for TN), and susceptibility (for CMB)-weighted MRI sequences. RIL was rated at whole-brain and hemispheric levels using a novel Fazekas scale-informed scoring system. The scoring system proved reliable (ICC > 0.85). Both groups developed moderate-to-severe RIL (62%[XRT]; 71%[PRT]) within 3 years; however, XRT was associated with persistent RIL increases in the contralesional hemisphere, whereas contralesional hemispheric RIL plateaued with PRT at 1-year post-radiotherapy (t = 2.180; P = .037). TN rates were greater with PRT (6%[XRT] vs 18%[PRT]; P = ns). CMB prevalence (76%[XRT]; 71%[PRT]) and burden (mean #CMB: 4.0[XRT]; 4.2[PRT]) were similar; however, XRT correlated with greater contralesional hemispheric CMB burden (27%[XRT]; 17%[PRT]; X2 = 4.986; P = .026), whereas PRT-specific CMB clustered at the radiation field margin (X2 = 14.7; P = .002). CNS radiotoxicity is common and progressive in glioma patients. Injury patterns suggest radiation modality-specificity as RIL, TN, and CMB exhibit unique spatiotemporal differences following XRT vs PRT, likely reflecting underlying dosimetric and radiobiological differences. Familiarity with such injury patterns is essential to improve patient management. Prospective studies are needed to validate these findings and assess their impacts on neurocognitive function.

Optimizing Image Quality with High-Resolution, Deep-Learning-Based Diffusion-Weighted Imaging in Breast Cancer Patients at 1.5 T.

The objective of this study was to evaluate a high-resolution deep-learning (DL)-based diffusion-weighted imaging (DWI) sequence for breast magnetic resonance imaging (MRI) in comparison to a standard DWI sequence (DWIStd) at 1.5 T. It is a prospective study of 38 breast cancer patients, who were scanned with DWIStd and DWIDL. Both DWI sequences were scored for image quality, sharpness, artifacts, contrast, noise, and diagnostic confidence with a Likert-scale from 1 (non-diagnostic) to 5 (excellent). The lesion diameter was evaluated on b 800 DWI, apparent diffusion coefficient (ADC), and the second subtraction (SUB) of the contrast-enhanced T1 VIBE. SNR was also calculated. Statistics included correlation analyses and paired t-tests. High-resolution DWIDL offered significantly superior image quality, sharpness, noise, contrast, and diagnostic confidence (each p < 0.02)). Artifacts were significantly higher in DWIDL by one reader (M = 4.62 vs. 4.36 Likert scale, p < 0.01) without affecting the diagnostic confidence. SNR was higher in DWIDL for b 50 and ADC maps (each p = 0.07). Acquisition time was reduced by 22% in DWIDL. The lesion diameters in DWI b 800DL and Std and ADCDL and Std were respectively 6% lower compared to the 2nd SUB. A DL-based diffusion sequence at 1.5 T in breast MRI offers a higher resolution and a faster acquisition, including only minimally more artefacts without affecting the diagnostic confidence.

DANTE-CAIPI Accelerated Contrast-Enhanced 3D T1: Deep learning-based image quality improvement for Vessel Wall MR.

Accelerated and blood-suppressed post-contrast 3D intracranial vessel wall MRI (IVW) enables high-resolution rapid scanning but is associated with low SNR. We hypothesized that a deep-learning (DL) denoising algorithm applied to accelerated, blood-suppressed post-contrast IVW can yield high-quality images with reduced artifacts and higher SNR in shorter scan times. Sixty-four consecutive patients underwent IVW, including conventional post-contrast 3D T1-sampling perfection with application-optimized contrasts by using different flip angle evolution (SPACE) and delay-alternating with nutation for tailored excitation (DANTE) blood-suppressed and CAIPIRINHIA-accelerated (CAIPI) 3D T1-weighted TSE post-contrast sequences (DANTE-CAIPI-SPACE). DANTE-CAIPI-SPACE acquisitions were then denoised using an unrolled deep convolutional network (DANTECAIPI-SPACE+DL). SPACE, DANTE-CAIPI-SPACE, and DANTE-CAIPI-SPACE+DL images were compared for overall image quality, SNR, severity of artifacts, arterial and venous suppression, and lesion assessment using 4-point or 5-point Likert scales. Quantitative evaluation of SNR and contrast-to-noise ratio (CNR) was performed. DANTE-CAIPI-SPACE+DL showed significantly reduced arterial (1 [1-1.75] vs. 3 [3-4], p<0.001) and venous flow artifacts (1 [1-2] vs. 3 [3-4], p<0.001) compared to SPACE. There was no significant difference between DANTE-CAIPI-SPACE+DL and SPACE in terms of image quality, SNR, artifact ratings and lesion assessment. For SNR ratings, DANTE-CAIPI-SPACE+DL was significantly better compared to DANTE-CAIPI-SPACE (2 [1-2], vs. 3 [2-3], p<0.001). No statistically significant differences were found between DANTECAIPI-SPACE and DANTE-CAIPI-SPACE+DL for image quality, artifact, arterial blood and venous blood flow artifacts, and lesion assessment. Quantitative vessel wall SNR and CNR median values were significantly higher for DANTE-CAIPI-SPACE+DL (SNR: 9.71, CNR: 4.24) compared to DANTE-CAIPI-SPACE (SNR: 5.50, CNR: 2.64), (p<0.001 for each), but there was no significant difference between SPACE (SNR: 10.82, CNR: 5.21) and DANTE-CAIPI-SPACE+DL. Deep-learning denoised post-contrast T1-weighted DANTE-CAIPI-SPACE accelerated and blood-suppressed IVW showed improved flow suppression with a shorter scan time and equivalent qualitative and quantitative SNR measures relative to conventional post-contrast IVW. It also improved SNR metrics relative to post-contrast DANTE-CAIPI-SPACE IVW. Implementing deep-learning denoised DANTE-CAIPI-SPACE IVW has the potential to shorten protocol time while maintaining or improving the image quality of IVW. DL=deep learning; IVW=Intracranial vessel wall MRI; SPACE=sampling perfection with application-optimized contrasts by using different flip angle evolution; DANTE=delay-alternating with nutation for tailored excitation; CAIPI=controlled aliasing in parallel imaging; CNR=contrast-to-noise ratio.

Optimization of micelle-encapsulated extremely small sized iron oxide nanoparticles as a T1 contrast imaging agent: biodistribution and safety profile

BackgroundIron oxide nanoparticles (IONPs) have been cleared by the Food and Drug Administration (FDA) for various clinical applications, such as tumor-targeted imaging, hyperthermia therapy, drug delivery, and live-cell tracking. However, the application of IONPs as T1 contrast agents has been restricted due to their high r2 values and r2/r1 ratios, which limit their effectiveness in T1 contrast enhancement. Notably, IONPs with diameters smaller than 5 nm, referred to as extremely small-sized IONPs (ESIONs), have demonstrated potential in overcoming these limitations. To advance the clinical application of ESIONs as T1 contrast agents, we have refined a scale-up process for micelle encapsulation aimed at improving the hydrophilization of ESIONs, and have carried out comprehensive in vivo biodistribution and preclinical toxicity assessments.ResultsThe optimization of the scale-up micelle-encapsulation process, specifically employing Tween60 at a concentration of 10% v/v, resulted in ESIONs that were uniformly hydrophilized, with an average size of 9.35 nm and a high purification yield. Stability tests showed that these ESIONs maintained consistent size over extended storage periods and dispersed effectively in blood and serum-mimicking environments. Relaxivity measurements indicated an r1 value of 3.43 mM− 1s− 1 and a favorable r2/r1 ratio of 5.36, suggesting their potential as T1 contrast agents. Biodistribution studies revealed that the ESIONs had extended circulation times in the bloodstream and were primarily cleared via the hepatobiliary route, with negligible renal excretion. We monitored blood clearance and organ distribution using positron emission tomography and magnetic resonance imaging (MRI). Additionally, MRI signal variations in a dose-dependent manner highlighted different behaviors at varying ESIONs concentrations, implying that optimal dosages might be specific to the intended imaging application. Preclinical safety evaluations indicated that ESIONs were tolerable in rats at doses up to 25 mg/kg.ConclusionsThis study effectively optimized a scale-up process for the micelle encapsulation of ESIONs, leading to the production of hydrophilic ESIONs at gram-scale levels. These optimized ESIONs showcased properties conducive to T1 contrast imaging, such as elevated r1 relaxivity and a reduced r2/r1 ratio. Biodistribution study underscored their prolonged bloodstream presence and efficient clearance through the liver and bile, without significant renal involvement. The preclinical toxicity tests affirmed the safety of the ESIONs, supporting their potential use as T1 contrast agent with versatile clinical application.

Prevalence of Rathke Cleft and Other Incidental Pituitary Gland Findings on Contrast-Enhanced 3D Fat-Saturated T1 MPRAGE at 7T MRI.

A cleftlike nonenhancing hypointensity was observed repeatedly in the pituitary gland at the adenohypophysis/neurohypophysis border on contrast-enhanced 3D fat-saturated T1-MPRAGE using clinical 7T MRI. Our primary goal was to assess the prevalence of this finding. The secondary goals were to evaluate the frequency of other incidental pituitary lesions, MRI artifacts, and their effect on pituitary imaging on the contrast-enhanced 3D fat-saturated T1 MPRAGE at 7T. One hundred patients who underwent 7T neuroimaging between October 27, 2021, and August 10, 2023, were included. Each case was evaluated for cleftlike pituitary hypointensity, pituitary masses, and artifacts on contrast-enhanced 3D fat-saturated T1 MPRAGE. Follow-up examinations were evaluated if present. The average prevalence for each finding was calculated, as were descriptive statistics for age and sex. A cleftlike hypointensity was present in 66% of 7T MRIs. There were no significant differences between the "cleftlike present" and "cleftlike absent" groups regarding sex (P = .39) and age (P = .32). The cleftlike hypointensity was demonstrated on follow-up MRIs in 3/3 patients with 7T, 1/12 with 3T, and 1/5 with 1.5T. A mass was found in 22%, while 75% had no mass and 3% were indeterminate. A mass was found in 18 (27%) of the cleftlike present and 4 (13%) of the cleftlike absent groups. The most common mass types were Rathke cleft cyst in 7 (31.8%) patients, "Rathke cleft cyst versus entrapped CSF" in 6 (27.3%), and microadenoma in 6 (22.2%) in the cleftlike present group. There were no significant differences in the mass types between the cleftlike present and cleftlike absent groups (P = .23). Susceptibility and/or motion artifacts were frequent using contrast-enhanced 3D fat-saturated T1 MPRAGE (54%). Artifact-free scans were significantly more frequent in the cleftlike present group (P = .03). A cleftlike nonenhancing hypointensity was frequently seen on the contrast-enhanced 3D fat-saturated T1 MPRAGE images at 7T MRI, which most likely represents a normal embryologic Rathke cleft remnant and cannot be seen in lower-field-strength MRIs. Susceptibility and motion artifacts are common in the sella. They may affect image quality, and the artifacts at 7T may lead to an underestimation of the prevalence of the Rathke cleft and other incidental findings.