T1-weighted Gradient-echo Images Research Articles

Cortical Microhemorrhage Presentation of Small Vessel Primary Angiitis of the Central Nervous System.

Primary angiitis of the central nervous system (PACNS) is a rare vasculitis restricted to the brain, spinal cord, and leptomeninges. This study aimed to describe the imaging characteristics of patients with small vessel PACNS (SV-PACNS) using 7 T magnetic resonance imaging (MRI). This ongoing prospective observational cohort study included patients who met the Calabrese and Mallek criteria and underwent 7 T MRI scan. The MRI protocol includes T1-weighted magnetization-prepared rapid gradient echo imaging, T2 star weighted imaging, and susceptibility-weighted imaging. Two experienced readers independently reviewed the neuroimages. Clinical data were extracted from the electronic patient records. The findings were then applied to a cohort of patients with large vessel central nervous system (CNS) vasculitis. We included 21 patients with SV-PACNS from December 2021 to November 2023. Of these, 12 (57.14%) had cerebral cortical microhemorrhages with atrophy. The pattern with microhemorrhages was described in detail based on the gradient echo sequence, leading to the identification of what we have termed the "coral-like sign." The onset age of patients with coral-like sign (33.83 ± 9.93 years) appeared younger than that of patients without coral-like sign (42.11 ± 14.18 years) (P = 0.131). Furthermore, the cerebral lesions in patients with cortical microhemorrhagic SV-PACNS showed greater propensity toward bilateral lesions (P = 0.03). The coral-like sign was not observed in patients with large vessel CNS vasculitis. The key characteristics of the coral-like sign represent cerebral cortical diffuse microhemorrhages with atrophy, which may be an important MRI pattern of SV-PACNS. ANN NEUROL 2024;96:194-203.

Brain Structures in a Human Embryo Imaged with MR Microscopy.

To delineate brain microstructures in human embryos during the formation of the various major primordia by MR microscopy, with different contrasts appropriate for each target. We focused mainly on the internal structures in the cerebral cortex and the accessory nerves of the brain. To find appropriate sequence parameters, we measured nuclear magnetic resonance (NMR) parameters and created kernel density plots of T1 and T2 values. We performed T1-weighted gradient echo imaging with parameters similar to those used in the previous studies. We performed T2*-weighted gradient echo imaging to delineate the target structures with the appropriate sequence parameters according to the NMR parameter and flip angle measurements. We also performed high-resolution imaging with both T1- and T2*-weighted sequences. T1, T2, and T2* values of the target tissues were positively correlated and shorter than those of the surrounding tissues. In T1-weighted images with a voxel size of (30 µm)3 and (20 µm)3, various organs and tissues and the agarose gel were differentiated as in previous studies, and the structure of approximately 40 µm in size was depicted, but the detailed structures within the cerebral cortex and the accessory nerves were not delineated. In T2*-weighted images with a voxel size of (30 µm)3, the layered structure within the cerebral cortex and the accessory nerves were clearly visualized. Overall, T1-weighted images provided more information than T2*-weighted images, but important internal brain structures of interest were visible only in T2*-weighted images. Therefore, it is essential to perform MR microscopy with different contrasts. We have visualized brain structures in a human embryo that had not previously been delineated by MR microscopy. We discussed pulse sequences appropriate for the structures of interest. This methodology would provide a way to visualize crucial embryological information about the anatomical structure of human embryos.

Application of a deep learning algorithm for three-dimensional T1-weighted gradient-echo imaging of gadoxetic acid-enhanced MRI in patients at a high risk of hepatocellular carcinoma.

To evaluate the efficacy of a vendor-specific deep learning reconstruction algorithm (DLRA) in enhancing image quality and focal lesion detection using three-dimensional T1-weighted gradient-echo images in gadoxetic acid-enhanced liver magnetic resonance imaging (MRI) in patients at a high risk of hepatocellular carcinoma. In this retrospective analysis, 83 high-risk patients with hepatocellular carcinoma underwent gadoxetic acid-enhanced liver MRI using a 3-T scanner. Triple arterial phase, high-resolution portal venous phase, and high-resolution hepatobiliary phase images were reconstructed using conventional reconstruction techniques and DLRA (AIRTM Recon DL; GE Healthcare) for subsequent comparison. Image quality and solid focal lesion detection were assessed by three abdominal radiologists and compared between conventional and DL methods. Focal liver lesion detection was evaluated using figures of merit (FOMs) from a jackknife alternative free-response receiver operating characteristic analysis on a per-lesion basis. DLRA-reconstructed images exhibited significantly improved overall image quality, image contrast, lesion conspicuity, vessel conspicuity, and liver edge sharpness and reduced subjective image noise, ringing artifacts, and motion artifacts compared to conventionally reconstructed images (all P < 0.05). Although there was no significant difference in the FOMs of non-cystic focal liver lesions between the conventional and DL methods, DLRA-reconstructed images showed notably higher pooled sensitivity than conventionally reconstructed images (P < 0.05) in all phases and higher detection rates for viable post-treatment HCCs in the arterial and hepatobiliary phases (all P < 0.05). Implementing DLRA can enhance the image quality in 3D T1-weighted gradient-echo sequences of gadoxetic acid-enhanced liver MRI examinations, leading to improved detection of viable post-treatment HCCs.

Clinical Breast MRI-based Radiomics for Distinguishing Benign and Malignant Lesions: An Analysis of Sequences and Enhanced Phases.

Previous studies have used different imaging sequences and different enhanced phases for breast lesion calsification in radiomics. The optimal sequence and contrast enhanced phase is unclear. To identify the optimal magnetic resonance imaging (MRI) radiomics model for lesion clarification, and to simulate its incremental value for multiparametric MRI (mpMRI)-guided biopsy. Retrospective. 329 female patients (138 malignant, 191 benign), divided into a training set (first site, n = 192) and an independent test set (second site, n = 137). 3.0-T, fast spoiled gradient-echo and fast spin-echo T1-weighted imaging (T1WI), fast spin-echo T2-weighted imaging (T2WI), echo-planar diffusion-weighted imaging (DWI), and fast spoiled gradient-echo contrast-enhanced MRI (CE-MRI). Two breast radiologists with 3 and 10 years' experience developed radiomics model on CE-MRI, CE-MRI + DWI, CE-MRI + DWI + T2WI, CE-MRI + DWI + T2WI + T1WI at each individual phase (P) and for multiple combinations of phases. The optimal radiomics model (Rad-score) was identified as having the highest area under the receiver operating characteristic curve (AUC) in the test set. Specificity was compared between a traditional mpMRI model and an integrated model (mpMRI + Rad-score) at sensitivity >98%. Wilcoxon paired-samples signed rank test, Delong test, McNemar test. Significance level was 0.05 and Bonferroni method was used for multiple comparisons (P = 0.007, 0.05/7). For radiomics models, CE-MRI/P3 + DWI + T2WI achieved the highest performance in the test set (AUC = 0.888, 95% confidence interval: 0.833-0.944). The integrated model had significantly higher specificity (55.3%) than the mpMRI model (31.6%) in the test set with a sensitivity of 98.4%. The CE-MRI/P3 + DWI + T2WI model is the optimized choice for breast lesion classification in radiomics, and has potential to reduce benign biopsies (100%-specificity) from 68.4% to 44.7% while retaining sensitivity >98%. 3 TECHNICAL EFFICACY: Stage 2.

Pediatric Hepatoblastoma After Neoadjuvant Chemotherapy: Diagnostic Performance of MR in Staging POSTTEXT and Vascular Involvement.

The assessment of resectability after neoadjuvant chemotherapy of hepatoblastoma is dependent on Post-Treatment EXTENT of Disease (POSTTEXT) staging and its annotation factors P (portal venous involvement) and V (hepatic venous/inferior vena cava [IVC] involvement), but MR performance in assessing them remains unclear. To assess the diagnostic performance of contrast-enhanced MR imaging for preoperative POSTTEXT staging and diagnosing vascular involvement in terms of annotation factors P and V in pediatric hepatoblastoma following neoadjuvant chemotherapy. Retrospective. Thirty-five consecutive patients (17 males, median age, 24 months; age range, 6-98 months) with proven hepatoblastoma underwent preoperative MR imaging following neoadjuvant chemotherapy. 3.0 T; T2-weighted imaging (T2WI), T2WI with fat suppression, diffusion weighted imaging, radial stack-of-the-star/Cartesian 3D Dixon T1-weighted gradient echo imaging. Three radiologists independently assessed the POSTTEXT stages and annotation factors P and V based on the 2017 PRE/POSTTEXT system. The sensitivities and specificities were calculated for 1) diagnosing each POSTTEXT stage; 2) discrimination of stages III and IV (advanced) from those stages I and II (non-advanced) hepatoblastomas; and 3) annotation factors P and V. The combination of pathologic findings and surgical records served as the reference standard. Sensitivity, specificity, Fleiss kappa test. The sensitivity and specificity ranges for discriminating advanced from non-advanced hepatoblastomas were 73.3%-80.0% and 80.0%-90.0%, respectively. For annotation factor P, they were 66.7%-100.0% and 90.6%, respectively. For factor V, they were 75.0% and 67.7%-83.9%, respectively. There was excellent, substantial, and moderate agreement on POSTTEXT staging (Fleiss kappa = 0.82), factors P (Fleiss kappa = 0.64), and factors V (Fleiss kappa = 0.60), respectively. MR POSTTEXT provides reliable discrimination between advanced and non-advanced tumors, and MR has moderate to excellent specificity at identifying portal venous and hepatic venous/IVC involvement. 3 TECHNICAL EFFICACY: Stage 3.

Multiple sclerosis imaging in clinical practice: a European-wide survey of 428 centers and conclusions by the ESNR Working Group.

To evaluate compliance with the available recommendations, we assessed the current clinical practice of imaging in the evaluation of multiple sclerosis (MS). An online questionnaire was emailed to all members and affiliates. Information was gathered on applied MR imaging protocols, gadolinium-based contrast agents (GBCA) use and image analysis. We compared the survey results with the Magnetic Resonance Imaging in MS (MAGNIMS) recommendations considered as the reference standard. A total of 428 entries were received from 44 countries. Of these, 82% of responders were neuroradiologists. 55% performed more than ten scans per week for MS imaging. The systematic use of 3T is rare (18%). Over 90% follow specific protocol recommendations with 3D FLAIR, T2-weighted and DWI being the most frequently used sequences. Over 50% use SWI at initial diagnosis and 3D gradient-echo T1-weighted imaging is the most used MRI sequence for pre- and post-contrast imaging. Mismatches with recommendations were identified including the use of only one sagittal T2-weighted sequence for spinal cord imaging, the systematic use of GBCA at follow-up (over 30% of institutions), a delay time shorter than 5min after GBCA administration (25%) and an inadequate follow-up duration in pediatric acute disseminated encephalomyelitis (80%). There is scarce use of automated software to compare images or to assess atrophy (13% and 7%). The proportions do not differ significantly between academic and non-academic institutions. While current practice in MS imaging is rather homogeneous across Europe, our survey suggests that recommendations are only partially followed. Hurdles were identified, mainly in the areas of GBCA use, spinal cord imaging, underuse of specific MRI sequences and monitoring strategies. This work will help radiologists to identify the mismatches between their own practices and the recommendations and act upon them. • While current practice in MS imaging is rather homogeneous across Europe, our survey suggests that available recommendations are only partially followed. • Several hurdles have been identified through the survey that mainly lies in the areas of GBCA use, spinal cord imaging, underuse of specific MRI sequences and monitoring strategies.

MRI-based synthetic CT in the detection of knee osteoarthritis: Comparison with CT.

Magnetic resonance Imagingis the gold standard for assessment of soft tissues; however, X-ray-based techniques are required for evaluating bone-related pathologies. This study evaluated the performance of synthetic computed tomography (sCT), a novel MRI-based bone visualization technique, compared with CT, for the scoring of knee osteoarthritis. sCT images were generated from the 3T T1-weighted gradient-echo MR images using a trained machine learning algorithm. Two readers scored the severity of osteoarthritis in tibiofemoral and patellofemoral joints according to OACT, which enables the evaluation of osteoarthritis, from its characteristics of joint space narrowing, osteophytes, cysts and sclerosis in CT (and sCT) images. Cohen's κ was used to assess the interreader agreement for each modality, and intermodality agreement of CT- and sCT-based scores for each reader. We also compared the confidence level of readers for grading CT and sCT images using confidence scores collected during grading. Inter-reader agreement for tibiofemoral and patellofemoral joints were almost-perfect for both modalities (κ = 0.83-0.88). The intermodality agreement of osteoarthritis scores between CT and sCT was substantial to almost-perfect for tibiofemoral (κ = 0.63 and 0.84 for the two readers) and patellofemoral joints (κ = 0.78 and 0.81 for the two readers). The analysis of diagnosis confidence scores showed comparable visual quality of the two modalities, where both are showing acceptable confidence levels for scoring OA. In conclusion, in this single-center study, sCT and CT were comparable for the scoring of knee OA.

Diaschisis Profiles in the Cerebellar Response to Hemodynamic Stimuli: Insights From Dynamic Measurement of Cerebrovascular Reactivity to Identify Occult and Transient Maxima.

Crossed cerebellar diaschisis (CCD) refers to depressions in perfusion and metabolism within the cerebellar hemisphere contralateral to supratentorial disease. Prior investigation into CCD in cerebrovascular reactivity (CVR) has been limited to terminal CVR estimations (CVRend ). We recently have demonstrated the presence of unsustained CVR maxima (CVRmax ) using dynamic CVR analysis, offering a fully dynamic characterization of CVR to hemodynamic stimuli. To investigate CCD in CVRmax from dynamic blood oxygen level-dependent (BOLD) MRI, by comparison with conventional CVRend estimation. Retrospective. A total of 23 patients (median age: 51 years, 10 females) with unilateral chronic steno-occlusive cerebrovascular disease, without prior knowledge of CCD status. A 3-T, T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) and acetazolamide-augmented BOLD imaging performed with a gradient-echo echo-planar imaging (EPI) sequence. A custom denoising pipeline was used to generate BOLD-CVR time signals. CVRend was established using the last minute of the BOLD response relative to the first-minute baseline. Following classification of healthy versus diseased cerebral hemispheres, CVRmax and CVRend were calculated for bilateral cerebral and cerebellar hemispheres. Three independent observers evaluated all data for the presence of CCD. Pearson correlations for comparing CVR across hemispheres, two-proportion Z-tests for comparing CCD prevalence, and Wilcoxon signed-rank tests for comparing median CVR. The level of statistical significance was set at P≤ 0.05. CCD-related changes were observed on both CVRend and CVRmax maps, with all CCD+ cases identifiable by inspection of either map. Diseased cerebral and contralateral cerebellar hemispheric CVR correlations in CCD+ patients were stronger when using CVRend (r=0.728) as compared to CVRmax (r=0.676). CVR correlations between healthy cerebral hemispheres and contralateral cerebellar hemispheres were stronger for CVRmax (r=0.739) than for CVRend (r=0.705). CCD-related alterations could be observed in CVR examinations. Conventional CVRend may underestimate CVR and could exaggerate CCD. 4. Stage 3.

Using Machine Learning Methods to Assess Lymphovascular Invasion and Survival in Breast Cancer: Performance of Combining Preoperative Clinical and MRI Characteristics.

Preoperative assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) is of high clinical relevance for treatment decision-making and prognosis. To investigate the associations of preoperative clinical and magnetic resonance imaging (MRI) characteristics with LVI and disease-free survival (DFS) by using machine learning methods in patients with IBC. Retrospective. Five hundred and seventy-five women (range: 24-79 years) with IBC who underwent preoperative MRI examinations at two hospitals, divided into the training (N=386) and validation datasets (N=189). Axial fat-suppressed T2-weighted turbo spin-echo sequence and dynamic contrast-enhanced with fat-suppressed T1-weighted three-dimensional gradient echo imaging. MRI characteristics (clinical T stage, breast edema score, MRI axillary lymph node status, multicentricity or multifocality, enhancement pattern, adjacent vessel sign, and increased ipsilateral vascularity) were reviewed independently by three radiologists. Logistic regression (LR), eXtreme Gradient Boosting (XGBoost), k-Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms were used to establish the models by combing preoperative clinical and MRI characteristics for assessing LVI status in the training dataset, and the methods were further applied in the validation dataset. The LVI score was calculated using the best-performing of the four models to analyze the association with DFS. Chi-squared tests, variance inflation factors, receiver operating characteristics (ROC), Kaplan-Meier curve, log-rank, Cox regression, and intraclass correlation coefficient were performed. The area under the ROC curve (AUC) and hazard ratios (HR) were calculated. A P-value <0.05 was considered statistically significant. The model established by the XGBoost algorithm had better performance than LR, SVM, and KNN models, achieving an AUC of 0.832 (95% confidence interval [CI]: 0.789, 0.876) in the training dataset and 0.838 (95% CI: 0.775, 0.901) in the validation dataset. The LVI score established by the XGBoost model was an independent indicator of DFS (adjusted HR: 2.66, 95% CI: 1.22-5.80). The XGBoost model based on preoperative clinical and MRI characteristics may help to investigate the LVI status and survival in patients with IBC. 3 TECHNICAL EFFICACY: Stage 2.

Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest

ObjectivesA deep learning-based super-resolution for postcontrast volume-interpolated breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to improve image quality, noise, artifacts and diagnostic confidence without change of acquisition parameters.Materials and methodsFifty patients who received VIBE postcontrast imaging of the chest at 1.5 T were included in this retrospective study. After acquisition of the standard VIBE (VIBES), a novel deep learning-based algorithm and a denoising algorithm were applied, resulting in enhanced images (VIBEDL). Two radiologists qualitatively evaluated both datasets independently, rating sharpness of soft tissue, vessels, bronchial structures, lymph nodes, artifacts, cardiac motion artifacts, noise levels and overall diagnostic confidence, using a Likert scale ranging from 1 to 4. In the presence of lung lesions, the largest lesion was rated regarding sharpness and diagnostic confidence using the same Likert scale as mentioned above. Additionally, the largest diameter of the lesion was measured.ResultsThe sharpness of soft tissue, vessels, bronchial structures and lymph nodes as well as the diagnostic confidence, the extent of artifacts, the extent of cardiac motion artifacts and noise levels were rated superior in VIBEDL (all P < 0.001).There was no significant difference in the diameter or the localization of the largest lung lesion in VIBEDL compared to VIBES. Lesion sharpness as well as detectability was rated significantly better by both readers with VIBEDL (both P < 0.001).ConclusionThe application of a novel deep learning-based super-resolution approach in T1-weighted VIBE postcontrast imaging resulted in an improvement in image quality, noise levels and diagnostic confidence as well as in a shortened acquisition time.

MRI detection of brain gadolinium retention in multiple sclerosis: Magnetization transfer vs. T1-weighted imaging.

Evidence of brain gadolinium retention has affected gadolinium-based contrast agent usage. It is, however, unclear to what extent macrocyclic agents are retained and whether their in vivo detection may necessitate nonconventional MRI. Magnetization transfer (MT) could prove suitable to detect gadolinium-related signal changes since dechelated gadolinium ions bind to macromolecules. Therefore, this study aimed to investigate associations of prior gadolinium administrations with MT and T1 signal abnormalities. A cohort of 23 persons with multiple sclerosis (MS) (18 females, 5 males, 57±8.0years) with multiple past gadolinium administrations (median 6, range 3-12) and 23 age- and sex-matched healthy controls underwent 1.5 Tesla MRI with MT, T1-weighted 2-dimensional spin echo, and T1-weighted 3-dimensional gradient echo. The signal intensity index was assessed by MRI in gadolinium retention predilection sites. There were dose-dependent associations of the globus pallidus signal on gradient echo (r=.55, p<.001) and spin echo (r=.38, p=.013) T1-weighted imaging, but not on MT. Relative to controls, MS patients had higher signal intensity index in the dentate nucleus on T1-weighted gradient echo (1.037±0.040 vs. 1.016±0.023, p=.04) with a similar trend in the globus pallidus on T1-weighted spin echo (1.091±0.034 vs. 1.076±0.014, p=.06). MT detected no group differences. Conventional T1-weighted imaging provided dose-dependent associations with gadolinium administrations in MS, while these could not be detected with 2-dimensional MT. Future studies could explore newer MT techniques like 3D and inhomogenous MT. Notably, these associations were identified with conventional MRI even though most patients had not received gadolinium administrations in the preceding 9years, suggestive of long-term retention.

Brain imaging of sequential acquisition using a flexible PET scanner and 3-T MRI: quantitative and qualitative assessment.

A mobile PET scanner termed flexible PET (fxPET) has been designed to fit existing MRI systems. The purpose of this study was to assess brain imaging with fxPET combined with 3-T MRI in comparison with conventional PET (cPET)/CT. In this prospective study, 29 subjects with no visible lesions except for mild leukoaraiosis on whole brain imaging underwent 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) cPET/CT followed by fxPET and MRI. The registration differences between fxPET and MRI and between cPET and CT were compared by measuring spatial coordinates. Three-dimensional magnetization-prepared rapid acquisition gradient-echo T1-weighted imaging (T1WI) was acquired. We applied two methods of attenuation correction to the fxPET images: MR-based attenuation correction, which yielded fxPETMRAC; and CT-based attenuation correction, which yielded fxPETCTAC. The three PET datasets were co-registered to the T1WI. Following subcortical segmentation and cortical parcellation, volumes of interest were placed in each PET image to assess physiological accumulation in the brain. SUVmean was obtained and compared between the three datasets. We also visually evaluated image distortion and clarity of fxPETMRAC. Mean misregistration of fxPET/MRI was < 3mm for each margin. Mean registration differences were significantly larger for fxPET/MRI than for cPET/CT except for the superior margin. There were high correlations between the three PET datasets regarding SUVmean. On visual evaluation of image quality, the grade of distortion was comparable between fxPETMRAC and cPET, and the grade of clarity was acceptable but inferior for fxPETMRAC compared with cPET. fxPET could successfully determine physiological [18F]FDG uptake; however, improved image clarity is desirable. In this study, fxPET/MRI at 3-T was feasible for brain imaging.

MR-self Noise2Noise: self-supervised deep learning-based image quality improvement of submillimeter resolution 3D MR images.

The study aimed to develop a deep neural network (DNN)-based noise reduction and image quality improvement by only using routine clinical scans and evaluate its performance in 3D high-resolution MRI. This retrospective study included T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) images from 185 clinical scans: 135 for DNN training, 11 for DNN validation, 20 for qualitative evaluation, and 19 for quantitative evaluation. Additionally, 18 vessel wall imaging (VWI) data were included to evaluate generalization. In each scan of the DNN training set, two noise-independent images were generated from the k-space data, resulting in an input-label pair. 2.5D U-net architecture was utilized for the DNN model. Qualitative evaluation between conventional MP-RAGE and DNN-based MP-RAGE was performed by two radiologists in image quality, fine structure delineation, and lesion conspicuity. Quantitative evaluation was performed with full sampled data as a reference by measuring quantitative error metrics and volumetry at 7 different simulated noise levels. DNN application on VWI was evaluated by two radiologists in image quality. Our DNN-based MP-RAGE outperformed conventional MP-RAGE in all image quality parameters (average scores = 3.7 vs. 4.9, p < 0.001). In the quantitative evaluation, DNN showed better error metrics (p < 0.001) and comparable (p > 0.09) or better (p < 0.02) volumetry results than conventional MP-RAGE. DNN application to VWI also revealed improved image quality (3.5 vs. 4.6, p < 0.001). The proposed DNN model successfully denoises 3D MR image and improves its image quality by using routine clinical scans only. • Our deep learning framework successfully improved conventional 3D high-resolution MRI in all image quality parameters, fine structure delineation, and lesion conspicuity. • Compared to conventional MRI, the proposed deep neural network-based MRI revealed better quantitative error metrics and comparable or better volumetry results. • Deep neural network application to 3D MRI whose pulse sequences and parameters were different from the training data showed improvement in image quality, revealing the potential to generalize on various clinical MRI.

Application of a Deep Learning Algorithm for Combined Super-Resolution and Partial Fourier Reconstruction Including Time Reduction in T1-Weighted Precontrast and Postcontrast Gradient Echo Imaging of Abdominopelvic MR Imaging.

Purpose: The purpose of this study was to test the technical feasibility and the impact on the image quality of a deep learning-based super-resolution reconstruction algorithm in 1.5 T abdominopelvic MR imaging. Methods: 44 patients who underwent abdominopelvic MRI were retrospectively included, of which 4 had to be subsequently excluded. After the acquisition of the conventional volume interpolated breath-hold examination (VIBEStd), images underwent postprocessing, using a deep learning-based iterative denoising super-resolution reconstruction algorithm for partial Fourier acquisitions (VIBESR). Image analysis of 40 patients with a mean age of 56 years (range 18–84 years) was performed qualitatively by two radiologists independently using a Likert scale ranging from 1 to 5, where 5 was considered the best rating. Results: Image analysis showed an improvement of image quality, noise, sharpness of the organs and lymph nodes, and sharpness of the intestine for pre- and postcontrast images in VIBESR compared to VIBEStd (each p < 0.001). Lesion detectability was better for VIBESR (p < 0.001), while there were no differences concerning the number of lesions. Average acquisition time was 16 s (±1) for the upper abdomen and 15 s (±1) for the pelvis for VIBEStd, and 15 s (±1) for the upper abdomen and 14 s (±1) for the pelvis for VIBESR. Conclusion: This study demonstrated the technical feasibility of a deep learning-based super-resolution algorithm including partial Fourier technique in abdominopelvic MR images and illustrated a significant improvement of image quality, noise, and sharpness while reducing TA.

Association of the Liver and Spleen Signal Intensity on MRI with Anemia in Gynecological Cancer.

This study investigates the association of the liver and spleen signal intensity on MRI with anemia in patients with gynecologic cancer. 332 patients with gynecological cancer and 78 healthy women underwent MRI examination. Liver and spleen MRI parameters and laboratory tests were obtained within 1 week. The signal intensity ratios of liver and spleen to the paraspinal muscle were calculated on gradient-echo T1-weighted images (T1WI) and T2-weighted images (T2WI) in both patients and healthy women, respectively. The ratios of liver and spleen to paraspinal muscle on T1WI and T2WI were lower in patients than in the healthy women, respectively (P<0.0001). The ratios of the liver and spleen to paraspinal muscle on T1WI and T2WI decreased with the increasing stage of anemia and decreasing hemoglobin levels (P<0.001). The ratios of the liver to paraspinal muscle on T1WI, spleen to paraspinal muscle on T1WI, and the liver and spleen to paraspinal muscle on T2WI could predict anemia stage≥1 (AUC=0.576, 0.643, 0.688, and 0.756, respectively), ≥2 (AUC=0.743, 0.714, 0.891, and 0.922, respectively) and 3 (AUC=0.851, 0.822, 0.854, and 0.949, respectively). T2WI-based spleen signal intensity ratios showed the highest potential for non-invasive evaluation of anemia in gynecological cancer.

Is the Impact Breast Implants with RFID Have on MRI Quality Investigated Thoroughly Enough?

Is the Impact Breast Implants with RFID Have on MRI Quality Investigated Thoroughly Enough?

Quantitative susceptibility mapping of both ring and non-ring white matter lesions in relapsing remitting multiple sclerosis

BackgroundRecent evidence suggests that the presence of magnetic susceptibility changes in MS lesions correlates with patients' expanded disability status scale (EDSS). PurposeThis study evaluated the presence of ring lesions (RLs) and non-RLs, as well as changes in lesion susceptibility and lesion volume over a two-year time period in relapsing-remitting multiple sclerosis (RRMS) patients and compared these measures to the EDSS. Study typeLongitudinal cohort. Materials and methodsA total of forty-three (43) patients with RRMS were recruited for this study. All subjects underwent 3 T MRI at baseline and forty-one (41) subjects had follow-up scans over a two-year period. The protocol included T2 fluid attenuated inversion recovery (FLAIR), pre- and post-contrast 2D T1-weighted gradient echo imaging and susceptibility weighted imaging (SWI) sequences. All data were acquired prior to gadolinium-based contrast agent injection except for SWI and post-contrast T1-weighted data. Mean and peak susceptibilities of lesions were measured using quantitative susceptibility mapping (QSM). For the RLs, inner−/outer-diameters from SWI data and absolute intensity from the FLAIR data were measured. The susceptibility and volumes of RLs and non-RLs were correlated with EDSS. ResultsThe number of RLs correlated positively with EDSS (p-value = 0.04), but the RL volumes did not show a correlation with EDSS. Measurements of the annular ring and regions with high susceptibility remained constant over time (p = 0.2). For non-ring QSM-positive (QSM+) lesions, the average susceptibility was significantly correlated to EDSS (p < 0.01.) This was also the case for lesion volume, as well as the product of volume and susceptibility when compared to EDSS (p < 0.01 for both). ConclusionsThe susceptibility distribution and lesion volumes for the RLs remained almost constant over time, suggesting that the changes in pathophysiology of the RLs is a gradual process. On the other hand, the presence of more than one RL along with both volume and susceptibility of non-RL QSM+ lesions were significantly associated with increased disability as measured by EDSS. These findings may strengthen the role of QSM in assessing MS patients radiologically.

From Dose Reduction to Contrast Maximization: Can Deep Learning Amplify the Impact of Contrast Media on Brain Magnetic Resonance Image Quality? A Reader Study.

The aim of this study was to evaluate a deep learning method designed to increase the contrast-to-noise ratio in contrast-enhanced gradient echo T1-weighted brain magnetic resonance imaging (MRI) acquisitions. The processed images are quantitatively evaluated in terms of lesion detection performance. A total of 250 multiparametric brain MRIs, acquired between November 2019 and March 2021 at Gustave Roussy Cancer Campus (Villejuif, France), were considered for inclusion in this retrospective monocentric study. Independent training (107 cases; age, 55 ± 14 years; 58 women) and test (79 cases; age, 59 ± 14 years; 41 women) samples were defined. Patients had glioma, brain metastasis, meningioma, or no enhancing lesion. Gradient echo and turbo spin echo with variable flip angles postcontrast T1 sequences were acquired in all cases. For the cases that formed the training sample, "low-dose" postcontrast gradient echo T1 images using 0.025 mmol/kg injections of contrast agent were also acquired. A deep neural network was trained to synthetically enhance the low-dose T1 acquisitions, taking standard-dose T1 MRI as reference. Once trained, the contrast enhancement network was used to process the test gradient echo T1 images. A read was then performed by 2 experienced neuroradiologists to evaluate the original and processed T1 MRI sequences in terms of contrast enhancement and lesion detection performance, taking the turbo spin echo sequences as reference. The processed images were superior to the original gradient echo and reference turbo spin echo T1 sequences in terms of contrast-to-noise ratio (44.5 vs 9.1 and 16.8; P < 0.001), lesion-to-brain ratio (1.66 vs 1.31 and 1.44; P < 0.001), and contrast enhancement percentage (112.4% vs 85.6% and 92.2%; P < 0.001) for cases with enhancing lesions. The overall image quality of processed T1 was preferred by both readers (graded 3.4/4 on average vs 2.7/4; P < 0.001). Finally, the proposed processing improved the average sensitivity of gradient echo T1 MRI from 88% to 96% for lesions larger than 10 mm ( P = 0.008), whereas no difference was found in terms of the false detection rate (0.02 per case in both cases; P > 0.99). The same effect was observed when considering all lesions larger than 5 mm: sensitivity increased from 70% to 85% ( P < 0.001), whereas false detection rates remained similar (0.04 vs 0.06 per case; P = 0.48). With all lesions included regardless of their size, sensitivities were 59% and 75% for original and processed T1 images, respectively ( P < 0.001), and the corresponding false detection rates were 0.05 and 0.14 per case, respectively ( P = 0.06). The proposed deep learning method successfully amplified the beneficial effects of contrast agent injection on gradient echo T1 image quality, contrast level, and lesion detection performance. In particular, the sensitivity of the MRI sequence was improved by up to 16%, whereas the false detection rate remained similar.

Magnetic resonance imaging (MRI) helps differentiate renal cell carcinoma with sarcomatoid differentiation from renal cell carcinoma without sarcomatoid differentiation.

The aim of the present study is to identify predictive imaging findings and construct a diagnostic model for differentiating renal cell carcinoma (RCC) with and without sarcomatoid dedifferentiation (sRCC and non-sRCC). This study is a single-center retrospective study. All patients had magnetic resonance imaging (MRI) with gradient-echo T1-weighted images, single-shot T2-weighted images (T2WI), and enhanced nephrographic phase images. Forty pathologically confirmed sRCCs and 80 non-sRCCs were included in this study. Control cases were selected by matching the tumor diameter and the year of MRI. Two radiologists independently evaluated the following findings: growth pattern, presence of low-intensity area on T2WI in the tumor (T2LIA), presence of non-enhancing area, local tumor stage, and presence of regional lymphadenopathy. Two radiologists measured the diameter of the tumor, T2LIA, and the non-enhancing area. Multivariable logistic regression analysis was used to identify independent predictive factors for differentiating sRCC from non-sRCC. Selected variables were entered in the logistic regression model, and the area under the curve (AUC) was calculated for each reader with 95% confidence intervals (CIs). Larger T2LIA-to-tumor diameter ratio, regional lymphadenopathy, and local tumor stage 4 were associated with sRCC, and selected for the subsequent construction of a logistic regression model. With this model, the AUCs were 0.76 (95% CI, 0.66-0.85) and 0.70 (95% CI, 0.59-0.81) for prediction of sRCC. In conclusion, larger T2LIA-to-tumor diameter ratio, regional lymphadenopathy, and local tumor stage 4 are predictive findings of sRCC. As a result, the model constructed using these findings demonstrated a moderate degree of diagnostic accuracy.

Loss of hippocampal dentation in hippocampal sclerosis and its relationship to memory dysfunction.

Hippocampal dentation (HD) is a "toothlike" morphological feature observed on the inferior aspect of the human hippocampus. It has been found that HD varies dramatically in healthy adults and is positively associated with verbal and visual memory. In this work, we evaluate the loss of HD and its association with memory dysfunction in patients with temporal lobe epilepsy (TLE) who have hippocampal sclerosis (HS). Fifty-eight unilateral HS patients with neuropsychological data were identified from a retrospective database. T1-weighted magnetization-prepared rapid acquisition gradient echo images (~1mm resolution) were upsampled to .25mm and were processed using ASHS software to obtain ultra-high-resolution segmentations and three-dimensional renderings. Dentes were counted on the epileptic and contralateral sides, and associations were tested between dentation on the epileptic versus contralateral sides and measures of verbal and visuospatial memory with respect to the dominant versus nondominant hemisphere. The median number of dentes in epileptic hippocampi was significantly lower than in contralateral hippocampi (p<.0001). Among cases with HS in the dominant hemisphere, verbal memory was significantly correlated with contralateral nondominant hemisphere dentation (r=.43, p=.04). Similarly, among cases of HS in the nondominant hemisphere, visual memory was significantly correlated with contralateral dominant hemisphere dentation (r=.48, p=.04). All other analyses were not significant. This is the first study characterizing dentation in TLE patients with HS and its memory correlates. There is marked loss of dentation in sclerotic hippocampi compared to the unaffected contralateral hippocampi. Material-specific measures of memory performance are paradoxically correlated with dentation contralateral to the side with HS, suggesting that contralateral functional capacity explains some of the variation in memory across TLE patients. HD is an important variable to consider in understanding memory loss in TLE.