. Thoracoabdominal MRI is limited by respiratory motion, especially in populations who cannot perform breath-holds. One approach for reducing motion blurring in radially-acquired MRI is respiratory ...gating. Straightforward 'hard-gating' uses only data from a specified respiratory window and suffers from reduced SNR. Proposed 'soft-gating' reconstructions may improve scan efficiency but reduce motion correction by incorporating data with nonzero weight acquired outside the specified window. However, previous studies report conflicting benefits, and importantly the choice of soft-gated weighting algorithm and effect on image quality has not previously been explored. The purpose of this study is to map how variable soft-gated weighting functions and parameters affect signal and motion blurring in respiratory-gated reconstructions of radial lung MRI, using neonates as a model population.
. Ten neonatal inpatients with respiratory abnormalities were imaged using a 1.5 T neonatal-sized scanner and 3D radial ultrashort echo-time (UTE) sequence. Images were reconstructed using ungated, hard-gated, and several soft-gating weighting algorithms (exponential, sigmoid, inverse, and linear weighting decay outside the period of interest), with %N
representing the relative amount of data included. The apparent SNR (aSNR) and motion blurring (measured by the maximum derivative of image intensity at the diaphragm, MDD) were compared between reconstructions.
. Soft-gating functions produced higher aSNR and lower MDD than hard-gated images using equivalent %N
, as expected. aSNR was not identical between different gating schemes for given %N
. While aSNR was approximately linear with %N
for each algorithm, MDD performance diverged between functions as %N
decreased. Algorithm performance was relatively consistent between subjects, except in images with high noise.
. The algorithm selection for soft-gating has a notable effect on image quality of respiratory-gated MRI; the timing of included data across the respiratory phase, and not simply the amount of data, plays an important role in aSNR. The specific soft-gating function and parameters should be considered for a given imaging application's requirements of signal and sharpness.
Stiffness plays an important role in diagnosing renal fibrosis. However, kidney stiffness is altered by perfusion changes in many kidney diseases. Therefore, the aim of the current study is to ...determine the correlation of kidney stiffness with water intake. We hypothesize that kidney stiffness will increase with 1 L of water intake due to increased water perfusion to the kidneys. Additionally, stiffness of the kidneys will correlate with apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values before and after water intake. A 3 T MRI scanner was used to perform magnetic resonance elastography and diffusion tensor imaging of the kidneys on 24 healthy subjects (age range: 22‐66 years) before and after water intake of 1 L. A 3D T1‐weighted bladder scan was also performed to measure bladder volume before and after water intake. A paired t‐test was performed to evaluate the effect of water intake on the stiffness of kidneys, in addition to bladder volume. A Spearman correlation test was performed to determine the association between stiffness, bladder volume, ADC and FA values of both kidneys before and after water intake. The results show a significant increase in stiffness in different regions of the kidney (ie, percentage increase ranged from 3.6% to 7.5%) and bladder volume after water intake (all P < 0.05). A moderate significant negative correlation was observed between change in kidney stiffness and bladder volume (concordance correlation coefficient = ‐0.468, P < 0.05). No significant correlation was observed between stiffness and ADC or FA values before and after water intake in both kidneys (P > 0.05). Water intake caused a significant increase in the stiffness of the kidneys. The negative correlation between the change in kidney stiffness and bladder volume, before and after water intake, indicates higher perfusion pressure in the kidneys, leading to increased stiffness.
Stiffness plays an important role in diagnosing renal fibrosis. However, it is established that perfusion is altered in different kidney diseases, leading to changes in kidney stiffness. This study demonstrated that water intake caused significant increase in the stiffness of the kidneys. Also, negative correlation between changes in kidney stiffness and bladder volume before and after water intake indicates higher perfusion pressure in the kidneys, leading to increased stiffness.
Percutaneous-transluminal renal angioplasty (PTRA) and stenting aim to halt the progression of kidney disease in patients with renal artery stenosis (RAS), but its outcome is often suboptimal. We ...hypothesized that a model incorporating markers of renal function and oxygenation extracted using radiomics analysis of blood oxygenation-level dependent (BOLD)-MRI images may predict renal response to PTRA in swine RAS.
Twenty domestic pigs with RAS were scanned with CT and BOLD MRI before and 4 weeks after PTRA. Stenotic (STK) and contralateral (CLK) kidney volume, blood flow (RBF), and glomerular filtration rate (GFR) were determined, and BOLD-MRI R2 * maps were generated before and after administration of furosemide, a tubular reabsorption inhibitor. Radiomics features were extracted from pre-PTRA BOLD maps and Robust features were determined by Intraclass correlation coefficients (ICC). Prognostic models were developed to predict post-PTRA renal function based on the baseline functional and BOLD-radiomics features, using Lasso-regression for training, and testing with resampling.
Twenty-six radiomics features passed the robustness test. STK oxygenation distribution pattern did not respond to furosemide, whereas in the CLK radiomics features sensitive to oxygenation heterogeneity declined. Radiomics-based model predictions of post-PTRA GFR (r = 0.58, p = 0.007) and RBF (r = 0.68; p = 0.001) correlated with actual measurements with sensitivity and specificity of 92% and 67%, respectively. Models were unsuccessful in predicting post-PTRA systemic measures of renal function.
Several radiomics features are sensitive to cortical oxygenation patterns and permit estimation of post-PTRA renal function, thereby distinguishing subjects likely to respond to PTRA and stenting.
Display omitted
•MR oxygenation maps unveil tissue engagement, and hypoxia susceptibility.•Radiomics mines spatial kidney oxygenation patterns characteristics.•Oxygenation heterogeneity may predict stenting kidney outcomes.•Heterogeneity signals tubular health; grave harm lessens revascularization gains.•Radiomics-guided renal BOLD probes noninvasively into histological tissue damage.
Varicocele is the most common reversible cause of male infertility, affecting up to 20% of healthy men and 40% of men with primary infertility. The objective of this study was to investigate the ...prevalence of varicocele in men evaluated for infertility, and to determine rates of subsequent varicocele repair. Since reproductive endocrinologists are the first specialists seen for male infertility care in North America, we hypothesized that varicocele would be underdiagnosed when compared to its reported prevalence among men with infertility. TriNetX, a large, multicenter electronic health record (EHR) database was queried to establish a cohort of all men (above 18 years of age) with a diagnosis of male infertility. This cohort was used to identify those with ensuing varicocele diagnosis. Men who received varicocelectomy or venous embolization after a diagnosis of varicocele were then identified. Out of 101,309 men with a diagnosis of male infertility in the network, only 9768 (9.6%) had a diagnosis of varicocele. Mean age of men with varicocele was 34. Varicocelectomy or venous embolization was performed in 1699 (20.2%) and 69 (0.76%) of men with varicocele, respectively. In this cross‐sectional EHR study, varicocele was underdiagnosed in men evaluated for infertility when compared with prior epidemiological studies.
Abstract BACKGROUND Standard MRI sequences, such as pre- and post-contrast T1w, T2w, and FLAIR images, are essential for optimizing segmentation of tumor subregions and evaluating treatment responses ...in pediatric brain tumors (PBTs). However, MRI sets are often incomplete due to imaging artifacts or inconsistent acquisition protocols across various centers. Generative Adversarial Networks (GANs) have been effectively utilized to generate missing MRI sequences for adult brain tumors. This study applies image-to-image translation models using GANs to synthesize missing FLAIR images from T2w images in PBTs. METHODS This retrospective study developed two GAN models, pix2pix in both 2D and 3D, trained and validated on T2w and FLAIR image pairs from 79 and 19 patients, respectively, with pediatric-type diffuse high-grade gliomas (diffuse midline glioma (DMG) including diffuse intrinsic pontine glioma (DIPG)), collected from the Children’s Brain Tumor Network (CBTN). The 2D pix2pix model processes each T2w MRI volume slice-by-slice, generating corresponding FLAIR slices to reconstruct the complete image volume. The 3D pix2pix model allows translation of 3D FLAIR volumes directly from T2w volumes without the need for individual slice processing and reconstruction. The quality of the generated FLAIR volumes was evaluated using Structural Similarity Index (SSI; best closer to 1) and Mean Squared Error (MSE; best near 0) on the validation set. RESULTS The 2D and 3D models achieved robust performance with median SSI, MSE of 0.88, 0.004, and 0.92, 0.002, respectively. CONCLUSIONS The GAN models developed in this study effectively generate missing FLAIR MRI volumes from corresponding T2w images in PBTs. The 3D model, outperforming 2D, speeds the overall processing pipeline by eliminating volume slicing and reconstruction. Future work includes assessing the impact of these synthesized images on the accuracy of our pretrained autosegmentation models in differentiating tumor subregions and measuring tumor volumes in longitudinal MRIs.
Background
Liver shear stiffness measurement using magnetic resonance elastography (MRE) aids in the noninvasive diagnosis and staging of liver fibrosis. Inadequate breath‐holds can lead to ...inaccurate stiffness estimation and/or failed MRE exams.
Purpose
To prospectively evaluate the performance of compressed sensitivity encoding (C‐SENSE) accelerated rapid MRE measurement of liver shear stiffness using displacement wave polarity‐inversion motion encoding.
Study Type
Retrospective.
Subjects
Eleven with liver disease and 10 asymptomatic subjects.
Field Strength/Sequence
1.5 T; gradient‐recalled‐echo (GRE) MRE.
Assessment
All participants underwent: 1) two‐dimensional (2D) GRE MRE with inflow saturation using SENSE acceleration factor (R) of 2 (standard of care SC); 2) 2D rapid MRE with (RwS); and 3) without (RnS) inflow saturation using C‐SENSE R = 3; and 4) spatial three‐dimensional (3D) rapid MRE with inflow saturation (R3D) using C‐SENSE R = 4; with nominally identical spatial resolution and coverage. Image analyst (D.G., 2 years of experience) drew identical and maximal regions of interest (ROIs) in right hepatic lobe.
Statistical Tests
Linear regression, intra‐class correlation coefficients (ICC), Bland–Altman analyses, and the Wilcoxon signed‐rank test were used to assess consistency and agreement of liver stiffness measurements for manually drawn identical and maximal ROIs.
Results
In 21 participants (37 ± 14 years) with liver stiffness (2.3 ± 0.7 kPa), body mass index (BMI 27 ± 7 kg/m2), proton density fat fraction (PDFF 9 ± 9%), and T2* (27 ± 4 msec); rapid MRE sequences showed excellent agreement (ICC > 0.95) with SC MRE and no correlation (r2 < 0.1) of the differences (mean difference <0.2 kPa, <6%; limits of agreement <0.4 kPa, <16%) with BMI, PDFF, and T2*. Breath‐hold times were: 14 seconds (SC), 5 seconds (RnS), 7 seconds (RwS) per slice, and 16 seconds for the R3D acquisition.
Data Conclusions
C‐SENSE accelerated GRE MRE sequences, using displacement wave polarity‐inversion motion encoding, produce equivalent measurements of liver stiffness and have potential clinical benefit in patients with limited breath‐holding capacity.
Level of Evidence
1
Technical Efficacy
Stage 1
Inside Cover Image Gandhi, Deep; Kalra, Prateek; Raterman, Brian ...
NMR in biomedicine,
April 2020, 2020-04-00, Letnik:
33, Številka:
4
Journal Article
Recenzirano
The cover image is based on the Research Article Magnetic resonance elastography‐derived stiffness of the kidneys and its correlation with water perfusion by Deep Gandhi et al., ...https://doi.org/10.1002/nbm.4237.
Abstract BACKGROUND Skull-stripping, the process of extracting brain tissue from MR images, is an important step for tumor segmentation and downstream imaging-based analytics such as AI-powered ...radiomic feature extraction. Existing skull-stripping models, designed for pediatric or adult patients, show limitations in accurately segmenting tumors in sellar/suprasellar regions. This limitation hinders their reliable application across different histologies of pediatric brain tumors. We propose a deep learning approach for fully automated skull-stripping, compatible with both single- or multi-parametric MRI sequences. METHODS We developed 3D nnU-Net models trained on preprocessed MRI sequences (including pre- and post-contrast T1w, T2w, and FLAIR) from 336 patients with brain tumors across multiple tumor histologies such as low-grade, high-grade and brainstem gliomas, medulloblastoma, ependymoma, etc., aged between 3 months and 20 years (median age, 8.5 years). The training utilized manually generated brain masks, including the sellar/suprasellar region, from 153 patients and employed 5-fold cross-validation to split the data into inner training-validation sets. The models were then tested on a withheld set of 183 subjects. Additionally, we trained a single-parametric model on individual images, resulting in 612 training and 732 testing cases. Model performance was evaluated using the Dice similarity metric for segmenting both the entire brain and slices specifically containing the sella turcica. RESULTS The multi-parametric and single-parametric models achieved mean±sd Dice scores of 0.981±0.008 (median=0.983) and 0.979±0.009 (median=0.981), respectively. For the sellar/suprasellar slices, the scores were 0.983±0.009 (median=0.986) and 0.981±0.012 (median=0.984), respectively. These results indicate a high precision in segmenting not only the entire brain volume, but also the sellar/suprasellar region. CONCLUSION Our proposed deep learning-based skull-stripping approach, leveraging both multi-parametric and single-parametric MRI inputs, demonstrates excellent accuracy. These models, made publicly available, have potential for improving auto-processing pipelines in pediatric brain tumors.
Abstract BACKGROUND Accurate radiographic diagnosis of pediatric brain tumors (PBTs) that originate in the brainstem and posterior fossa, including medulloblastoma (MB), pilocytic astrocytoma (PA), ...ependymoma (EPN), atypical teratoid/rhabdoid tumor (ATRT), and diffuse intrinsic pontine glioma (DIPG), is crucial for optimizing surgical approaches and enhancing neoadjuvant therapies. Existing research on the radiographic differential diagnosis of posterior fossa tumors has limitations, including small sample sizes, lack of inclusion of certain histologies especially rarer tumors such as ATRTs, and incomplete analysis of the whole tumor, including peritumoral edema. In this study, we aimed to perform a comprehensive analysis using radiomics and machine learning to differentiate among the common posterior fossa and brainstem tumors. METHODS We employed 927 radiomic features extracted from whole tumor regions within treatment-naïve, standard multiparametric MRI sequences (pre-/post-contrast T1-weighted, T2-weighted, FLAIR) of 264 patients (106 MBs, 78 PAs, 28 EPNs, 27 ATRTs, and 25 DIPGs), collected from the Children’s Brain Tumor Network (CBTN). We adopted a one-versus-rest classification strategy, employing Support Vector Machines combined with the Least Absolute Shrinkage and Selection Operator (LASSO) for feature selection, and implementing nested cross-validation for robustness. RESULTS The performances of the classifiers were evaluated using the Area Under the Receiver Operating Characteristic Curve, yielding values of 0.84 for MBs, 0.84 for PAs, 0.70 for EPNs, 0.75 for ATRTs, and 0.71 for DIPGs. CONCLUSIONS Our method effectively differentiates between various tumor types in the posterior fossa and brainstem, paving the path towards the development of comprehensive diagnostic and prognostic AI tools for pre-treatment histological diagnosis of these tumors. These AI tools can lead to more tailored, risk-adjusted treatments for PBTs, reducing morbidities and improving patient outcomes. Based on our promising initial results, we will expand our dataset to include more samples and incorporate rarer tumor types, as well as piloting in different molecular subclassifications.
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