Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.

BACKGROUND: Early prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) and investigate its value to improve pCR prediction as well as its association with tumor heterogeneity in breast cancer patients. METHODS: The DCE-MRI, clinicopathologic record, and full transcriptomic data of 785 breast cancer patients receiving neoadjuvant chemotherapy were retrospectively included from a public dataset. Dynamic features of DCE-MRI were computed from extracted phase-varying radiomic feature series using 22 CAnonical Time-sereis CHaracteristics. Dynamic model and radiomic model were developed by logistic regression using dynamic features and traditional radiomic features respectively. Various combined models with clinical factors were also developed to find the optimal combination and the significance of each components was evaluated. All the models were evaluated in independent test set in terms of area under receiver operating characteristic curve (AUC). To explore the potential underlying biological mechanisms, radiogenomic analysis was implemented on patient subgroups stratified by dynamic model to identify differentially expressed genes (DEGs) and enriched pathways. RESULTS: A 10-feature dynamic model and a 4-feature radiomic model were developed (AUC = 0.688, 95%CI: 0.635-0.741 and AUC = 0.650, 95%CI: 0.595-0.705) and tested (AUC = 0.686, 95%CI: 0.594-0.778 and AUC = 0.626, 95%CI: 0.529-0.722), with the dynamic model showing slightly higher AUC (train p = 0.181, test p = 0.222). The combined model of clinical, radiomic, and dynamic achieved the highest AUC in pCR prediction (train: 0.769, 95%CI: 0.722-0.816 and test: 0.762, 95%CI: 0.679-0.845). Compared with clinical-radiomic combined model (train AUC = 0.716, 95%CI: 0.665-0.767 and test AUC = 0.695, 95%CI: 0.656-0.714), adding the dynamic component brought significant improvement in model performance (train p < 0.001 and test p = 0.005). Radiogenomic analysis identified 297 DEGs, including CXCL9, CCL18, and HLA-DPB1 which are known to be associated with breast cancer prognosis or angiogenesis. Gene set enrichment analysis further revealed enrichment of gene ontology terms and pathways related to immune system. CONCLUSION: Dynamic characteristics of DCE-MRI were quantified and used to develop dynamic model for improving pCR prediction in breast cancer patients. The dynamic model was associated with tumor heterogeniety in prognostic-related gene expression and immune-related pathways.

The gut-brain axis: Correlation of choroid plexus volume and permeability with inflammatory biomarkers in Crohn's disease.

BACKGROUND: The dysregulation of the gut-brain axis in chronic inflammatory bowel diseases can cause neuro-psychological disturbances, but the underlying mechanisms are still not fully understood. The choroid plexus (CP) maintains brain homeostasis and nourishment through the secretion and clearance of cerebrospinal fluid. Recent research has demonstrated the existence of a CP vascular barrier in mice which is modulated during intestinal inflammation. This study investigates possible correlations between CP modifications and inflammatory activity in patients with Crohn's disease (CD). METHODS: In this prospective study, 17 patients with CD underwent concomitant abdominal and brain 3 T MRI. The volume and permeability of CP were compared with levels of C-reactive protein (CRP), fecal calprotectin (FC), sMARIA and SES-CD scores. RESULTS: The CP volume was negatively correlated with CRP levels (R = -0.643, p-value = 0.024) and FC (R = -0.571, p-value = 0.050). DCE metrics normalized by CP volume were positively correlated with CRP (K-trans: R = 0.587, p-value = 0.045; Vp: R = 0.706, p-value = 0.010; T1: R = 0.699, p-value = 0.011), and FC (Vp: R = 0.606, p-value = 0.037). CONCLUSIONS: Inflammatory activity in patients with CD is associated with changes in CP volume and permeability, thus supporting the hypothesis that intestinal inflammation could affect the brain through the modulation of CP vascular barrier also in humans.

Current status in spatiotemporal analysis of contrast-based perfusion MRI.

In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions-of-interest as isolated systems supplied by a single global source. This simplification not only leads to long-recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between-voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state-of-the-art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research.

A community-endorsed open-source lexicon for contrast agent-based perfusion MRI: A consensus guidelines report from the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI).

This manuscript describes the ISMRM OSIPI (Open Science Initiative for Perfusion Imaging) lexicon for dynamic contrast-enhanced and dynamic susceptibility-contrast MRI. The lexicon was developed by Taskforce 4.2 of OSIPI to provide standardized definitions of commonly used quantities, models, and analysis processes with the aim of reducing reporting variability. The taskforce was established in February 2020 and consists of medical physicists, engineers, clinicians, data and computer scientists, and DICOM (Digital Imaging and Communications in Medicine) standard experts. Members of the taskforce collaborated via a slack channel and quarterly virtual meetings. Members participated by defining lexicon items and reporting formats that were reviewed by at least two other members of the taskforce. Version 1.0.0 of the lexicon was subject to open review from the wider perfusion imaging community between January and March 2022, and endorsed by the Perfusion Study Group of the ISMRM in the summer of 2022. The initial scope of the lexicon was set by the taskforce and defined such that it contained a basic set of quantities, processes, and models to enable users to report an end-to-end analysis pipeline including kinetic model fitting. We also provide guidance on how to easily incorporate lexicon items and definitions into free-text descriptions (e.g., in manuscripts and other documentation) and introduce an XML-based pipeline encoding format to encode analyses using lexicon definitions in standardized and extensible machine-readable code. The lexicon is designed to be open-source and extendable, enabling ongoing expansion of its content. We hope that widespread adoption of lexicon terminology and reporting formats described herein will increase reproducibility within the field.

The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge.

PURPOSE: K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize K trans $$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate K trans $$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for K trans $$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' K trans $$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in K trans $$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods.

PURPOSE: To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods. METHODS: A simulation framework in a form of DRO toolkit has been developed using the ultrafast and conventional breast DCE-MRI data of 53 women with malignant (n = 25) or benign (n = 28) lesions. We segmented five anatomical regions and performed pharmacokinetic analysis to determine the ranges of pharmacokinetic parameters for each segmented region. A database of the segmentations and their pharmacokinetic parameters is included in the DRO toolkit that can generate a large number of realistic breast DCE-MRI data. We provide two potential examples for our DRO toolkit: assessing the accuracy of an image reconstruction method using undersampled simulated radial k-space data and assessing the impact of the B 1 + $$ {\mathrm{B}}_1^{+} $$ field inhomogeneity on estimated parameters. RESULTS: The estimated pharmacokinetic parameters for each region showed agreement with previously reported values. For the assessment of the reconstruction method, it was found that the temporal regularization resulted in significant underestimation of estimated parameters by up to 57% and 10% with the weighting factor λ = 0.1 and 0.01, respectively. We also demonstrated that spatial discrepancy of v p $$ {v}_p $$ and PS $$ \mathrm{PS} $$ increase to about 33% and 51% without correction for B 1 + $$ {\mathrm{B}}_1^{+} $$ field. CONCLUSION: We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.

Motion-compensated image reconstruction for improved kidney function assessment using dynamic contrast-enhanced MRI.

Accurately measuring renal function is crucial for pediatric patients with kidney conditions. Traditional methods have limitations, but dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides a safe and efficient approach for detailed anatomical evaluation and renal function assessment. However, motion artifacts during DCE-MRI can degrade image quality and introduce misalignments, leading to unreliable results. This study introduces a motion-compensated reconstruction technique for DCE-MRI data acquired using golden-angle radial sampling. Our proposed method achieves three key objectives: (1) identifying and removing corrupted data (outliers) using a Gaussian process model fitting with a k -space center navigator, (2) efficiently clustering the data into motion phases and performing interphase registration, and (3) utilizing a novel formulation of motion-compensated radial reconstruction. We applied the proposed motion correction (MoCo) method to DCE-MRI data affected by varying degrees of motion, including both respiratory and bulk motion. We compared the outcomes with those obtained from the conventional radial reconstruction. Our evaluation encompassed assessing the quality of images, concentration curves, and tracer kinetic model fitting, and estimating renal function. The proposed MoCo reconstruction improved the temporal signal-to-noise ratio for all subjects, with a 21.8% increase on average, while total variation values of the aorta, right, and left kidney concentration were improved for each subject, with 32.5%, 41.3%, and 42.9% increases on average, respectively. Furthermore, evaluation of tracer kinetic model fitting indicated that the median standard deviation of the estimated filtration rate ( σ F T ), mean normalized root-mean-squared error (nRMSE), and chi-square goodness-of-fit of tracer kinetic model fit were decreased from 0.10 to 0.04, 0.27 to 0.24, and, 0.43 to 0.27, respectively. The proposed MoCo technique enabled more reliable renal function assessment and improved image quality for detailed anatomical evaluation in the case of bulk and respiratory motion during the acquisition of DCE-MRI.

MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.

BACKGROUND: Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)-based tumor heterogeneity in assessing ALN metastasis in BC is unclear. PURPOSE: To assess the value of deep learning (DL)-derived kinetic heterogeneity parameters based on BC dynamic contrast-enhanced (DCE)-MRI to infer the ALN status. STUDY TYPE: Retrospective. SUBJECTS: 1256/539/153/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively. FIELD STRENGTH/SEQUENCE: 1.5 T/3.0 T, non-contrast T1-weighted spin-echo sequence imaging (T1WI), DCE-T1WI, and diffusion-weighted imaging. ASSESSMENT: Clinical pathological and MRI semantic features were obtained by reviewing histopathology and MRI reports. The segmentation of the tumor lesion on the first phase of T1WI DCE-MRI images was applied to other phases after registration. A DL architecture termed convolutional recurrent neural network (ConvRNN) was developed to generate the KHimage (kinetic heterogeneity of DCE-MRI image) score that indicated the ALN status in patients with BC. The model was trained and optimized on training and internal validation cohorts, tested on two external validation cohorts. We compared ConvRNN model with other 10 models and the subgroup analyses of tumor size, magnetic field strength, and molecular subtype were also evaluated. STATISTICAL TESTS: Chi-squared, Fisher's exact, Student's t, Mann-Whitney U tests, and receiver operating characteristics (ROC) analysis were performed. P < 0.05 was considered significant. RESULTS: The ConvRNN model achieved area under the curve (AUC) of 0.802 in the internal validation cohort and 0.785-0.806 in the external validation cohorts. The ConvRNN model could well evaluate the ALN status of the four molecular subtypes (AUC = 0.685-0.868). The patients with larger tumor sizes (>5 cm) were more susceptible to ALN metastasis with KHimage scores of 0.527-0.827. DATA CONCLUSION: A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Cotyledon-Specific Flow Evaluation of Rhesus Macaque Placental Injury Using Ferumoxytol Dynamic Contrast-Enhanced MRI.

BACKGROUND: Recently, dynamic contrast-enhanced (DCE) MRI with ferumoxytol as contrast agent has recently been introduced for the noninvasive assessment of placental structure and function throughout. However, it has not been demonstrated under pathological conditions. PURPOSE: To measure cotyledon-specific rhesus macaque maternal placental blood flow using ferumoxytol DCE MRI in a novel animal model for local placental injury. STUDY TYPE: Prospective animal model. SUBJECTS: Placental injections of Tisseel (three with 0.5 mL and two with 1.5 mL), monocyte chemoattractant protein 1 (three with 100 µg), and three with saline as controls were performed in a total of 11 rhesus macaque pregnancies at approximate gestational day (GD 101). DCE MRI scans were performed prior (GD 100) and after (GD 115 and GD 145) the injection (term = GD 165). FIELD STRENGTH/SEQUENCE: 3 T, T1-weighted spoiled gradient echo sequence (product sequence, DISCO). ASSESSMENT: Source images were inspected for motion artefacts from the mother or fetus. Placenta segmentation and DCE processing were performed for the dynamic image series to measure cotyledon specific volume, flow, and normalized flow. Overall placental histopathology was conducted for controls, Tisseel, and MCP-1 animals and regions of tissue infarctions and necrosis were documented. Visual inspections for potential necrotic tissue were conducted for the two Tisseelx3 animals. STATISTICAL TESTS: Wilcoxon rank sum test, significance level P < 0.05. RESULTS: No motion artefacts were observed. For the group treated with 1.5 mL of Tisseel, significantly lower cotyledon volume, flow, and normalized flow per cotyledon were observed for the third gestational time point of imaging (day ~145), with mean normalized flow of 0.53 minute-1 . Preliminary histopathological analysis shows areas of tissue necrosis from a selected cotyledon in one Tisseel-treated (single dose) animal and both Tisseelx3 (triple dose) animals. DATA CONCLUSION: This study demonstrates the feasibility of cotyledon-specific functional analysis at multiple gestational time points and injury detection in a placental rhesus macaque model through ferumoxytol-enhanced DCE MRI. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.

Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions.

OBJECTIVES: To propose a novel model-free data-driven approach based on the voxel-wise mapping of DCE-MRI time-intensity-curve (TIC) profiles for quantifying and visualizing hemodynamic heterogeneity and to validate its potential clinical applications. MATERIALS AND METHODS: From December 2018 to July 2022, 259 patients with 325 pathologically confirmed breast lesions who underwent breast DCE-MRI were retrospectively enrolled. Based on the manually segmented breast lesions, the TIC of each voxel within the 3D whole lesion was classified into 19 subtypes based on wash-in rate (nonenhanced, slow, medium, and fast), wash-out enhancement (persistent, plateau, and decline), and wash-out stability (steady and unsteady), and the composition ratio of these 19 subtypes for each lesion was calculated as a new feature set (type-19). The three-type TIC classification, semiquantitative parameters, and type-19 features were used to build machine learning models for identifying lesion malignancy and classifying histologic grades, proliferation status, and molecular subtypes. RESULTS: The type-19 feature-based model significantly outperformed models based on the three-type TIC method and semiquantitative parameters both in distinguishing lesion malignancy (respectively; AUC = 0.875 vs. 0.831, p = 0.01 and 0.875vs. 0.804, p = 0.03), predicting tumor proliferation status (AUC = 0.890 vs. 0.548, p = 0.006 and 0.890 vs. 0.596, p = 0.020), but not in predicting histologic grades (p = 0.820 and 0.970). CONCLUSION: In addition to conventional methods, the proposed computational approach provides a novel, model-free, data-driven approach to quantify and visualize hemodynamic heterogeneity. CLINICAL RELEVANCE STATEMENT: Voxel-wise intra-lesion mapping of TIC profiles allows for visualization of hemodynamic heterogeneity and its composition ratio for differentiation of malignant and benign breast lesions. KEY POINTS: • Voxel-wise TIC profiles were mapped, and their composition ratio was compared between various breast lesions. • The model based on the composition ratio of voxel-wise TIC profiles significantly outperformed the three-type TIC classification model and the semiquantitative parameters model in lesion malignancy differentiation and tumor proliferation status prediction in breast lesions. • This novel, data-driven approach allows the intuitive visualization and quantification of the hemodynamic heterogeneity of breast lesions.

Dynamic contrast-enhanced MR imaging in identifying active anal fistula after surgery.

BACKGROUND: It is challenging to identify residual or recurrent fistulas from the surgical region, while MR imaging is feasible. The aim was to use dynamic contrast-enhanced MR imaging (DCE-MRI) technology to distinguish between active anal fistula and postoperative healing (granulation) tissue. METHODS: Thirty-six patients following idiopathic anal fistula underwent DCE-MRI. Subjects were divided into Group I (active fistula) and Group IV (postoperative healing tissue), with the latter divided into Group II (≤ 75 days) and Group III (> 75 days) according to the 75-day interval from surgery to postoperative MRI reexamination. MRI classification and quantitative analysis were performed. Correlation between postoperative time intervals and parameters was analyzed. The difference of parameters between the four groups was analyzed, and diagnostic efficiency was tested by receiver operating characteristic curve. RESULTS: Wash-in rate (WI) and peak enhancement intensity (PEI) were significantly higher in Group I than in Group II (p = 0.003, p = 0.040), while wash-out rate (WO), time to peak (TTP), and normalized signal intensity (NSI) were opposite (p = 0.031, p = 0.007, p = 0.010). Area under curves for discriminating active fistula from healing tissue within 75 days were 0.810 in WI, 0.708 in PEI, 0.719 in WO, 0.783 in TTP, 0.779 in NSI. All MRI parameters were significantly different between Group I and Group IV, but not between Group II and Group III, and not related to time intervals. CONCLUSION: In early postoperative period, DCE-MRI can be used to identify active anal fistula in the surgical area. TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR2000033072.

MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis.

BACKGROUND: Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) plays an important role in the diagnosis and treatment of breast cancer. However, obtaining complete eight temporal images of DCE-MRI requires a long scanning time, which causes patients' discomfort in the scanning process. Therefore, to reduce the time, the multi temporal feature fusing neural network with Co-attention (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables the acquisition of DCE-MRI images without scanning. In order to reduce the time, multi-temporal feature fusion cooperative attention mechanism neural network (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables DCE-MRI image acquisition without scanning. METHODS: In this paper, we propose multi temporal feature fusing neural network with Co-attention (MTFN) for DCE-MRI Synthesis, in which the Co-attention module can fully fuse the features of the first and third temporal image to obtain the hybrid features. The Co-attention explore long-range dependencies, not just relationships between pixels. Therefore, the hybrid features are more helpful to generate the eighth temporal images. RESULTS: We conduct experiments on the private breast DCE-MRI dataset from hospitals and the multi modal Brain Tumor Segmentation Challenge2018 dataset (BraTs2018). Compared with existing methods, the experimental results of our method show the improvement and our method can generate more realistic images. In the meanwhile, we also use synthetic images to classify the molecular typing of breast cancer that the accuracy on the original eighth time-series images and the generated images are 89.53% and 92.46%, which have been improved by about 3%, and the classification results verify the practicability of the synthetic images. CONCLUSIONS: The results of subjective evaluation and objective image quality evaluation indicators show the effectiveness of our method, which can obtain comprehensive and useful information. The improvement of classification accuracy proves that the images generated by our method are practical.

Diagnosis and monitoring denosumab therapy of giant cell tumors of bone: radiologic-pathologic correlation.

OBJECTIVE: To determine the value of CT and dynamic contrast-enhanced (DCE-)MRI for monitoring denosumab therapy of giant cell tumors of bone (GCTB) by correlating it to histopathology. MATERIALS AND METHODS: Patients with GCTB under denosumab treatment and monitored with CT and (DCE-)MRI (2012-2021) were retrospectively included. Imaging and (semi-)quantitative measurements were used to assess response/relapse. Tissue samples were analyzed using computerized segmentation for vascularization and number of neoplastic and giant cells. Pearson's correlation/Spearman's rank coefficient and Kruskal-Wallis tests were used to assess correlations between histopathology and radiology. RESULTS: Six patients (28 ± 8years; five men) were evaluated. On CT, good responders showed progressive re-ossification (+7.8HU/month) and cortical remodeling (woven bone). MRI showed an SI decrease relative to muscle on T1-weighted (-0.01 A.U./month) and on fat-saturated T2-weighted sequences (-0.03 A.U./month). Time-intensity-curves evolved from a type IV with high first pass, high amplitude, and steep wash-out to a slow type II. An increase in time-to-peak (+100%) and a decrease in Ktrans (-71%) were observed. This is consistent with microscopic examination, showing a decrease of giant cells (-76%), neoplastic cells (-63%), and blood vessels (-28%). There was a strong statistical significant inverse correlation between time-to-peak and microvessel density (ρ = -0.9, p = 0.01). Significantly less neoplastic (p = 0.03) and giant cells (p = 0.04) were found with a time-intensity curve type II, compared to a type IV. Two patients showed relapse after initial good response when stopping denosumab. Inverse imaging and pathological findings were observed. CONCLUSION: CT and (DCE-)MRI show a good correlation with pathology and allow adequate evaluation of response to denosumab and detection of therapy failure.

Earlier consolidation and improved knee function of medial open wedge high tibial osteotomy with autologous bone graft.

PURPOSE: Medial knee osteoarthritis can be treated with medial open wedge high tibial osteotomy (OWHTO). We sought to investigate osseous consolidation of the osteotomy with and without autologous bone grafts (ABG) to detect possible benefits of ABG in osseous healing and functional outcome. METHODS: In this prospective study, patients without graft transplantation were compared to those receiving ABG after medial OWHTO. They were followed up 6 weeks, 12 weeks, 6 months and 12 months postoperatively. Radiographic progress of consolidation, clinical scores, contrast-enhanced ultrasound (CEUS) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were assessed at each appointment. RESULTS: A total of 35 patients were enrolled, 20 without and 15 with graft transplantation. Radiologic evaluation showed a significantly earlier consolidation of the osteotomy gaps (p = 0.012) in patients with ABG, resulting in a significantly higher rate of consolidation 12 months after surgery (60% without bone graft vs. 100% with bone graft, p = 0.006). At 6 weeks as well as 6-month follow-up, a tendency of earlier consolidation with ABG was apparent, but not statistically significant (6 weeks: 50% vs. 80%, p = 0.089; 6 months: 30% vs. 60%, p = 0.097). CEUS and DCE-MRI showed physiological perfusion of the osteotomy gaps in both groups. A tendency to better function and less pain in patients with ABG was recognizable. CONCLUSION: In our study, autologous bone grafting evocated earlier osseous consolidation after medial OWHTO and showed a tendency to a better functional outcome.

Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function.

PURPOSE: In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time. MATERIALS AND METHOD: A total of 13 healthy subjects (younger (<40 y/o): 8, older (> 67 y/o): 5) were recruited and underwent 25-min DCE-MRI scans. The 25 min data were retrospectively truncated to 10 min to simulate a reduced scan time of 10 min. A deep learning network was trained to predict the CIF using simulated tissue contrast dynamics with two vascular transport models. The BBB permeability (PS) was measured using 3 methods: (i) Ca-25min, using DCE-MRI data of 25 min with individually sampled AIF (Ca); (ii) Ca-10min, using truncated 10min data with AIF (Ca); and (iii) Cp-10min, using truncated 10 min data with CIF (Cp). The PS estimates from the Ca-25min method were used as reference standard values to assess the accuracy of the Ca-10min and Cp-10min methods in estimating the PS values. RESULTS: When compared to the reference method(Ca-25min), the Ca-10min and Cp-10min methods resulted in an overestimation of PS by 217 ± 241 % and 48.0 ± 30.2 %, respectively. The Bland Altman analysis showed that the mean difference from the reference was 8.85 ± 1.78 (x10-4 min-1) with the Ca-10min, while it was reduced to 1.63 ± 2.25 (x10-4 min-1) with the Cp-10min, resulting in an average reduction of 81%. The limits of agreement also reduced by up to 39.2% with the Cp-10min. We found a 75% increase of BBB permeability in the gray matter and a 35% increase in the white matter, when comparing the older group to the younger group. CONCLUSIONS: We demonstrated the feasibility of estimating the capillary-level input functions using a deep learning network. We also showed that this method can be used to estimate subtle age-related changes in BBB permeability with reduced scan time, without compromising accuracy. Moreover, the trained deep learning network can automatically select CIF, reducing the potential uncertainty resulting from manual user-intervention.

Self-gated, dynamic contrast-enhanced magnetic resonance imaging with compressed-sensing reconstruction for evaluating endothelial permeability in the aortic root of atherosclerotic mice.

High-risk atherosclerotic plaques are characterized by active inflammation and abundant leaky microvessels. We present a self-gated, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition with compressed sensing reconstruction and apply it to assess longitudinal changes in endothelial permeability in the aortic root of Apoe-/- atherosclerotic mice during natural disease progression. Twenty-four, 8-week-old, female Apoe-/- mice were divided into four groups (n = 6 each) and imaged with self-gated DCE-MRI at 4, 8, 12, and 16 weeks after high-fat diet initiation, and then euthanized for CD68 immunohistochemistry for macrophages. Eight additional mice were kept on a high-fat diet and imaged longitudinally at the same time points. Aortic-root pseudo-concentration curves were analyzed using a validated piecewise linear model. Contrast agent wash-in and washout slopes (b1 and b2 ) were measured as surrogates of aortic root endothelial permeability and compared with macrophage density by immunohistochemistry. b2 , indicating contrast agent washout, was significantly higher in mice kept on an high-fat diet for longer periods of time (p = 0.03). Group comparison revealed significant differences between mice on a high-fat diet for 4 versus 16 weeks (p = 0.03). Macrophage density also significantly increased with diet duration (p = 0.009). Spearman correlation between b2 from DCE-MRI and macrophage density indicated a weak relationship between the two parameters (r = 0.28, p = 0.20). Validated piecewise linear modeling of the DCE-MRI data showed that the aortic root contrast agent washout rate is significantly different during disease progression. Further development of this technique from a single-slice to a 3D acquisition may enable better investigation of the relationship between in vivo imaging of endothelial permeability and atherosclerotic plaques' genetic, molecular, and cellular makeup in this important model of disease.

4D Golden-Angle Radial MRI at Subsecond Temporal Resolution.

Intraframe motion blurring, as a major challenge in free-breathing dynamic MRI, can be reduced if high temporal resolution can be achieved. To address this challenge, this work proposes a highly accelerated 4D (3D + time) dynamic MRI framework with subsecond temporal resolution that does not require explicit motion compensation. The method combines standard stack-of-stars golden-angle radial sampling and tailored GRASP-Pro (Golden-angle RAdial Sparse Parallel imaging with imProved performance) reconstruction. Specifically, 4D dynamic MRI acquisition is performed continuously without motion gating or sorting. The k-space centers in stack-of-stars radial data are organized to guide estimation of a temporal basis, with which GRASP-Pro reconstruction is employed to enforce joint low-rank subspace and sparsity constraints. This new basis estimation strategy is the new feature proposed for subspace-based reconstruction in this work to achieve high temporal resolution (e.g., subsecond/3D volume). It does not require sequence modification to acquire additional navigation data, it is compatible with commercially available stack-of-stars sequences, and it does not need an intermediate reconstruction step. The proposed 4D dynamic MRI approach was tested in abdominal motion phantom, free-breathing abdominal MRI, and dynamic contrast-enhanced MRI (DCE-MRI). Our results have shown that GRASP-Pro reconstruction with the new basis estimation strategy enables highly-accelerated 4D dynamic imaging at subsecond temporal resolution (with five spokes or less for each dynamic frame per image slice) for both free-breathing non-DCE-MRI and DCE-MRI. In the abdominal phantom, better image quality with lower root mean square error and higher structural similarity index was achieved using GRASP-Pro compared with standard GRASP. With the ability to acquire each 3D image in less than 1 s, intraframe respiratory blurring can be intrinsically reduced for body applications with our approach, which eliminates the need for explicit motion detection and motion compensation.

A delta-radiomic lymph node model using dynamic contrast enhanced MRI for the early prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients.

BACKGROUND: The objective of this paper is to explore the value of a delta-radiomic model of the axillary lymph node (ALN) using dynamic contrast-enhanced (DCE) MRI for early prediction of the axillary pathological complete response (pCR) of breast cancer patients after neoadjuvant chemotherapy (NAC). METHODS: A total of 120 patients with ALN-positive breast cancer who underwent breast MRI before and after the first cycle of NAC between October 2018 and May 2021 were prospectively included in this study. Patients were divided into a training (n = 84) and validation (n = 36) cohort based on the temporal order of their treatments. Radiomic features were extracted from the largest slice of targeted ALN on DCE-MRI at pretreatment and after one cycle of NAC, and their changes (delta-) were calculated and recorded. Logistic regression was then applied to build radiomic models using the pretreatment (pre-), first-cycle(1st-), and changes (delta-) radiomic features separately. A clinical model was also built and combined with the radiomic models. The models were evaluated by discrimination, calibration, and clinical application and compared using DeLong test. RESULTS: Among the three radiomic models, the ALN delta-radiomic model performed the best with AUCs of 0.851 (95% CI: 0.770-0.932) and 0.822 (95% CI: 0.685-0.958) in the training and validation cohorts, respectively. The clinical model yielded moderate AUCs of 0.742 (95% CI: 0.637-0.846) and 0.723 (95% CI: 0.550-0.896), respectively. After combining clinical features to the delta-radiomics model, the efficacy of the combined model (AUC = 0.932) in the training cohort was significantly higher than that of both the delta-radiomic model (Delong p = 0.017) and the clinical model (Delong p < 0.001) individually. Additionally, in the validation cohort, the combined model had the highest AUC (0.859) of any of the models we tested although this was not statistically different from any other individual model's validation AUC. Calibration and decision curves showed a good agreement and a high clinical benefit for the combined model. CONCLUSION: This preliminary study indicates that ALN-based delta-radiomic model combined with clinical features is a promising strategy for the early prediction of downstaging ALN status after NAC. Future axillary MRI applications need to be further explored.

A Cox Nomogram for Assessing Recurrence Free Survival in Hepatocellular Carcinoma Following Surgical Resection Using Dynamic Contrast-Enhanced MRI Radiomics.

BACKGROUND: The prognosis of hepatocellular carcinoma (HCC) is difficult to predict and carries high mortality. This study utilized radiomic techniques with clinical examinations to assess recurrence in HCC. PURPOSE: To develop a Cox nomogram to assess the risk of postoperative recurrence in HCC using radiomic features of three volumes of interest (VOIs) in preoperative dynamic contrast-enhanced MRI (DCE-MRI), along with clinical findings. STUDY TYPE: Retrospective. SUBJECTS: 249 patients with pathologically proven HCCs undergoing surgical resection at three institutions were selected. FIELD STRENGTH/SEQUENCE: Fat saturated T2-weighted, Fat saturated T1-weighted, and DCE-MRI performed at 1.5 T and 3.0 T. ASSESSMENT: Three VOIs were generated; the tumor VOI corresponds to the area from the tumor core to the outer perimeter of the tumor, the tumor +10 mm VOI represents the area from the tumor perimeter to 10 mm distal to the tumor in all directions, finally, the background liver parenchyma VOI represents the hepatic tissue outside the tumor. Three models were generated. The total radiomic model combined information from the three listed VOI's above. The clinical-radiological model combines physical examination findings with imaging characteristics such as tumor size, margin features, and metastasis. The combined radiomic model includes features from both models listed above and showed the highest reliability for assessing 24-month survival for HCC. STATISTICAL TESTS: The least absolute shrinkage and selection operator (LASSO) Cox regression, univariable, and multivariable Cox regression, Kmeans clustering, and Kaplan-Meier analysis. The discrimination performance of each model was quantified by the C-index. A P value <0.05 was considered statistically significant. RESULTS: The combined radiomic model, which included features from the radiomic VOI's and clinical imaging provided the highest performance (C-index: training cohort = 0.893, test cohort = 0.851, external cohort = 0.797) in assessing the survival of HCC. CONCLUSION: The combined radiomic model provides superior ability to discern the possibility of recurrence-free survival in HCC over the total radiomic and the clinical-radiological models. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 2.

Influence of Different Measurement Methods of Arterial Input Function on Quantitative Dynamic Contrast-Enhanced MRI Parameters in Head and Neck Cancer.

BACKGROUND: Head and neck cancer (HNC) is the sixth most prevalent cancer worldwide. Dynamic contrast-enhanced MRI (DCE-MRI) helps in diagnosis and prognosis. Quantitative DCE-MRI requires an arterial input function (AIF), which affects the values of pharmacokinetic parameters (PKP). PURPOSE: To evaluate influence of four individual AIF measurement methods on quantitative DCE-MRI parameters values (Ktrans , ve , kep , and vp ), for HNC and muscle. STUDY TYPE: Prospective. POPULATION: A total of 34 HNC patients (23 males, 11 females, age range 24-91) FIELD STRENGTH/SEQUENCE: A 3 T; 3D SPGR gradient echo sequence with partial saturation of inflowing spins. ASSESSMENT: Four AIF methods were applied: automatic AIF (AIFa) with up to 50 voxels selected from the whole FOV, manual AIF (AIFm) with four voxels selected from the internal carotid artery, both conditions without (Mc-) or with (Mc+) motion correction. Comparison endpoints were peak AIF values, PKP values in tumor and muscle, and tumor/muscle PKP ratios. STATISTICAL TESTS: Nonparametric Friedman test for multiple comparisons. Nonparametric Wilcoxon test, without and with Benjamini Hochberg correction, for pairwise comparison of AIF peak values and PKP values for tumor, muscle and tumor/muscle ratio, P value ≤ 0.05 was considered statistically significant. RESULTS: Peak AIF values differed significantly for all AIF methods, with mean AIFmMc+ peaks being up to 66.4% higher than those for AIFaMc+. Almost all PKP values were significantly higher for AIFa in both, tumor and muscle, up to 76% for mean Ktrans values. Motion correction effect was smaller. Considering tumor/muscle parameter ratios, most differences were not significant (0.068 ≤ Wilcoxon P value ≤ 0.8). DATA CONCLUSION: We observed important differences in PKP values when using either AIFa or AIFm, consequently choice of a standardized AIF method is mandatory for DCE-MRI on HNC. From the study findings, AIFm and inflow compensation are recommended. The use of the tumor/muscle PKP ratio should be of interest for multicenter studies. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.