Classic models for dynamic contrast-enhanced MRI

Exercise-induced calf muscle hyperemia quantified with dynamic blood oxygen level-dependent (BOLD) imaging

Muscle hyperemia in exercise is usually the combined result of increased cardiac output and local muscle vasodilation, with the latter reflecting muscle's capacity for increased blood perfusion to support exercise. In this study, we aim to quantify muscle's vasodilation capability with dynamic BOLD imaging. A deoxyhemoglobin-kinetics model is proposed to analyze dynamic BOLD signals acquired during exercise recovery, deriving a hyperemia index (HI) for a muscle group of interest. We demonstrated the method's validity with calf muscles of healthy subjects who performed plantar flexion for muscle stimulation. In a test with exercise load incrementally increasing from 0 to 16 lbs., gastrocnemius HI showed considerable variance among the 4 subjects, but with a consistent trend, i.e. low at light load (e.g. 0-6 lbs) and linearly increasing at heavy load. The high variability among different subjects was confirmed with the other 10 subjects who exercised with a same moderate load of 8 lbs., with coefficient of variance among subjects' medial gastrocnemius 87.8%, lateral gastrocnemius 111.8% and soleus 132.3%. These findings align with the fact that intensive exercise induces high muscle hyperemia, but a comparison among different subjects is hard to make, presumably due to the subjects' different rate of oxygen utilization. For the same 10 subjects who exercised with load of 8 lbs., we also performed dynamic contrast enhanced (DCE) MRI to measure muscle perfusion (F). With a moderate correlation of 0.654, HI and F displayed three distinctive responses of calf muscles: soleus of all the subjects were in the cluster of low F and low HI, and gastrocnemius of most subjects had high F and either low or high HI. This finding suggests that parameter F encapsulates blood flow through vessels of all sizes, but BOLD-derived HI focuses on capillary flow and therefore is a more specific indicator of muscle vasodilation. In conclusion, the proposed hyperemia index has the potential of quantitatively assessing muscle vasodilation induced with exercise.

Changes in perfusion and permeability in glioblastoma model induced by the anti-angiogenic agents cediranib and thalidomide

BACKGROUND AND PURPOSE

The poor delivery of drugs to infiltrating tumor cells contributes to therapeutic failure in glioblastoma. During the early phase of an anti-angiogenic treatment, a remodeling of the tumor vasculature could occur, leading to a more functional vessel network that could enhance drug delivery. However, the restructuration of blood vessels could increase the proportion of normal endothelial cells that could be a barrier for the free diffusion of drugs. The net balance, in favor or not, of a better delivery of compounds during the course of an antiangiogenic treatment remains to be established. This study explored whether cediranib and thalidomide could modulate perfusion and vessel permeability in the brain U87 tumor mouse model.

METHODS

The dynamic evolution of the diffusion of agents outside the tumor core using the fluorescent dye Evans Blue in histology and Gd-DOTA using dynamic contrast-enhanced (DCE)-MRI. CD31 labelling of endothelial cells was used to measure the vascular density.

RESULTS AND INTERPRETATION

Cediranib and thalidomide effectively reduced tumor size over time. The accessibility of Evans Blue outside the tumor core continuously decreased over time. The vascular density was significantly decreased after treatment while the proportion of normal vessels remained unchanged over time. In contrast to histological studies, DCE-MRI did not tackle any significant change in hemodynamic parameters, in the core or margins of the tumor, whatever the parameter used or the pharmacokinetic model used. While cediranib and thalidomide were effective in decreasing the tumor size, they were ineffective in transiently increasing the delivery of agents in the core and the margins of the tumor.

Challenges, limitations, and pitfalls of PET and advanced MRI in patients with brain tumors: A report of the PET/RANO group

Brain tumor diagnostics have significantly evolved with the use of positron emission tomography (PET) and advanced magnetic resonance imaging (MRI) techniques. In addition to anatomical MRI, these modalities may provide valuable information for several clinical applications such as differential diagnosis, delineation of tumor extent, prognostication, differentiation between tumor relapse and treatment-related changes, and the evaluation of response to anticancer therapy. In particular, joint recommendations of the Response Assessment in Neuro-Oncology (RANO) Group, the European Association of Neuro-oncology, and major European and American Nuclear Medicine societies highlighted that the additional clinical value of radiolabeled amino acids compared to anatomical MRI alone is outstanding and that its widespread clinical use should be supported. For advanced MRI and its steadily increasing use in clinical practice, the Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition provided more recently an updated acquisition protocol for the widely used dynamic susceptibility contrast perfusion MRI. Besides amino acid PET and perfusion MRI, other PET tracers and advanced MRI techniques (e.g. MR spectroscopy) are of considerable clinical interest and are increasingly integrated into everyday clinical practice. Nevertheless, these modalities have shortcomings which should be considered in clinical routine. This comprehensive review provides an overview of potential challenges, limitations, and pitfalls associated with PET imaging and advanced MRI techniques in patients with gliomas or brain metastases. Despite these issues, PET imaging and advanced MRI techniques continue to play an indispensable role in brain tumor management. Acknowledging and mitigating these challenges through interdisciplinary collaboration, standardized protocols, and continuous innovation will further enhance the utility of these modalities in guiding optimal patient care.

Bone marrow lesions in knee osteoarthritis assessed by dynamic contrast-enhanced MRI with histopathological correlations

BACKGROUND

Bone marrow lesions (BMLs) in knee osteoarthritis (OA) have been assessed histopathologically and by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI); however, a direct comparison of the results has not been reported.

PURPOSE

To evaluate and compare the findings by DCE-MRI and histopathology of subchondral BMLs in knee OA.

MATERIAL AND METHODS

In total, 19 patients with medial tibiofemoral knee OA undergoing total knee arthroplasty were analyzed. Preoperative MRI, including a DCE sequence, was performed, and bone biopsies were obtained from the resected specimens corresponding to BML areas. The contrast enhancement by DCE-MRI was analyzed using semi-quantitative (area under the curve [AUC]), peak enhancement [PE]), and quantitative (Ktrans, Kep) methods. Enhancement in the medial OA compartment was compared with similar areas in a normal lateral compartment, and the DCE characteristics of BMLs were correlated with semi-quantitatively graded histopathological features.

RESULTS

AUC and PE were significantly higher in medial tibial and femoral BMLs compared with the values in the lateral condyles; Ktrans and Kep were only significantly higher in the tibial plateau. In the tibia, AUC and PE were significantly correlated with the grade of vascular proliferation, and PE also with the degree of marrow fibrosis. There was no significant correlation between AUC/PE and histopathological findings in the femur and no correlation between quantitative DCE parameters and histopathological findings.

CONCLUSION

BML characteristics by semi-quantitative DCE in the form of AUC and PE may be used as parameters for the degree of histopathological vascularization in the bone marrow whereas quantitative DCE data were less conclusive.

Blind deconvolution decreases requirements on temporal resolution of DCE-MRI: Application to 2nd generation pharmacokinetic modeling

PURPOSE

Dynamic Contrast-Enhanced (DCE) MRI with 2nd generation pharmacokinetic models provides estimates of plasma flow and permeability surface-area product in contrast to the broadly used 1st generation models (e.g. the Tofts models). However, the use of 2nd generation models requires higher frequency with which the dynamic images are acquired (around 1.5 s per image). Blind deconvolution can decrease the demands on temporal resolution as shown previously for one of the 1st generation models. Here, the temporal-resolution requirements achievable for blind deconvolution with a 2nd generation model are studied.

METHODS

The 2nd generation model is formulated as the distributed-capillary adiabatic-tissue-homogeneity (DCATH) model. Blind deconvolution is based on Parker's model of the arterial input function. The accuracy and precision of the estimated arterial input functions and the perfusion parameters is evaluated on synthetic and real clinical datasets with different levels of the temporal resolution.

RESULTS

The estimated arterial input functions remained unchanged from their reference high-temporal-resolution estimates (obtained with the sampling interval around 1 s) when increasing the sampling interval up to about 5 s for synthetic data and up to 3.6-4.8 s for real data. Further increasing of the sampling intervals led to systematic distortions, such as lowering and broadening of the 1st pass peak. The resulting perfusion-parameter estimation error was below 10% for the sampling intervals up to 3 s (synthetic data), in line with the real data perfusion-parameter boxplots which remained unchanged up to the sampling interval 3.6 s.

CONCLUSION

We show that use of blind deconvolution decreases the demands on temporal resolution in DCE-MRI from about 1.5 s (in case of measured arterial input functions) to 3-4 s. This can be exploited in increased spatial resolution or larger organ coverage.

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.

Alterations of the blood-brain barrier during aging

The blood-brain barrier (BBB) is a complex and dynamic interface that regulates the exchange of molecules and cells between the blood and the central nervous system. It undergoes structural and functional changes during aging, which may compromise its integrity and contribute to the pathogenesis of neurodegenerative diseases. In recent years, advances in microscopy and high-throughput bioinformatics have allowed a more in-depth investigation of the aging mechanisms of BBB. This review summarizes age-related alterations of the BBB structure and function from six perspectives: endothelial cells, astrocytes, pericytes, basement membrane, microglia and perivascular macrophages, and fibroblasts, ranging from the molecular level to the human multi-system level. These basic components are essential for the proper functioning of the BBB. Recent imaging methods of BBB were also reviewed. Elucidation of age-associated BBB changes may offer insights into BBB homeostasis and may provide effective therapeutic strategies to protect it during aging.

The relationship between parameters measured using intravoxel incoherent motion and dynamic contrast-enhanced MRI in patients with breast cancer undergoing neoadjuvant chemotherapy: a longitudinal cohort study

Purpose

The primary aim of this study was to explore whether intravoxel incoherent motion (IVIM) can offer a contrast-agent-free alternative to dynamic contrast-enhanced (DCE)-MRI for measuring breast tumor perfusion. The secondary aim was to investigate the relationship between tissue diffusion measures from DWI and DCE-MRI measures of the tissue interstitial and extracellular volume fractions.

Materials and methods

A total of 108 paired DWI and DCE-MRI scans were acquired at 1.5 T from 40 patients with primary breast cancer (median age: 44.5 years) before and during neoadjuvant chemotherapy (NACT). DWI parameters included apparent diffusion coefficient (ADC), tissue diffusion (Dt), pseudo-diffusion coefficient (Dp), perfused fraction (f), and the product f×Dp (microvascular blood flow). DCE-MRI parameters included blood flow (Fb), blood volume fraction (vb), interstitial volume fraction (ve) and extracellular volume fraction (vd). All were extracted from three tumor regions of interest (whole-tumor, ADC cold-spot, and DCE-MRI hot-spot) at three MRI visits: pre-treatment, after one, and three cycles of NACT. Spearman's rank correlation was used for assessing between-subject correlations (r), while repeated measures correlation was employed to assess within-subject correlations (rrm) across visits between DWI and DCE-MRI parameters in each region.

Results

No statistically significant between-subject or within-subject correlation was found between the perfusion parameters estimated by IVIM and DCE-MRI (f versus vb and f×Dp versus Fb; P=0.07-0.81). Significant moderate positive between-subject and within-subject correlations were observed between ADC and ve (r=0.461, rrm=0.597) and between Dt and ve (r=0.405, rrm=0.514) as well as moderate positive within-subject correlations between ADC and vd and between Dt and vd (rrm=0.619 and 0.564, respectively) in the whole-tumor region.

Conclusion

No correlations were observed between the perfusion parameters estimated by IVIM and DCE-MRI. This may be attributed to imprecise estimates of fxDp and vb, or an underlying difference in what IVIM and DCE-MRI measure. Care should be taken when interpreting the IVIM parameters (f and f×Dp) as surrogates for those measured using DCE-MRI. However, the moderate positive correlations found between ADC and Dt and the DCE-MRI parameters ve and vd confirms the expectation that as the interstitial and extracellular volume fractions increase, water diffusion increases.

Applying dynamic contrast-enhanced MRI tracer kinetic models to differentiate benign and malignant soft tissue tumors

BACKGROUND

To explore the potential of different quantitative dynamic contrast-enhanced (qDCE)-MRI tracer kinetic (TK) models and qDCE parameters in discriminating benign from malignant soft tissue tumors (STTs).

METHODS

This research included 92 patients (41females, 51 males; age range 16-86 years, mean age 51.24 years) with STTs. The qDCE parameters (Ktrans, Kep, Ve, Vp, F, PS, MTT and E) for regions of interest of STTs were estimated by using the following TK models: Tofts (TOFTS), Extended Tofts (EXTOFTS), adiabatic tissue homogeneity (ATH), conventional compartmental (CC), and distributed parameter (DP). We established a comprehensive model combining the morphologic features, time-signal intensity curve shape, and optimal qDCE parameters. The capacities to identify benign and malignant STTs was evaluated using the area under the curve (AUC), degree of accuracy, and the analysis of the decision curve.

RESULTS

TOFTS-Ktrans, EXTOFTS-Ktrans, EXTOFTS-Vp, CC-Vp and DP-Vp demonstrated good diagnostic performance among the qDCE parameters. Compared with the other TK models, the DP model has a higher AUC and a greater level of accuracy. The comprehensive model (AUC, 0.936, 0.884-0.988) demonstrated superiority in discriminating benign and malignant STTs, outperforming the qDCE models (AUC, 0.899-0.915) and the traditional imaging model (AUC, 0.802, 0.712-0.891) alone.

CONCLUSIONS

Various TK models successfully distinguish benign from malignant STTs. The comprehensive model is a noninvasive approach incorporating morphological imaging aspects and qDCE parameters, and shows significant potential for further development.

Exploring the Impact of Irradiation on Glioblastoma Blood-Brain-Barrier Permeability: Insights from Dynamic-Contrast-Enhanced-MRI and Histological Analysis

(1) Background: Glioblastoma (GB) presents a formidable challenge in neuro-oncology due to its aggressive nature, limited treatment options, and poor prognosis. The blood-brain barrier (BBB) complicates treatment by hindering drug delivery to the tumor site, particularly to the infiltrative cells in the margin of the tumor, which are mainly responsible for tumor recurrence. Innovative strategies are therefore needed to enhance drug delivery in the margins of the tumor. This study explores whether irradiation can enhance BBB permeability by assessing hemodynamic changes and the distribution of contrast agents in the core and the margins of GB tumors. (2) Methods: Mice grafted with U-87MG cells were exposed to increasing irradiation doses. The distribution of contrast agents and hemodynamic parameters was evaluated using both non-invasive magnetic resonance imaging (MRI) techniques with gadolinium-DOTA as a contrast agent and invasive histological analysis with Evans blue, a fluorescent vascular leakage marker. Diffusion-MRI was also used to assess cytotoxic effects. (3) Results: The histological study revealed a complex dose-dependent effect of irradiation on BBB integrity, with increased vascular leakage at 5 Gy but reduced leakage at higher doses (10 and 15 Gy). However, there was no significant increase in the diffusion of Gd-DOTA outside the tumor area by MRI. (4) Conclusions: The increase in BBB permeability could be an interesting approach to enhance drug delivery in glioblastoma margins for low irradiation doses. In this model, DCE-MRI analysis was of limited value in assessing the BBB opening in glioblastoma after irradiation.

Unpaired deep learning for pharmacokinetic parameter estimation from dynamic contrast-enhanced MRI without AIF measurements

DCE-MRI provides information about vascular permeability and tissue perfusion through the acquisition of pharmacokinetic parameters. However, traditional methods for estimating these pharmacokinetic parameters involve fitting tracer kinetic models, which often suffer from computational complexity and low accuracy due to noisy arterial input function (AIF) measurements. Although some deep learning approaches have been proposed to tackle these challenges, most existing methods rely on supervised learning that requires paired input DCE-MRI and labeled pharmacokinetic parameter maps. This dependency on labeled data introduces significant time and resource constraints and potential noise in the labels, making supervised learning methods often impractical. To address these limitations, we present a novel unpaired deep learning method for estimating pharmacokinetic parameters and the AIF using a physics-driven CycleGAN approach. Our proposed CycleGAN framework is designed based on the underlying physics model, resulting in a simpler architecture with a single generator and discriminator pair. Crucially, our experimental results indicate that our method does not necessitate separate AIF measurements and produces more reliable pharmacokinetic parameters than other techniques.

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.

Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI)

PURPOSE

Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework.

METHODS

A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results.

RESULTS

The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions.

CONCLUSIONS

The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.

Blood-brain barrier biomarkers

The blood-brain barrier (BBB) is a dynamic interface that regulates the exchange of molecules and cells between the brain parenchyma and the peripheral blood. The BBB is mainly composed of endothelial cells, astrocytes and pericytes. The integrity of this structure is essential for maintaining brain and spinal cord homeostasis and protection from injury or disease. However, in various neurological disorders, such as traumatic brain injury, Alzheimer's disease, and multiple sclerosis, the BBB can become compromised thus allowing passage of molecules and cells in and out of the central nervous system parenchyma. These agents, however, can serve as biomarkers of BBB permeability and neuronal damage, and provide valuable information for diagnosis, prognosis and treatment. Herein, we provide an overview of the BBB and changes due to aging, and summarize current knowledge on biomarkers of BBB disruption and neurodegeneration, including permeability, cellular, molecular and imaging biomarkers. We also discuss the challenges and opportunities for developing a biomarker toolkit that can reliably assess the BBB in physiologic and pathophysiologic states.

3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR)

Objective. 3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly under-sampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly under-sampled data.Approach. STINR-MR used a joint reconstruction and deformable registration approach to achieve a high acceleration factor for cine volumetric imaging. It addressed the ill-posed spatiotemporal reconstruction problem by solving a reference-frame 3D MR image and a corresponding motion model that deforms the reference frame to each cine frame. The reference-frame 3D MR image was reconstructed as a spatial implicit neural representation (INR) network, which learns the mapping from input 3D spatial coordinates to corresponding MR values. The dynamic motion model was constructed via a temporal INR, as well as basis deformation vector fields (DVFs) extracted from prior/onboard 4D-MRIs using principal component analysis. The learned temporal INR encodes input time points and outputs corresponding weighting factors to combine the basis DVFs into time-resolved motion fields that represent cine-frame-specific dynamics. STINR-MR was evaluated using MR data simulated from the 4D extended cardiac-torso (XCAT) digital phantom, as well as two MR datasets acquired clinically from human subjects. Its reconstruction accuracy was also compared with that of the model-based non-rigid motion estimation method (MR-MOTUS) and a deep learning-based method (TEMPEST).Main results. STINR-MR can reconstruct 3D cine-MR images with high temporal (<100 ms) and spatial (3 mm) resolutions. Compared with MR-MOTUS and TEMPEST, STINR-MR consistently reconstructed images with better image quality and fewer artifacts and achieved superior tumor localization accuracy via the solved dynamic DVFs. For the XCAT study, STINR reconstructed the tumors to a mean ± SD center-of-mass error of 0.9 ± 0.4 mm, compared to 3.4 ± 1.0 mm of the MR-MOTUS method. The high-frame-rate reconstruction capability of STINR-MR allows different irregular motion patterns to be accurately captured.Significance. STINR-MR provides a lightweight and efficient framework for accurate 3D cine-MRI reconstruction. It is a 'one-shot' method that does not require external data for pre-training, allowing it to avoid generalizability issues typically encountered in deep learning-based methods.

Imaging Role in Diagnosis, Prognosis, and Treatment Response Prediction Associated with High-grade Glioma

Background

Glioma is one of the most drug and radiation-resistant tumors. Gliomas suffer from inter- and intratumor heterogeneity which makes the outcome of similar treatment protocols vary from patient to patient. This article is aimed to overview the potential imaging markers for individual diagnosis, prognosis, and treatment response prediction in malignant glioma. Furthermore, the correlation between imaging findings and biological and clinical information of glioma patients is reviewed.

Materials and Methods

The search strategy in this study is to select related studies from scientific websites such as PubMed, Scopus, Google Scholar, and Web of Science published until 2022. It comprised a combination of keywords such as Biomarkers, Diagnosis, Prognosis, Imaging techniques, and malignant glioma, according to Medical Subject Headings.

Results

Some imaging parameters that are effective in glioma management include: ADC, FA, Ktrans, regional cerebral blood volume (rCBV), cerebral blood flow (CBF), ve, Cho/NAA and lactate/lipid ratios, intratumoral uptake of 18F-FET (for diagnostic application), RD, ADC, ve, vp, Ktrans, CBFT1, rCBV, tumor blood flow, Cho/NAA, lactate/lipid, MI/Cho, uptakes of 18F-FET, 11C-MET, and 18F-FLT (for prognostic and predictive application). Cerebral blood volume and Ktrans are related to molecular markers such as vascular endothelial growth factor (VEGF). Preoperative ADCmin value of GBM tumors is associated with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. 2-hydroxyglutarate metabolite and dynamic 18F-FDOPA positron emission tomography uptake are related to isocitrate dehydrogenase (IDH) mutations.

Conclusion

Parameters including ADC, RD, FA, rCBV, Ktrans, vp, and uptake of 18F-FET are useful for diagnosis, prognosis, and treatment response prediction in glioma. A significant correlation between molecular markers such as VEGF, MGMT, and IDH mutations with some diffusion and perfusion imaging parameters has been identified.

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.

Dynamic Contrast-enhanced MRI Quantification of Altered Vascular Permeability in Autoimmune Encephalitis

Background Blood-brain barrier (BBB) permeability change is a possible pathologic mechanism of autoimmune encephalitis. Purpose To evaluate the change in BBB permeability in patients with autoimmune encephalitis as compared with healthy controls by using dynamic contrast-enhanced (DCE) MRI and to explore its predictive value for treatment response in patients. Materials and Methods This single-center retrospective study included consecutive patients with probable or possible autoimmune encephalitis and healthy controls who underwent DCE MRI between April 2020 and May 2021. Automatic volumetric segmentation was performed on three-dimensional T1-weighted images, and volume transfer constant (Ktrans) values were calculated at encephalitis-associated brain regions. Ktrans values were compared between the patients and controls, with adjustment for age and sex with use of a nonparametric approach. The Wilcoxon rank sum test was performed to compare Ktrans values of the good (improvement in modified Rankin Scale [mRS] score of at least two points or achievement of an mRS score of  ≤2) and poor (improvement in mRS score of less than two points and achievement of an mRS score >2) treatment response groups among the patients. Results Thirty-eight patients with autoimmune encephalitis (median age, 38 years [IQR, 29-59 years]; 20 [53%] female) and 17 controls (median age, 71 years [IQR, 63-77 years]; 12 [71%] female) were included. All brain regions showed higher Ktrans values in patients as compared with controls (P < .001). The median difference in Ktrans between the patients and controls was largest in the right parahippocampal gyrus (25.1 × 10-4 min-1 [95% CI: 17.6, 43.4]). Among patients, the poor treatment response group had higher baseline Ktrans values in both cerebellar cortices (P = .03), the left cerebellar cortex (P = .02), right cerebellar cortex (P = .045), left cerebral cortex (P = .045), and left postcentral gyrus (P = .03) than the good treatment response group. Conclusion DCE MRI demonstrated that BBB permeability was increased in all brain regions in patients with autoimmune encephalitis as compared with controls, and baseline Ktrans values were higher in patients with poor treatment response in the cerebellar cortex, left cerebral cortex, and left postcentral gyrus as compared with the good response group. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Filippi and Rocca in this issue.

A Generalized Kinetic Model of Fractional Order Transport Dynamics with Transit Time Heterogeneity in Microvascular Space

The aim of this study is to develop and validate a unifying kinetic model for microvascular transport by introducing an impulse response function that incorporates essential physiological parameters and integrates key features of existing models. This new methodology combines a one-compartment model of fractional order with a model that uses the gamma distribution to describe the distribution of capillary transit times. Central to this model are two primary parameters: [Formula: see text], representing the kurtosis of residue times, and [Formula: see text], signifying the width of the distribution of capillary transit times within a tissue voxel. To validate this proposed model, data from dynamic contrast-enhanced magnetic resonance imaging (DCI-MRI) were employed and the findings were compared with three existing models. Using the Akaike information criterion for model selection, the results demonstrate that the integrative model, especially at elevated blood flow rates, frequently offers superior fits in comparison to constrained models.

Chemical Reactions in Living Systems

The term "in vivo ("in the living") chemistry" refers to chemical reactions that take place in a complex living system such as cells, tissue, body liquids, or even in an entire organism. In contrast, reactions that occur generally outside living organisms in an artificial environment (e.g., in a test tube) are referred to as in vitro. Over the past decades, significant contributions have been made in this rapidly growing field of in vivo chemistry, but it is still not fully understood, which transformations proceed efficiently without the formation of by-products or how product formation in such complex environments can be characterized. Potential applications can be imagined that synthesize drug molecules directly within the cell or confer new cellular functions through controlled chemical transformations that will improve the understanding of living systems and develop new therapeutic strategies. The guiding principles of this contribution are twofold: 1) Which chemical reactions can be translated from the laboratory to the living system? 2) Which characterization methods are suitable for studying reactions and structure formation in complex living environments?

Perfusion Imaging of the Musculoskeletal System

Perfusion imaging is the aspect of functional imaging, which is most applicable to the musculoskeletal system. In this review, the anatomy and physiology of bone perfusion is briefly outlined as are the methods of acquiring perfusion data on MR imaging. The current clinical indications of perfusion related to the assessment of soft tissue and bone tumors, synovitis, osteoarthritis, avascular necrosis, Keinbock's disease, diabetic foot, osteochondritis dissecans, and Paget's disease of bone are reviewed. Challenges and opportunities related to perfusion imaging of the musculoskeletal system are also briefly addressed.

The value of multiparametric MRI combined with clinical prognostic parameters in predicting the 5-year survival of stage IIIC1 cervical squamous cell carcinoma

OBJECTIVES

To explore the value of multiparametric magnetic resonance imaging（MRI）in predicting the 5-year progression-free survival (PFS) and overall survival (OS) of cervical squamous cell carcinoma (CSCC) in 2018 FIGO stage IIIC1.

METHODS

This retrospective study collected156 patients with CSCC from Dec. 2014 to Jul. 2018. Sixty-one patients underwent radical hysterectomy (RH), and 95 patients underwent concurrent chemoradiotherapy (CCRT). Clinical and MR parameters of primary tumours were analysed. A 1:1 ratio propensity score matching (PSM) was performed for the RH group and CCRT group according to T stage. The Cox proportional hazard model was used to evaluate the associations between imaging or clinical variables and PFS and OS.

RESULTS

The 5-year PFS and OS rates were 72.6% and 78.3%, respectively. The analysis results show that the treatment method, ADCmin < 0.604 × 10-3 mm2/s, and Ktrans < 0.699 min-1 correlated with worse PFS, while SCC-Ag > 6.7 ng/L, ADCmin < 0.604 × 10-3 mm2/s, and Ktrans < 0.699 min-1 correlated with worse OS. After PSM, we confirmed that the treatment methods did not affect the long-term survival of patients with stage IIIC1 disease, and a low Ktrans value was an independent poor prognostic factor.

CONCLUSION

Functional MRI parameters and SCC-Ag have potential predictive value for the 5-year survival of 2018 FIGOIIIC1 CSCC. There were no significant differences in survival between CCRT and RH + adjuvant therapy for IIIC1 stage CSCC if the T stage was earlier.

Unified Bayesian network for uncertainty quantification of physiological parameters in dynamic contrast enhanced (DCE) MRI of the liver

Objective. Physiological parameter estimation is affected by intrinsic ambiguity in the data such as noise and model inaccuracies. The aim of this work is to provide a deep learning framework for accurate parameter and uncertainty estimates for DCE-MRI in the liver.Approach. Concentration time curves are simulated to train a Bayesian neural network (BNN). Training of the BNN involves minimization of a loss function that jointly minimizes the aleatoric and epistemic uncertainties. Uncertainty estimation is evaluated for different noise levels and for different out of distribution (OD) cases, i.e. where the data during inference differs strongly to the data during training. The accuracy of parameter estimates are compared to a nonlinear least squares (NLLS) fitting in numerical simulations andin vivodata of a patient suffering from hepatic tumor lesions.Main results. BNN achieved lower root-mean-squared-errors (RMSE) than the NLLS for the simulated data. RMSE of BNN was on overage of all noise levels lower by 33% ± 1.9% forktrans, 22% ± 6% forveand 89% ± 5% forvpthan the NLLS. The aleatoric uncertainties of the parameters increased with increasing noise level, whereas the epistemic uncertainty increased when a BNN was evaluated with OD data. For thein vivodata, more robust parameter estimations were obtained by the BNN than the NLLS fit. In addition, the differences between estimated parameters for healthy and tumor regions-of-interest were significant (p< 0.0001).Significance. The proposed framework allowed for accurate parameter estimates for quantitative DCE-MRI. In addition, the BNN provided uncertainty estimates which highlighted cases of high noise and in which the training data did not match the data during inference. This is important for clinical application because it would indicate cases in which the trained model is inadequate and additional training with an adapted training data set is required.

Neuroinflammation in the Evolution of Motor Function in Stroke and Trauma Patients: Treatment and Potential Biomarkers

Neuroinflammation has a significant impact on different pathologies, such as stroke or spinal cord injury, intervening in their pathophysiology: expansion, progression, and resolution. Neuroinflammation involves oxidative stress, damage, and cell death, playing an important role in neuroplasticity and motor dysfunction by affecting the neuronal connection responsible for motor control. The diagnosis of this pathology is performed using neuroimaging techniques and molecular diagnostics based on identifying and measuring signaling molecules or specific markers. In parallel, new therapeutic targets are being investigated via the use of bionanomaterials and electrostimulation to modulate the neuroinflammatory response. These novel diagnostic and therapeutic strategies have the potential to facilitate the development of anticipatory patterns and deliver the most beneficial treatment to improve patients' quality of life and directly impact their motor skills. However, important challenges remain to be solved. Hence, the goal of this study was to review the implication of neuroinflammation in the evolution of motor function in stroke and trauma patients, with a particular focus on novel methods and potential biomarkers to aid clinicians in diagnosis, treatment, and therapy. A specific analysis of the strengths, weaknesses, threats, and opportunities was conducted, highlighting the key challenges to be faced in the coming years.

Perfusion estimation using synthetic MRI-based measurements and a porous media flow model

The measurement of perfusion and filtration of blood in biological tissue give rise to important clinical parameters used in diagnosis, follow-up, and therapy. In this paper, we address techniques for perfusion analysis using processed contrast agent concentration data from dynamic MRI acquisitions. A new methodology for analysis is evaluated and verified using synthetic data generated on a tissue geometry.

Assessment of Hyperosmolar Blood-Brain Barrier Opening in Glioblastoma via Histology with Evans Blue and DCE-MRI

BACKGROUND

While the blood-brain barrier (BBB) is often compromised in glioblastoma (GB), the perfusion and consequent delivery of drugs are highly heterogeneous. Moreover, the accessibility of drugs is largely impaired in the margins of the tumor and for infiltrating cells at the origin of tumor recurrence. In this work, we evaluate the value of methods to assess hemodynamic changes induced by a hyperosmolar shock in the core and the margins of a tumor in a GB model.

METHODS

Osmotic shock was induced with an intracarotid infusion of a hypertonic solution of mannitol in mice grafted with U87-MG cells. The distribution of fluorescent dye (Evans blue) within the brain was assessed via histology. Dynamic contrast-enhanced (DCE)-MRI with an injection of Gadolinium-DOTA as the contrast agent was also used to evaluate the effect on hemodynamic parameters and the diffusion of the contrast agent outside of the tumor area.

RESULTS

The histological study revealed that the fluorescent dye diffused much more largely outside of the tumor area after osmotic shock than in control tumors. However, the study of tumor hemodynamic parameters via DCE-MRI did not reveal any change in the permeability of the BBB, whatever the studied MRI parameter.

CONCLUSIONS

The use of hypertonic mannitol infusion seems to be a promising method to increase the delivery of compounds in the margins of GB. Nevertheless, the DCE-MRI analysis method using gadolinium-DOTA as a contrast agent seems of limited value for determining the efficacy of opening the BBB in GB after osmotic shock.

Open source code for the generation of digital reference objects for dynamic contrast-enhanced MRI analysis software validation

OBJECTIVES

Dynamic contrast-enhanced MR images can be analyzed through the application of a wide range of kinetic models. This process is prone to variability and a lack of standardization that can affect the measured metrics. There is a need for customized digital reference objects (DROs) for the validation of DCE-MRI software packages that undertake kinetic model analysis. DROs are currently available only for a small subset of the kinetic models commonly applied to DCE-MRI data. This work aimed to address this gap.

METHODS

Code was written in the MATLAB programming environment to generate customizable DROs. This modular code allows the insertion of a plug-in to describe the kinetic model to be tested. We input our generated DROs into three commercial and open-source analysis packages and assessed the agreement of kinetic model parameter values output with the 'ground-truth' values used in the DRO generation.

RESULTS

For the five kinetic models tested, the concordance correlation coefficient values were >98%, indicating excellent agreement of the results with 'ground-truth'.

CONCLUSIONS

Testing our DROs on three independent software packages produced concordant results, strongly suggesting our DRO generation code is correct. This implies that our DROs can be used to validate other third party software for the kinetic model analysis of DCE-MRI data.

ADVANCES IN KNOWLEDGE

This work extends published work of others to allow customized generation of test objects for any applied kinetic model and allows the incorporation of B1 mapping into the DRO for application at higher field strengths.

Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models

We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, Ktrans, plasma volume fraction, vp, and extravascular, extracellular space, ve, directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, vp, Ktrans, and ve, respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches.

Transfer Learning Approach to Vascular Permeability Changes in Brain Metastasis Post-Whole-Brain Radiotherapy

The purpose of this study is to further validate the utility of our previously developed CNN in an alternative small animal model of BM through transfer learning. Unlike the glioma model, the BM mouse model develops multifocal intracranial metastases, including both contrast enhancing and non-enhancing lesions on DCE MRI, thus serving as an excellent brain tumor model to study tumor vascular permeability. Here, we conducted transfer learning by transferring the previously trained GBM CNN to DCE MRI datasets of BM mice. The CNN was re-trained to learn about the relationship between BM DCE images and target permeability maps extracted from the Extended Tofts Model (ETM). The transferred network was found to accurately predict BM permeability and presented with excellent spatial correlation with the target ETM PK maps. The CNN model was further tested in another cohort of BM mice treated with WBRT to assess vascular permeability changes induced via radiotherapy. The CNN detected significantly increased permeability parameter Ktrans in WBRT-treated tumors (p < 0.01), which was in good agreement with the target ETM PK maps. In conclusion, the proposed CNN can serve as an efficient and accurate tool for characterizing vascular permeability and treatment responses in small animal brain tumor models.

Imaging human lung perfusion with contrast media: A meta-analysis

PURPOSE

To pool and summarise published data of pulmonary blood flow (PBF), pulmonary blood volume (PBV) and mean transit time (MTT) of the human lung, obtained with perfusion MRI or CT to provide reliable reference values of healthy lung tissue. In addition, the available data regarding diseased lung was investigated.

METHODS

PubMed was systematically searched to identify studies that quantified PBF/PBV/MTT in the human lung by injection of contrast agent, imaged by MRI or CT. Only data analysed by 'indicator dilution theory' were considered numerically. Weighted mean (wM), weighted standard deviation (wSD) and weighted coefficient of variance (wCoV) were obtained for healthy volunteers (HV), weighted according to the size of the datasets. Signal to concentration conversion method, breath holding method and presence of 'pre-bolus' were noted.

RESULTS

PBV was obtained from 313 measurements from 14 publications (wM: 13.97 ml/100 ml, wSD: 4.21 ml/100 ml, wCoV 0.30). MTT was obtained from 188 measurements from 10 publications (wM: 5.91 s, wSD: 1.84 s wCoV 0.31). PBF was obtained from 349 measurements from 14 publications (wM: 246.26 ml/100 ml ml/min, wSD: 93.13 ml/100 ml ml/min, wCoV 0.38). PBV and PBF were higher when the signal was normalised than when it was not. No significant differences were found for PBV and PBF between breathing states or between pre-bolus and no pre-bolus. Data for diseased lung were insufficient for meta-analysis.

CONCLUSION

Reference values for PBF, MTT and PBV were obtained in HV. The literature data are insufficient to draw strong conclusions regarding disease reference values.

Multiparametric MRI of Solid Renal Masses: Principles and Applications of Advanced Quantitative and Functional Methods for Tumor Diagnosis and Characterization

Solid renal masses (SRMs) are increasingly detected and encompass both benign and malignant masses, with renal cell carcinoma (RCC) being the most common malignant SRM. Most patients with SRMs will undergo management without a priori pathologic confirmation. There is an unmet need to noninvasively diagnose and characterize RCCs, as significant variability in clinical behavior is observed and a wide range of differing management options exist. Cross-sectional imaging modalities, including magnetic resonance imaging (MRI), are increasingly used for SRM characterization. Multiparametric (mp) MRI techniques can provide insight into tumor biology by probing different physiologic/pathophysiologic processes noninvasively. These include sequences that probe tissue microstructure, including intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and T₁ relaxometry; oxygen metabolism (blood oxygen level dependent [BOLD-MRI]); as well as vascular flow and perfusion (dynamic contrast-enhanced MRI [DCE-MRI] and arterial spin labeling [ASL]). In this review, we will discuss each mpMRI method in terms of its principles, roles, and discuss the results of human studies for SRM assessment. Future validation of these methods may help to enable a personalized management approach for patients with SRM in the emerging era of precision medicine. EVIDENCE LEVEL: 5. TECHNICAL EFFICACY: 2.

Use of In Vivo Imaging and Physiologically-Based Kinetic Modelling to Predict Hepatic Transporter Mediated Drug-Drug Interactions in Rats

Gadoxetate, a magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 and multidrug resistance-associated protein 2. Six drugs, with varying degrees of transporter inhibition, were used to assess gadoxetate dynamic contrast enhanced MRI biomarkers for transporter inhibition in rats. Prospective prediction of changes in gadoxetate systemic and liver AUC (AUCR), resulting from transporter modulation, were performed by physiologically-based pharmacokinetic (PBPK) modelling. A tracer-kinetic model was used to estimate rate constants for hepatic uptake (khe), and biliary excretion (kbh). The observed median fold-decreases in gadoxetate liver AUC were 3.8- and 1.5-fold for ciclosporin and rifampicin, respectively. Ketoconazole unexpectedly decreased systemic and liver gadoxetate AUCs; the remaining drugs investigated (asunaprevir, bosentan, and pioglitazone) caused marginal changes. Ciclosporin decreased gadoxetate khe and kbh by 3.78 and 0.09 mL/min/mL, while decreases for rifampicin were 7.20 and 0.07 mL/min/mL, respectively. The relative decrease in khe (e.g., 96% for ciclosporin) was similar to PBPK-predicted inhibition of uptake (97-98%). PBPK modelling correctly predicted changes in gadoxetate systemic AUCR, whereas underprediction of decreases in liver AUCs was evident. The current study illustrates the modelling framework and integration of liver imaging data, PBPK, and tracer-kinetic models for prospective quantification of hepatic transporter-mediated DDI in humans.

In vivo methods for imaging blood-brain barrier function and dysfunction

The blood-brain barrier (BBB) is the interface between the central nervous system and systemic circulation. It tightly regulates what enters and is removed from the brain parenchyma and is fundamental in maintaining brain homeostasis. Increasingly, the BBB is recognised as having a significant role in numerous neurological disorders, ranging from acute disorders (traumatic brain injury, stroke, seizures) to chronic neurodegeneration (Alzheimer's disease, vascular dementia, small vessel disease). Numerous approaches have been developed to study the BBB in vitro, in vivo, and ex vivo. The complex multicellular structure and effects of disease are difficult to recreate accurately in vitro, and functional aspects of the BBB cannot be easily studied ex vivo. As such, the value of in vivo methods to study the intact BBB cannot be overstated. This review discusses the structure and function of the BBB and how these are affected in diseases. It then discusses in depth several established and novel methods for imaging the BBB in vivo, with a focus on MRI, nuclear imaging, and high-resolution intravital fluorescence microscopy.

Role of Imaging in Initial Prognostication of Locally Advanced Soft Tissue Sarcomas

BACKGROUND

Although imaging is central in the initial staging of patients with soft tissue sarcomas (STS), it remains underused and few radiological features are currently used in practice for prognostication and to help guide the best therapeutic strategy. Yet, several prognostic qualitative and quantitative characteristics from magnetic resonance imaging (MRI) and positron emission tomography (PET) have been identified over these last decades.

OBJECTIVE

After an overview of the current validated prognostic features based on baseline imaging and their integration into prognostic tools, such as nomograms used by clinicians, the aim of this review is to summarize more complex and innovative MRI, PET, and radiomics features, and to highlight their role to predict indirectly (through histologic grade) or directly the patients' outcomes.

Model-free dynamic contrast-enhanced MRI analysis: differentiation between active tumor and necrotic tissue in patients with glioblastoma

OBJECTIVE

Treatment response assessment in patients with high-grade gliomas (HGG) is heavily dependent on changes in lesion size on MRI. However, in conventional MRI, treatment-related changes can appear as enhancing tissue, with similar presentation to that of active tumor tissue. We propose a model-free data-driven method for differentiation between these tissues, based on dynamic contrast-enhanced (DCE) MRI.

MATERIALS AND METHODS

The study included a total of 66 scans of patients with glioblastoma. Of these, 48 were acquired from 1 MRI vendor and 18 scans were acquired from a different MRI vendor and used as test data. Of the 48, 24 scans had biopsy results. Analysis included semi-automatic arterial input function (AIF) extraction, direct DCE pharmacokinetic-like feature extraction, and unsupervised clustering of the two tissue types. Validation was performed via (a) comparison to biopsy result (b) correlation to literature-based DCE curves for each tissue type, and (c) comparison to clinical outcome.

RESULTS

Consistency between the model prediction and biopsy results was found in 20/24 cases. An average correlation of 82% for active tumor and 90% for treatment-related changes was found between the predicted component and population-based templates. An agreement between the predicted results and radiologist's assessment, based on RANO criteria, was found in 11/12 cases.

CONCLUSION

The proposed method could serve as a non-invasive method for differentiation between lesion tissue and treatment-related changes.

Multitasking dynamic contrast enhanced magnetic resonance imaging can accurately differentiate chronic pancreatitis from pancreatic ductal adenocarcinoma

Background and aims

Accurate differentiation of chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) is an area of unmet clinical need. In this study, a novel Multitasking dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) technique was used to quantitatively evaluate the microcirculation properties of pancreas in CP and PDAC and differentiate between them.

Methods

The Multitasking DCE technique was able to acquire one 3D image per second during the passage of MRI contrast agent, allowing the quantitative estimation of microcirculation properties of tissue, including blood flow F_p, plasma volume fraction v_p, transfer constant K^trans, and extravascular extracellular volume fraction v_e. Receiver operating characteristic (ROC) analysis was performed to differentiate the CP pancreas, PDAC pancreas, normal control pancreas, PDAC tumor, PDAC upstream, and PDAC downstream. ROCs from quantitative analysis and conventional analysis were compared.

Results

Fourteen PDAC patients, 8 CP patients and 20 healthy subjects were prospectively recruited. The combination of F_p, v_p, K^trans, and v_e can differentiate CP versus PDAC pancreas with good AUC (AUC [95% CI] = 0.821 [0.654 - 0.988]), CP versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), PDAC pancreas versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC tumor with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC downstream with excellent AUC (0.917 [0.795 - 1.000]), and CP versus PDAC upstream with fair AUC (0.722 [0.465 - 0.980]). This quantitative analysis outperformed conventional analysis in differentiation of each pair.

Conclusion

Multitasking DCE MRI is a promising clinical tool that is capable of unbiased quantitative differentiation between CP from PDAC.

A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion by dynamic contrast-agent imaging: Direct simulation and inverse tracer-kinetic procedures

Dynamic contrast-enhanced (DCE) perfusion imaging has shown great potential to non-invasively assess cancer development and its treatment by their characteristic tissue signatures. Different tracer kinetics models are being applied to estimate tissue and tumor perfusion parameters from DCE perfusion imaging. The goal of this work is to provide an in silico model-based pipeline to evaluate how these DCE imaging parameters may relate to the true tissue parameters. As histology data provides detailed microstructural but not functional parameters, this work can also help to better interpret such data. To this aim in silico vasculatures are constructed and the spread of contrast agent in the tissue is simulated. As a proof of principle we show the evaluation procedure of two tracer kinetic models from in silico contrast-agent perfusion data after a bolus injection. Representative microvascular arterial and venous trees are constructed in silico. Blood flow is computed in the different vessels. Contrast-agent input in the feeding artery, intra-vascular transport, intra-extravascular exchange and diffusion within the interstitial space are modeled. From this spatiotemporal model, intensity maps are computed leading to in silico dynamic perfusion images. Various tumor vascularizations (architecture and function) are studied and show spatiotemporal contrast imaging dynamics characteristic of in vivo tumor morphotypes. The Brix II also called 2CXM, and extended Tofts tracer-kinetics models common in DCE imaging are then applied to recover perfusion parameters that are compared with the ground truth parameters of the in silico spatiotemporal models. The results show that tumor features can be well identified for a certain permeability range. The simulation results in this work indicate that taking into account space explicitly to estimate perfusion parameters may lead to significant improvements in the perfusion interpretation of the current tracer-kinetics models.

Cognitive changes are associated with increased blood-brain barrier leakage in non-brain metastases lung cancer patients

To explore the relationship between cognitive function and blood-brain barrier leakage in non-brain metastasis lung cancer and healthy controls. 75 lung cancers without brain metastasis and 29 healthy controls matched with age, sex, and education were evaluated by cognitive assessment, and the Patlak pharmacokinetic model was used to calculate the average leakage in each brain region according to the automated anatomical labeling atlas. After that, the relationships between cognitive and blood-brain barrier leakage were evaluated. Compared with healthy controls, the leakage of bilateral temporal gyrus and whole brain gyrus were higher in patients with lung cancers (P < 0.05), mainly in patients with advanced lung cancer (P < 0.05), but not in patients with early lung cancer (P > 0.05). The cognitive impairment of advanced lung cancers was mainly reflected in the damage of visuospatial/executive, and delayed recall. The left temporal gyrus with increased blood-brain barrier leakage showed negative correlations with delayed recall (r = -0.201, P = 0.042). An increase in blood-brain barrier leakage was found in non-brain metastases advanced lung cancers that corresponded to decreased delayed recall. With progression in lung cancer staging, blood-brain barrier shows higher leakage and may lead to brain metastases and lower cognitive development.

Whole-tumor histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for soft tissue sarcoma: correlation with HIF-1alpha expression

OBJECTIVE

To investigate the correlation of histogram metrics from diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters with HIF-1alpha expression in soft tissue sarcoma (STS).

METHODS

We enrolled 71 patients with STS who underwent 3.0-T MRI, including conventional MRI, DWI, and DCE-MRI sequences. Location, maximum tumor diameter, envelope, T2-weighted tumor heterogeneity, peritumoral edema, peritumoral enhancement, necrosis, tail-like pattern, bone invasion, and vessel/nerve invasion and/or encasement were determined using conventional MRI images. The whole-tumor histogram metrics were calculated on the apparent diffusion coefficient (ADC), K^trans, K_ep, and V_e maps. Independent-samples t test and one-way ANOVA were used for testing the differences between normally distributed categorical data with HIF-1alpha expression. Pearson and Spearman correlations and multiple linear regression analyses were performed to determine the correlations between histogram metrics and HIF-1alpha expression. Survival curves were plotted using the Kaplan-Meier method.

RESULTS

Regarding conventional MRI features, only highly heterogeneous on T2-weighted images (55.6 ± 19.9% vs. 45.4 ± 20.5%, p = 0.041) and more than 50% necrotic area (57.3 ± 20.4% vs. 43.9 ± 19.7%, p = 0.002) were prone to indicate STS with higher HIF-1alpha expression. Histogram metrics obtained from ADC (mean, median, 10th, and 25th percentile values), K^trans (mean, median, 75th, and 90th percentile values), and K_ep (90th percentile values) were significantly correlated with HIF-1alpha expression. Multiple linear regression analysis demonstrated that more than 50% necrosis, ADC_skewness, K^trans_90th, and grade III were independently associated with HIF-1alpha expression.

CONCLUSION

DWI and DCE-MRI histogram parameters were significantly correlated with HIF-1alpha expression in STS.

KEY POINTS

• DWI and DCE-MRI histogram parameters are correlated with HIF-1alpha expression in STS. • More than 50% necrosis, ADC_skewness, K^trans_90th, and grade III were independently associated with HIF-1alpha expression in STS.

Diagnostic Performance of Dynamic Contrast-Enhanced MRI and 18F-FDG PET/CT for Evaluation of Soft Tissue Tumors and Correlation with Pathology Parameters

RATIONALE AND OBJECTIVES

To assess the diagnostic performance of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and fluorine-18-fluorodeoxyglucose (¹⁸F-FDG) positron-emission tomography/computed tomography (PET/CT) parameters in evaluating the biological behavior of soft tissue tumors.

MATERIALS AND METHODS

We retrospectively analyzed DCE-MRI and ¹⁸F-FDG PET/CT parameters in 78 patients with pathology-confirmed soft tissue tumors. A total of 78 patients had undergone DCE-MRI examination, while 24 patients with malignant soft tissue tumor had undergone ¹⁸F-FDG PET/CT examination. Microvessel density (MVD) and the Ki-67 labeling index (LI) were detected using immunohistochemistry. Differences in parameters (Ktrans, Kep, Ve, MVD, and Ki-67 LI) between benign and malignant tumors were compared. Differences in parameters (Ktrans, Kep, Ve, MVD, and SUV_max) between high- and low-proliferation malignant tumors (grouped by Ki-67 LI) were compared. Correlation of the DCE-MRI and ¹⁸F-FDG PET/CT parameters with MVD and Ki-67 LI was analyzed.

RESULTS

Only the Ktrans, Kep, MVD, and Ki-67 LI differed significantly between the benign and malignant soft tissue tumors (all p < 0.001). Only Kep (p = 0.033) and SUV_max (p = 0.001) differed significantly between high- and low-proliferation malignant soft tissue tumors. Ktrans, Kep, and SUV_max correlated positively with MVD (r = 0.805, 0.778, 0.730, respectively; all p < 0.001), and with Ki-67 LI (r = 0.721, 0.685, 0.655, respectively; all p < 0.001).

CONCLUSION

DCE-MRI and ¹⁸F-FDG PET/CT parameters indicate soft tissue tumor biological behavior and can be used to differentiate between benign and malignant soft tissue tumors and between high- and low-proliferation malignant soft tissue tumors.

Visualising spatial heterogeneity in glioblastoma using imaging habitats

Glioblastoma is a high-grade aggressive neoplasm characterised by significant intra-tumoral spatial heterogeneity. Personalising therapy for this tumour requires non-invasive tools to visualise its heterogeneity to monitor treatment response on a regional level. To date, efforts to characterise glioblastoma's imaging features and heterogeneity have focussed on individual imaging biomarkers, or high-throughput radiomic approaches that consider a vast number of imaging variables across the tumour as a whole. Habitat imaging is a novel approach to cancer imaging that identifies tumour regions or 'habitats' based on shared imaging characteristics, usually defined using multiple imaging biomarkers. Habitat imaging reflects the evolution of imaging biomarkers and offers spatially preserved assessment of tumour physiological processes such perfusion and cellularity. This allows for regional assessment of treatment response to facilitate personalised therapy. In this review, we explore different methodologies to derive imaging habitats in glioblastoma, strategies to overcome its technical challenges, contrast experiences to other cancers, and describe potential clinical applications.

Dynamic contrast-enhanced MRI and Doppler sonography in patients with squamous cell carcinoma of head and neck treated with induction chemotherapy

In view of the inherent limitations associated with performing dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) in clinical settings, current study was designed to provide a proof of principle that Doppler sonography and DCE-MRI derived perfusion parameters yield similar hemodynamic information from metastatic lymph nodes in squamous cell carcinomas of head and neck (HNSCCs). Strong positive correlations between volume fraction of plasma space in tissues (V_p ) and blood volume (r = 0.72, p = 0.02) and between V_p and %area perfused (r = 0.65, p = 0.04) were observed. Additionally, a moderate positive correlation trending towards significance was obtained between volume transfer constant (K^trans ) and %area perfused (r = 0.49, p = 0.09).

Dynamic contrast enhanced MRI for characterization of blood-brain-barrier dysfunction after traumatic brain injury

BACKGROUND AND PURPOSE

Dysfunction of the blood-brain-barrier (BBB) is a recognized pathological consequence of traumatic brain injury (TBI) which may play an important role in chronic TBI pathophysiology. We hypothesized that BBB disruption can be detected with dynamic contrast-enhanced (DCE) MRI not only in association with focal traumatic lesions but also in normal-appearing brain tissue of TBI patients, reflecting microscopic microvascular injury. We further hypothesized that BBB integrity would improve but not completely normalize months after TBI.

MATERIALS AND METHODS

DCE MRI was performed in 40 adult patients a median of 23 days after hospitalized TBI and in 21 healthy controls. DCE data was analyzed using Patlak and linear models, and derived metrics of BBB leakage including the volume transfer constant (Ktrans) and the normalized permeability index (NPI) were compared between groups. BBB metrics were compared with focal lesion distribution as well as with contemporaneous measures of symptomatology and cognitive function in TBI patients. Finally, BBB metrics were examined longitudinally among 18 TBI patients who returned for a second MRI a median of 204 days postinjury.

RESULTS

TBI patients exhibited higher mean Ktrans (p = 0.0028) and proportion of suprathreshold NPI voxels (p = 0.001) relative to controls. Tissue-based analysis confirmed greatest TBI-related BBB disruption in association with focal lesions, however elevated Ktrans was also observed in perilesional (p = 0.011) and nonlesional (p = 0.044) regions. BBB disruption showed inverse correlation with quality of life (rho = -0.51, corrected p = 0.016). Among the subset of TBI patients who underwent a second MRI several months after the initial evaluation, metrics of BBB disruption did not differ significantly at the group level, though variable longitudinal changes were observed at the individual subject level.

CONCLUSIONS

This pilot investigation suggests that TBI-related BBB disruption is detectable in the early post-injury period in association with focal and diffuse brain injury.

Blood-brain barrier leakage years after pre-eclampsia: dynamic contrast-enhanced 7-Tesla MRI study

OBJECTIVE

Pre-eclampsia is a hypertensive complication of pregnancy that is associated with an increased risk of long-term cardiovascular and cerebrovascular disorders. Although the underlying mechanism of persistent susceptibility to cerebral complications after pre-eclampsia remains largely unclear, impaired blood-brain barrier (BBB) integrity has been suggested to precede several cerebrovascular diseases. In this study, we aimed to investigate the integrity of the BBB years after pre-eclampsia.

METHODS

This was an observational study of premenopausal formerly pre-eclamptic women and controls with a history of normotensive pregnancy who underwent cerebral magnetic resonance imaging (MRI) at ultra-high field (7 Tesla) to assess the integrity of the BBB. Permeability of the BBB was determined by assessing leakage rate and fractional leakage volume of the contrast agent gadobutrol using dynamic contrast-enhanced MRI. BBB leakage measures were determined for the whole brain and lobar white and gray matter. Multivariable analyses were performed, and odds ratios were calculated to compare women with and those without a history of pre-eclampsia, adjusting for potential confounding effects of age, hypertension status at MRI and Fazekas score.

RESULTS

Twenty-two formerly pre-eclamptic women (mean age, 37.8 ± 5.4 years) and 13 control women with a history of normotensive pregnancy (mean age, 40.8 ± 5.5 years) were included in the study. The time since the index pregnancy was 6.6 ± 3.2 years in the pre-eclamptic group and 9.0 ± 3.7 years in controls. The leakage rate and fractional leakage volume were significantly higher in formerly pre-eclamptic women than in controls in the global white (P = 0.001) and gray (P = 0.02) matter. Regionally, the frontal (P = 0.04) and parietal (P = 0.009) cortical gray matter, and the frontal (P = 0.001), temporal (P &lt; 0.05) and occipital (P = 0.007) white matter showed higher leakage rates in formerly pre-eclamptic women. The odds of a high leakage rate after pre-eclampsia were generally higher in white-matter regions than in gray-matter regions.

CONCLUSION

This observational study demonstrates global impairment of the BBB years after a pre-eclamptic pregnancy, which could be an early marker of long-term cerebrovascular disorders. © 2022 The Authors. Ultrasound in Obstetrics &amp; Gynecology published by John Wiley &amp; Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Quantitative CT perfusion imaging in patients with pancreatic cancer: a systematic review

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related death with a 5-year survival rate of 10%. Quantitative CT perfusion (CTP) can provide additional diagnostic information compared to the limited accuracy of the current standard, contrast-enhanced CT (CECT). This systematic review evaluates CTP for diagnosis, grading, and treatment assessment of PDAC. The secondary goal is to provide an overview of scan protocols and perfusion models used for CTP in PDAC. The search strategy combined synonyms for 'CTP' and 'PDAC.' Pubmed, Embase, and Web of Science were systematically searched from January 2000 to December 2020 for studies using CTP to evaluate PDAC. The risk of bias was assessed using QUADAS-2. 607 abstracts were screened, of which 29 were selected for full-text eligibility. 21 studies were included in the final analysis with a total of 760 patients. All studies comparing PDAC with non-tumorous parenchyma found significant CTP-based differences in blood flow (BF) and blood volume (BV). Two studies found significant differences between pathological grades. Two other studies showed that BF could predict neoadjuvant treatment response. A wide variety in kinetic models and acquisition protocol was found among included studies. Quantitative CTP shows a potential benefit in PDAC diagnosis and can serve as a tool for pathological grading and treatment assessment; however, clinical evidence is still limited. To improve clinical use, standardized acquisition and reconstruction parameters are necessary for interchangeability of the perfusion parameters.

Diagnostic yield of simultaneous dynamic contrast-enhanced magnetic resonance perfusion measurements and [18F]FET PET in patients with suspected recurrent anaplastic astrocytoma and glioblastoma

Purpose

Both amino acid positron emission tomography (PET) and magnetic resonance imaging (MRI) blood volume (BV) measurements are used in suspected recurrent high-grade gliomas. We compared the separate and combined diagnostic yield of simultaneously acquired dynamic contrast-enhanced (DCE) perfusion MRI and O-(2-[¹⁸F]-fluoroethyl)-L-tyrosine ([¹⁸F]FET) PET in patients with anaplastic astrocytoma and glioblastoma following standard therapy.

Methods

A total of 76 lesions in 60 hybrid [¹⁸F]FET PET/MRI scans with DCE MRI from patients with suspected recurrence of anaplastic astrocytoma and glioblastoma were included retrospectively. BV was measured from DCE MRI employing a 2-compartment exchange model (2CXM). Diagnostic performances of maximal tumour-to-background [¹⁸F]FET uptake (TBR_max), maximal BV (BV_max) and normalised BV_max (nBV_max) were determined by ROC analysis using 6-month histopathological (n = 28) or clinical/radiographical follow-up (n = 48) as reference. Sensitivity and specificity at optimal cut-offs were determined separately for enhancing and non-enhancing lesions.

Results

In progressive lesions, all BV and [¹⁸F]FET metrics were higher than in non-progressive lesions. ROC analyses showed higher overall ROC AUCs for TBR_max than both BV_max and nBV_max in both lesion-wise (all lesions, p = 0.04) and in patient-wise analysis (p < 0.01). Combining TBR_max with BV metrics did not increase ROC AUC. Lesion-wise positive fraction/sensitivity/specificity at optimal cut-offs were 55%/91%/84% for TBR_max, 45%/77%/84% for BV_max and 59%/84%/72% for nBV_max. Combining TBR_max and best-performing BV cut-offs yielded lesion-wise sensitivity/specificity of 75/97%. The fraction of progressive lesions was 11% in concordant negative lesions, 33% in lesions only BV positive, 64% in lesions only [¹⁸F]FET positive and 97% in concordant positive lesions.

Conclusion

The overall diagnostic accuracy of DCE BV imaging is good, but lower than that of [¹⁸F]FET PET. Adding DCE BV imaging did not improve the overall diagnostic accuracy of [¹⁸F]FET PET, but may improve specificity and allow better lesion-wise risk stratification than [¹⁸F]FET PET alone.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05917-3.

Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma

Pseudoprogression (PsP) refers to treatment-related clinico-radiologic changes mimicking true progression (TP) that occurs in patients with glioblastoma (GBM), predominantly within the first 6 months after the completion of surgery and concurrent chemoradiation therapy (CCRT) with temozolomide. Accurate differentiation of TP from PsP is essential for making informed decisions on appropriate therapeutic intervention as well as for prognostication of these patients. Conventional neuroimaging findings are often equivocal in distinguishing between TP and PsP and present a considerable diagnostic dilemma to oncologists and radiologists. These challenges have emphasized the need for developing alternative imaging techniques that may aid in the accurate diagnosis of TP and PsP. In this review, we encapsulate the current state of knowledge in the clinical applications of commonly used metabolic and physiologic magnetic resonance (MR) imaging techniques such as diffusion and perfusion imaging and proton spectroscopy in distinguishing TP from PsP. We also showcase the potential of promising imaging techniques, such as amide proton transfer and amino acid-based positron emission tomography, in providing useful information about the treatment response. Additionally, we highlight the role of "radiomics", which is an emerging field of radiology that has the potential to change the way in which advanced MR techniques are utilized in assessing treatment response in GBM patients. Finally, we present our institutional experiences and discuss future perspectives on the role of multiparametric MR imaging in identifying PsP in GBM patients treated with "standard-of-care" CCRT as well as novel/targeted therapies.

Soft Tissue Sarcomas: The Role of Quantitative MRI in Treatment Response Evaluation

BACKGROUND

Although curative surgery remains the cornerstone of the therapeutic strategy in patients with soft tissue sarcomas (STS), neoadjuvant radiotherapy and chemotherapy (NART and NACT, respectively) are increasingly used to improve operability, surgical margins and patient outcome. The best imaging modality for locoregional assessment of STS is MRI but these tumors are mostly evaluated in a qualitative manner.

OBJECTIVE

After an overview of the current standard of care regarding treatment for patients with locally advanced STS, this review aims to summarize the principles and limitations of (i) the current methods used to evaluate response to neoadjuvant treatment in clinical practice and clinical trials in STS (RECIST 1.1 and modified Choi criteria), (ii) quantitative MRI sequences (i.e., diffusion weighted imaging and dynamic contrast enhanced MRI), and (iii) texture analyses and (delta-) radiomics.

Blood–Brain Barrier Dysfunction and the Potential Mechanisms in Chronic Cerebral Hypoperfusion Induced Cognitive Impairment

Chronic cerebral hypoperfusion (CCH) is a major cause of vascular cognitive impairment and dementia (VCID). Although the underlying mechanisms have not been fully elucidated, the emerging data suggest that blood–brain barrier (BBB) dysfunction is one of the pivotal pathological changes in CCH. BBB dysfunction appears early in CCH, contributing to the deterioration of white matter and the development of cognitive impairment. In this review, we summarize the latest experimental and clinical evidence implicating BBB disruption as a major cause of VCID. We discuss the mechanisms of BBB dysfunction in CCH, focusing on the cell interactions within the BBB, as well as the potential role of APOE genotype. In summary, we provide novel insights into the pathophysiological mechanisms underlying BBB dysfunction and the potential clinical benefits of therapeutic interventions targeting BBB in CCH.