Automatic selection of arterial input function using cluster analysis

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.

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.

Improved reliability of perfusion estimation in dynamic susceptibility contrast MRI by using the arterial input function from dynamic contrast enhanced MRI

The arterial input function (AIF) plays a crucial role in estimating quantitative perfusion properties from dynamic susceptibility contrast (DSC) MRI. An important issue, however, is that measuring the AIF in absolute contrast-agent concentrations is challenging, due to uncertainty in relation to the measuredR 2 ∗ -weighted signal, signal depletion at high concentration, and partial-volume effects. A potential solution could be to derive the AIF from separately acquired dynamic contrast enhanced (DCE) MRI data. We aim to compare the AIF determined from DCE MRI with the AIF from DSC MRI, and estimated perfusion coefficients derived from DSC data using a DCE-driven AIF with perfusion coefficients determined using a DSC-based AIF. AIFs were manually selected in branches of the middle cerebral artery (MCA) in both DCE and DSC data in each patient. In addition, a semi-automatic AIF-selection algorithm was applied to the DSC data. The amplitude and full width at half-maximum of the AIFs were compared statistically using the Wilcoxon rank-sum test, applying a 0.05 significance level. Cerebral blood flow (CBF) was derived with different AIF approaches and compared further. The results showed that the AIFs extracted from DSC scans yielded highly variable peaks across arteries within the same patient. The semi-automatic DSC-AIF had significantly narrower width compared with the manual AIFs, and a significantly larger peak than the manual DSC-AIF. Additionally, the DCE-based AIF provided a more stable measurement of relative CBF and absolute CBF values estimated with DCE-AIFs that were compatible with previously reported values. In conclusion, DCE-based AIFs were reproduced significantly better across vessels, showed more realistic profiles, and delivered more stable and reasonable CBF measurements. The DCE-AIF can, therefore, be considered as an alternative AIF source for quantitative perfusion estimations in DSC MRI.

Dynamic susceptibility contrast perfusion-weighted and diffusion-weighted magnetic resonance imaging findings in pilocytic astrocytoma and H3.3 and H3.1 variant diffuse midline glioma, H3K27-altered

OBJECTIVE

This study compared the dynamic susceptibility contrast (DSC) magnetic resonance imaging parameters and apparent diffusion coefficient (ADC) between pilocytic astrocytoma (PA) and diffuse midline glioma, H3K27-altered (DMG) variants.

METHODS

The normalized relative cerebral blood volume (nrCBV), normalized relative flow (nrCBF), percentile signal recovery (PSR), and normalized mean ADC (nADCmean) of 23 patients with midline PAs (median age, 13 years [range, 1-71 years]; 13 female patients) and 40 patients with DMG (8.5 years [1-35 years]; 19 female patients), including 35 patients with H3.3- and five patients with H3.1-mutant tumors, treated between January 2016 and May 2022 were statistically compared.

RESULTS

DMG had a significantly lower nADCmean (median: 1.48 vs. 1.96; p = 0.00075) and lower PSR (0.97 vs. 1.23, p = 0.13) but higher nrCBV and nrCBF (1.66 vs. 1.17, p = 0.058, respectively, and 1.87 vs. 1.19, p = 0.028, respectively) than PA. The H3.3 variant had a lower nADCmean than the H3.1 variant (1.46 vs. 1.80, p = 0.10).

CONCLUSION

DMG had lower ADC and PSR and higher rCBV and rCBF than PA. The H3.3 variant had a lower ADC than the H3.1 variant. Recognizing the differences and similarities in the DSC parameters and ADC between these tumors may help presurgical diagnosis.

Effect of Temporal Sampling Rate on Estimates of the Perfusion Parameters for Patients with Moyamoya Disease Assessed with Simultaneous Multislice Dynamic Susceptibility Contrast-enhanced MR Imaging

PURPOSE

The effect of temporal sampling rate (TSR) on perfusion parameters has not been fully investigated in Moyamoya disease (MMD); therefore, this study evaluated the influence of different TSRs on perfusion parameters quantitatively and qualitatively by applying simultaneous multi-slice (SMS) dynamic susceptibility contrast-enhanced MR imaging (DSC-MRI).

METHODS

DSC-MRI datasets were acquired from 28 patients with MMD with a TSR of 0.5 s. Cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), time to peak (TTP), and time to maximum tissue residue function (Tmax) were calculated for eight TSRs ranging from 0.5 to 4.0 s in 0.5-s increments that were subsampled from a TSR of 0.5 s datasets. Perfusion measurements and volume for chronic ischemic (Tmax ≥ 2 s) and non-ischemic (Tmax < 2 s) areas for each TSR were compared to measurements with a TSR of 0.5 s, as was visual perfusion map analysis.

RESULTS

CBF, CBV, and Tmax values tended to be underestimated, whereas MTT and TTP values were less influenced, with a longer TSR. Although Tmax values were overestimated in the TSR of 1.0 s in non-ischemic areas, differences in perfusion measurements between the TSRs of 0.5 and 1.0 s were generally minimal. The volumes of the chronic ischemic areas with a TSR ≥ 3.0 s were significantly underestimated. In CBF and CBV maps, no significant deterioration was noted in image quality up to 3.0 and 2.5 s, respectively. The image quality of MTT, TTP, and Tmax maps for the TSR of 1.0 s was similar to that for the TSR of 0.5 s but was significantly deteriorated for the TSRs of ≥ 1.5 s.

CONCLUSION

In the assessment of MMD by SMS DSC-MRI, application of TSRs of ≥ 1.5 s may lead to deterioration of the perfusion measurements; however, that was less influenced in TSRs of ≤ 1.0 s.

Utility of dynamic susceptibility contrast MRI for differentiation between paragangliomas and meningiomas in the cerebellopontine angle and jugular foramen region

PURPOSE

Differentiation of paragangliomas and meningiomas can be a challenge. This study aimed to assess the utility of dynamic susceptibility contrast perfusion MRI (DSC-MRI) to distinguish paragangliomas from meningiomas.

METHODS

This retrospective study included 40 patients with paragangliomas and meningiomas in the cerebellopontine angle and jugular foramen region between March 2015 and February 2022 in a single institution. Pretreatment DSC-MRI and conventional MRI were performed in all cases. Normalized relative cerebral blood volume (nrCBV), relative cerebral blood flow (nrCBF), relative mean transit time (nrMTT), and time to peak (nTTP) as well as conventional MRI features were compared between the 2 tumor types and between meningioma subtypes as appropriate. Receiver operating characteristic curve and multivariate logistic regression analysis were performed.

RESULTS

Twenty-eight meningiomas including 8 WHO grade II meningiomas (12 males, 16 females; median age 55 years) and 12 paragangliomas (5 males, 7 females; median age 35 years) were included in this study. Paragangliomas had a higher rate of cystic/necrotic changes (10/12 vs 10/28; P = 0.014), a higher rate of internal flow voids (9/12 vs 8/28; P = 0.013), higher nrCBV (median 9.78 vs 6.64; P = 0.04), and shorter nTTP (median 0.78 vs 1.06; P < 0.001) than meningiomas. There was no difference in conventional imaging features and DSC-MRI parameters between meningioma subtypes. nTTP was identified as the most significant parameter for the 2 tumor types in the multivariate logistic regression analysis (P = 0.009).

CONCLUSIONS

In this small retrospective study, DSC-MRI perfusion differences were observed between paragangliomas and meningiomas, but not between grade I and II meningiomas.

Unchanged perfusion in normal-appearing white and grey matter of glioma patients nine months after proton beam irradiation

Purpose: Radio(chemo)therapy is used as a standard treatment for glioma patients. The surrounding normal tissue is inevitably affected by the irradiation. The aim of this longitudinal study was to investigate perfusion alterations in the normal-appearing tissue after proton irradiation and assess the dose sensitivity of the normal tissue perfusion.Methods: In 14 glioma patients, a sub-cohort of a prospective clinical trial (NCT02824731), perfusion changes in normal-appearing white matter (WM), grey matter (GM) and subcortical GM structures, i.e. caudate nucleus, hippocampus, amygdala, putamen, pallidum and thalamus, were evaluated before treatment and at three-monthly intervals after proton beam irradiation. The relative cerebral blood volume (rCBV) was assessed with dynamic susceptibility contrast MRI and analysed as the percentage ratio between follow-up and baseline image (ΔrCBV). Radiation-induced alterations were evaluated using Wilcoxon signed rank test. Dose and time correlations were investigated with univariate and multivariate linear regression models.Results: No significant ΔrCBV changes were found in any normal-appearing WM and GM region after proton beam irradiation. A positive correlation with radiation dose was observed in the multivariate regression model applied to the combined ΔrCBV values of low (1-20 Gy), intermediate (21-40 Gy) and high (41-60 Gy) dose regions of GM (p < 0.001), while no time dependency was detected in any normal-appearing area.Conclusion: The perfusion in normal-appearing brain tissue remained unaltered after proton beam therapy. In further studies, a direct comparison with changes after photon therapy is recommended to confirm the different effect of proton therapy on the normal-appearing tissue.

Dynamic Contrast-Enhanced MRI Parameters and Normalized ADC Values Could Aid Differentiation of Skull Base Osteomyelitis from Nasopharyngeal Cancer

BACKGROUND AND PURPOSE

The skull base osteomyelitis sometimes can be difficult to distinguish from nasopharyngeal cancer. This study aimed to investigate the differences between skull base osteomyelitis and nasopharyngeal cancer using dynamic contrast-enhanced MR imaging and normalized ADC values.

MATERIALS AND METHODS

This study included 8 and 12 patients with skull base osteomyelitis and nasopharyngeal cancer, respectively, who underwent dynamic contrast-enhanced MR imaging and DWI before primary treatment. Quantitative dynamic contrast-enhanced MR imaging parameters and ADC values of the ROIs were analyzed. Normalized ADC parameters were calculated by dividing the ROIs of the lesion by that of the spinal cord.

RESULTS

The rate transfer constant between extravascular extracellular space and blood plasma per minute (Kep) was significantly lower in patients with skull base osteomyelitis than in those with nasopharyngeal cancer (median, 0.43 versus 0.57; P = .04). The optimal cutoff value of Kep was 0.48 (area under the curve, 0.78; 95% CI, 0.55-1). The normalized mean ADC was significantly higher in patients with skull base osteomyelitis than in those with nasopharyngeal cancer (median, 1.90 versus 0.87; P < .001). The cutoff value of normalized mean ADC was 1.55 (area under the curve, 0.96; 95% CI, 0.87-1). The area under the curve of the combination of dynamic contrast-enhanced MR imaging parameters (Kep and extravascular extracellular space volume per unit tissue volume) was 0.89 (95% CI, 0.73-1), and the area under the curve of the combination of dynamic contrast-enhanced MR imaging parameters and normalized mean ADC value was 0.98 (95% CI, 0.93-1).

CONCLUSIONS

Quantitative dynamic contrast-enhanced MR imaging parameters and normalized ADC values may be useful in differentiating skull base osteomyelitis and nasopharyngeal cancer. The combination of dynamic contrast-enhanced MR imaging parameters and normalized ADC values outperformed each measure in isolation.

Differentiation of pilocytic astrocytoma, medulloblastoma, and hemangioblastoma on diffusion-weighted and dynamic susceptibility contrast perfusion MRI

This study aimed to evaluate the diagnostic performance of dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging and apparent diffusion coefficient (ADC) for differentiating common posterior fossa tumors, pilocytic astrocytoma (PA), medulloblastoma (MB), and hemangioblastoma (HB). Between January 2016 and April 2022, we enrolled 23 (median age, 7 years [range, 2-26]; 12 female), 13 (10 years [1-24]; 3 female), and 12 (43 years [23-73]; 7 female) patients with PA, MB, and HB, respectively. Normalized relative cerebral blood volume and flow (nrCBV and nrCBF) and normalized mean ADC (nADCmean) were calculated from volume-of-interest and statistically compared. nADCmean was significantly higher in PA than in MB (PA: median, 2.2 [range, 1.59-2.65] vs MB: 0.93 [0.70-1.37], P < .001). nrCBF was significantly higher in HB than in PA and MB (PA: 1.10 [0.54-2.26] vs MB: 1.62 [0.93-3.16] vs HB: 7.83 [2.75-20.1], all P < .001). nrCBV was significantly different between all 3 tumor types (PA: 0.89 [0.34-2.28] vs MB: 1.69 [0.93-4.23] vs HB: 8.48 [4.59-16.3], P = .008 for PA vs MB; P < .001 for PA vs HB and MB vs HB). All tumors were successfully differentiated using an algorithmic approach with a threshold value of 4.58 for nrCBV and subsequent threshold value of 1.38 for nADCmean. DSC parameters and nADCmean were significantly different between PA, MB, and HB. An algorithmic approach combining nrCBV and nADCmean may be useful for differentiating these tumor types.

Skull metastases and osseous venous malformations: The role of diffusion-weighted and dynamic contrast-enhanced MRI

BACKGROUND AND PURPOSE

Skull metastasis (SM) is a common secondary malignancy. We evaluated the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (MRI) in differentiating SM from osseous venous malformations and SM of various origins.

METHODS

This study included 31 patients with SM (median age, 64 years; range, 41-87 years; 29 women; 24 and 7 patients with breast and non-small cell lung cancer, respectively) and 16 with osseous venous malformations (median age, 68 years; range, 20-81 years; 10 women) who underwent both DWI and dynamic contrast-enhanced MRI between January 2015 and October 2021. Normalized mean apparent diffusion coefficients (ADCs) and dynamic contrast-enhanced MRI parameters were compared between SM and osseous venous malformations, and between breast cancer and non-small cell lung cancer. Multivariate stepwise logistic regression analyses were performed to identify statistically significant parameters.

RESULTS

Plasma volume and time-to-maximum enhancement were the most statistically significant parameters for differentiating SM from osseous venous malformations, with an area under the receiver operating characteristic curve of 0.962. The normalized mean ADC and peak enhancement values were the most statistically significant parameters for differentiating breast cancer from non-small cell lung cancer, with an area under the curve of 0.924.

CONCLUSIONS

Our results highlight the efficacious diagnostic performance of DWI and dynamic contrast-enhanced MRI in distinguishing SM from osseous venous malformations and differentiating SM of various origins.

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 (K^trans , v_e , k_ep , and v_p ), 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 K^trans 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.

Oxygen extraction efficiency and white matter lesion burden in older adults exhibiting radiological evidence of capillary shunting

White matter lesions (WML) have been linked to cognitive decline in aging as well as in Alzheimer's disease. While hypoperfusion is frequently considered a cause of WMLs due to the resulting reduction in oxygen availability to brain tissue, such reductions could also be caused by impaired oxygen exchange. Here, we tested the hypothesis that venous hyperintense signal (VHS) in arterial spin labeling (ASL) magnetic resonance imaging (MRI) may represent a marker of impaired oxygen extraction in aging older adults. In participants aged 60-80 years (n = 30), we measured cerebral blood flow and VHS with arterial spin labeling, maximum oxygen extraction fraction (OEFmax) with dynamic susceptibility contrast, and WML volume with T1-weighted MRI. We found a significant interaction between OEFmax and VHS presence on WML volume (p = 0.02), where lower OEFmax was associated with higher WML volume in participants with VHS, and higher OEFmax was associated with higher WML volume in participants without VHS. These results indicate that VHS in perfusion-weighted ASL data may represent a distinct cerebrovascular aging pattern involving oxygen extraction inefficiency as well as hypoperfusion.

Perfusion and diffusion-weighted imaging parameters: Comparison between pre- and postbiopsy MRI for high-grade glioma

We aimed to evaluate the differences in dynamic susceptibility contrast (DSC)- magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) parameters between the pre- and postbiopsy MRI obtained before treatment in patients with diffuse midline glioma, H3K27-altered. The data of 25 patients with pathologically proven diffuse midline glioma, H3K27-altered, were extracted from our hospital's database between January 2017 and August 2021. Twenty (median age, 13 years; range, 3-52 years; 12 women) and 8 (13.5 years; 5-68 years; 1 woman) patients underwent preoperative DSC-MRI and DWI before and after biopsy, respectively. The normalized corrected relative cerebral blood volume (ncrCBV), normalized relative cerebral blood flow (nrCBF), and normalized maximum, mean, and minimum apparent diffusion coefficient (ADC) were calculated using the volumes-of-interest of the tumor and normal-appearing reference region. The macroscopic postbiopsy changes (i.e., biopsy tract, tissue defect, and hemorrhage) were meticulously excluded from the postbiopsy measurements. The DSC-MRI and DWI parameters of the pre- and postbiopsy groups were compared using the Mann-Whitney U test. The ncrCBV was significantly lower in the postbiopsy group than in the prebiopsy group [prebiopsy group: median 1.293 (range, 0.513 to 2.547) versus postbiopsy group: 0.877 (0.748 to 1.205), P = .016]. No significant difference was observed in the nrCBF and normalized ADC values, although the median nrCBF was lower in the postbiopsy group. The DSC-MRI parameters differed between the pre- and postbiopsy MRI obtained pretreatment, although the macroscopic postbiopsy changes were carefully excluded from the analysis. The results emphasize the potential danger of integrating and analyzing DSC-MRI parameters derived from pre- and postbiopsy MRI.

Comparison of Two Software Packages for Perfusion Imaging: Ischemic Core and Penumbra Estimation and Patient Triage in Acute Ischemic Stroke

Purpose: Automated postprocessing packages have been developed for managing acute ischemic stroke (AIS). These packages identify ischemic core and penumbra using either computed tomographic perfusion imaging (CTP) data or magnetic resonance imaging (MRI) data. Measurements of abnormal tissues and treatment decisions derived from different vendors can vary. The purpose of this study is to investigate the agreement of volumetric and decision-making outcomes derived from two software packages. Methods: A total of 594 AIS patients (174 underwent CTP and 420 underwent MRI) were included. Imaging data were accordingly postprocessed by two software packages: RAPID and RealNow. Volumetric outputs were compared between packages by performing intraclass correlation coefficient (ICC), Wilcoxon paired test and Bland–Altman analysis. Concordance of selecting patients eligible for mechanical thrombectomy (MT) was assessed based on neuroimaging criteria proposed in DEFUSE3. Results: In the group with CTP data, mean ischemic core volume (ICV)/penumbral volume (PV) was 14.9/81.1 mL via RAPID and 12.6/83.2 mL via RealNow. Meanwhile, in the MRI group, mean ICV/PV were 52.4/68.4 mL and 48.9/61.6 mL via RAPID and RealNow, respectively. Reliability, which was measured by ICC of ICV and PV in CTP and MRI groups, ranged from 0.87 to 0.99. The bias remained small between measurements (CTP ICV: 0.89 mL, CTP PV: −2 mL, MRI ICV: 3.5 mL and MRI PV: 6.8 mL). In comparison with CTP ICV with follow-up DWI, the ICC was 0.92 and 0.94 for RAPID and Realnow, respectively. The bias remained small between CTP ICV and follow-up DWI measurements (Rapid: −4.65 mL, RealNow: −3.65 mL). Wilcoxon paired test showed no significant difference between measurements. The results of patient triage were concordant in 159/174 cases (91%, ICC: 0.90) for CTP and 400/420 cases (95%, ICC: 0.93) for MRI. Conclusion: The CTP ICV derived from RealNow was more accurate than RAPID. The similarity in volumetric measurement between packages did not necessarily relate to equivalent patient triage. In this study, RealNow showed excellent agreement with RAPID in measuring ICV and PV as well as patient triage.

Normalized Parameters of Dynamic Contrast-Enhanced Perfusion MRI and DWI-ADC for Differentiation between Posttreatment Changes and Recurrence in Head and Neck Cancer

BACKGROUND AND PURPOSE

Differentiating recurrence from benign posttreatment changes has clinical importance in the imaging follow-up of head and neck cancer. This study aimed to investigate the utility of normalized dynamic contrast-enhanced MR imaging and ADC for their differentiation.

MATERIALS AND METHODS

This study included 51 patients with a history of head and neck cancer who underwent follow-up dynamic contrast-enhanced MR imaging with DWI-ADC, of whom 25 had recurrences and 26 had benign posttreatment changes. Quantitative and semiquantitative dynamic contrast-enhanced MR imaging parameters and ADC of the ROI and reference region were analyzed. Normalized dynamic contrast-enhanced MR imaging parameters and normalized DWI-ADC parameters were calculated by dividing the ROI by the reference region.

RESULTS

Normalized plasma volume, volume transfer constant between extravascular extracellular space and blood plasma per minute (K ^trans), area under the curve, and wash-in were significantly higher in patients with recurrence than in those with benign posttreatment change (P = .003 to <.001). The normalized mean ADC was significantly lower in patients with recurrence than in those with benign posttreatment change (P < .001). The area under the receiver operating characteristic curve of the combination of normalized dynamic contrast-enhanced MR imaging parameters with significance (normalized plasma volume, normalized extravascular extracellular space volume per unit tissue volume, normalized K ^trans, normalized area under the curve, and normalized wash-in) and normalized mean ADC was 0.97 (95% CI, 0.93-1).

CONCLUSIONS

Normalized dynamic contrast-enhanced MR imaging parameters, normalized mean ADC, and their combination were effective in differentiating recurrence and benign posttreatment changes in head and neck cancer.

Dynamic Susceptibility Contrast MRI May Contribute in Prediction of Stereotactic Radiosurgery Outcome in Brain Metastases

Background

Following stereotactic radiosurgery (SRS), predicting treatment response is not possible at an early stage using structural imaging alone. Hence, the current study aims at investigating whether dynamic susceptibility contrast (DSC)-MRI estimated prior to SRS can provide predictive biomarkers in response to SRS treatment and characterize vascular characteristics of pseudo-progression.

Methods

In this retrospective study, perfusion-weighted DSC-MRI image data acquired with a temporal resolution of 1.45 seconds were collected from 41 patients suffering from brain metastases. Outcome was defined based on lesion volume changes in time (determined on structural images) or death. Motion correction and manual lesion delineation were performed prior to semi-automated, voxel-wise perfusion analysis. Statistical testing was performed using linear regression and a significance threshold at P = .05. Age, sex, primary cancers (pulmonary cancer and melanoma), lesion volume, and dichotomized survival time were added as covariates in the linear regression models (ANOVA).

Results

Relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) were found to be significantly lower prior to SRS treatment in patients with increasing lesion volume or early death post-SRS (P ≤ .01).

Conclusion

Unfavorable treatment outcome may be linked to low perfusion prior to SRS. Pseudo-progression may be preceded by a transient rCBF increase post-SRS. However, results should be verified in different or larger patient material.

Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach

PURPOSE

To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI.

METHODS

A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n = 25) and benign (n = 28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy.

RESULT

The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with arterial input function (AIF) showed an area under the curve (AUC) between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81.

CONCLUSION

This study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.

Prediction of Wound Failure in Patients with Head and Neck Cancer Treated with Free Flap Reconstruction: Utility of CT Perfusion and MR Perfusion in the Early Postoperative Period

BACKGROUND AND PURPOSE

Free flap reconstruction in patients with head and neck cancer carries a risk of postoperative complications, and radiologic predictive factors have been limited. The aim of this study was to assess the factors that predict free flap reconstruction failure using CT and MR perfusion.

MATERIALS AND METHODS

This single-center prospective study included 24 patients (mean age, 62.7 [SD, 9.0] years; 16 men) who had free flap reconstruction from January 2016 to May 2018. CT perfusion and dynamic contrast-enhanced MR imaging with conventional CT and MR imaging were performed between 2 and 4 days after the free flap surgery, and the wound assessments within 14 days after the surgery were conducted by the surgical team. The parameters of CT perfusion and dynamic contrast-enhanced MR imaging with conventional imaging findings and patient demographics were compared between the patients with successful free flap reconstruction and those with wound failure as appropriate. P < .05 was considered significant.

RESULTS

There were 19 patients with successful free flap reconstruction and no wound complications (mean age, 63.9 [SD, 9.5] years; 14 men), while 5 patients had wound failure (mean age, 58.0 [SD, 5.7] years; 2 men). Blood flow, blood volume, MTT, and time maximum intensity projection (P = .007, .007, .015, and .004, respectively) in CT perfusion, and fractional plasma volume, volume transfer constant, peak enhancement, and time to maximum enhancement (P = .006, .039, .004, and .04, respectively) in dynamic contrast-enhanced MR imaging were significantly different between the 2 groups.

CONCLUSIONS

CT perfusion and dynamic contrast-enhanced MR imaging are both promising imaging techniques to predict wound complications after head and neck free flap reconstruction.

Assessing Tumour Haemodynamic Heterogeneity and Response to Choline Kinase Inhibition Using Clustered Dynamic Contrast Enhanced MRI Parameters in Rodent Models of Glioblastoma

To investigate the utility of DCE-MRI derived pharmacokinetic parameters in evaluating tumour haemodynamic heterogeneity and treatment response in rodent models of glioblastoma, imaging was performed on intracranial F98 and GL261 glioblastoma bearing rodents. Clustering of the DCE-MRI-based parametric maps (using Tofts, extended Tofts, shutter speed, two-compartment, and the second generation shutter speed models) was performed using a hierarchical clustering algorithm, resulting in areas with poor fit (reflecting necrosis), low, medium, and high valued pixels representing parameters Ktrans, ve, Kep, vp, τi and Fp. There was a significant increase in the number of necrotic pixels with increasing tumour volume and a significant correlation between ve and tumour volume suggesting increased extracellular volume in larger tumours. In terms of therapeutic response in F98 rat GBMs, a sustained decrease in permeability and perfusion and a reduced cell density was observed during treatment with JAS239 based on Ktrans, Fp and ve as compared to control animals. No significant differences in these parameters were found for the GL261 tumour, indicating that this model may be less sensitive to JAS239 treatment regarding changes in vascular parameters. This study demonstrates that region-based clustered pharmacokinetic parameters derived from DCE-MRI may be useful in assessing tumour haemodynamic heterogeneity with the potential for assessing therapeutic response.

Quantitative Analysis of DCE and DSC-MRI: From Kinetic Modeling to Deep Learning

BACKGROUND

 Perfusion MRI is a well-established imaging modality with a multitude of applications in oncological and cardiovascular imaging. Clinically used processing methods, while stable and robust, have remained largely unchanged in recent years. Despite promising results from novel methods, their relatively minimal improvement compared to established methods did not generally warrant significant changes to clinical perfusion processing.

RESULTS AND CONCLUSION

 Machine learning in general and deep learning in particular, which are currently revolutionizing computer-aided diagnosis, may carry the potential to change this situation and truly capture the potential of perfusion imaging. Recent advances in the training of recurrent neural networks make it possible to predict and classify time series data with high accuracy. Combining physics-based tissue models and deep learning, using either physics-informed neural networks or universal differential equations, simplifies the training process and increases the interpretability of the resulting models. Due to their versatility, these methods will potentially be useful in bridging the gap between microvascular architecture and perfusion parameters, akin to MR fingerprinting in structural MR imaging. Still, further research is urgently needed before these methods may be used in clinical practice.

KEY POINTS

· Machine learning offers promising methods for processing of perfusion data.. · Recurrent neural networks can classify time series with high accuracy.. · Data augmentation is essentially especially when using small datasets..

CITATION FORMAT

· Rotkopf LT, Zhang KS, Tavakoli AA et al. Quantitative Analysis of DCE and DSC-MRI: From Kinetic Modeling to Deep Learning. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1762-5854.

Evaluation of normal-appearing white matter with perfusion and diffusion MRI in patients with treated glioblastoma

OBJECTIVE

We tried to reveal how the normal appearing white matter (NAWM) was affected in patients with glioblastoma treated with chemo-radiotherapy (CRT) in the period following the treatment, by multiparametric MRI.

MATERIALS AND METHODS

43 multiparametric MRI examinations of 17 patients with glioblastoma treated with CRT were examined. A total of six different series or maps were analyzed in the examinations: Apparent Diffusion Coefficient (ADC) and Fractional Anisotropy (FA) maps, Gradient Echo (GRE) sequence, Dynamic susceptibility contrast (DSC) and Arterial spin labeling (ASL) perfusion sequences. Each sequence in each examination was examined in detail with 14 Region of Interest (ROI) measurements. The obtained values were proportioned to the contralateral NAWM values and the results were recorded as normalized values. Time dependent changes of normalized values were statistically analyzed.

RESULTS

The most prominent changes in follow-up imaging occurred in the perilesional region. In perilesional NAWM, we found a decrease in normalized FA (nFA), rCBV (nrCBV), rCBF (nrCBF), ASL (nASL)values (p < 0.005) in the first 3 months after treatment, followed by a plateau and an increase approaching pretreatment values, although it did not reach. Similar but milder findings were present in other NAWM areas. In perilesional NAWM, nrCBV values were found to be positively high correlated with nrCBF and nASL, and negatively high correlated with nADC values (r: 0.963, 0.736, - 0.973, respectively). We also found high correlations between the mean values of nrCBV, nrCBF, nASL in other NAWM areas (r: 0.891, 0.864, respectively).

DISCUSSION

We showed that both DSC and ASL perfusion values decreased correlatively in the first 3 months and showed a plateau after 1 year in patients with glioblastoma treated with CRT, unlike the literature. Although it was not as evident as perfusion MRI, it was observed that the ADC values also showed a plateau pattern following the increase in the first 3 months. Further studies are needed to explain late pathophysiological changes. Because of the high correlation, our results support ASL perfusion instead of contrast enhanced perfusion methods.

Dynamic susceptibility contrast-MRI parameters, ADC values, and the T2-FLAIR mismatch sign are useful to differentiate between H3-mutant and H3-wild-type high-grade midline glioma

OBJECTIVES

Diffuse midline gliomas, H3K27-altered (DMG-A), are malignant gliomas with an unfavorable prognosis. Knowledge of dynamic susceptibility contrast (DSC) MRI findings and imaging differences with high-grade midline glioma without H3K27 alteration (DMG-W) has been limited. We compared the DSC, ADC, and conventional MRI findings between DMG-A and DMG-W.

METHODS

In this single institutional retrospective study, the electronic database of our hospital between June 2015 and May 2021 was searched. Twenty and 17 patients with DMG-A (median, 13 years; range, 3-52 years; 11 females) and DMG-W (median, 40 years; 7-73 years; 9 females), respectively, were found. Normalized relative cerebral blood flow (nrCBF) and normalized corrected relative cerebral blood volume (ncrCBV); normalized maximum, mean, and minimum ADC values; and the prevalence of T2-FLAIR mismatch sign were compared between the two groups using Mann-Whitney U tests and Fisher's exact test.

RESULTS

The nrCBF and ncrCBV were significantly lower in DMG-A compared with DMG-W (nrCBF: median 0.88 [range, 0.19-2.67] vs. 1.47 [range, 0.57-4.90] (p < 0.001); ncrCBV: 1.17 [0.20-2.67] vs. 1.56 [0.60-4.03] (p = 0.008)). Normalized maximum ADC (nADC_max) was significantly higher in DMG-A (median 2.37 [1.25-3.98] vs. 1.95 [1.23-2.77], p = 0.02). T2-FLAIR mismatch sign was significantly more common in DMG-A (11/20 (55.0%) vs. 1/17 (5.9%), p = 0.0017). When at least two of nrCBF < 1.11, nADC_max ≥ 2.48, and T2-FLAIR mismatch sign were positive, the diagnostic performance was the highest with accuracy of 0.81.

CONCLUSION

DSC-MRI parameters, ADC values, and the T2-FLAIR mismatch sign are useful to differentiate between DMG-A and DMG-W.

KEY POINTS

• Diffuse midline glioma, H3K27-altered (DMG-A), showed a significantly lower normalized relative cerebral blood flow and volume compared with H3K27-wild-type counterparts (DMG-W). • T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign was significantly more frequent in DMG-A compared to DMG-W. • Indicators that combined DSC parameters, ADC values, and T2-FLAIR mismatch sign, with or without age, are useful to distinguish the two tumors.

Dynamic susceptibility contrast and diffusion-weighted MRI in posterior fossa pilocytic astrocytoma and medulloblastoma

BACKGROUND AND PURPOSE

The utility of perfusion MRI in distinguishing between pilocytic astrocytoma (PA) and medulloblastoma (MB) is unclear. This study aimed to evaluate the diagnostic and prognostic performance of dynamic susceptibility contrast (DSC)-MRI parameters and apparent diffusion coefficient (ADC) values between PA and MB.

METHODS

Between January 2012 and August 2021, 49 (median, 7 years [range, 1-28 years]; 28 females) and 35 (median, 8 years [1-24 years]; 12 females) patients with pathologically confirmed PA and MB, respectively, were included. The normalized relative cerebral blood volume and flow (nrCBV and nrCBF) and mean and minimal normalized ADC (nADCmean and nADCmin) values were calculated using volume-of-interest analyses. Diagnostic performance and Pearson's correlation with progression-free survival were also evaluated.

RESULTS

The MB group showed a significantly higher nrCBV and nrCBF (nrCBV: 1.69 [0.93-4.23] vs. 0.95 [range, 0.37-2.28], p = .0032; nrCBF: 1.62 [0.93-3.16] vs. 1.07 [0.46-2.26], p = .0084) and significantly lower nADCmean and nADCmin (nADCmean: 0.97 [0.70-1.68] vs. 2.21 [1.44-2.80], p < .001; nADCmin: 0.50 [0.19-0.89] vs. 1.42 [0.89-2.20], p < .001) than the PA group. All parameters exhibited good diagnostic ability (accuracy >0.80) with nADCmin achieving the highest score (accuracy = 1). A moderate correlation was found between nADCmean and progression-free survival for MB (r = 0.44, p = .0084).

CONCLUSIONS

DSC-MRI parameters and ADC values were useful for distinguishing between PA and MB. A lower ADC indicated an unfavorable MB prognosis, but the DSC-MRI parameters did not correlate with progression-free survival in either group.

Perfusion Imaging: An Advection Diffusion Approach

Perfusion imaging is of great clinical importance and is used to assess a wide range of diseases including strokes and brain tumors. Commonly used approaches for the quantitative analysis of perfusion images are based on measuring the effect of a contrast agent moving through blood vessels and into tissue. Contrast-agent free approaches, for example, based on intravoxel incoherent motion and arterial spin labeling, also exist, but are so far not routinely used clinically. Existing contrast-agent-dependent methods typically rely on the estimation of the arterial input function (AIF) to approximately model tissue perfusion. These approaches neglect spatial dependencies. Further, as reliably estimating the AIF is non-trivial, different AIF estimates may lead to different perfusion measures. In this work we therefore propose PIANO, an approach that provides additional insights into the perfusion process. PIANO estimates the velocity and diffusion fields of an advection-diffusion model best explaining the contrast dynamics without using an AIF. PIANO accounts for spatial dependencies and neither requires estimating the AIF nor relies on a particular contrast agent bolus shape. Specifically, we propose a convenient parameterization of the estimation problem, a numerical estimation approach, and extensively evaluate PIANO. Simulation experiments show the robustness and effectiveness of PIANO, along with its ability to distinguish between advection and diffusion. We further apply PIANO on a public brain magnetic resonance (MR) perfusion dataset of acute stroke patients, and demonstrate that PIANO can successfully resolve velocity and diffusion field ambiguities and results in sensitive measures for the assessment of stroke, comparing favorably to conventional measures of perfusion.

Cerebral hemodynamics and capillary dysfunction in late-onset major depressive disorder

In major depressive disorder (MDD), perfusion changes in cortico-limbic pathways are interpreted as altered neuronal activity, but they could also signify changes in neurovascular coupling due to altered capillary function. To examine capillary function in late-onset MDD, 22 patients and 22 age- and gender-matched controls underwent perfusion MRI. We measured normalized cerebral blood flow (nCBF), cerebral blood volume (nCBV), and relative transit-time heterogeneity (RTH). Resulting brain oxygenation was estimated in terms of oxygen tension and normalized metabolic rate of oxygen (nCMRO₂). Patients revealed signs of capillary dysfunction (elevated RTH) in the anterior prefrontal cortex and ventral anterior cingulate cortex bilaterally and in the left insulate cortex compared to controls, bilateral hypometabolism (parallel reductions of nCBV, nCBF, and CMRO₂) but preserved capillary function in the subthalamic nucleus and globus pallidus bilaterally, and hyperactivity with preserved capillary function (increased nCBF) in the cerebellum and brainstem. Our data support that perfusion changes in deep nuclei and cerebellum reflect abnormally low and high activity, respectively, in MDD patients, but suggest that microvascular pathology affects neurovascular coupling in ventral circuits. We speculate that microvascular pathology is important for our understanding of etiology of late-onset MDD as well as infererences about resulting brain activity changes.

Tissue outcome prediction in hyperacute ischemic stroke: Comparison of machine learning models

Machine Learning (ML) has been proposed for tissue fate prediction after acute ischemic stroke (AIS), with the aim to help treatment decision and patient management. We compared three different ML models to the clinical method based on diffusion-perfusion thresholding for the voxel-based prediction of final infarct, using a large MRI dataset obtained in a cohort of AIS patients prior to recanalization treatment. Baseline MRI (MRI₀), including diffusion-weighted sequence (DWI) and Tmax maps from perfusion-weighted sequence, and 24-hr follow-up MRI (MRI_24h) were retrospectively collected in consecutive 394 patients AIS patients (median age = 70 years; final infarct volume = 28mL). Manually segmented DWI_24h lesion was considered the final infarct. Gradient Boosting, Random Forests and U-Net were trained using DWI, apparent diffusion coefficient (ADC) and Tmax maps on MRI₀ as inputs to predict final infarct. Tissue outcome predictions were compared to final infarct using Dice score. Gradient Boosting had significantly better predictive performance (median [IQR] Dice Score as for median age, maybe you can replace the comma with an equal sign for consistency 0.53 [0.29-0.68]) than U-Net (0.48 [0.18-0.68]), Random Forests (0.51 [0.27-0.66]), and clinical thresholding method (0.45 [0.25-0.62]) (P < 0.001). In this benchmark of ML models for tissue outcome prediction in AIS, Gradient Boosting outperformed other ML models and clinical thresholding method and is thus promising for future decision-making.

Forward Model and Deep Learning Based Iterative Deconvolution for Robust Dynamic CT Perfusion

Perfusion maps obtained from low-dose computed tomography (CT) data suffer from poor signal to noise ratio. To enhance the quality of the perfusion maps, several works rely on denoising the low-dose CT (LD-CT) images followed by conventional regularized deconvolution. Recent works employ deep neural networks (DNN) for learning a direct mapping between the noisy and the clean perfusion maps ignoring the convolution-based forward model. DNN-based methods are not robust to practical variations in the data that are seen in real-world applications such as stroke. In this work, we propose an iterative framework that combines the perfusion forward model with a DNN-based regularizer to obtain perfusion maps directly from the LD-CT dynamic data. To improve the robustness of the DNN, we leverage the anatomical information from the contrast-enhanced LD-CT images to learn the mapping between low-dose and standard-dose perfusion maps. Through empirical experiments, we show that our model is robust both qualitatively and quantitatively to practical perturbations in the data.

Extraction of a vascular function for a fully automated dynamic contrast-enhanced magnetic resonance brain image processing pipeline

PURPOSE

To develop a deep-learning model that leverages the spatial and temporal information from dynamic contrast-enhanced magnetic resonance (DCE MR) brain imaging in order to automatically estimate a vascular function (VF) for quantitative pharmacokinetic (PK) modeling.

METHODS

Patients with glioblastoma multiforme were scanned post-resection approximately every 2 months using a high spatial and temporal resolution DCE MR imaging sequence ( ≈ 5 s and ≈ 2 cm³ ). A region over the transverse sinus was manually drawn in the dynamic T1-weighted images to provide a ground truth VF. The manual regions and their resulting VF curves were used to train a deep-learning model based on a 3D U-net architecture. The model concurrently utilized the spatial and temporal information in DCE MR images to predict the VF. In order to analyze the contribution of the spatial and temporal terms, different weighted combinations were examined. The manual and deep-learning predicted regions and VF curves were compared.

RESULTS

Forty-three patients were enrolled in this study and 155 DCE MR scans were processed. The 3D U-net was trained using a loss function that combined the spatial and temporal information with different weightings. The best VF curves were obtained when both spatial and temporal information were considered. The predicted VF curve was similar to the manual ground truth VF curves.

CONCLUSION

The use of spatial and temporal information improved VF curve prediction relative to when only the spatial information is used. The method generalized well for unseen data and can be used to automatically estimate a VF curve suitable for quantitative PK modeling. This method allows for a more efficient clinical pipeline and may improve automation of permeability mapping.

Applications of artificial intelligence for DWI and PWI data processing in acute ischemic stroke: Current practices and future directions

Multimodal Magnetic Resonance Imaging (MRI) techniques of Perfusion-Weighted Imaging (PWI) and Diffusion-Weighted Imaging (DWI) data are integral parts of the diagnostic workup in the acute stroke setting. The visual interpretation of PWI/DWI data is the most likely procedure to triage Acute Ischemic Stroke (AIS) patients who will access reperfusion therapy, especially in those exceeding 6 h of stroke onset. In fact, this process defines two classes of tissue: the ischemic core, which is presumed to be irreversibly damaged, visualized on DWI data and the penumbra which is the reversibly injured brain tissue around the ischemic tissue, visualized on PWI data. AIS patients with a large ischemic penumbra and limited infarction core have a high probability of benefiting from endovascular treatment. However, it is a tedious and time-consuming procedure. Consequently, it is subject to high inter- and intra-observer variability. Thus, the assessment of the potential risks and benefits of endovascular treatment is uncertain. Fast, accurate and automatic post-processing of PWI and DWI data is important for clinical diagnosis and is necessary to help the decision making for therapy. Therefore, an automated procedure that identifies stroke slices, stroke hemisphere, segments stroke regions in DWI, and measures hypoperfused tissue in PWI enhances considerably the reproducibility and the accuracy of stroke assessment. In this work, we draw an overview of several applications of Artificial Intelligence (AI) for the automation processing and their potential contributions in clinical practices. We compare the current approaches among each other's with respect to some key requirements.

DCE-MRI of Brain Fluid Barriers: In Vivo Water Cycling at the Human Choroid Plexus

Metabolic deficits at brain-fluid barriers are an increasingly recognized feature of cognitive decline in older adults. At the blood-cerebrospinal fluid barrier, water is transported across the choroid plexus (CP) epithelium against large osmotic gradients via processes tightly coupled to activity of the sodium/potassium pump. Here, we quantify CP homeostatic water exchange using dynamic contrast-enhanced MRI and investigate the association of the water efflux rate constant (k_co) with cognitive dysfunction in older individuals. Temporal changes in the longitudinal relaxation rate constant (R₁) after contrast agent bolus injection were measured in a CP region of interest in 11 participants with mild cognitive dysfunction [CI; 73 ± 6 years] and 28 healthy controls [CN; 72 ± 7 years]. k_co was determined from a modified two-site pharmacokinetic exchange analysis of the R₁ time-course. K^trans, a measure of contrast agent extravasation to the interstitial space was also determined. Cognitive function was assessed by neuropsychological test performance. k_co averages 5.8 ± 2.7 s^-1 in CN individuals and is reduced by 2.4 s^-1 [ca. 40%] in CI subjects. Significant associations of k_co with global cognition and multiple cognitive domains are observed. K^trans averages 0.13 ± 0.07 min^-1 and declines with age [-0.006 ± 0.002 min^-1 yr^-1], but shows no difference between CI and CN individuals or association with cognitive performance. Our findings suggest that the CP water efflux rate constant is associated with cognitive dysfunction and shows an age-related decline in later life, consistent with the metabolic disturbances that characterize brain aging.

AIFNet: Automatic vascular function estimation for perfusion analysis using deep learning

Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the optimal reperfusion treatment. In perfusion CT imaging, deconvolution methods are used to obtain clinically interpretable perfusion parameters that allow identifying brain tissue abnormalities. Deconvolution methods require the selection of two reference vascular functions as inputs to the model: the arterial input function (AIF) and the venous output function, with the AIF as the most critical model input. When manually performed, the vascular function selection is time demanding, suffers from poor reproducibility and is subject to the professionals' experience. This leads to potentially unreliable quantification of the penumbra and core lesions and, hence, might harm the treatment decision process. In this work we automatize the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable deep learning approach for estimating the vascular functions. Unlike previous methods using clustering or segmentation techniques to select vascular voxels, AIFNet is directly optimized at the vascular function estimation, which allows to better recognise the time-curve profiles. Validation on the public ISLES18 stroke database shows that AIFNet almost reaches inter-rater performance for the vascular function estimation and, subsequently, for the parameter maps and core lesion quantification obtained through deconvolution. We conclude that AIFNet has potential for clinical transfer and could be incorporated in perfusion deconvolution software.

Cerebral Macro- and Microcirculation during Ephedrine versus Phenylephrine Treatment in Anesthetized Brain Tumor Patients: A Randomized Clinical Trial Using Magnetic Resonance Imaging

BACKGROUND

This study compared ephedrine versus phenylephrine treatment on cerebral macro- and microcirculation, measured by cerebral blood flow, and capillary transit time heterogeneity, in anesthetized brain tumor patients. The hypothesis was that capillary transit time heterogeneity in selected brain regions is greater during phenylephrine than during ephedrine, thus reducing cerebral oxygen tension.

METHODS

In this single-center, double-blinded, randomized clinical trial, 24 anesthetized brain tumor patients were randomly assigned to ephedrine or phenylephrine. Magnetic resonance imaging of peritumoral and contralateral hemispheres was performed before and during vasopressor infusion. The primary endpoint was between-group difference in capillary transit time heterogeneity. Secondary endpoints included changes in cerebral blood flow, estimated oxygen extraction fraction, and brain tissue oxygen tension.

RESULTS

Data from 20 patients showed that mean (± SD) capillary transit time heterogeneity in the contralateral hemisphere increased during phenylephrine from 3.0 ± 0.5 to 3.2 ± 0.7 s and decreased during ephedrine from 3.1 ± 0.8 to 2.7 ± 0.7 s (difference phenylephrine versus difference ephedrine [95% CI], -0.6 [-0.9 to -0.2] s; P = 0.004). In the peritumoral region, the mean capillary transit time heterogeneity increased during phenylephrine from 4.1 ± 0.7 to 4.3 ± 0.8 s and decreased during ephedrine from 3.5 ± 0.9 to 3.3 ± 0.9 s (difference phenylephrine versus difference ephedrine [95%CI], -0.4[-0.9 to 0.1] s; P = 0.130). Cerebral blood flow (contralateral hemisphere ratio difference [95% CI], 0.3 [0.06 to 0.54]; P = 0.018; and peritumoral ratio difference [95% CI], 0.3 [0.06 to 0.54; P = 0.018) and estimated brain tissue oxygen tension (contralateral hemisphere ratio difference [95% CI], 0.34 [0.09 to 0.59]; P = 0.001; and peritumoral ratio difference [95% CI], 0.33 [0.09 to 0.57]; P = 0.010) were greater during ephedrine than phenylephrine in both regions.

CONCLUSIONS

Phenylephrine caused microcirculation in contralateral tissue, measured by the change in capillary transit time heterogeneity, to deteriorate compared with ephedrine, despite reaching similar mean arterial pressure endpoints. Ephedrine improved cerebral blood flow and tissue oxygenation in both brain regions and may be superior to phenylephrine in improving cerebral macro- and microscopic hemodynamics and oxygenation.

EDITOR’S PERSPECTIVE

Operator dependency of arterial input function in dynamic contrast-enhanced MRI

Objective

To investigate the effect of inter-operator variability in arterial input function (AIF) definition on kinetic parameter estimates (KPEs) from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas.

Methods

The study included 118 DCE series from 23 patients. AIFs were measured by three domain experts (DEs), and a population AIF (pop-AIF) was constructed from the measured AIFs. The DE-AIFs, pop-AIF and AUC-normalized DE-AIFs were used for pharmacokinetic analysis with the extended Tofts model. AIF-dependence of KPEs was assessed by intraclass correlation coefficient (ICC) analysis, and the impact on relative longitudinal change in K^trans was assessed by Fleiss’ kappa (κ).

Results

There was a moderate to substantial agreement (ICC 0.51–0.76) between KPEs when using DE-AIFs, while AUC-normalized AIFs yielded ICC 0.77–0.95 for K^trans, k_ep and v_e and ICC 0.70 for v_p. Inclusion of the pop-AIF did not reduce agreement. Agreement in relative longitudinal change in K^trans was moderate (κ = 0.591) using DE-AIFs, while AUC-normalized AIFs gave substantial (κ = 0.809) agreement.

Discussion

AUC-normalized AIFs can reduce the variation in kinetic parameter results originating from operator input. The pop-AIF presented in this work may be applied in absence of a satisfactory measurement.

Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance

PURPOSE

Surveillance of patients with high-grade glioma (HGG) and identification of disease progression remain a major challenge in neurooncology. This study aimed to develop a support vector machine (SVM) classifier, employing combined longitudinal structural and perfusion MRI studies, to classify between stable disease, pseudoprogression and progressive disease (3-class problem).

METHODS

Study participants were separated into two groups: group I (total cohort: 64 patients) with a single DSC time point and group II (19 patients) with longitudinal DSC time points (2-3). We retrospectively analysed 269 structural MRI and 92 dynamic susceptibility contrast perfusion (DSC) MRI scans. The SVM classifier was trained using all available MRI studies for each group. Classification accuracy was assessed for different feature dataset and time point combinations and compared to radiologists' classifications.

RESULTS

SVM classification based on combined perfusion and structural features outperformed radiologists' classification across all groups. For the identification of progressive disease, use of combined features and longitudinal DSC time points improved classification performance (lowest error rate 1.6%). Optimal performance was observed in group II (multiple time points) with SVM sensitivity/specificity/accuracy of 100/91.67/94.7% (first time point analysis) and 85.71/100/94.7% (longitudinal analysis), compared to 60/78/68% and 70/90/84.2% for the respective radiologist classifications. In group I (single time point), the SVM classifier also outperformed radiologists' classifications with sensitivity/specificity/accuracy of 86.49/75.00/81.53% (SVM) compared to 75.7/68.9/73.84% (radiologists).

CONCLUSION

Our results indicate that utilisation of a machine learning (SVM) classifier based on analysis of longitudinal perfusion time points and combined structural and perfusion features significantly enhances classification outcome (p value= 0.0001).

Differentiation between glioblastoma and primary CNS lymphoma: application of DCE-MRI parameters based on arterial input function obtained from DSC-MRI

OBJECTIVE

This study aimed to evaluate whether arterial input functions (AIFs) obtained from dynamic susceptibility contrast (DSC)-MRI (AIF_DSC) improve the reliability and diagnostic accuracy of dynamic contrast-enhanced (DCE)-derived pharmacokinetic (PK) parameters for differentiating glioblastoma from primary CNS lymphoma (PCNSL) compared with AIFs derived from DCE-MRI (AIF_DCE).

METHODS

This retrospective study included 172 patients with glioblastoma (n = 147) and PCNSL (n = 25). All patients had undergone preoperative DSC- and DCE-MRI. The volume transfer constant (K^trans), volume of the vascular plasma space (v_p), and volume of the extravascular extracellular space (v_e) were acquired using AIF_DSC and AIF_DCE. The relative cerebral blood volume (rCBV) was obtained from DSC-MRI. Intraclass correlation coefficients (ICC) and ROC curves were used to assess the reliability and diagnostic accuracy of individual parameters.

RESULTS

The mean K^trans, v_p, and v_e values revealed better ICCs with AIF_DSC than with AIF_DCE (K^trans, 0.911 vs 0.355; v_p, 0.766 vs 0.503; v_e, 0.758 vs 0.657, respectively). For differentiating all glioblastomas from PCNSL, the mean rCBV (AUC = 0.856) was more accurate than the AIF_DSC-driven mean K^trans, which had the largest AUC (0.711) among the DCE-derived parameters (p = 0.02). However, for glioblastomas with low rCBV (≤ 75th percentile of PCNSL; n = 30), the AIF_DSC-driven mean K^trans and v_p were more accurate than rCBV (AUC: K^trans, 0.807 vs rCBV, 0.515, p = 0.004; v_p, 0.715 vs rCBV, p = 0.045).

CONCLUSION

DCE-derived PK parameters using the AIF_DSC showed improved reliability and diagnostic accuracy for differentiating glioblastoma with low rCBV from PCNSL.

KEY POINTS

• An accurate differential diagnosis of glioblastoma and PCNSL is crucial because of different therapeutic strategies. • In contrast to the rCBV from DSC-MRI, another perfusion imaging technique, the DCE parameters for the differential diagnosis have been limited because of the low reliability of AIFs from DCE-MRI. • When we analyzed DCE-MRI data using AIFs from DSC-MRI (AIF_DSC), AIF_DSC-driven DCE parameters showed improved reliability and better diagnostic accuracy than rCBV for differentiating glioblastoma with low rCBV from PCNSL.

Impaired cerebral microcirculation in isolated REM sleep behaviour disorder

During the prodromal period of Parkinson's disease and other α-synucleinopathy-related parkinsonisms, neurodegeneration is thought to progressively affect deep brain nuclei, such as the locus coeruleus, caudal raphe nucleus, substantia nigra, and the forebrain nucleus basalis of Meynert. Besides their involvement in the regulation of mood, sleep, behaviour, and memory functions, these nuclei also innervate parenchymal arterioles and capillaries throughout the cortex, possibly to ensure that oxygen supplies are adjusted according to the needs of neural activity. The aim of this study was to examine whether patients with isolated REM sleep behaviour disorder, a parasomnia considered to be a prodromal phenotype of α-synucleinopathies, reveal microvascular flow disturbances consistent with disrupted central blood flow control. We applied dynamic susceptibility contrast MRI to characterize the microscopic distribution of cerebral blood flow in the cortex of 20 polysomnographic-confirmed patients with isolated REM sleep behaviour disorder (17 males, age range: 54-77 years) and 25 healthy matched controls (25 males, age range: 58-76 years). Patients and controls were cognitively tested by Montreal Cognitive Assessment and Mini Mental State Examination. Results revealed profound hypoperfusion and microvascular flow disturbances throughout the cortex in patients compared to controls. In patients, the microvascular flow disturbances were seen in cortical areas associated with language comprehension, visual processing and recognition and were associated with impaired cognitive performance. We conclude that cortical blood flow abnormalities, possibly related to impaired neurogenic control, are present in patients with isolated REM sleep behaviour disorder and associated with cognitive dysfunction. We hypothesize that pharmacological restoration of perivascular neurotransmitter levels could help maintain cognitive function in patients with this prodromal phenotype of parkinsonism.

Dynamics of cerebral perfusion and oxygenation parameters following endovascular treatment of acute ischemic stroke

BACKGROUND

We studied the effects of endovascular treatment (EVT) and the impact of the extent of recanalization on cerebral perfusion and oxygenation parameters in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO).

METHODS

Forty-seven patients with anterior LVO underwent computed tomography perfusion (CTP) before and immediately after EVT. The entire ischemic region (T_max >6 s) was segmented before intervention, and tissue perfusion (time-to-maximum (T_max), time-to-peak (TTP), mean transit time (MTT), cerebral blood volume (CBV), cerebral blood flow (CBF)) and oxygenation (coefficient of variation (COV), capillary transit time heterogeneity (CTH), metabolic rate of oxygen (CMRO₂), oxygen extraction fraction (OEF)) parameters were quantified from the segmented area at baseline and the corresponding area immediately after intervention, as well as within the ischemic core and penumbra. The impact of the extent of recanalization (modified Treatment in Cerebral Infarction (mTICI)) on CTP parameters was assessed with the Wilcoxon test and Pearson's correlation coefficients.

RESULTS

The T_max, MTT, OEF and CTH values immediately after EVT were lower in patients with complete (as compared with incomplete) recanalization, whereas CBF and COV values were higher (P<0.05) and no differences were found in other parameters. The ischemic penumbra immediately after EVT was lower in patients with complete recanalization as compared with those with incomplete recanalization (P=0.002), whereas no difference was found for the ischemic core (P=0.12). Specifically, higher mTICI scores were associated with a greater reduction of ischemic penumbra volumes (R²=-0.48 (95% CI -0.67 to -0.22), P=0.001) but not of ischemic core volumes (P=0.098).

CONCLUSIONS

Our study demonstrates that the ischemic penumbra is the key target of successful EVT in patients with AIS and largely determines its efficacy on a tissue level. Furthermore, we confirm the validity of the mTICI score as a surrogate parameter of interventional success on a tissue perfusion level.

Measuring global impairment of cerebral perfusion using dynamic susceptibility contrast perfusion-weighted imaging in out-of-hospital cardiac arrest survivors: A prospective preliminary study

BACKGROUND AND PURPOSE

This study aimed to assess the global impairment and prognostic performance of cerebral perfusions (CP) measured by dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) in out-of-hospital cardiac arrest (OHCA) patients after sustained restoration of spontaneous circulation (ROSC).

MATERIALS AND METHODS

This is a single-centre, prospective observational study. OHCA patients performed DSC-PWI within 8 h after ROSC were enrolled. We quantified the CP parameters, such as cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), time to peak (TTP), and time to maximum of the residue function (Tmax) either by normalization or arterial input function (AIF). The primary and secondary outcomes were survival to discharge and comparison of prognostic performance between CP parameters and serum neuron-specific enolase (NSE) using area under the receiver operating characteristic (AUROC) and sensitivity values.

RESULTS

Thirty-one patients were included in this study. CBV and TTP quantified by normalization, and MTT and Tmax quantified by AIF showed significantly higher CP values in the non-survival group (p = 0.02, 0.03, 0.02, and <0.01, respectively). Their AUROCs and 100% specific sensitivities were 0.74/25.0%, 0.60/33.3%, 0.75/56.3%, and 0.79/43.8%, respectively. MTT quantified by AIF showed sensitivity in predicting mortality at an early stage of PCA care, comparable with NSE.

CONCLUSION

Hyperaemia and delayed CP were generally observed in OHCA patients regardless of outcomes. MTT and Tmax quantified by AIF have prognostic performance in predicting mortality, comparable with NSE. Further prospective multicentre studies are required to confirm our results.

Deterministic Arterial Input Function selection in DCE-MRI for automation of quantitative perfusion calculation of colorectal cancer

Development of a deterministic algorithm for automated detection of the Arterial Input Function (AIF) in DCE-MRI of colorectal cancer. Using a filter pipeline to determine the AIF region of interest. Comparison to algorithms from literature with mean squared error and quantitative perfusion parameter K^trans. The AIF found by our algorithm has a lower mean squared error (0.0022 ± 0.0021) in reference to the manual annotation than comparable algorithms. The error of K^trans (21.52 ± 17.2%) is lower than that of other algorithms. Our algorithm generates reproducible results and thus supports a robust and comparable perfusion analysis.

Novel Estimation of Penumbra Zone Based on Infarct Growth Using Machine Learning Techniques in Acute Ischemic Stroke

While the penumbra zone is traditionally assessed based on perfusion-diffusion mismatch, it can be assessed based on machine learning (ML) prediction of infarct growth. The purpose of this work was to develop and validate an ML method for the prediction of infarct growth distribution and volume, in cases of successful (SR) and unsuccessful recanalization (UR). Pre-treatment perfusion-weighted, diffusion-weighted imaging (DWI) data, and final infarct lesions annotated from day-7 DWI from patients with middle cerebral artery occlusion were utilized to develop and validate two ML models for prediction of tissue fate. SR and UR models were developed from data in patients with modified treatment in cerebral infarction (mTICI) scores of 2b-3 and 0-2a, respectively. When compared to manual infarct annotation, ML-based infarct volume predictions resulted in an intraclass correlation coefficient (ICC) of 0.73 (95% CI = 0.31-0.91, p < 0.01) for UR, and an ICC of 0.87 (95% CI = 0.73-0.94, p < 0.001) for SR. Favorable outcomes for mismatch presence and absence in SR were 50% and 36%, respectively, while they were 61%, 56%, and 25%, respectively, for the low, intermediate, and high infarct growth groups. The presented method can offer novel and alternative insights into selecting patients for recanalization therapy and predicting functional outcome.

DSC Brain Perfusion Using Advanced Deconvolution Models in the Diagnostic Work-up of Dementia and Mild Cognitive Impairment: A Semiquantitative Comparison with HMPAO-SPECT-Brain Perfusion

BACKGROUND

SPECT (single-photon emission-computed tomography) is used for the detection of hypoperfusion in cognitive impairment and dementia but is not widely available and related to radiation dose exposure. We compared the performance of DSC (dynamic susceptibility contrast) perfusion using semi- and fully adaptive deconvolution models to HMPAO-SPECT (99mTc-hexamethylpropyleneamine oxime-SPECT).

MATERIAL AND METHODS

Twenty-seven patients with dementia of different subtypes including frontotemporal dementia (FTD) and mild cognitive impairment (MCI) received a multimodal diagnostic work-up including DSC perfusion at a clinical 3T high-field scanner and HMPAO-SPECT. Nineteen healthy control individuals received DSC perfusion. For calculation of the hemodynamic parameter maps, oscillation-index standard truncated singular value decomposition (oSVD, semi-adaptive) as well as Bayesian parameter estimation (BAY, fully adaptive) were performed.

RESULTS

Patients showed decreased cortical perfusion in the left frontal lobe compared to controls (relative cerebral blood volume corrected, rBVc: 0.37 vs 0.27, p = 0.048, adjusted for age and sex). Performance of rBVc (corrected for T1 effects) was highest compared to SPECT for detection of frontal hypoperfusion (sensitivity 83%, specificity 80% for oSVD and BAY, area under curve (AUC) = 0.833 respectively, p < 0.05) in FTD and MCI. For nonleakage-corrected rBV and for rBF (relative cerebral blood flow), sensitivity of frontal hypoperfusion was above 80% for oSVD and for BAY (rBV: sensitivity 83%, specificity 75%, AUC = 0.908 for oSVD and 0.917 for BAY, p < 0.05 respectively; rBF: sensitivity 83%, specificity 65%, AUC = 0.825, p < 0.05 for oSVD).

CONCLUSION

Advanced deconvolution DSC can reliably detect pathological perfusion alterations in FTD and MCI. Hence, this widely accessible technique has the potential to improve the diagnosis of dementia and MCI as part of an interdisciplinary multimodal imaging work-up. Advances in knowledge: Advanced DSC perfusion has a high potential in the work-up of suspected dementia and correlates with SPECT brain perfusion results in dementia and MCI.

Impaired perfusion and capillary dysfunction in prodromal Alzheimer's disease

INTRODUCTION

Cardiovascular disease increases the risk of developing Alzheimer's disease (AD), and growing evidence suggests an involvement of cerebrovascular pathology in AD. Capillary dysfunction, a condition in which capillary flow disturbances rather than arterial blood supply limit brain oxygen extraction, could represent an overlooked vascular contributor to neurodegeneration. We examined whether cortical capillary transit-time heterogeneity (CTH), an index of capillary dysfunction, is elevated in amyloid-positive patients with mild cognitive impairment (prodromal AD [pAD]).

METHODS

We performed structural and perfusion weighted MRI in 22 pAD patients and 21 healthy controls.

RESULTS

We found hypoperfusion, reduced blood volume, and elevated CTH in the parietal and frontal cortices of pAD-patients compared to controls, while only the precuneus showed focal cortical atrophy.

DISCUSSION

We propose that microvascular flow disturbances antedate cortical atrophy and may limit local tissue oxygenation in pAD. We speculate that capillary dysfunction contributes to the development of neurodegeneration in AD.

T2/FLAIR-mismatch sign for noninvasive detection of IDH-mutant 1p/19q non-codeleted gliomas: validity and pathophysiology

Background

This study aimed to assess the validity and pathophysiology of the T2/FLAIR-mismatch sign for noninvasive identification of isocitrate dehydrogenase (IDH)-mutant 1p/19q non-codeleted glioma.

Methods

Magnetic resonance imaging scans from 408 consecutive patients with newly diagnosed glioma (113 lower-grade gliomas and 295 glioblastomas) were evaluated for the presence of T2/FLAIR-mismatch sign by 2 independent reviewers. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the performance of the T2/FLAIR-mismatch sign for identifying IDH-mutant 1p/19q non-codeleted tumors. An exploratory analysis of differences in contrast-enhancing tumor volumes, apparent diffusion coefficient (ADC) values, and relative cerebral blood volume (rCBV) values in IDH-mutant gliomas with versus without the presence of a T2/FLAIR-mismatch sign (as well as analysis of spatial differences within tumors with the presence of a T2/FLAIR-mismatch sign) was performed.

Results

The T2/FLAIR-mismatch sign was present in 12 cases with lower-grade glioma (10.6%), all of them being IDH-mutant 1p/19q non-codeleted tumors (sensitivity = 10.9%, specificity = 100%, PPV = 100%, NPV = 3.0%, accuracy = 13.3%). There was a substantial interrater agreement to identify the T2/FLAIR-mismatch sign (Cohen's kappa = 0.75 [95% CI, 0.57-0.93]). The T2/FLAIR-mismatch sign was not identified in any other molecular subgroup, including IDH-mutant glioblastoma cases (n = 5). IDH-mutant gliomas with a T2/FLAIR-mismatch sign showed significantly higher ADC (P < .0001) and lower rCBV values (P = .0123) as compared to IDH-mutant gliomas without a T2/FLAIR-mismatch sign. Moreover, in IDH-mutant gliomas with T2/FLAIR-mismatch sign the ADC values were significantly lower in the FLAIR-hyperintense rim as compared to the FLAIR-hypointense core of the tumor (P = .0005).

Conclusions

This study confirms the high specificity of the T2/FLAIR-mismatch sign for noninvasive identification of IDH-mutant 1p/19q non-codeleted gliomas; however, sensitivity is low and applicability is limited to lower-grade gliomas. Whether the higher ADC and lower rCBV values in IDH-mutant gliomas with a T2/FLAIR-mismatch sign (as compared to those without) translate into a measurable prognostic effect requires investigation in future studies. Moreover, spatial differences in ADC values between the core and rim of tumors with a T2/FLAIR-mismatch sign potentially reflect specific distinctions in tumor cellularity and microenvironment.

Automated ASPECTS in Acute Ischemic Stroke: A Comparative Analysis with CT Perfusion

BACKGROUND AND PURPOSE

Automated ASPECTS has the potential of reducing interobserver variability in the determination of early ischemic changes. We aimed to assess the performance of an automated ASPECTS software against the assessment of a neuroradiologist in a comparative analysis with concurrent CTP-based CBV ASPECTS.

MATERIALS AND METHODS

Patients with anterior circulation stroke who had baseline NCCT and CTP and underwent successful mechanical thrombectomy were included. NCCT-ASPECTS was assessed by 2 neuroradiologists, and discrepancies were resolved by consensus. CTP-CBV ASPECTS was assessed by a different neuroradiologist. Automated ASPECTS was provided by Brainomix software. ASPECTS was dichotomized (ASPECTS ≥6 or <6) and was also based on the time from onset (>6 or ≤6 hours).

RESULTS

A total of 58 patients were included. The interobserver agreement for NCCT ASPECTS was moderate (κ = 0.48) and marginally improved (κ = 0.64) for dichotomized data. Automated ASPECTS showed excellent agreement with consensus reads (κ = 0.84) and CTP-CBV ASPECTS (κ = 0.84). Intraclass correlation coefficients for ASPECTS across all 3 groups were 0.84 (95% CI, 0.76-0.90, raw scores) and 0.94 (95% CI, 0.91-0.96, dichotomized scores). Automated scores were comparable with consensus reads and CTP-CBV ASPECTS in patients when grouped on the basis of time from symptom onset (>6 or ≤6 hours). There was significant (P < .001) negative correlation with final infarction volume and the 3 ASPECTS groups (r = -0.52, consensus reads; -0.58, CTP-CBV; and -0.66, automated).

CONCLUSIONS

ASPECTS derived from an automated software performs equally as well as consensus reads of expert neuroradiologists and concurrent CTP-CBV ASPECTS and can be used to standardize ASPECTS reporting and minimize interpretation variability.

Oxygenation differs among white matter hyperintensities, intersected fiber tracts and unaffected white matter

White matter hyperintensities of presumed vascular origin are frequently observed on magnetic resonance imaging in normal aging. They are typically found in cerebral small vessel disease and suspected culprits in the etiology of complex age- and small vessel disease-related conditions, such as late-onset depression. White matter hyperintensities may interfere with surrounding white matter metabolic demands by disrupting fiber tract integrity. Meanwhile, risk factors for small vessel disease are thought to reduce tissue oxygenation, not only by reducing regional blood supply, but also by impairing capillary function. To address white matter oxygen supply–demand balance, we estimated voxel-wise capillary density as an index of resting white matter metabolism, and combined estimates of blood supply and capillary function to calculate white matter oxygen availability. We conducted a cross-sectional study with structural, perfusion- and diffusion-weighted magnetic resonance imaging in 21 patients with late-onset depression and 21 controls. We outlined white matter hyperintensities and used tractography to identify the tracts they intersect. Perfusion data comprised cerebral blood flow, blood volume, mean transit time and relative transit time heterogeneity—the latter a marker of capillary dysfunction. Based on these, white matter oxygenation was calculated as the steady state cerebral metabolic rate of oxygen under the assumption of normal tissue oxygen tension and vice versa. The number, volume and perfusion characteristics of white matter hyperintensities did not differ significantly between groups. Hemodynamic data showed white matter hyperintensities to have lower blood flow and blood volume, but higher relative transit time heterogeneity, than normal-appearing white matter, resulting in either reduced capillary metabolic rate of oxygen or oxygen tension. Intersected tracts showed significantly lower blood flow, blood volume and capillary metabolic rate of oxygen than normal-appearing white matter. Across groups, lower lesion oxygen tension was associated with higher lesion number and volume. Compared with normal-appearing white matter, tissue oxygenation is significantly reduced in white matter hyperintensities as well as the fiber tracts they intersect, independent of parallel late-onset depression. In white matter hyperintensities, reduced microvascular blood volume and concomitant capillary dysfunction indicate a severe oxygen supply–demand imbalance with hypoxic tissue injury. In intersected fiber tracts, parallel reductions in oxygenation and microvascular blood volume are consistent with adaptations to reduced metabolic demands. We speculate, that aging and vascular risk factors impair white matter hyperintensity perfusion and capillary function to create hypoxic tissue injury, which in turn affect the function and metabolic demands of the white matter tracts they disrupt.

Multi-stage automated local arterial input function selection in perfusion MRI

OBJECTIVE

Cerebral blood flow (CBF) quantification using dynamic-susceptibility contrast MRI can be achieved via model-independent deconvolution, with local arterial input function (AIF) deconvolution methods identifying multiple arterial regions with unique corresponding arterial input functions. The clinical application of local AIF methods necessitates an efficient and fully automated solution. To date, such local AIF methods have relied on the computation of a singular surrogate measure of bolus arrival time or custom arterial scoring functions to infer vascular supply origins. This paper aims to introduce a new local AIF method that alternatively utilises a multi-stage approach to perform AIF selection.

MATERIAL AND METHODS

A fully automated, multi-stage local AIF method is proposed, leveraging both signal-based cluster analysis and priority flooding to define arterial regions and their corresponding vascular supply origins. The introduced method was applied to data from four patients with cerebrovascular disease who showed significant artefacts when using a prevailing automated local AIF method.

RESULTS

The immediately apparent image artefacts found using the pre-existing method due to poor AIF selection were found to be absent when using the proposed method.

CONCLUSION

The results suggest the proposed solution provides a more robust approach to perfusion quantification than currently available fully automated local AIF methods.

Standardized acquisition and post-processing of dynamic susceptibility contrast perfusion in patients with brain tumors, cerebrovascular disease and dementia: comparability of post-processing software

OBJECTIVE

MR-perfusion post-processing still lacks standardization. This study evaluates the results of perfusion analysis with two established software solutions in a large series of patients with different diseases when a highly standardized processing workflow is ensured.

METHODS

Multicenter data of 260 patients (80 with brain tumors, 124 with cerebrovascular disease and 56 with dementia examined with the same MR protocol) were analyzed. Raw data sets were processed with two software suites: Olea sphere and NordicICE. Group differences were analyzed with paired t-tests and one-way ANOVA.

RESULTS

Perfusion metrics were significantly different for all examined diseases in the unaffected brain for both software suites [ratio cortex/white matter left hemisphere: mean transit time (MTT) 0.991 vs 0.847, p < 0.05; relative cerebral bloodflow (rBF) 3.23 vs 4.418, p < 0.001; relative cerebral bloodvolume (rBVc) 2.813 vs 3.884, p < 0.001; right hemisphere: MTT 1.079 vs 0.854, p < 0.05; rBF 3.262 vs 4.378, p < 0.001; rBVc 2.762 vs 3.935, p < 0.001)]. Perfusion results were also significantly different in patients with stroke (ratio cortex/white matter affected hemisphere: MTT 1.058 vs 0.784; p < 0.001), dementia (ratio cortex/white matter left hemisphere: rBVc 1.152 vs 1.795, p < 0.001; right hemisphere: rBVc 1.396 vs 1.662, p < 0.05) and brain tumors (ratio cortex/whole tumor rBVc: 0.778 vs 0.919, p < 0.001 and ratio cortex/tumor hotspot rBVc: 0.529 vs 0.512, p < 0.05).

CONCLUSION

Despite a highly standardized workflow, parametric perfusion maps are depended on the chosen software. Radiologists should consider software related variances when using dynamic susceptibility contrast perfusion for clinical imaging and research.

ADVANCES IN KNOWLEDGE

This multicenter study compared perfusion parameters calculated by two commercial dynamic susceptibility contrast perfusion post-processing software solutions in different central nervous system disorders with a large sample size and a highly standardized processing workflow. Despite, parametric perfusion maps are depended on the chosen software which impacts clinical imaging and research.