Tumor Blood Volume Measurement in Brain Gliomas Using Velocity-Selective Arterial Spin Labeling

doi:10.21203/rs.3.rs-4099762/v1

Background

To evaluate the feasibility and performance of velocity selective (VS) ASL based cerebral blood volume (CBV) mapping among glioma patients in clinical practice, comparing with the VSASL based cerebral blood flow (CBF) mapping and dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI).

Methods

This study included patients with histologically proven brain glioma who underwent preoperative MRI including VSASL based CBV, CBF and DSC-PWI between 2017 and 2021. Visual inspection was performed to evaluate the lesion conspicuity on VSASL derived CBV maps based on 1–3 criteria by comparing to the surrounding parenchyma. The relative values of maximum tumor blood volume (rTBV) and tumor blood flow (rTBF), derived from VSASL or DSC-PWI were compared between low-grade and high-grade glioma. Linear regression and Bland–Altman analyses were constructed to evaluate the correlation and agreement of rTBV measurements between VSASL method and DSC-PWI. The diagnosis ability for discrimination between low- and high-grade glioma were evaluated using ROC curves.

Results

Forty-eight participants (mean age, 45 ± 13 years; 25 men; 23 high-grade gliomas) were evaluated. The lesion conspicuity of VSASL based CBV maps was good on visual inspection (averaged scores: 2.26 ± 0.76, weighted kappa of 0.8 between readers). Moreover, VSASL provided highly correlated quantifications of rTBV (R² = 0.83, p < 0.001) compared to DSC-PWI, and further improved diagnostic performance than VSASL derived rTBF measurements (ROC AUC = 0.94 vs. 0.89).

Conclusions

VSASL served as a promising and accurate means in quantification of TBV for glioma stratification in clinical patients, indicating its potential as a viable non-contrast alternative to DSC-PWI for brain tumor applications.

Perfusion imaging

Cerebral blood volume

Glioma

Magnetic resonance imaging

Diagnosis

Differential

Higher tumor vascularity is an important biomarker in diagnosis, staging, and evaluating the therapeutic effect of brain tumor [1]. The clinical MRI method for cerebral perfusion measurement is dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI), which obtains T₂*-weighted images during the first pass of the gadolinium contrast agent. DSC-PWI yields multiple hemodynamic parameters, including cerebral blood flow (CBF) and cerebral blood volume (CBV). For brain tumor imaging, CBV is the more common perfusion metric depicting angiogenesis [2, 3].

Arterial spin labeling (ASL) MRI is a completely noninvasive method by magnetically labeling water in the blood as an endogenous tracer, which offers the great advantages of not requiring an exogenous contrast agent and is ideal for frequent longitudinal monitoring. ASL techniques typically obtain CBF-weighted perfusion signal by applying spatially selective labeling modules on the feeding arteries through the neck followed by a post-labeling delay (PLD) before the readout. However, this approach, such as pseudo-continuous ASL (PCASL), is sensitive to prolonged arterial transit time (ATT), and would lead to underestimation of CBF for patients with slow flow, most prominently after stroke with large-vessel stenosis or occlusion [4, 5].

In order to remove the susceptibility to ATT delay, velocity-selective (VS) ASL was proposed to label all the upstream blood in the vascular tree that is flowing above a cutoff velocity (V_cut) [6]. Further methodological developments and reliability evaluation have been performed to advance this approach [7–10]. Its technical aspects and clinical implementation were summarized in a recent review [11]. In a study on untreated brain gliomas, VSASL derived CBF measurements were more comparable with DSC-PWI than PCASL [12], reflecting the abnormal vascularity of tumors as poorly connected, tortuous, and heterogeneous vessel networks formed out of neoangiogenesis.

Additionally, quantitative mapping of CBV has been demonstrated among healthy subjects by applying VS label and control modules right before acquisition with minimal PLD (~ 0 s) [13–15]. As CBV derived from DSC-PWI is the most widely adopted perfusion marker of brain tumor angiogenesis, the clinical utility for neurooncology imaging of VSASL based CBV quantification is worth further investigation. This study aimed to evaluate the feasibility and performance of VSASL based CBV mapping on preoperative patients with brain gliomas by comparing with VSASL based CBF and DSC-PWI based CBV mapping.

Patient population

The local institutional ethics committee approved this retrospective study (NO. 2020-KY-058-01), and informed consent was not required. The study comprised consecutive individuals diagnosed with brain glioma by histopathology who had preoperative VSASL based CBV perfusion MRI between November 2017 and August 2021. Patients having inadequate imaging quality, post-processing errors, or partial availability of the WHO (World Health Organization) 4-tier categorization were not included. Overall, 48 patients were enrolled. An overview of the patient selection process is in Fig. 1. For 22 of 48 patients who were included, VSASL based CBF values were analyzed in a previous study [12] which were compared with CBF derived from PCASL and DSC-PWI.

MR Imaging Protocol

A 3.0T Philips Ingenia scanner (Philips Medical System, Best, The Netherlands) equipped with a 16-channel head coil was used to perform the MRI scans. Anatomic MRI, DWI, and VSASL based CBV mapping were performed for each subject, along with VSASL based CBF mapping and DSC-PWI conducted on subgroups of patients.

Conventional anatomic sequences included axial T₁-weighted-imaging (T₁WI: repetition time [TR]/ echo time [TE] = 2000/20 ms, field of view [FOV] = 230 × 187 mm², matrix = 272 × 166, slice thickness = 6 mm, gap = 1 mm, 18 slices); axial T₂-weighted-imaging (T₂WI: TR/TE = 3000/90 ms, FOV = 230 × 185 mm², matrix = 328 × 185, slice thickness = 6 mm, gap = 1 mm, 18 slices); and fluid attenuated inversion recovery (FLAIR: TR/TE/TI = 9000/120/2500 ms, FOV = 230 × 183 mm², matrix = 352 × 143, section thickness = 6 mm, gap = 1 mm, 18 slices). A routine clinical diffusion-weighted-imaging (DWI) was required with followed parameters: TR/TE = 2530/99 ms, FOV = 230 × 231 mm², matrix = 156 × 122, slice thickness = 6 mm, gap = 1 mm, b-values = 0 and 1000 s/mm², 18 slices.

The specifics of the VSASL sequence for CBF and CBV mapping were detailed in Refs [7, 12] and [14], respectively: their Fourier transform (FT) based velocity-selective saturation pulse train (FT-VSS) pulse trains were composed of nine hard excitation pulses (10° each) with eight velocity-encoding steps, with each step (6 for CBF and 16 ms for CBV) interleaved with paired and phase-cycled refocusing pulses and four triangular gradient lobes with alternating polarity (CBF: 27 mT/m, 0.6 ms duration, 0.3 ms ramp time, V_cut = 2.1 cm/s; CBV: 22 mT/m, 0.6 ms duration, 0.3 ms ramp time, V_cut = 1.0 cm/s). V_cut was defined in the recent VSASL guideline paper [11]. To allow fresh arterial blood to enter the imaging volume during CBF mapping, there was a 3.6-second delay between the global pre-saturation and the labeling modules. Three non-selective adiabatic inversion pulses were administered during the 1.5 s PLD at 0.02 s, 1.12 s, and 1.46 s after labeling for background suppression. For CBV mapping, the CSF-suppression module with T₂prep (TE_prep = 300 ms) followed by a global inversion was applied at 3.5 s after the global pre-saturation and 1.7 s before the VS label/control module for CSF nulling, and the acquisition was immediately (10 ms) applied after the FT-VSS pulse train.

The VSASL-CBF and VSASL-CBV methods employed the identical 2D multi-slice single-shot echo-planar imaging (EPI) readout. Acquisition parameters: field of view = 186 x 208 mm², matrix = 56 x 60, the acquisition resolution = 3.3 x 3.4 mm², the reconstructed voxel size = 1.9 x 1.9 mm²; EPI factor = 25, sensitivity encoding factor = 2.5, effective echo time = 10 ms, ten slices acquired at a slice thickness of 4.4 mm without gaps. After 24 averages of interleaved label and control, the overall measurement duration with repetition times of 5.6 and 6.0 s was around 4.6 min for VSASL based CBF and 5.0 min for CBV mapping. Furthermore, using the same resolution and acquisition strategy as the ASL protocols, a proton density-weighted picture (repetition time = 10 s, scan time = 0.3 min) was obtained for quantification.

DSC-PWI was performed by scanning a multi-slice gradient echo echo-planar imaging sequence for 270 repetitions within 2.5 min during the first-pass of a gadolinium-based contrast agent, Gadavist (gadobutrol, Bayer Healthcare, Berlin, Germany). A power injector was utilized to administer the bolus injection into the antecubital vein, adhering to the standard protocol (dose: 0.1 mmol/kg; injection rate: 5 mL/s) followed by a flush of 20 mL saline at the same rate. Twenty-four axial slices with an in-plane resolution of 1.9 mm and 4.4 mm thickness with no gap covered complete tumor volume. The DSC imaging acquisition parameters included the following: TR/TE = 1800/45 ms, FOV = 186 × 208 mm², matrix = 84 × 91, the acquisition resolution = 2.2 x 2.3 mm²; flip angle = 90°, EPI factor = 37 and sensitivity encoding factor = 2.5. Postcontrast T1WI in Axial, coronal, and sagittal orientations were acquired following DSC-PWI acquisition.

Data Processing and Image Analysis

Matlab R2016a (Mathworks Inc, Natick, MA, USA) was used to quantify voxel-wise CBF and CBV in ASL data, using the usual equations published in Ref [7, 14]. The Olea Sphere program (Olea Medical, La Ciotat, France) was used to process DSC-MRI raw data for CBF and CBV maps, which included a delay-insensitive deconvolution algorithm [16] and leakage-correction [17].

Qualitative Analysis

Two neuroradiologists (with more than 8 and 3 years of experience, respectively) who were blinded to the pathology reports, clinical data, and perfusion procedures assessed the subjective judgments of VSASL-based CBV maps in combination with conventional anatomic pictures in random order. Prior to analysis, the windowing of the pseudo-color image from the perfusion technique was adjusted to the appropriate scale for each example.To standardize the subjective evaluation, the radiologists were trained for consensus on image series obtained from ten patients with brain tumors not included in the study. A consensus reading was performed if disagreement occurred between these two readers.

For image quality, a semi-quantitative score was assigned by independent reading of two readers for the visibility of the lesion on VSASL CBV map by comparing lesion signal intensity to the surrounding parenchyma using a three-point scale described as follows [18, 19]: score of 1 was defined as: “signal/shape at the location of the lesion cannot be distinguished from surrounding parenchyma,” 2: “suspicion that signal/shape at the location of the lesion is deviant from surrounding parenchyma,” 3: “signal/shape at the location of the lesion can be identified from surrounding parenchyma.

Quantitative Analysis

The independent evaluation of CBV or CBF maps derived from VSASL or DSC-PWI was performed at a 4-week interval by the same two neuroradiologists aforementioned with the same blinded manner. Three representative 3×3 pixel ROIs were manually chosen from the location of the glioma with the visually identifiable maximal perfusion signal on CBV or CBF maps, and the areas of hemorrhage, necrosis, or cysts were avoided using a combined evaluation of conventional anatomic images. Additional sets of ROIs were placed on the contralateral normal-appearing white matter and grey matter, respectively, to obtain the mean values of the white matter blood volume or gray matter blood flow (CBV_WM or CBF_GM). The rTBV and rTBF were calculated as a ratio of tumor blood volume (TBV) to CBV_WM and tumor blood flow (TBF) to CBF_GM. The maximum values among the three tumor ROIs were recorded. The mean values of each variable from the two readers were used for further analyses.

Statistical Analysis

The normality of the data was assessed with the Shapiro–Wilk’s test. Categorical data were compared using Fisher’s exact test. Interobserver agreement for the two radiologists for the qualitative and quantitative analysis was calculated by using Cohen weighted k analysis and intraclass correlation coefficient, respectively. Group differences in rTBF or rTBV between low-grade and high-grade glioma were compared with either a student’s t-test or Mann–Whitney U-test as appropriate. Linear regression and Bland-Altman analyses were performed to evaluate the correlation and agreement of CBV between VSASL and DSC-PWI. The receiver-operating characteristic (ROC) curves were constructed and each area under the curve (AUC) was calculated to determine the diagnostic accuracy of perfusion parameters derived from either VSASL or DSC-PWI methods for differentiating low-grade from high-grade glioma. Data were analyzed by using GraphPad Prism 8.0.1(GraphPad Software, San Diego, CA, USA) and Medcalc 19.8 (Ostend, Belgium). All reported P values were two sided and P less than 0.05 was indicative of a statistically significant difference.

Participants Characteristics

Among the 53 eligible patients, five were excluded due to lack of WHO 4-tier classification (n=1), poor imaging quality (n=3), or corrupt data following post-processing (n=1) (Fig.1). Forty-eight patients with untreated glioma were ultimately enrolled, including 25 in low-grade glioma (WHO grade II; mean age of 38 ±11 years, 10 females/15 males) and 23 in high-grade glioma (WHO grade III and IV; mean age of 53 ±10 years, 13 females/10 males). All the tumor tissue were obtained by surgical operation, and IDH gene mutation status were identified in 47 gliomas. Patient demographic, histopathology, molecular and image characteristics are shown in Table 1. Among these 48 patients with VSASL-CBV images, 32 patients had evaluable DSC-PWI imaging, and 44 had evaluable VSASL-CBF for comparison.

Table1. Baseline patient characteristics and perfusion measures for low-grade and high-grade gliomas.

Parameters	Low-Grade Glioma (n=25)	High-Grade Glioma (n=23)	P Value
Clinical data
Sex			.25
No. of women	10 (40)	13 (57)
No. of men	15 (60)	10 (43)
Mean age ±(y)	38±11	53±10	<.001
WHO grade
II	25	-	-
III	-	9	-
IV	-	14	-
Quantitative parameters at baseline perfusion MRI
VSASL rTBF^a	1.32 (1.01– 1.63)	2.58 (1.81 – 3.17)	<.001
VSASL rTBV^a,b	2.93 (2.20– 3.98)	6.49 (5.14– 7.78)	<.001
DSC-PWI rTBV^a,c	3.41 (1.82– 3.95)	6.28 (5.10– 7.23)	<.001

Note. Unless otherwise indicated, data are numbers and data in parentheses are percentages.

DSC-PWI = dynamic susceptibility contrast-enhanced perfusion-weighted imaging, VSASL = velocity-selective arterial spin labeling, rTBF = relative tumor blood flow, rTBV = relative tumor blood volume.

^a Data are median and interquartile range.

^bWith 44 patients analyzed.

^cWith 32 patients analyzed.

Visual Assessment

The two independent readers evaluated the conspicuity of the glioma with an overall weighted kappa of 0.80 (95% CI: 0.70 - 0.90), indicating good agreement. Averaged scores (2.26 ± 0.76) of visual assessments of the two readers showed good conspicuity of glioma on VSASL based CBV maps with all patients, and slightly better conspicuity on high-grade glioma with averaged scores of 2.30 ± 0.66 compared to low-grade (2.24 ± 0.84). Representative images of astrocytoma and glioblastoma are shown in two cases (Fig.2a, b). CBV and CBF maps derived from VSASL and DSC-PWI are largely comparable on visual inspection. As expected, low-grade astrocytoma did not show elevated CBV (Fig.2a), in contrast to glioblastoma with markedly elevated CBV (Fig.2b). Representative cases of different visual scores between low-grade and high-grade glioma are shown on Supplement Fig.1.

Correlation and Agreement between VSASL and DSC-PWI derived rTBV

The inter-observer agreement for quantitative parameters was excellent, with ICCs between 0.934 and 0.987 (Table 2). A high correlation was observed between rTBV values of VSASL and DSC-PWI in 32 patients (R² = 0.83, P<0.001, Fig.3a). Bland-Altman plots further confirmed the agreement in rTBV between the two techniques (Fig.3b).

Table 2. Raw data and inter-reader agreement of measurements of all the measured quantitative parameters between two readers.

	Reader 1	Reader 2	ICCs (95% CI)
VSASL rTBF	1.93±0.98	1.91±0.90	0.987 (0.976 - 0.993)
VSASL rTBV	4.64±1.91	4.68±1.89	0.934 (0.883 - 0.963)
DSC-PWI rTBV	4.54±2.14	4.72±2.12	0.971 (0.940 - 0.986)

DSC-PWI = dynamic susceptibility contrast-enhanced perfusion-weighted imaging,

VSASL = velocity-selective arterial spin labeling,

rTBF = relative tumor blood flow, rTBV = relative tumor blood volume,

CI = confidence interval, ICC = intraclass correlation coefficients,

Glioma Grading

Both VSASL and DSC-PWI derived rTBV, as well as VSASL derived rTBF showed clear distinction between low-grade and high-grade gliomas (Fig.4). The median rTBF and rTBV values in low-grade glioma were all significantly lower than those of high-grade for VSASL-rTBF (1.32 vs. 2.58), VSASL-rTBV (2.93 vs. 6.49), and DSC-PWI-rTBV (3.41 vs. 6.28), P<0.001 (Table 1). The ROC curves showed that VSASL-derived rTBV yielded excellent diagnostic performance in glioma grading with rTBV showing slightly higher accuracy than VSASL-derived rTBF (AUC: 0.94 vs. 0.89) (Fig.5), and comparable with DSC-PWI derived rTBV (AUC: 0.94 vs. 0.93). The optimal threshold and diagnostic performance of each technique are listed in Table 3. Compared to rTBF derived from VSASL, rTBV derived from VSASL showed consistently higher diagnostic sensitivity (87.0% vs. 73.7%), positive (95.2% vs. 93.3%) or negative (88.9% vs. 82.8%) predictive values, and accuracy (91.7% vs. 86.4%) for glioma grading (Table 3). The diagnostic performance of rTBV derived between VSASL and DSC-PWI was comparable (Fig.5, Table 3).

Table 3. Diagnostic performances of VSASL derived rTBF, rTBV, and DSC-PWI derived rTBV in preoperatively grading of gliomas.

Quantitative parameters at baseline perfusion MRI	AUC ^a	Threshold	Sensitivity (%)^b	Specificity (%)^b	PPV (%)^b	NPV (%)^b	Accuracy (%)^b	P value
VSASL rTBF	0.89 (0.79 – 0.99)	>1.97	73.7 (14/19)	96.0 (24/25)	93.3 (14/15)	82.8 (24/29)	86.4 (38/44)	<.001
VSASL rTBV	0.94 (0.88 – 1.00)	>4.84	87.0 (20/23)	96.0 (24/25)	95.2 (20/21)	88.9 (24/27)	91.7 (44/48)	<.001
DSC-PWI rTBV	0.93 (0.84 – 1.00)	>4.38	87.5 (14/16)	93.8 (15/16)	93.3 (14/15)	88.2 (15/17)	90.6 (29/32)	<.001

DSC-PWI = dynamic susceptibility contrast-enhanced perfusion-weighted imaging,

VSASL = velocity-selective arterial spin labeling,

rTBF = relative tumor blood flow, rTBV = relative tumor blood volume,

AUC = area under the receiver operating characteristic curve, PPV = positive predictive value, NPV = negative predictive value,

^a Numbers in parentheses are 95% confidence intervals.

^b Numbers in parentheses are raw data.

This study demonstrated the feasibility and performance of VSASL based CBV mapping on preoperative patients with brain gliomas. VSASL provided good lesion conspicuity in visual assessment. There was excellent correlation in quantifications of rTBV between VSASL and DSC-PWI, and further improved diagnostic performance than VSASL derived rTBF measurements with higher diagnostic sensitivity, positive or negative predictive value and accuracy.

Perfusion imaging, especially DSC-PWI, is widely used in the current clinical practice of glioma imaging and is also of considerable interest in the context of clinical trials [20, 21]. As a semi-quantitative parameter of tumor physiology, rCBV is commonly used as an in vivo biomarker of tumor vascularity, but conventional ASL can only result in an estimate of CBF. The VSASL based CBV method employed in the present study was demonstrated as a noninvasive alternative to DSC-PWI, which is useful for patient with contraindication to the use of gadolinium-based contrast agents.

In visual assessment, the lesion conspicuity was good with excellent interobserver agreement on VSASL based CBV maps, indicating that the VSASL based CBV maps could provide competent image quality for lesion identification and evaluation, which may improve the clinical radiology practical values. The results also showed that the lesion visibility of low-grade gliomas was slightly lower than that of high-grade, which was in line with our expectations, as the degree of vascular proliferation in low-grade gliomas is lower [1], and the lesions of low-grade gliomas do not differ much from the surrounding tissue.

Our results are consistent with the previous studies demonstrating higher rTBV and rTBF for gliomas with high grade [22–24], which was mainly attributed to the higher microvascular proliferation, along with the more prominent presence of deranged vascular morphology according to the glioma grade [25]. Both rTBV and rTBF, derived by either DSC or VSASL, showed high diagnostic accuracy for grading gliomas from the ROC curves. The superior performance of rTBV derived from VSASL compared to that of rTBF shown in this work can be partly attributed to the fact that the spin-labeling technique for CBV mapping is able to deliver higher SNR than CBF mapping [26, 27] as the labeled blood signal does not experience T₁ decay when PLD is set to zero.

ROI placement for internal reference for normalization is highly variant in the published literature [24, 28, 29], and it may have an important influence on the results of perfusion research. Contralateral normal-appearing white matter or mirrored regions are common references for normalization in TBV perfusion research [23, 30–32], but normalization of TBF to contralateral normal-appearing grey matter has most often been performed in MRI studies [12, 33]. As in the previous study [34], contralateral normal-appearing white matter or grey matter, respectively, were chosen for internal normalization for the maximum value of TBV and TBF in the present study. White matter, the most accepted normalization reference [25], was chosen here because of its better reproducibility [35]. With the higher SNR, gray matter was chosen as a reference for TBF. There was excellent agreement between VSASL and DSC-PWI (Fig. 3). Moreover, the ratios of TBV reported in this study between different grades were comparable to those recently published studies [23], indicating that VSASL may provide a useful and reliable means of perfusion measure in routine brain glioma imaging.

This study had several limitations. First, our sample was retrospectively collected from a single center and the sample size was relatively small, so the results of this study need to be validated in a larger scale multicenter study. Secondly, in this pilot study we just focused on the potential value of VSASL based CBV map in predicting tumor grading, a larger sample size would be desired for evaluating molecular genetic profiles, monitoring treatment responses, tumor progression and predicting prognosis. Finally, 3D readout would be preferred to achieve larger spatial coverage and simpler perfusion quantification than 2D multi-slice acquisition for VSASL based CBV map.

In conclusion, these results indicate that VSASL provided highly correlated quantifications of relative tumor blood volume when compared to DSC-PWI and further improved diagnostic performance than VSASL derived tumor blood flow measurements. The clinical feasibility of VSASL-CBV would be further tested for brain tumor imaging with 3D acquisition for evaluating its potential as a viable non-contrast alternative to the more widely implemented DSC-PWI perfusion methods.

ASL Arterial spin labeling

ATT Arterial transit time

AUC Area under the curve

CBF Cerebral blood flow

CBV Cerebral blood volume

DSC-PWI Dynamic susceptibility contrast-enhanced perfusion-weighted imaging

PCASL pseudo-continuous arterial spin labeling

PLD post-labeling delay

ROC Receiver operating characteristic

rTBF relative tumor blood flow

rTBV relative tumor blood volume

TBF Tumor blood flow

TBV Tumor blood volume

VSASL Velocity-selective arterial spin labeling

WHO World Health Organization

Ethics approval and consent to participate:

This retrospective study was performed under the approval of the local institutional ethics committee (approval number: 2020-KY-058-01) with a waiver of obtaining informed consent.

Consent for publication:

Not applicable.

Availability of data and materials:

All data were available from the corresponding author upon reasonable requests.

Competing interests:

All authors declared no confict of interest.

Funding:

The authors declare that there was no funding received for this research.

Authors’ contributions:

Yaoming Qu: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing original draft; Andong Ma, Xinran Yan, Xiaochan Ou: Data curation, Formal analysis; Xia Zou, Haitao Wen, Qihong Rui, Xianlong Wan: Data curation; Dan Zhu: Methodology, Software, Writing – review & editing; Qin Qin: Conceptualization, Methodology, Software, Project administration, Writing – review & editing; Zhibo Wen: Conceptualization, Funding acquisition, Methodology, Project administration, Writing – review & editing. The authors read and approved the final manuscript.

Acknowledgements:

The authors thank the radiologist and nurse colleagues who helped during the research study. A special thank you is also expressed to the patients for participating in the study.

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No competing interests reported.

SupplementaryMaterial.docx

Tumor Blood Volume Measurement in Brain Gliomas Using Velocity-Selective Arterial Spin Labeling

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and Methods

Patient population

MR Imaging Protocol

Data Processing and Image Analysis

Qualitative Analysis

Quantitative Analysis

Statistical Analysis

Results

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1