Image-derived input function estimation on a TOF-enabled PET/MR for cerebral blood flow mapping

Turning brain MRI into diagnostic PET: 15O-water PET CBF synthesis from multi-contrast MRI via attention-based encoder-decoder networks

Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of a wide range of neurological diseases. Positron emission tomography (PET) with radiolabeled water (15O-water) is the gold-standard for the measurement of CBF in humans, however, it is not widely available due to its prohibitive costs and the use of short-lived radiopharmaceutical tracers that require onsite cyclotron production. Magnetic resonance imaging (MRI), in contrast, is more accessible and does not involve ionizing radiation. This study presents a convolutional encoder-decoder network with attention mechanisms to predict the gold-standard 15O-water PET CBF from multi-contrast MRI scans, thus eliminating the need for radioactive tracers. The model was trained and validated using 5-fold cross-validation in a group of 126 subjects consisting of healthy controls and cerebrovascular disease patients, all of whom underwent simultaneous 15O-water PET/MRI. The results demonstrate that the model can successfully synthesize high-quality PET CBF measurements (with an average SSIM of 0.924 and PSNR of 38.8 dB) and is more accurate compared to concurrent and previous PET synthesis methods. We also demonstrate the clinical significance of the proposed algorithm by evaluating the agreement for identifying the vascular territories with impaired CBF. Such methods may enable more widespread and accurate CBF evaluation in larger cohorts who cannot undergo PET imaging due to radiation concerns, lack of access, or logistic challenges.

Automated extraction of the arterial input function from brain images for parametric PET studies

BACKGROUND

Accurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images.

RESULTS

Total body 18F-FDG PET data from 12 subjects were split into a model adjustment group (n = 6) and a validation group (n = 6). We developed an AIF estimation framework using wavelet-based methods and unsupervised machine learning to distinguish arterial and venous activity curves, compared to the IDIF from the descending aorta. All of the automatically extracted AIFs in the validation group had similar shape to the IDIF derived from the descending aorta IDIF. The average area under the curve error and normalised root mean square error across validation data were - 1.59 ± 2.93% and 0.17 ± 0.07.

CONCLUSIONS

Our automated AIF framework accurately estimates the AIF from brain images. It reduces operator-dependence, and could facilitate the clinical adoption of parametric PET.

Early-Frame [18F]Florbetaben PET/MRI for Cerebral Blood Flow Quantification in Patients with Cognitive Impairment: Comparison to an [15O]Water Gold Standard

Cerebral blood flow (CBF) may be estimated from early-frame PET imaging of lipophilic tracers, such as amyloid agents, enabling measurement of this important biomarker in participants with dementia and memory decline. Although previous methods could map relative CBF, quantitative measurement in absolute units (mL/100 g/min) remained challenging and has not been evaluated against the gold standard method of [15O]water PET. The purpose of this study was to develop and validate a minimally invasive quantitative CBF imaging method combining early [18F]florbetaben (eFBB) with phase-contrast MRI using simultaneous PET/MRI. Methods: Twenty participants (11 men and 9 women; 8 cognitively normal, 9 with mild cognitive impairment, and 3 with dementia; 10 β-amyloid negative and 10 β-amyloid positive; 69 ± 9 y old) underwent [15O]water PET, phase-contract MRI, and eFBB imaging in a single session on a 3-T PET/MRI scanner. Quantitative CBF images were created from the first 2 min of brain activity after [18F]florbetaben injection combined with phase-contrast MRI measurement of total brain blood flow. These maps were compared with [15O]water CBF using concordance correlation (CC) and Bland-Altman statistics for gray matter, white matter, and individual regions derived from the automated anatomic labeling (AAL) atlas. Results: The 2 methods showed similar results in gray matter ([15O]water, 55.2 ± 14.7 mL/100 g/min; eFBB, 55.9 ± 14.2 mL/100 g/min; difference, 0.7 ± 2.4 mL/100 g/min; P = 0.2) and white matter ([15O]water, 21.4 ± 5.6 mL/100 g/min; eFBB, 21.2 ± 5.3 mL/100 g/min; difference, -0.2 ± 1.0 mL/100 g/min; P = 0.4). The intrasubject CC for AAL-derived regions was high (0.91 ± 0.04). Intersubject CC in different AAL-derived regions was similarly high, ranging from 0.86 for midfrontal regions to 0.98 for temporal regions. There were no significant differences in performance between the methods in the amyloid-positive and amyloid-negative groups as well as participants with different cognitive statuses. Conclusion: We conclude that eFBB PET/MRI can provide robust CBF measurements, highlighting the capability of simultaneous PET/MRI to provide measurements of both CBF and amyloid burden in a single imaging session in participants with memory disorders.

Simultaneous estimation of a model-derived input function for quantifying cerebral glucose metabolism with [18F]FDG PET

BACKGROUND

Quantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [18F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials. Alternatively, we propose a SIME approach based on the two-tissue compartment model to extract a high signal-to-noise ratio (SNR) model-derived input function (MDIF) from the whole-brain TAC. The purpose of this study is to present the MDIF approach and its implementation in the analysis of animal and human data.

METHODS

Simulations were performed to assess the accuracy of the MDIF approach. Animal experiments were conducted to compare derived MDIFs to measured AIFs (n = 5). Using dynamic [18F]FDG PET data from neurologically healthy volunteers (n = 18), the MDIF method was compared to the original SIME-IDIF. Lastly, the feasibility of extracting parametric images was investigated by implementing a variational Bayesian parameter estimation approach.

RESULTS

Simulations demonstrated that the MDIF can be accurately extracted from a whole-brain TAC. Good agreement between MDIFs and measured AIFs was found in the animal experiments. Similarly, the MDIF-to-IDIF area-under-the-curve ratio from the human data was 1.02 ± 0.08, resulting in good agreement in grey matter CMRGlu: 24.5 ± 3.6 and 23.9 ± 3.2 mL/100 g/min for MDIF and IDIF, respectively. The MDIF method proved superior in characterizing the first pass of [18F]FDG. Groupwise parametric images obtained with the MDIF showed the expected spatial patterns.

CONCLUSIONS

A model-driven SIME method was proposed to derive high SNR input functions. Its potential was demonstrated by the good agreement between MDIFs and AIFs in animal experiments. In addition, CMRGlu estimates obtained in the human study agreed to literature values. The MDIF approach requires fewer fitting parameters than the original SIME method and has the advantage that it can model the shape of any input function. In turn, the high SNR of the MDIFs has the potential to facilitate the extraction of voxelwise parameters when combined with robust parameter estimation methods such as the variational Bayesian approach.

An update on the use of image-derived input functions for human PET studies: new hopes or old illusions?

BACKGROUND

The need for arterial blood data in quantitative PET research limits the wider usability of this imaging method in clinical research settings. Image-derived input function (IDIF) approaches have been proposed as a cost-effective and non-invasive alternative to gold-standard arterial sampling. However, this approach comes with its own limitations-partial volume effects and radiometabolite correction among the most important-and varying rates of success, and the use of IDIF for brain PET has been particularly troublesome.

MAIN BODY

This paper summarizes the limitations of IDIF methods for quantitative PET imaging and discusses some of the advances that may make IDIF extraction more reliable. The introduction of automated pipelines (both commercial and open-source) for clinical PET scanners is discussed as a way to improve the reliability of IDIF approaches and their utility for quantitative purposes. Survey data gathered from the PET community are then presented to understand whether the field's opinion of the usefulness and validity of IDIF is improving. Finally, as the introduction of next-generation PET scanners with long axial fields of view, ultra-high sensitivity, and improved spatial and temporal resolution, has also brought IDIF methods back into the spotlight, a discussion of the possibilities offered by these state-of-the-art scanners-inclusion of large vessels, less partial volume in small vessels, better description of the full IDIF kinetics, whole-body modeling of radiometabolite production-is included, providing a pathway for future use of IDIF.

CONCLUSION

Improvements in PET scanner technology and software for automated IDIF extraction may allow to solve some of the major limitations associated with IDIF, such as partial volume effects and poor temporal sampling, with the exciting potential for accurate estimation of single kinetic rates. Nevertheless, until individualized radiometabolite correction can be performed effectively, IDIF approaches remain confined at best to a few tracers.

Image-derived input functions from dynamic 15O-water PET scans using penalised reconstruction

BACKGROUND

Quantitative positron emission tomography (PET) scans of the brain typically require arterial blood sampling but this is complicated and logistically challenging. One solution to remove the need for arterial blood sampling is the use of image-derived input functions (IDIFs). Obtaining accurate IDIFs, however, has proved to be challenging, mainly due to the limited resolution of PET. Here, we employ penalised reconstruction alongside iterative thresholding methods and simple partial volume correction methods to produce IDIFs from a single PET scan, and subsequently, compare these to blood-sampled input curves (BSIFs) as ground truth. Retrospectively we used data from sixteen subjects with two dynamic ¹⁵O-labelled water PET scans and continuous arterial blood sampling: one baseline scan and another post-administration of acetazolamide.

RESULTS

IDIFs and BSIFs agreed well in terms of the area under the curve of input curves when comparing peaks, tails and peak-to-tail ratios with R² values of 0.95, 0.70 and 0.76, respectively. Grey matter cerebral blood flow (CBF) values showed good agreement with an average difference between the BSIF and IDIF CBF values of 2% ± and a coefficient of variation (CoV) of 7.3%.

CONCLUSION

Our results show promising results that a robust IDIF can be produced for dynamic ¹⁵O-water PET scans using only the dynamic PET scan images with no need for a corresponding MRI or complex analytical techniques and thereby making routine clinical use of quantitative CBF measurements with ¹⁵O-water feasible.

Using arterial spin labeling to measure cerebrovascular reactivity in Moyamoya disease: Insights from simultaneous PET/MRI

Cerebrovascular reactivity (CVR) reflects the CBF change to meet different physiological demands. The reference CVR technique is PET imaging with vasodilators but is inaccessible to most patients. DSC can measure transit time to evaluate patients suspected of stroke, but the use of gadolinium may cause side-effects. Arterial spin labeling (ASL) is a non-invasive MRI technique for CBF measurements. Here, we investigate the effectiveness of ASL with single and multiple post labeling delays (PLD) to replace PET and DSC for CVR and transit time mapping in 26 Moyamoya patients. Images were collected using simultaneous PET/MRI with acetazolamide. CVR, CBF, arterial transit time (ATT), and time-to-maximum (Tmax) were measured in different flow territories. Results showed that CVR was lower in occluded regions than normal regions (by 68 ± 12%, 52 ± 5%, and 56 ± 9%, for PET, single- and multi-PLD PCASL, respectively, all p < 0.05). Multi-PLD PCASL correlated slightly higher with PET (CCC = 0.36 and 0.32 in affected and unaffected territories respectively). Vasodilation caused ATT to reduce by 4.5 ± 3.1% (p < 0.01) in occluded regions. ATT correlated significantly with Tmax (R² > 0.35, p < 0.01). Therefore, multi-PLD ASL is recommended for CVR studies due to its high agreement with the reference PET technique and the capability of measuring transit time.

Noninvasive Quantification of Cerebral Blood Flow Using Hybrid PET/MR Imaging to Extract the [15 O]H2 O Image-Derived Input Function Free of Partial Volume Errors

BACKGROUND

Quantification of cerebral blood flow (CBF) with [¹⁵ O]H₂ O-positron emission tomography (PET) requires arterial sampling to measure the input function. This invasive procedure can be avoided by extracting an image-derived input function (IDIF); however, IDIFs are sensitive to partial volume errors due to the limited spatial resolution of PET.

PURPOSE

To present an alternative hybrid PET/MR imaging of CBF (PMRFlow_IDIF ) that uses phase-contrast (PC) MRI measurements of whole-brain (WB) CBF to calibrate an IDIF extracted from a WB [¹⁵ O]H₂ O time-activity curve.

STUDY TYPE

Technical development and validation.

ANIMAL MODEL

Twelve juvenile Duroc pigs (83% female).

POPULATION

Thirteen healthy individuals (38% female).

FIELD STRENGTH/SEQUENCES

3 T; gradient-echo PC-MRI.

ASSESSMENT

PMRFlow_IDIF was validated against PET-only in a porcine model that included arterial sampling. CBF maps were generated by applying PMRFlow_IDIF and two previous PMRFlow methods (PC-PET and double integration method [DIM]) to [¹⁵ O]H₂ O-PET data acquired from healthy individuals.

STATISTICAL TESTS

PMRFlow and PET CBF measurements were compared with regression and correlation analyses. Paired t-tests were performed to evaluate differences. Potential biases were assessed using one-sample t-tests. Reliability was assessed by intraclass correlation coefficients. Statistical significance: α  = 0.05.

RESULTS

In the animal study, strong agreement was observed between PMRFlow_IDIF (average voxel-wise CBF, 58.0 ± 16.9 mL/100 g/min) and PET (63.0 ± 18.9 mL/100 g/min). In the human study, PMRFlow_DIM (y = 1.11x - 5.16, R²  = 0.99 ± 0.01) and PMRFlow_PC-PET (y = 0.87x + 3.82, R²  = 0.97 ± 0.02) performed similarly to PMRFlow_IDIF, and CBF was within the expected range (eg, 49.7 ± 7.2 mL/100 g/min for gray matter).

DATA CONCLUSION

Accuracy of PMRFlow_IDIF was confirmed in the animal study with the primary source of error attributed to differences in WB CBF measured by PC MRI and PET. In the human study, differences in CBF from PMRFlow_IDIF , PMRFlow_DIM , and PMRFlow_PC-PET were due to the latter two not accounting for blood-borne activity.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 1.

Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function

Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of 15O-labeled water by using tracer kinetic modelling. However, for quantification of regional CBF, an arterial input function (AIF), obtained from arterial blood sampling, is required. In this work we evaluated a novel, non-invasive approach for input function prediction based on machine learning (MLIF), against AIF for CBF PET measurements in human subjects.Twenty-five subjects underwent two 10 min dynamic 15O-water brain PET scans with continuous arterial blood sampling, before (baseline) and following acetazolamide medication. Three different image-derived time-activity curves were automatically segmented from the carotid arteries and used as input into a Gaussian process-based AIF prediction model, considering both baseline and acetazolamide scans as training data. The MLIF approach was evaluated by comparing AIF and MLIF curves, as well as whole-brain grey matter CBF values estimated by kinetic modelling derived with either AIF or MLIF.The results showed that AIF and MLIF curves were similar and that corresponding CBF values were highly correlated and successfully differentiated before and after acetazolamide medication. In conclusion, our non-invasive MLIF method shows potential to replace the AIF obtained from blood sampling for CBF measurements using 15O-water PET and kinetic modelling.

Cerebrovascular reactivity measurements using simultaneous 15O-water PET and ASL MRI: Impacts of arterial transit time, labeling efficiency, and hematocrit

Cerebrovascular reactivity (CVR) reflects the capacity of the brain to meet changing physiological demands and can predict the risk of cerebrovascular diseases. CVR can be obtained by measuring the change in cerebral blood flow (CBF) during a brain stress test where CBF is altered by a vasodilator such as acetazolamide. Although the gold standard to quantify CBF is PET imaging, the procedure is invasive and inaccessible to most patients. Arterial spin labeling (ASL) is a non-invasive and quantitative MRI method to measure CBF, and a consensus guideline has been published for the clinical application of ASL. Despite single post labeling delay (PLD) pseudo-continuous ASL (PCASL) being the recommended ASL technique for CBF quantification, it is sensitive to variations to the arterial transit time (ATT) and labeling efficiency induced by the vasodilator in CVR studies. Multi-PLD ASL controls for the changes in ATT, and velocity selective ASL is in theory insensitive to both ATT and labeling efficiency. Here we investigate CVR using simultaneous ¹⁵O-water PET and ASL MRI data from 19 healthy subjects. CVR and CBF measured by the ASL techniques were compared using PET as the reference technique. The impacts of blood T1 and labeling efficiency on ASL were assessed using individual measurements of hematocrit and flow velocity data of the carotid and vertebral arteries measured using phase-contrast MRI. We found that multi-PLD PCASL is the ASL technique most consistent with PET for CVR quantification (group mean CVR of the whole brain = 42 ± 19% and 40 ± 18% respectively). Single-PLD ASL underestimated the CVR of the whole brain significantly by 15 ± 10% compared with PET (p<0.01, paired t-test). Changes in ATT pre- and post-acetazolamide was the principal factor affecting ASL-based CVR quantification. Variations in labeling efficiency and blood T1 had negligible effects.

Validation of kinetic modeling of [15O]H2O PET using an image derived input function on hybrid PET/MRI

In present study we aimed to validate the use of image-derived input functions (IDIF) in the kinetic modeling of cerebral blood flow (CBF) measured by [¹⁵O]H₂O PET by comparing with the accepted reference standard arterial input function (AIF). Additional comparisons were made to mean cohort AIF and CBF values acquired by methodologically independent phase-contrast mapping (PCM) MRI. Using hybrid PET/MRI an IDIF was generated by measuring the radiotracer concentration in the internal carotid arteries and correcting for partial volume effects using the intravascular volume measured from MRI-angiograms. Seven patients with carotid steno-occlusive disease and twelve healthy controls were examined at rest, after administration of acetazolamide, and, in the control group, during hyperventilation. Agreement between the techniques was examined by linear regression and Bland-Altman analysis. Global CBF values modeled using IDIF correlated with values from AIF across perfusion states in both patients (p<10^-6, R²=0.82, 95% limits of agreement (LoA)=[-11.3-9.9] ml/100 g/min) and controls (p<10^-6, R²=0.87, 95% LoA=[-17.1-13.7] ml/100 g/min). The reproducibility of gCBF using IDIF was identical to AIF (15.8%). Values from IDIF and AIF had equally good correlation to measurements by PCM MRI, R²=0.86 and R²=0.84, (p<10^-6), respectively. Mean cohort AIF performed substantially worse than individual IDIFs (p<10^-6, R²=0.63, LoA=[-12.8-25.3] ml/100 g/min). In the patient group, use of IDIF provided similar reactivity maps compared to AIF. In conclusion, global CBF values modeled using IDIF correlated with values modeled by AIF and similar perfusion deficits could be established in a patient group.

Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic 18F-FDG PET Brain Studies

This work set out to develop a motion-correction approach aided by conditional generative adversarial network (cGAN) methodology that allows reliable, data-driven determination of involuntary subject motion during dynamic ¹⁸F-FDG brain studies. Methods: Ten healthy volunteers (5 men/5 women; mean age ± SD, 27 ± 7 y; weight, 70 ± 10 kg) underwent a test–retest ¹⁸F-FDG PET/MRI examination of the brain (n = 20). The imaging protocol consisted of a 60-min PET list-mode acquisition contemporaneously acquired with MRI, including MR navigators and a 3-dimensional time-of-flight MR angiography sequence. Arterial blood samples were collected as a reference standard representing the arterial input function (AIF). Training of the cGAN was performed using 70% of the total datasets (n = 16, randomly chosen), which was corrected for motion using MR navigators. The resulting cGAN mappings (between individual frames and the reference frame [55–60 min after injection]) were then applied to the test dataset (remaining 30%, n = 6), producing artificially generated low-noise images from early high-noise PET frames. These low-noise images were then coregistered to the reference frame, yielding 3-dimensional motion vectors. Performance of cGAN-aided motion correction was assessed by comparing the image-derived input function (IDIF) extracted from a cGAN-aided motion-corrected dynamic sequence with the AIF based on the areas under the curves (AUCs). Moreover, clinical relevance was assessed through direct comparison of the average cerebral metabolic rates of glucose (CMRGlc) values in gray matter calculated using the AIF and the IDIF. Results: The absolute percentage difference between AUCs derived using the motion-corrected IDIF and the AIF was (1.2% + 0.9%). The gray matter CMRGlc values determined using these 2 input functions differed by less than 5% (2.4% + 1.7%). Conclusion: A fully automated data-driven motion-compensation approach was established and tested for ¹⁸F-FDG PET brain imaging. cGAN-aided motion correction enables the translation of noninvasive clinical absolute quantification from PET/MR to PET/CT by allowing the accurate determination of motion vectors from the PET data itself.

Predicting 15O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias

To improve the quality of MRI-based cerebral blood flow (CBF) measurements, a deep convolutional neural network (dCNN) was trained to combine single- and multi-delay arterial spin labeling (ASL) and structural images to predict gold-standard ¹⁵O-water PET CBF images obtained on a simultaneous PET/MRI scanner. The dCNN was trained and tested on 64 scans in 16 healthy controls (HC) and 16 cerebrovascular disease patients (PT) with 4-fold cross-validation. Fidelity to the PET CBF images and the effects of bias due to training on different cohorts were examined. The dCNN significantly improved CBF image quality compared with ASL alone (mean ± standard deviation): structural similarity index (0.854 ± 0.036 vs. 0.743 ± 0.045 [single-delay] and 0.732 ± 0.041 [multi-delay], P < 0.0001); normalized root mean squared error (0.209 ± 0.039 vs. 0.326 ± 0.050 [single-delay] and 0.344 ± 0.055 [multi-delay], P < 0.0001). The dCNN also yielded mean CBF with reduced estimation error in both HC and PT (P < 0.001), and demonstrated better correlation with PET. The dCNN trained with the mixed HC and PT cohort performed the best. The results also suggested that models should be trained on cases representative of the target population.

Potentials and caveats of AI in hybrid imaging

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research.

Quantification of brain oxygen extraction and metabolism with [15O]-gas PET: A technical review in the era of PET/MRI

Oxygen extraction fraction (OEF) and the cerebral metabolic rate of oxygen (CMRO₂) are key cerebral physiological parameters to identify at-risk cerebrovascular patients and understand brain health and function. PET imaging with [¹⁵O]-oxygen tracers, either through continuous or bolus inhalation, provides non-invasive assessment of OEF and CMRO₂. Numerous tracer delivery, PET acquisition, and kinetic modeling approaches have been adopted to map brain oxygenation. The purpose of this technical review is to critically evaluate different methods for [¹⁵O]-gas PET and its impact on the accuracy and reproducibility of OEF and CMRO₂ measurements. We perform a meta-analysis of brain oxygenation PET studies in healthy volunteers and compare between continuous and bolus inhalation techniques. We also describe OEF metrics that have been used to detect hemodynamic impairment in cerebrovascular disease. For these patients, advanced techniques to accelerate the PET scans and potential synthesis with MRI to avoid arterial blood sampling would facilitate broader use of [¹⁵O]-oxygen PET for brain physiological assessment.

Pharmacokinetic analysis of [68Ga]Ga-DOTA-TOC PET in meningiomas for assessment of in vivo somatostatin receptor subtype 2

PURPOSE

DOTA-D-Phe¹-Tyr³-octreotide with gallium-68 ([⁶⁸Ga]Ga-DOTA-TOC) is one of the PET tracers that forms the basis for peptide receptor radionuclide therapy based on somatostatin receptor subtype 2 (SSTR2) expression in meningiomas. Yet, the quantitative relationship between [⁶⁸Ga]Ga-DOTA-TOC accumulation and SSTR2 is unknown. We conducted a correlative analysis of a range of [⁶⁸Ga]Ga-DOTA-TOC PET metric(s) as imaging surrogate(s) of the receptor binding in meningiomas by correlating the PET results with SSTR2 expression from surgical specimens. We additionally investigated possible influences of secondary biological factors such as vascularization, inflammation and proliferation.

METHODS

Fifteen patients with MRI-presumed or recurrent meningiomas underwent a 60-min dynamic [⁶⁸Ga]Ga-DOTA-TOC PET/CT before surgery. The PET data comprised maximum and mean standardized uptake values (SUV_max, SUV_mean) with and without normalization to reference regions, and quantitative measurements derived from kinetic modelling using a reversible two-tissue compartment model with the fractional blood volume (V_B). Expressions of SSTR2 and proliferation (Ki-67, phosphohistone-H3, proliferating cell nuclear antigen) were determined by immunohistochemistry and/or quantitative polymerase chain reaction (qPCR), while biomarkers of vascularization (vascular endothelial growth factor A (VEGFA), endothelial marker CD34) and inflammation (cytokine interleukin-18, microglia/macrophage-specific marker CD68) by qPCR.

RESULTS

Histopathology revealed 12 World Health Organization (WHO) grade I and three WHO grade II meningiomas showing no link to SSTR2. The majority of [⁶⁸Ga]Ga-DOTA-TOC PET metrics showed significant associations with SSTR2 protein, while all PET metrics were positively correlated with SSTR2 mRNA with the best results for mean tumour-to-blood ratio (TBR_mean) (r = 0.757, P = 0.001) and SUV_mean (r = 0.714, P = 0.003). Significant positive correlations were also found between [⁶⁸Ga]Ga-DOTA-TOC PET metrics, and VEGFA and V_B. SSTR2 mRNA was moderately correlated with VEGFA (r = 0.539, P = 0.038). Neither [⁶⁸Ga]Ga-DOTA-TOC PET metrics nor SSTR2 were correlated with proliferation or inflammation.

CONCLUSION

[⁶⁸Ga]Ga-DOTA-TOC accumulation in meningiomas is associated with SSTR2 binding and vascularization with TBR_mean being the best PET metric for assessing SSTR2.

High Intravascular Signal Arterial Transit Time Artifacts Have Negligible Effects on Cerebral Blood Flow and Cerebrovascular Reserve Capacity Measurement Using Single Postlabel Delay Arterial Spin-Labeling in Patients with Moyamoya Disease

BACKGROUND AND PURPOSE

Arterial spin-labeling-derived CBF values may be affected by arterial transit time artefacts. Thus, our aim was to assess to what extent arterial spin-labeling-derived CBF and cerebrovascular reserve capacity values in major vascular regions are overestimated due to the arterial transit time artifacts in patients with Moyamoya disease.

MATERIALS AND METHODS

Eight patients with Moyamoya disease were included before or after revascularization surgery. CBF maps were acquired using a 3D pseudocontinuous arterial spin-labeling sequence, before and 5, 15, and 25 minutes after an IV acetazolamide injection and were registered to each patient's 3D-T1-weighted images. Vascular regions were defined by spatial normalization to a Montreal Neurological Institute-based vascular regional template. The arterial transit time artifacts were defined as voxels with high signal intensity corresponding to the right tail of the histogram for a given vascular region, with the cutoff selected by visual inspection. Arterial transit time artifact maps were created and applied as masks to exclude arterial transit time artifacts on CBF maps, to create corrected CBF maps. The cerebrovascular reserve capacity was calculated as CBF after acetazolamide injection relative to CBF at baseline for corrected and uncorrected CBF values, respectively.

RESULTS

A total of 16 examinations were analyzed. Arterial transit time artifacts were present mostly in the MCA, whereas the posterior cerebral artery was generally unaffected. The largest differences between corrected and uncorrected CBF and cerebrovascular reserve capacity values, reported as patient group average ratio and percentage point difference, respectively, were 0.978 (95% CI, 0.968-0.988) and 1.8 percentage points (95% CI, 0.3-3.2 percentage points). Both were found in the left MCA, 15 and 5 minutes post-acetazolamide injection, respectively.

CONCLUSIONS

Arterial transit time artifacts have negligible overestimation effects on calculated vascular region-based CBF and cerebrovascular reserve capacity values derived from single-delay 3D pseudocontinuous arterial spin-labeling.

In vivo imaging of cell proliferation in meningioma using 3'-deoxy-3'-[18F]fluorothymidine PET/MRI

PURPOSE

Positron emission tomography (PET) with 3'-deoxy-3'-[¹⁸F]fluorothymidine ([¹⁸F]FLT) provides a noninvasive assessment of tumour proliferation in vivo and could be a valuable imaging modality for assessing malignancy in meningiomas. We investigated a range of static and dynamic [¹⁸F]FLT metrics by correlating the findings with cellular biomarkers of proliferation and angiogenesis.

METHODS

Seventeen prospectively recruited adult patients with intracranial meningiomas underwent a 60-min dynamic [¹⁸F]FLT PET following surgery. Maximum and mean standardized uptake values (SUV_max, SUV_mean) with and without normalization to healthy brain tissue and blood radioactivity obtained from 40 to 60 min summed dynamic images (PET_40-60) and ~ 60-min blood samples were calculated. Kinetic modelling using a two-tissue reversible compartmental model with a fractioned blood volume (V_B) was performed to determine the total distribution volume (V_T). Expressions of proliferation and angiogenesis with key parameters including Ki-67 index, phosphohistone-H3 (phh3), MKI67, thymidine kinase 1 (TK1), proliferating cell nuclear antigen (PCNA), Kirsten RAt Sarcoma viral oncogene homolog (KRAS), TIMP metallopeptidase inhibitor 3 (TIMP3), and vascular endothelial growth factor A (VEGFA) were determined by immunohistochemistry and/or quantitative polymerase chain reaction.

RESULTS

Immunohistochemistry revealed 13 World Health Organization (WHO) grade I and four WHO grade II meningiomas. SUV_max and SUV_mean normalized to blood radioactivity from PET_40-60 and blood sampling, and V_T were able to significantly differentiate between WHO grades with the best results for maximum and mean tumour-to-whole-blood ratios (sensitivity 100%, specificity 94-95%, accuracy 99%; P = 0.003). Static [¹⁸F]FLT metrics were significantly correlated with proliferative biomarkers, especially Ki-67 index, phh3, and TK1, while no correlations were found with VEGFA or V_B. Using Ki-67 index with a threshold > 4%, the majority of [¹⁸F]FLT metrics showed a high ability to identify aggressive meningiomas with SUV_mean demonstrating the best performance (sensitivity 80%, specificity 81%, accuracy 80%; P = 0.024).

CONCLUSION

[¹⁸F]FLT PET could be a useful imaging modality for assessing cellular proliferation in meningiomas.

Assessment of the kidney function parameters split function, mean transit time, and outflow efficiency using dynamic FDG-PET/MRI in healthy subjects

Background

Traditionally, isotope nephrography is considered as the method of choice to assess kidney function parameters in nuclear medicine. We propose a novel approach to determine the split function (SF), mean transit time (MTT), and outflow efficiency (OE) with 2-deoxy-2-[18F]fluoro-D-glucose (FDG) dynamic positron emission tomography (PET).

Materials and methods

Healthy adult subjects underwent dynamic simultaneous FDG-PET and magnetic resonance imaging (PET/MRI). Time-activity curves (TACs) of total kidneys, renal cortices, and the aorta were prospectively obtained from dynamic PET series. MRI images were used for anatomical correlation. The same individuals were subjected to dynamic renal Technetium-99 m-mercaptoacetyltriglycine (MAG3) scintigraphy and TACs of kidneys; the perirenal background and the left ventricle were determined. SF was calculated on the basis of integrals over the TACs, MTT was determined from renal retention functions after deconvolution analysis, and OE was determined from MTT. Values obtained from PET series were compared with scintigraphic parameters, which served as the reference.

Results

Twenty-four subjects underwent both examinations. Total kidney SF, MTT, and OE as estimated by dynamic PET/MRI correlated to their reference values by r = 0.75, r = 0.74 and r = 0.81, respectively, with significant difference (p < 0.0001) between the means of MTT and OE. No correlations were found for cortex FDG values.

Conclusions

The study proofs the concept that SF, MTT, and OE can be estimated with dynamic FDG PET/MRI scans in healthy kidneys. This has advantages for patients receiving a routine PET/MRI scan, as kidney parameters can be estimated simultaneously to functional and morphological imaging with high accuracy.

Simultaneous PET-MRI imaging of cerebral blood flow and glucose metabolism in the symptomatic unilateral internal carotid artery/middle cerebral artery steno-occlusive disease

Purpose

Cerebral blood flow (CBF) and glucose metabolism are important and significant factors in ischaemic cerebrovascular disease. The objective of this study was to use quantitative hybrid PET/MR to evaluate the effects of surgery treatment on the symptomatic unilateral internal carotid artery/middle cerebral artery steno-occlusive disease.

Methods

Fifteen patients diagnosed with ischaemic cerebrovascular disease were evaluated using a hybrid TOF PET/MR system (Signa, GE Healthcare). The CBF value measured by arterial spin labelling (ASL) and the standardized uptake value ratio (SUVR) measured by ¹⁸F-FDG PET were obtained, except for the infarct area and its contralateral side, before and after bypass surgery. The asymmetry index (AI) was calculated from the CBF and SUVR of the ipsilateral and contralateral cerebral hemispheres, respectively. The ΔCBF and ΔSUVR were calculated as the percent changes of CBF and SUVR between before and after surgery, and paired t tests were used to determine whether a significant change occurred. Spearman’s rank correlation was also used to compare CBF with glucose metabolism in the same region.

Results

The analysis primarily revealed that after bypass surgery, a statistically significant increase occurred in the CBF on the affected side (P < 0.01). The postprocedural SUVR was not significantly higher than the preprocedural SUVR (P > 0.05). However, the postprocedural AI values for CBF and SUVR were significantly lower after surgery than before surgery (P < 0.01). A significant correlation was found between the AI values for preoperative CBF and SUVR on the ipsilateral hemisphere (P < 0.01).

Conclusions

The present study demonstrates that a combination of ASL and ¹⁸F-FDG PET could be used to simultaneously analyse changes in patients’ cerebral haemodynamic patterns and metabolism between before and after superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery. This therefore represents an essential tool for the evaluation of critical haemodynamic and metabolic status in patients with symptomatic unilateral ischaemic cerebrovascular disease.

Human non-REM sleep and the mean global BOLD signal

A hallmark of non-rapid eye movement (REM) sleep is the decreased brain activity as measured by global reductions in cerebral blood flow, oxygen metabolism, and glucose metabolism. It is unknown whether the blood oxygen level dependent (BOLD) signal undergoes similar changes. Here we show that, in contrast to the decreases in blood flow and metabolism, the mean global BOLD signal increases with sleep depth in a regionally non-uniform manner throughout gray matter. We relate our findings to the circulatory and metabolic processes influencing the BOLD signal and conclude that because oxygen consumption decreases proportionately more than blood flow in sleep, the resulting decrease in paramagnetic deoxyhemoglobin accounts for the increase in mean global BOLD signal.

Identifying Hypoperfusion in Moyamoya Disease With Arterial Spin Labeling and an [15O]-Water Positron Emission Tomography/Magnetic Resonance Imaging Normative Database

Background and Purpose- Noninvasive imaging of brain perfusion has the potential to elucidate pathophysiological mechanisms underlying Moyamoya disease and enable clinical imaging of cerebral blood flow (CBF) to select revascularization therapies for patients. We used hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI) technology to characterize the distribution of hypoperfusion in Moyamoya disease and its relationship to vessel stenosis severity, through comparisons with a normative perfusion database of healthy controls. Methods- To image CBF, we acquired [¹⁵O]-water PET as a reference and simultaneously acquired arterial spin labeling (ASL) MRI scans in 20 Moyamoya patients and 15 age-matched, healthy controls on a PET/MRI scanner. The ASL MRI scans included a standard single-delay ASL scan with postlabel delay of 2.0 s and a multidelay scan with 5 postlabel delays (0.7-3.0s) to estimate and account for arterial transit time in CBF quantification. The percent volume of hypoperfusion in patients (determined as the fifth percentile of CBF values in the healthy control database) was the outcome measure in a logistic regression model that included stenosis grade and location. Results- Logistic regression showed that anterior ( P<0.0001) and middle cerebral artery territory regions ( P=0.003) in Moyamoya patients were susceptible to hypoperfusion, whereas posterior regions were not. Cortical regions supplied by arteries with stenosis on MR angiography showed more hypoperfusion than normal arteries ( P=0.001), but the extent of hypoperfusion was not different between mild-moderate versus severe stenosis. Multidelay ASL did not perform differently from [¹⁵O]-water PET in detecting perfusion abnormalities, but standard ASL overestimated the extent of hypoperfusion in patients ( P=0.003). Conclusions- This simultaneous PET/MRI study supports the use of multidelay ASL MRI in clinical evaluation of Moyamoya disease in settings where nuclear medicine imaging is not available and application of a normative perfusion database to automatically identify abnormal CBF in patients.

Promise of Fully Integrated PET/MRI: Noninvasive Clinical Quantification of Cerebral Glucose Metabolism

We describe a fully automated processing pipeline to support the noninvasive absolute quantification of the cerebral metabolic rate for glucose (CMRGlc) in a clinical setting. This pipeline takes advantage of “anatometabolic” information associated with fully integrated PET/MRI. Methods: Ten healthy volunteers (5 men and /5 women; 27 ± 7 y old; 70 ± 10 kg) underwent a test-retest ¹⁸F-FDG PET/MRI examination of the brain. The imaging protocol consisted of a 60-min PET list-mode acquisition with parallel MRI acquisitions, including 3-dimensional time-of-flight MR angiography, MRI navigators, and a T1-weighted MRI scan. State-of-the-art MRI-based attenuation correction was derived from T1-weighted MRI (pseudo-CT [pCT]). For validation purposes, a low-dose CT scan was also performed. Arterial blood samples were collected as the reference standard (arterial input function [AIF]). The developed pipeline allows the derivation of an image-derived input function (IDIF), which is subsequently used to create CMRGlc maps by means of a Patlak analysis. The pipeline also includes motion correction using the MRI navigator sequence as well as a novel partial-volume correction that accounts for background heterogeneity. Finally, CMRGlc maps are used to generate a normative database to facilitate the detection of metabolic abnormalities in future patient scans. To assess the performance of the developed pipeline, IDIFs extracted by both CT-based attenuation correction (CT-IDIF) and MRI-based attenuation correction (pCT-IDIF) were compared with the reference standard (AIF) using the absolute percentage difference between the areas under the curves as well as the absolute percentage difference in regional CMRGlc values. Results: The absolute percentage differences between the areas under the curves for CT-IDIF and pCT-IDIF were determined to be 1.4% ± 1.0% and 3.4% ± 2.6%, respectively. The absolute percentage difference in regional CMRGlc values based on CT-IDIF and pCT-IDIF differed by less than 6% from the reference values obtained from the AIF. Conclusion: By taking advantage of the capabilities of fully integrated PET/MRI, we developed a fully automated computational pipeline that allows the noninvasive determination of regional CMRGlc values in a clinical setting. This methodology might facilitate the proliferation of fully quantitative imaging into the clinical arena and, as a result, might contribute to improved diagnostic efficacy.

Towards quantitative [18F]FDG-PET/MRI of the brain: Automated MR-driven calculation of an image-derived input function for the non-invasive determination of cerebral glucose metabolic rates

Absolute quantification of PET brain imaging requires the measurement of an arterial input function (AIF), typically obtained invasively via an arterial cannulation. We present an approach to automatically calculate an image-derived input function (IDIF) and cerebral metabolic rates of glucose (CMRGlc) from the [18F]FDG PET data using an integrated PET/MRI system. Ten healthy controls underwent test-retest dynamic [18F]FDG-PET/MRI examinations. The imaging protocol consisted of a 60-min PET list-mode acquisition together with a time-of-flight MR angiography scan for segmenting the carotid arteries and intermittent MR navigators to monitor subject movement. AIFs were collected as the reference standard. Attenuation correction was performed using a separate low-dose CT scan. Assessment of the percentage difference between area-under-the-curve of IDIF and AIF yielded values within ±5%. Similar test-retest variability was seen between AIFs (9 ± 8) % and the IDIFs (9 ± 7) %. Absolute percentage difference between CMRGlc values obtained from AIF and IDIF across all examinations and selected brain regions was 3.2% (interquartile range: (2.4-4.3) %, maximum < 10%). High test-retest intravariability was observed between CMRGlc values obtained from AIF (14%) and IDIF (17%). The proposed approach provides an IDIF, which can be effectively used in lieu of AIF.

MRI-Driven PET Image Optimization for Neurological Applications

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are established imaging modalities for the study of neurological disorders, such as epilepsy, dementia, psychiatric disorders and so on. Since these two available modalities vary in imaging principle and physical performance, each technique has its own advantages and disadvantages over the other. To acquire the mutual complementary information and reinforce each other, there is a need for the fusion of PET and MRI. This combined dual-modality (either sequential or simultaneous) could generate preferable soft tissue contrast of brain tissue, flexible acquisition parameters, and minimized exposure to radiation. The most unique superiority of PET/MRI is mainly manifested in MRI-based improvement for the inherent limitations of PET, such as motion artifacts, partial volume effect (PVE) and invasive procedure in quantitative analysis. Head motion during scanning significantly deteriorates the effective resolution of PET image, especially for the dynamic scan with lengthy time. Hybrid PET/MRI device can offer motion correction (MC) for PET data through MRI information acquired simultaneously. Regarding the PVE associated with limited spatial resolution, the process and reconstruction of PET data can be further optimized by using acquired MRI either sequentially or simultaneously. The quantitative analysis of dynamic PET data mainly relies upon an invasive arterial blood sampling procedure to acquire arterial input function (AIF). An image-derived input function (IDIF) method without the need of arterial cannulization, can serve as a potential alternative estimation of AIF. Compared with using PET data only, combining anatomical or functional information from MRI for improving the accuracy in IDIF approach has been demonstrated. Yet, due to the interference and inherent disparity between the two modalities, these methods for optimizing PET image based on MRI still have many technical challenges. This review discussed upon the most recent progress, current challenges and future directions of MRI-driven PET data optimization for neurological applications, with either sequential or simultaneous acquisition approach.

Kinetic [18F]-Fluoride of the Knee in Normal Volunteers

PURPOSE

[F]-sodium fluoride ([F]NaF) is a well-established bone-seeking agent that has shown promise to assess bone turnover in a variety of disorders, but its distribution in healthy knee joints has not been explored. This study aimed to investigate parametric values for [F]NaF uptake in various bone tissues types of the knee and their spatial distributions.

METHODS

Twelve healthy subjects were hand-injected with 92.5 MBq of [F]NaF and scanned on a 3-T PET/MRI system. Listmode PET data for both knees were acquired for 50 minutes from injection simultaneously with MRI Dixon and angiography data. The image-derived input function was determined from the popliteal artery. Using the Hawkins model, Patlak analysis was performed to obtain Ki (Ki) values and nonlinear regression analysis to obtain Ki, K1, k3/(k2 + k3), and blood volume. Comparisons for the measured kinetic parameters, SUV, and SUVmax were made between tissue types (subchondral, cortical, and trabecular bone) and between regional subsections of subchondral bone.

RESULTS

Cortical bone had the highest [F]NaF uptake differing significantly in all measured parameters when compared with trabecular bone and significantly higher SUVmax and K1 than subchondral bone. Subchondral bone also had significantly higher SUV, SUVmax, and Ki than trabecular bone tissue. Regional differences were observed in K1 and k3/(k2 + k3) values.

CONCLUSIONS

Quantitative [F]NaF PET is sensitive to variations in bone vascularization and metabolism in the knee joint.

Simultaneous phase-contrast MRI and PET for noninvasive quantification of cerebral blood flow and reactivity in healthy subjects and patients with cerebrovascular disease

BACKGROUND

H₂ ¹⁵ O-positron emission tomography (PET) is considered the reference standard for absolute cerebral blood flow (CBF). However, this technique requires an arterial input function measured through continuous sampling of arterial blood, which is invasive and has limitations with tracer delay and dispersion.

PURPOSE

To demonstrate a new noninvasive method to quantify absolute CBF with a PET/MRI hybrid scanner. This blood-free approach, called PC-PET, takes the spatial CBF distribution from a static H₂ ¹⁵ O-PET scan, and scales it to the whole-brain average CBF value measured by simultaneous phase-contrast MRI.

STUDY TYPE

Observational.

SUBJECTS

Twelve healthy controls (HC) and 13 patients with Moyamoya disease (MM) as a model of chronic ischemic disease.

FIELD STRENGTH/SEQUENCES

3T/2D cardiac-gated phase-contrast MRI and H₂ ¹⁵ O-PET.

ASSESSMENT

PC-PET CBF values from whole brain (WB), gray matter (GM), and white matter (WM) in HCs were compared with literature values since 2000. CBF and cerebrovascular reactivity (CVR), which is defined as the percent CBF change between baseline and post-acetazolamide (vasodilator) scans, were measured by PC-PET in MM patients and HCs within cortical regions corresponding to major vascular territories. Statistical Tests: Linear, mixed effects models were created to compare CBF and CVR, respectively, between patients and controls, and between different degrees of stenosis.

RESULTS

The mean CBF values in WB, GM, and WM in HC were 42 ± 7 ml/100 g/min, 50 ± 7 ml/100 g/min, and 23 ± 3 ml/100 g/min, respectively, which agree well with literature values. Compared with normal regions (57 ± 23%), patients showed significantly decreased CVR in areas with mild/moderate stenosis (47 ± 17%, P = 0.011) and in severe/occluded areas (40 ± 16%, P = 0.016). Data Conclusion: PC-PET identifies differences in cerebrovascular reactivity between healthy controls and cerebrovascular patients. PC-PET is suitable for CBF measurement when arterial blood sampling is not accessible, and warrants comparison to fully quantitative H₂ ¹⁵ O-PET in future studies.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;51:183-194.

Hybrid PET/MRI Methodology

The hybrid PET/MR scanner represents the first implementation of the effective integration of two modalities allowing truly synchronous/simultaneous acquisition of their imaging signals. This integration, resulting from the innovation and development of specific hardware components has paved the way for new approaches in the study of neurodegenerative diseases. This chapter will describe the hardware development that has led to the availability of different clinical solutions for PET/MR imaging as well as the still-open technological challenges and opportunities related to the processing and exploitation of the simultaneous acquisition in neurological studies.

Noninvasive method for measurement of cerebral blood flow using O-15 water PET/MRI with ASL correlation

PURPOSE

A noninvasive image derived input function (IDIF) method was applied to estimate arterial input function from brain H₂¹⁵O-PET/MRI images for the measurement of cerebral blood flow (CBF) because of difficulty in arterial blood sampling during PET/MRI scans. To evaluate accuracy and reproducibility of radioactivity in the internal carotid arteries (ICA) for the IDIF method, a new phantom using a skull bone was applied in the cross-calibration process between the scanner and a gamma-well counter.

METHODS

Eleven healthy volunteers (9 males, 43.9 ± 10.9y) underwent PET/MRI studies with a 3-min H₂¹⁵O-PET and several MRI scans including arterial spin labeling (ASL) perfusion MRI. PET images were reconstructed as dynamic data using two sets of reconstruction parameters, which were determined by basic assessment of radioactivity concentration reproducibility in the tubes of the phantom. The IDIF method extracted the time-activity curves of the ICA from several image slices in the PET data. CBF images were calculated using the autoradiographic (ARG) method and a one-tissue compartment model (1-TCM).

RESULTS

The global means of CBF from the ARG, 1-TCM, and ASL-MRI were 44.8 ± 4.3, 47.9 ± 5.9 and 57.9 ± 8.6 (mL/min/100 g), respectively. CBF from ASL-MRI was significantly greater compared with CBF from H₂¹⁵O-PET (P < 0.001). However, these CBF values were significantly correlated with each other in the scatter plots (P < 0.05).

CONCLUSIONS

Noninvasive measurement of CBF using H₂¹⁵O-PET/MRI and IDIF with the cross-calibration method with a skull phantom experiment provided reasonable quantitative values. The IDIF method allowed reliable estimation of arterial radioactivity concentration, which is useful for clinical application. The ASL-MRI perfusion image from the simultaneous acquisition tended to overestimate CBF.

Assessing the kidney function parameters glomerular filtration rate and effective renal plasma flow with dynamic FDG-PET/MRI in healthy subjects

Background

A method was developed to assess the kidney parameters glomerular filtration rate (GFR) and effective renal plasma flow (ERPF) from 2-deoxy-2-[¹⁸F]fluoro-d-glucose (FDG) concentration behavior in kidneys, measured with positron emission tomography (PET) scans.

Twenty-four healthy adult subjects prospectively underwent dynamic simultaneous PET/magnetic resonance imaging (MRI) examination. Time activity curves (TACs) were obtained from the dynamic PET series, with the guidance of MR information. Patlak analysis was performed to determine the GFR, and based on integrals, ERPF was calculated. Results were compared to intra-individually obtained reference values determined from venous blood samples.

Results

Total kidney GFR and ERPF as estimated by dynamic PET/MRI were highly correlated to their reference values (r = 0.88/p < 0.0001 and r = 0.82/p < 0.0001, respectively) with no significant difference between their means.

Conclusions

The study is a proof of concept that GFR and ERPF can be assessed with dynamic FDG PET/MRI scans in healthy kidneys. This has advantages for patients getting a routine scan, where additional examinations for kidney function estimation could be avoided. Further studies are required for transferring this PET/MRI method to PET/CT applications.

Electronic supplementary material

The online version of this article (10.1186/s13550-018-0389-1) contains supplementary material, which is available to authorized users.

Long-Delay Arterial Spin Labeling Provides More Accurate Cerebral Blood Flow Measurements in Moyamoya Patients: A Simultaneous Positron Emission Tomography/MRI Study

BACKGROUND AND PURPOSE

Arterial spin labeling (ASL) MRI is a promising, noninvasive technique to image cerebral blood flow (CBF) but is difficult to use in cerebrovascular patients with abnormal, long arterial transit times through collateral pathways. To be clinically adopted, ASL must first be optimized and validated against a reference standard in these challenging patient cases.

METHODS

We compared standard-delay ASL (post-label delay=2.025 seconds), multidelay ASL (post-label delay=0.7-3.0 seconds), and long-label long-delay ASL acquisitions (post-label delay=4.0 seconds) against simultaneous [¹⁵O]-positron emission tomography (PET) CBF maps in 15 Moyamoya patients on a hybrid PET/MRI scanner. Dynamic susceptibility contrast was performed in each patient to identify areas of mild, moderate, and severe time-to-maximum (Tmax) delays. Relative CBF measurements by each ASL scan in 20 cortical regions were compared with the PET reference standard, and correlations were calculated for areas with moderate and severe Tmax delays.

RESULTS

Standard-delay ASL underestimated relative CBF by 20% in areas of severe Tmax delays, particularly in anterior and middle territories commonly affected by Moyamoya disease (P<0.001). Arterial transit times correction by multidelay acquisitions led to improved consistency with PET, but still underestimated CBF in the presence of long transit delays (P=0.02). Long-label long-delay ASL scans showed the strongest correlation relative to PET, and there was no difference in mean relative CBF between the modalities, even in areas of severe delays.

CONCLUSIONS

Post-label delay times of ≥4 seconds are needed and may be combined with multidelay strategies for robust ASL assessment of CBF in Moyamoya disease.