Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data

Transforming clinical cardiology through neural networks and deep learning: A guide for clinicians

The rapid evolution of neural networks and deep learning has revolutionized various fields, with clinical cardiology being no exception. As traditional methods in cardiology encounter limitations, the integration of advanced computational techniques offers unprecedented opportunities in diagnostics and patient care. This review explores the transformative role of neural networks and deep learning in clinical cardiology, particularly focusing on their applications in electrocardiogram (ECG) analysis, imaging technologies, and cardiac prediction models. Among others, Deep Neural Networks (DNNs) have significantly surpassed traditional approaches in accuracy and efficiency in automatic ECG diagnosis. Convolutional Neural Networks (CNNs) are successfully applied in PET/CT and PET/MR imaging, enhancing diagnostic capabilities. Furthermore, deep learning algorithms have shown potential in improving cardiac prediction models, although challenges in interpretability and clinical integration remain. The review also addresses the 'black box' nature of neural networks and the ethical considerations surrounding their use in clinical settings. Overall, this review underscores the significant impact of neural networks and deep learning in cardiology, providing insights into current applications and future directions in the field.

The use of PET/MRI in radiotherapy

Positron emission tomography/magnetic resonance imaging (PET/MRI) is a hybrid imaging technique that quantitatively combines the metabolic and functional data from positron emission tomography (PET) with anatomical and physiological information from MRI. As PET/MRI technology has advanced, its applications in cancer care have expanded. Recent studies have demonstrated that PET/MRI provides unique advantages in the field of radiotherapy and has become invaluable in guiding precision radiotherapy techniques. This review discusses the rationale and clinical evidence supporting the use of PET/MRI for radiation positioning, target delineation, efficacy evaluation, and patient surveillance.Critical relevance statement This article critically assesses the transformative role of PET/MRI in advancing precision radiotherapy, providing essential insights into improved radiation positioning, target delineation, efficacy evaluation, and patient surveillance in clinical radiology practice.Key points• The emergence of PET/MRI will be a key bridge for precise radiotherapy.• PET/MRI has unique advantages in the whole process of radiotherapy.• New tracers and nanoparticle probes will broaden the use of PET/MRI in radiation.• PET/MRI will be utilized more frequently for radiotherapy.

Systematic evaluation of human soft tissue attenuation correction in whole-body PET/MR: Implications from PET/CT for optimization of MR-based AC in patients with normal lung tissue

BACKGROUND

Attenuation correction (AC) is an important methodical step in positron emission tomography/magnetic resonance imaging (PET/MRI) to correct for attenuated and scattered PET photons.

PURPOSE

The overall quality of magnetic resonance (MR)-based AC in whole-body PET/MRI was evaluated in direct comparison to computed tomography (CT)-based AC serving as reference. The quantitative impact of isolated tissue classes in the MR-AC was systematically investigated to identify potential optimization needs and strategies.

METHODS

Data of n = 60 whole-body PET/CT patients with normal lung tissue and without metal implants/prostheses were used to generate six different AC-models based on the CT data for each patient, simulating variations of MR-AC. The original continuous CT-AC (CT-org) is referred to as reference. A pseudo MR-AC (CT-mrac), generated from CT data, with four tissue classes and a bone atlas represents the MR-AC. Relative difference in linear attenuation coefficients (LAC) and standardized uptake values were calculated. From the results two improvements regarding soft tissue AC and lung AC were proposed and evaluated.

RESULTS

The overall performance of MR-AC is in good agreement compared to CT-AC. Lungs, heart, and bone tissue were identified as the regions with most deviation to the CT-AC (myocardium -15%, bone tissue -14%, and lungs ±20%). Using single-valued LACs for AC in the lung only provides limited accuracy. For improved soft tissue AC, splitting the combined soft tissue class into muscles and organs each with adapted LAC could reduce the deviations to the CT-AC to < ±1%. For improved lung AC, applying a gradient LAC in the lungs could remarkably reduce over- or undercorrections in PET signal compared to CT-AC (±5%).

CONCLUSIONS

The AC is important to ensure best PET image quality and accurate PET quantification for diagnostics and radiotherapy planning. The optimized segment-based AC proposed in this study, which was evaluated on PET/CT data, inherently reduces quantification bias in normal lung tissue and soft tissue compared to the CT-AC reference.

A Clinical Role of PET-MRI in Prostate Cancer?

PET/MRI is a relevant application field for prostate cancer management, offering advantages in early diagnosis, staging, and therapy planning. Despite drawbacks such as higher costs, longer acquisition time, and the need for skilled personnel, the technical integration of PET and MRI provides valuable information for detecting primary tumors, identifying metastases, and characterizing the disease, leading to more accurate staging and personalized treatment strategies. However, PET/MRI adoption has been slow, but ongoing technological advancements and AI integration might overcome challenges and improve clinical utility. As precision medicine gains importance in oncology, PET/MRI's multiparametric data can tailor treatment plans to individual patients, providing a comprehensive assessment of tumor biology and aggressiveness for more effective therapeutic strategies.

Hybrid PET/MR in Cardiac Imaging

In the last few years, technological advances in MR imaging, PET detectors, and attenuation correction algorithms have allowed the creation of truly integrated PET/MR imaging systems, for both clinical and research applications. These machines allow a comprehensive investigation of cardiovascular diseases, by offering a wide variety of detailed anatomical and functional data in combination. Despite significant pathophysiologic mechanisms being clarified by this new data, its clinical relevance and prognostic significance have not been demonstrated yet.

A review of PET attenuation correction methods for PET-MR

Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.

Detection of distant metastases and distant second primary cancers in head and neck squamous cell carcinoma: comparison of [18F]FDG PET/MRI and [18F]FDG PET/CT

PURPOSE

This prospective study aimed to compare the diagnostic performance of [¹⁸]FDG PET/MRI and PET/CT for the detection of distant metastases and distant second primary cancers in patients with head and neck squamous cell carcinoma (HNSCC).

METHODS

A total of 103 [¹⁸F]FDG PET/MRI examinations immediately followed by PET/CT were obtained in 82 consecutive patients for staging of primary HNSCC (n = 38), suspected loco-regional recurrence/follow-up (n = 41) or unknown primary HNSCC (n = 3). Histology and follow-up > 2 years formed the standard of reference. Blinded readers evaluated the anonymized PET/MRI and PET/CT examinations separately using a 5-point Likert score. Statistical analysis included: receiver operating characteristic (ROC) analysis, jackknife alternative free-response ROC (JAFROC) and region-of-interest (ROI)-based ROC to account for data clustering and sensitivity/specificity/accuracy comparisons for a score ≥ 3.

RESULTS

Distant metastases and distant second primary cancers were present in 23/103 (22%) examinations in 16/82 (19.5%) patients, and they were more common in the post-treatment group (11/41, 27%) than in the primary HNSCC group (3/38, 8%), p = 0.039. The area under the curve (AUC) per patient/examination/lesion was 0.947 [0.927-1]/0.965 [0.917-1]/0.957 [0.928-0.987] for PET/MRI and 0.975 [0.950-1]/0.968 [0.920-1]/0.944 [0.910-0.979] for PET/CT, respectively (p > 0.05). The diagnostic performance of PET/MRI and PET/CT was similar according to JAFROC (p = 0.919) and ROI-based ROC analysis (p = 0.574). Sensitivity/specificity/accuracy for PET/MRI and PET/CT for a score ≥ 3 was 94%/88%/89% and 94%/91%/91% per patient, 96%/90%/91% and 96%/93%/93% per examination and 95%/85%/90% and 90%/86%/88% per lesion, respectively, p > 0.05.

CONCLUSIONS

In HNSCC patients, PET/MRI and PET/CT had a high and similar diagnostic performance for detecting distant metastases and distant second primary cancers.

Bone marrow activation in response to metabolic syndrome and early atherosclerosis

AIMS

Experimental studies suggest that increased bone marrow (BM) activity is involved in the association between cardiovascular risk factors and inflammation in atherosclerosis. However, human data to support this association are sparse. The purpose was to study the association between cardiovascular risk factors, BM activation, and subclinical atherosclerosis.

METHODS AND RESULTS

Whole body vascular 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) was performed in 745 apparently healthy individuals [median age 50.5 (46.8-53.6) years, 83.8% men] from the Progression of Early Subclinical Atherosclerosis (PESA) study. Bone marrow activation (defined as BM 18F-FDG uptake above the median maximal standardized uptake value) was assessed in the lumbar vertebrae (L3-L4). Systemic inflammation was indexed from circulating biomarkers. Early atherosclerosis was evaluated by arterial metabolic activity by 18F-FDG uptake in five vascular territories. Late atherosclerosis was evaluated by fully formed plaques on MRI. Subjects with BM activation were more frequently men (87.6 vs. 80.0%, P = 0.005) and more frequently had metabolic syndrome (MetS) (22.2 vs. 6.7%, P < 0.001). Bone marrow activation was significantly associated with all MetS components. Bone marrow activation was also associated with increased haematopoiesis-characterized by significantly elevated leucocyte (mainly neutrophil and monocytes) and erythrocyte counts-and with markers of systemic inflammation including high-sensitivity C-reactive protein, ferritin, fibrinogen, P-selectin, and vascular cell adhesion molecule-1. The associations between BM activation and MetS (and its components) and increased erythropoiesis were maintained in the subgroup of participants with no systemic inflammation. Bone marrow activation was significantly associated with high arterial metabolic activity (18F-FDG uptake). The co-occurrence of BM activation and arterial 18F-FDG uptake was associated with more advanced atherosclerosis (i.e. plaque presence and burden).

CONCLUSION

In apparently healthy individuals, BM 18F-FDG uptake is associated with MetS and its components, even in the absence of systemic inflammation, and with elevated counts of circulating leucocytes. Bone marrow activation is associated with early atherosclerosis, characterized by high arterial metabolic activity. Bone marrow activation appears to be an early phenomenon in atherosclerosis development.[Progression of Early Subclinical Atherosclerosis (PESA); NCT01410318].

PET/MR Technology: Advancement and Challenges

When this article was written, it coincided with the 11th anniversary of the installation of our PET/MR device in Munich. In fact, this was the first fully integrated device to be in clinical use. During this time, we have observed many interesting behaviors, to put it kindly. However, it is more critical that in this process, our understanding of the system also improved - including the advantages and limitations from a technical, logistical, and medical perspective. The last decade of PET/MRI research has certainly been characterized by most sites looking for a "key application." There were many ideas in this context and before and after the devices became available, some of which were based on the earlier work with integrating data from single devices. These involved validating classical PET methods with MRI (eg, perfusion or oncology diagnostics). More important, however, were the scenarios where intermodal synergies could be expected. In this review, we look back on this decade-long journey, at the challenges overcome and those still to come.

Dictionary Learning Constrained Direct Parametric Estimation in Dynamic Myocardial Perfusion PET

In myocardial perfusion imaging with dynamic positron emission tomography (PET), direct parametric reconstruction from the projection data allows accurate modeling of the Poisson noise in the projection domain to provide more reliable estimate of the parametric images. In this study, we propose to incorporate a superior denoiser to efficiently suppress the unfavorable noise propagation during the direct reconstruction. The dictionary learning (DL) based sparse representation serves as a regularization term to constrain the intermediate K₁ estimation. We rewrite the DL regularizer into a voxel-separable form to facilitate the decoupling of a DL penalized curve fitting from the reconstruction of dynamic frames. The nonlinear fitting is then solved by a damped Newton method with uniform initialization. Using simulated and patient ⁸²Rb dynamic PET data, we study the performance of the proposed DL direct algorithm and quantitatively compare it with the indirect method with or without post-filtering, the direct reconstruction without regularization, and the quadratic penalty regularized direct algorithm. The DL regularized direct reconstruction achieves improved noise versus bias performance in the reconstructed K₁ images as well as superior recovery of a reduced myocardial blood flow defect. The dictionary learned from a 3D self-created hollow sphere image yields comparable results to those using the dictionary learned from the corresponding magnetic resonance image. The uniform initializations converge to K₁ estimations similar to the result from initializing with the indirect reconstruction. To summarize, we demonstrate the potential of the proposed DL constrained direct parametric reconstruction in improving quantitative dynamic PET imaging.

MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers

Purpose

We compare the performance of three commonly used MRI‐guided attenuation correction approaches in torso PET/MRI, namely segmentation‐, atlas‐, and deep learning‐based algorithms.

Methods

Twenty‐five co‐registered torso ¹⁸F‐FDG PET/CT and PET/MR images were enrolled. PET attenuation maps were generated from in‐phase Dixon MRI using a three‐tissue class segmentation‐based approach (soft‐tissue, lung, and background air), voxel‐wise weighting atlas‐based approach, and a residual convolutional neural network. The bias in standardized uptake value (SUV) was calculated for each approach considering CT‐based attenuation corrected PET images as reference. In addition to the overall performance assessment of these approaches, the primary focus of this work was on recognizing the origins of potential outliers, notably body truncation, metal‐artifacts, abnormal anatomy, and small malignant lesions in the lungs.

Results

The deep learning approach outperformed both atlas‐ and segmentation‐based methods resulting in less than 4% SUV bias across 25 patients compared to the segmentation‐based method with up to 20% SUV bias in bony structures and the atlas‐based method with 9% bias in the lung. The deep learning‐based method exhibited superior performance. Yet, in case of sever truncation and metallic‐artifacts in the input MRI, this approach was outperformed by the atlas‐based method, exhibiting suboptimal performance in the affected regions. Conversely, for abnormal anatomies, such as a patient presenting with one lung or small malignant lesion in the lung, the deep learning algorithm exhibited promising performance compared to other methods.

Conclusion

The deep learning‐based method provides promising outcome for synthetic CT generation from MRI. However, metal‐artifact and body truncation should be specifically addressed.

The impact of MR-based attenuation correction in spinal cord FDG-PET/MR imaging for neurological studies

Purpose

Positron emission tomography (PET) attenuation correction (AC) in positron emission tomography‐magnetic resonance (PET/MR) scanners constitutes a critical and barely explored issue in spinal cord investigation, mainly due to the limitations in accounting for highly attenuating bone structures which surround the spinal canal. Our study aims at evaluating the clinical suitability of MR‐driven AC (MRAC) for 18‐fluorodeoxy‐glucose positron emission tomography (¹⁸F‐FDG‐PET) in spinal cord.

Methods

Thirty‐six patients, undergoing positron emission tomography‐computed tomography (PET/CT) and PET/MR in the same session for oncological examination, were retrospectively analyzed.

For each patient, raw PET data from PET/MR scanner were reconstructed with 4‐ and 5‐class MRAC maps, generated by hybrid PET/MR system (PET_MRAC4 and PET_MRAC5, respectively, where PET_MRAC is PET images reconstructed using MR‐based attenuation correction map), and an AC map derived from CT data after a custom co‐registration pipeline (PET_rCTAC, where PET_rCTAC is PET images reconstructed using CT‐based attenuation correction map), which served as reference. Mean PET standardized uptake values (SUVm) were extracted from the three reconstructed PET images by regions of interest (ROIs) identified on T2‐weighted MRI, in the spinal cord, lumbar cerebrospinal fluid (CSF), and vertebral marrow at five levels (C2, C5, T6, T12, and L3). SUVm values from PET_MRAC4 and PET_MRAC5 were compared with each other and with the reference by means of paired t‐test, and correlated using Pearson's correlation (r) to assess their consistency. Cohen's d was calculated to assess the magnitude of differences between PET images.

Results

SUVmvalues from PET_MRAC4 were lower than those from PET_MRAC5 in almost all analyzed ROIs, with a mean difference ranging from 0.03 to 0.26 (statistically significant in the vertebral marrow at C2 and C5, spinal cord at T6 and T2, and CSF at L3). This was also confirmed by the effect size, with highest values at low spinal levels (d = 0.45 at T12 in spinal cord, d = 0.95 at L3 in CSF). SUVm values from PET_MRAC4 and PET_MRAC5 showed a very good correlation (0.81 < r < 0.97, p < 0.05) in all spinal ROIs. Underestimation of SUVm between PET_MRAC4 and PET_rCTAC was observed at each level, with a mean difference ranging from 0.02 to 0.32 (statistically significant in the vertebral marrow at C2 and T6, and CSF at L3). Although PET_MRAC5 underestimates PET_rCTAC (mean difference ranging from 0.02 to 0.3), an overall decrease in effect size could be observed for PET_MRAC5, mainly at lower spinal levels (T12, L3). SUVm from both PET_MRAC4 and PET_MRAC5 methods showed r value from good to very good with respect to PET_rCTAC (0.67 < r < 0.9 and 0.73 < r < 0.94, p < 0.05, respectively).

Conclusions

Our results showed that neglecting bones in AC can underestimate the FDG uptake measurement of the spinal cord. The inclusion of bones in MRAC is far from negligible and improves the AC in spinal cord, mainly at low spinal levels. Therefore, care must be taken in the spinal canal region, and the use of AC map reconstruction methods accounting for bone structures could be beneficial.

Assessment of deep learning-based PET attenuation correction frameworks in the sinogram domain

This study set out to investigate various deep learning frameworks for PET attenuation correction in the sinogram domain. Different models for both time-of-flight (TOF) and non-TOF PET emission data were implemented, including direct estimation of the attenuation corrected (AC) emission sinograms from the nonAC sinograms, estimation of the attenuation correction factors (ACFs) from PET emission data, correction of scattered photons prior to training of the models, and separate training of the models for each segment of the emission sinograms. A segmentation-based 2-class AC map was included as a bottom-line technique for comparison of the different models considering PET/CT AC as reference. Fifty clinical TOF PET/CT brain scans were employed for training whereas 20 were used for evaluation of the models. Quantitative analysis of the resulting PET images was carried out through region-wise standardized uptake value (SUV) bias calculation. The models relying on TOF information significantly outperformed the nonTOF models as well as the segmentation-based AC map resulting in maximum SUV bias of 6.5%, 9.5%, and 14.0%, respectively. Estimation of ACFs from either TOF or nonTOF PET emission data was very sensitive to prior scatter correction. However, direct estimation of AC sinograms from nonAC sinograms revealed no sensitivity to scatter correction, thus obviating the need for prior scatter estimation. For TOF PET data, though direct prediction of the AC sinograms does not require prior estimation of scattered photons, it requires input/output channels equal to the number of TOF bins which might be computationally or memory-wise expensive. Prediction of the ACF matrices from TOF emission data is less demanding in terms of memory as it requires only a single channel for output. AC in the sinogram domain of TOF PET data exhibited superior performance compared to both nonTOF and segmentation-based methods. However, such models require multiple input/output channels.

Deep learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation correction in dementia neuroimaging

PURPOSE

The accuracy of existing PET/MR attenuation correction (AC) has been limited by a lack of correlation between MR signal and tissue electron density. Based on our finding that longitudinal relaxation rate, or R1 , is associated with CT Hounsfield unit in bone and soft tissues in the brain, we propose a deep learning T1 -enhanced selection of linear attenuation coefficients (DL-TESLA) method to incorporate quantitative R1 for PET/MR AC and evaluate its accuracy and longitudinal test-retest repeatability in brain PET/MR imaging.

METHODS

DL-TESLA uses a 3D residual UNet (ResUNet) for pseudo-CT (pCT) estimation. With a total of 174 participants, we compared PET AC accuracy of DL-TESLA to 3 other methods adopting similar 3D ResUNet structures but using UTE R 2 ∗ , or Dixon, or T1 -MPRAGE as input. With images from 23 additional participants repeatedly scanned, the test-retest differences and within-subject coefficient of variation of standardized uptake value ratios (SUVR) were compared between PET images reconstructed using either DL-TESLA or CT for AC.

RESULTS

DL-TESLA had (1) significantly lower mean absolute error in pCT, (2) the highest Dice coefficients in both bone and air, (3) significantly lower PET relative absolute error in whole brain and various brain regions, (4) the highest percentage of voxels with a PET relative error within both ±3% and ±5%, (5) similar to CT test-retest differences in SUVRs from the cerebrum and mean cortical (MC) region, and (6) similar to CT within-subject coefficient of variation in cerebrum and MC.

CONCLUSION

DL-TESLA demonstrates excellent PET/MR AC accuracy and test-retest repeatability.

An international expert opinion statement on the utility of PET/MR for imaging of skeletal metastases

BACKGROUND

MR is an important imaging modality for evaluating musculoskeletal malignancies owing to its high soft tissue contrast and its ability to acquire multiparametric information. PET provides quantitative molecular and physiologic information and is a critical tool in the diagnosis and staging of several malignancies. PET/MR, which can take advantage of its constituent modalities, is uniquely suited for evaluating skeletal metastases. We reviewed the current evidence of PET/MR in assessing for skeletal metastases and provided recommendations for its use.

METHODS

We searched for the peer reviewed literature related to the usage of PET/MR in the settings of osseous metastases. In addition, expert opinions, practices, and protocols of major research institutions performing research on PET/MR of skeletal metastases were considered.

RESULTS

Peer-reviewed published literature was included. Nuclear medicine and radiology experts, including those from 13 major PET/MR centers, shared the gained expertise on PET/MR use for evaluating skeletal metastases and contributed to a consensus expert opinion statement. [18F]-FDG and non [18F]-FDG PET/MR may provide key advantages over PET/CT in the evaluation for osseous metastases in several primary malignancies.

CONCLUSION

PET/MR should be considered for staging of malignancies where there is a high likelihood of osseous metastatic disease based on the characteristics of the primary malignancy, hight clinical suspicious and in case, where the presence of osseous metastases will have an impact on patient management. Appropriate choice of tumor-specific radiopharmaceuticals, as well as stringent adherence to PET and MR protocols, should be employed.

Diagnostic efficiency of whole-body 18F-FDG PET/MRI, MRI alone, and SUV and ADC values in staging of primary uterine cervical cancer

Background

The use of PET/MRI for gynecological cancers is emerging. The purpose of this study was to assess the additional diagnostic value of PET over MRI alone in local and whole-body staging of cervical cancer, and to evaluate the benefit of standardized uptake value (SUV) and apparent diffusion coefficient (ADC) in staging.

Methods

Patients with histopathologically-proven cervical cancer and whole-body ¹⁸F-FDG PET/MRI obtained before definitive treatment were retrospectively registered. Local tumor spread, nodal involvement, and distant metastases were evaluated using PET/MRI or MRI dataset alone. Histopathology or clinical consensus with follow-up imaging were used as reference standard. Tumor SUVmax and ADC were measured and SUVmax/ADC ratio calculated. Area under the curve (AUC) was determined to predict diagnostic performance and Mann-Whitney U test was applied for group comparisons.

Results

In total, 33 patients who underwent surgery (n = 23) or first-line chemoradiation (n = 10) were included. PET/MRI resulted in higher AUC compared with MRI alone in detecting parametrial (0.89 versus 0.73), vaginal (0.85 versus 0.74), and deep cervical stromal invasion (0.96 versus 0.74), respectively. PET/MRI had higher diagnostic confidence than MRI in identifying patients with radical cone biopsy and no residual at hysterectomy (sensitivity 89% versus 44%). PET/MRI and MRI showed equal AUC for pelvic nodal staging (both 0.73), whereas AUC for distant metastases was higher using PET/MRI (0.80 versus 0.67). Tumor SUVmax/ADC ratio, but not SUVmax or ADC alone, was significantly higher in the presence of metastatic pelvic lymph nodes (P < 0.05).

Conclusions

PET/MRI shows higher accuracy than MRI alone for determining local tumor spread and distant metastasis emphasizing the added value of PET over MRI alone in staging of cervical cancer. Tumor SUVmax/ADC ratio may predict pelvic nodal involvement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-020-00372-5.

Evaluation of a model-based attenuation correction method on whole-body 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging

The bone cannot be evaluated using magnetic resonance attenuation correction (MRAC) with the Dixon sequence. To solve this issue, the present study aimed to evaluate model-based AC for whole-body 2-[fluorine-18]-fluoro-2-deoxy-D-glucose (¹⁸F-FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI) by creating bone segmentation. We analyzed and evaluated the data of 31 consecutive patients. The Biograph mMR (Siemens Healthcare) was used for clinical whole-body ¹⁸F-FDG PET/MRI with the conventional MRAC method, and OSIRIX MD software was used to analyze the images. After the examination, the new model-based post-processing MRAC was applied to create μ-maps with bone segmentation, and retrospective PET reconstruction was performed using this μ-map. The bone structures of all patients created using model-based MRAC were visually evaluated. Standard uptake values (SUVs) at 11 anatomical positions in PET images, corrected using the μ-map with and without bone segmentation, were measured and compared. The model-based post-processing MRAC was run for all patients, without errors. Visual evaluation revealed that the model-based post-processing MRAC exhibited poor results for six patients. Furthermore, it exhibited an increasing trend of SUV in the brain compared to the conventional method. Locations other than the brain indicated a similar or decreasing trend. The two methods showed a good linear correlation for all patients. However, patients aged < 20 years exhibited a different trend from those aged ≥ 20 years. We should exercise caution when applying this model-based MRAC for younger patients.

Transforming UTE-mDixon MR Abdomen-Pelvis Images Into CT by Jointly Leveraging Prior Knowledge and Partial Supervision

Computed tomography (CT) provides information for diagnosis, PET attenuation correction (AC), and radiation treatment planning (RTP). Disadvantages of CT include poor soft tissue contrast and exposure to ionizing radiation. While MRI can overcome these disadvantages, it lacks the photon absorption information needed for PET AC and RTP. Thus, an intelligent transformation from MR to CT, i.e., the MR-based synthetic CT generation, is of great interest as it would support PET/MR AC and MR-only RTP. Using an MR pulse sequence that combines ultra-short echo time (UTE) and modified Dixon (mDixon), we propose a novel method for synthetic CT generation jointly leveraging prior knowledge as well as partial supervision (SCT-PK-PS for short) on large-field-of-view images that span abdomen and pelvis. Two key machine learning techniques, i.e., the knowledge-leveraged transfer fuzzy c-means (KL-TFCM) and the Laplacian support vector machine (LapSVM), are used in SCT-PK-PS. The significance of our effort is threefold: 1) Using the prior knowledge-referenced KL-TFCM clustering, SCT-PK-PS is able to group the feature data of MR images into five initial clusters of fat, soft tissue, air, bone, and bone marrow. Via these initial partitions, clusters needing to be refined are observed and for each of them a few additionally labeled examples are given as the partial supervision for the subsequent semi-supervised classification using LapSVM; 2) Partial supervision is usually insufficient for conventional algorithms to learn the insightful classifier. Instead, exploiting not only the given supervision but also the manifold structure embedded primarily in numerous unlabeled data, LapSVM is capable of training multiple desired tissue-recognizers; 3) Benefiting from the joint use of KL-TFCM and LapSVM, and assisted by the edge detector filter based feature extraction, the proposed SCT-PK-PS method features good recognition accuracy of tissue types, which ultimately facilitates the good transformation from MR images to CT images of the abdomen-pelvis. Applying the method on twenty subjects' feature data of UTE-mDixon MR images, the average score of the mean absolute prediction deviation (MAPD) of all subjects is 140.72 ± 30.60 HU which is statistically significantly better than the 241.36 ± 21.79 HU obtained using the all-water method, the 262.77 ± 42.22 HU obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method, and the 197.05 ± 76.53 HU obtained via the conventional SVM method. These results demonstrate the effectiveness of our method for the intelligent transformation from MR to CT on the body section of abdomen-pelvis.

Attenuation correction for human PET/MRI studies

Attenuation correction has been one of the main methodological challenges in the integrated positron emission tomography and magnetic resonance imaging (PET/MRI) field. As standard transmission or computed tomography approaches are not available in integrated PET/MRI scanners, MR-based attenuation correction approaches had to be developed. Aspects that have to be considered for implementing accurate methods include the need to account for attenuation in bone tissue, normal and pathological lung and the MR hardware present in the PET field-of-view, to reduce the impact of subject motion, to minimize truncation and susceptibility artifacts, and to address issues related to the data acquisition and processing both on the PET and MRI sides. The standard MR-based attenuation correction techniques implemented by the PET/MRI equipment manufacturers and their impact on clinical and research PET data interpretation and quantification are first discussed. Next, the more advanced methods, including the latest generation deep learning-based approaches that have been proposed for further minimizing the attenuation correction related bias are described. Finally, a future perspective focused on the needed developments in the field is given.

Additional value of hybrid PET/MR imaging versus MR or PET performed separately to assess cardiovascular disease

INTRODUCTION AND OBJECTIVES

Hybrid positron emission tomography (PET) and magnetic resonance (MR) imaging is an emerging technology in the diagnosis of cardiovascular disease; however, there have been no reports of its use in the national clinical setting. Our objective was to evaluate the additional value of integrated PET/MR systems compared with MR and PET performed separately in this setting.

METHODS

We prospectively included 49 patients, 30 to assess myocardial viability (coronary group) and 19 to assess inflammatory, infectious, and tumoral diseases (noncoronary heart disease group). All patients underwent cardiac ¹⁸F-fluorodeoxyglucose PET/MR. PET/MR studies included attenuation correction sequences, followed by simultaneous cardiac PET and cardiac MR acquisition, with protocols adapted to the clinical indication (cine, tissue characterization and/or late enhancement imaging).

RESULTS

Most (87.8%) PET/MR studies were initially interpretable. Use of PET/MR improved diagnosis vs PET or MR performed separately in 42.1% of coronary cases and 88.9% of noncoronary cases. PET/MR enabled reclassification of 87.5% of coronary cases initially classified as showing inconclusive results on MR or PET and 70% of noncoronary cases.

CONCLUSIONS

In our series, multimodality PET/MR technology provided additional diagnostic value in some patients with cardiovascular disease compared with MR and PET performed separately, especially in cases of noncoronary heart disease and in those with inconclusive results on MR or PET. In our experience, the main benefits of PET/MR include the possibility of simultaneous acquisition, the in vivo integration of anatomical/functional/metabolic aspects, and the interaction of different experts in imaging modalities.

Positron emission tomography/MRI for cardiac diseases assessment

Functional imaging tools have emerged in the last few decades and are increasingly used to assess the function of the human heart in vivo. Positron emission tomography (PET) is used to evaluate myocardial metabolism and blood flow. Magnetic resonance imaging (MRI) is an essential tool for morphological and functional evaluation of the heart. In cardiology, PET is successfully combined with CT for hybrid cardiac imaging. The effective integration of two imaging modalities allows simultaneous data acquisition combining functional, structural and molecular imaging. After PET/CT has been successfully accepted for clinical practices, hybrid PET/MRI is launched. This review elaborates the current evidence of PET/MRI in cardiovascular imaging and its expected clinical applications for a comprehensive assessment of cardiovascular diseases while highlighting the advantages and limitations of this hybrid imaging approach.

Almost 10 years of PET/MR attenuation correction: the effect on lesion quantification with PSMA: clinical evaluation on 200 prostate cancer patients

Purpose

After a decade of PET/MR, the case of attenuation correction (AC) remains open. The initial four-compartment (air, water, fat, soft tissue) Dixon-based AC scheme has since been expanded with several features, the latest being MR field-of-view extension and a bone atlas. As this potentially changes quantification, we evaluated the impact of these features in PET AC in prostate cancer patients.

Methods

Two hundred prostate cancer patients were examined with either ¹⁸F- or ⁶⁸Ga-prostate-specific membrane antigen (PSMA) PET/MR. Qualitative and quantitative analysis (SUV_mean, SUV_max, correlation, and statistical significance) was performed on images reconstructed using different AC schemes: Dixon, Dixon+MLAA, Dixon+HUGE, and Dixon+HUGE+bones for ¹⁸F-PSMA data; Dixon and Dixon+bones for ⁶⁸Ga-PSMA data. Uptakes were compared using linear regression against standard Dixon.

Results

High correlation and no visually perceivable differences between all evaluated methods (r > 0.996) were found. The mean relative difference in lesion uptake of ¹⁸F-PSMA and ⁶⁸Ga-PSMA remained, respectively, within 4% and 3% in soft tissue, and within 10% and 9% in bones for all evaluated methods. Bone registration errors were detected, causing mean uptake change of 5% in affected lesions.

Conclusions

Based on these results and the encountered bone atlas registration inaccuracy, we deduce that including bones and extending the MR field-of-view did not introduce clinically significant differences in PSMA diagnostic accuracy and tracer uptake quantification in prostate cancer pelvic lesions, facilitating the analysis of serial studies respectively. However, in the absence of ground truth data, we advise against atlas-based methods when comparing serial scans for bone lesions.

Deep learning-guided estimation of attenuation correction factors from time-of-flight PET emission data

PURPOSE

Attenuation correction (AC) is essential for quantitative PET imaging. In the absence of concurrent CT scanning, for instance on hybrid PET/MRI systems or dedicated brain PET scanners, an accurate approach for synthetic CT generation is highly desired. In this work, a novel framework is proposed wherein attenuation correction factors (ACF) are estimated from time-of-flight (TOF) PET emission data using deep learning.

METHODS

In this approach, referred to as called DL-EM), the different TOF sinogram bins pertinent to the same slice are fed into a multi-input channel deep convolutional network to estimate a single ACF sinogram associated with the same slice. The clinical evaluation of the proposed DL-EM approach consisted of 68 clinical brain TOF PET/CT studies, where CT-based attenuation correction (CTAC) served as reference. A two-tissue class consisting of background-air and soft-tissue segmentation of the TOF PET non-AC images (SEG) as a proxy of the technique used in the clinic was also included in the comparative evaluation. Qualitative and quantitative PET analysis was performed through SUV bias maps quantification in 63 different brain regions.

RESULTS

The DL-EM approach resulted in 6.1 ± 9.7% relative mean absolute error (RMAE) in bony structures compared to SEG AC method with RMAE of 16.1 ± 8.2% (p-value <0.001). Considering the entire head region, DL-EM led to a root mean square error (RMSE) of 0.3 ± 0.01 outperforming the SEG method with RMSE of 0.8 ± 0.02 SUV (p-value <0.001). The region-wise analysis of brain PET studies revealed less than 7% absolute SUV bias for the DL-EM approach, whereas the SEG method resulted in more than 14% absolute SUV bias (p-value <0.05).

CONCLUSIONS

Qualitative assessment and quantitative PET analysis demonstrated the superior performance of the DL-EM approach over the segmentation-based technique with clinically acceptable SUV bias. The results obtained using the DL-EM approach are comparable to state-of-the-art MRI-guided AC methods. Yet, this approach enables the extraction of interesting features about patient-specific attenuation which could be employed not only as a stand-alone AC approach but also as complementary/prior information in other AC algorithms.

MR-based PET attenuation correction using a combined ultrashort echo time/multi-echo Dixon acquisition

PURPOSE

To develop a magnetic resonance (MR)-based method for estimation of continuous linear attenuation coefficients (LACs) in positron emission tomography (PET) using a physical compartmental model and ultrashort echo time (UTE)/multi-echo Dixon (mUTE) acquisitions.

METHODS

We propose a three-dimensional (3D) mUTE sequence to acquire signals from water, fat, and short T₂ components (e.g., bones) simultaneously in a single acquisition. The proposed mUTE sequence integrates 3D UTE with multi-echo Dixon acquisitions and uses sparse radial trajectories to accelerate imaging speed. Errors in the radial k-space trajectories are measured using a special k-space trajectory mapping sequence and corrected for image reconstruction. A physical compartmental model is used to fit the measured multi-echo MR signals to obtain fractions of water, fat, and bone components for each voxel, which are then used to estimate the continuous LAC map for PET attenuation correction.

RESULTS

The performance of the proposed method was evaluated via phantom and in vivo human studies, using LACs from computed tomography (CT) as reference. Compared to Dixon- and atlas-based MRAC methods, the proposed method yielded PET images with higher correlation and similarity in relation to the reference. The relative absolute errors of PET activity values reconstructed by the proposed method were below 5% in all of the four lobes (frontal, temporal, parietal, and occipital), cerebellum, whole white matter, and gray matter regions across all subjects (n = 6).

CONCLUSIONS

The proposed mUTE method can generate subject-specific, continuous LAC map for PET attenuation correction in PET/MR.

Cardiac PET/MRI: Current Clinical Status and Future Perspectives

Combined PET/MRI has now been in clinical routine for almost 10 years. Since then, it has not only had to face validation, comparison and research questions, it has also been increasingly used in clinical routine. A number of cardiovascular applications have become established here, whereby viability imaging and assessment of inflammatory and infiltrative processes in the heart are to be emphasized. However, further interesting applications are expected in the near future. This review summarizes the most important clinical applications on the one hand and mentions interesting areas of application in research on the other.

Vascular Inflammation in Subclinical Atherosclerosis Detected by Hybrid PET/MRI

BACKGROUND

Atherosclerosis is a chronic inflammatory disease, but data on arterial inflammation at early stages is limited.

OBJECTIVES

The purpose of this study was to characterize vascular inflammation by hybrid ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/magnetic resonance imaging (PET/MRI).

METHODS

Carotid, aortic, and ilio-femoral ¹⁸F-FDG PET/MRI was performed in 755 individuals (age 40 to 54 years; 83.7% men) with known plaques detected by 2-/3-dimensional vascular ultrasound and/or coronary calcification in the PESA (Progression of Early Subclinical Atherosclerosis) study. The authors evaluated the presence, distribution, and number of arterial inflammatory foci (increased ¹⁸F-FDG uptake) and plaques with or without inflammation (coincident ¹⁸F-FDG uptake).

RESULTS

Arterial inflammation was present in 48.2% of individuals (24.4% femorals, 19.3% aorta, 15.8% carotids, and 9.3% iliacs) and plaques in 90.1% (73.9% femorals, 55.8% iliacs, and 53.1% carotids). ¹⁸F-FDG arterial uptakes and plaques significantly increased with cardiovascular risk factors (p < 0.01). Coincident ¹⁸F-FDG uptakes were present in 287 of 2,605 (11%) plaques, and most uptakes were detected in plaque-free arterial segments (459 of 746; 61.5%). Plaque burden, defined by plaque presence, number, and volume, was significantly higher in individuals with arterial inflammation than in those without (p < 0.01). The number of plaques and ¹⁸F-FDG uptakes showed a positive albeit weak correlation (r = 0.25; p < 0.001).

CONCLUSIONS

Arterial inflammation is highly prevalent in middle-aged individuals with known subclinical atherosclerosis. Large-scale multiterritorial PET/MRI allows characterization of atherosclerosis-related arterial inflammation and demonstrates ¹⁸F-FDG uptake in plaque-free arterial segments and, less frequently, within plaques. These findings suggest an arterial inflammatory state at early stages of atherosclerosis. (Progression of Early Subclinical Atherosclerosis [PESA]; NCT01410318).

PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques

Positron emission tomography/magnetic resonance imaging (PET/MRI) potentially offers several advantages over positron emission tomography/computed tomography (PET/CT), for example, no CT radiation dose and soft tissue images from MR acquired at the same time as the PET. However, obtaining accurate linear attenuation correction (LAC) factors for the lung remains difficult in PET/MRI. LACs depend on electron density and in the lung, these vary significantly both within an individual and from person to person. Current commercial practice is to use a single‐valued population‐based lung LAC, and better estimation is needed to improve quantification. Given the under‐appreciation of lung attenuation estimation as an issue, the inaccuracy of PET quantification due to the use of single‐valued lung LACs, the unique challenges of lung estimation, and the emerging status of PET/MRI scanners in lung disease, a review is timely. This paper highlights past and present methods, categorizing them into segmentation, atlas/mapping, and emission‐based schemes. Potential strategies for future developments are also presented.

Attenuation Correction Approaches for Serotonin Transporter Quantification With PET/MRI

Background

Several MR-based attenuation correction (AC) approaches were developed to conquer the challenging AC in hybrid PET/MR imaging. These AC methods are commonly evaluated on standardized uptake values or tissue concentration. However, in neurotransmitter system studies absolute quantification is more favorable due to its accuracy. Therefore, our aim was to investigate the accuracy of segmentation- and atlas-based MR AC approaches on serotonin transporter (SERT) distribution volumes and occupancy after a drug challenge.

Methods

18 healthy subjects (7 male) underwent two [¹¹C]DASB PET/MRI measurements in a double-blinded, placebo controlled, cross-over design. After 70 min the selective serotonin reuptake inhibitor (SSRI) citalopram or a placebo was infused. The parameters total and specific volume of distribution (V_T, V_S = BP_P) and occupancy were quantified. All subjects underwent a low-dose CT scan as reference AC method. Besides the standard AC approaches DIXON and UTE, a T1-weighted structural image was recorded to estimate a pseudo-CT based on an MR/CT database (pseudoCT). Another evaluated AC approach superimposed a bone model on AC DIXON. Lastly, an approach optimizing the segmentation of UTE images was analyzed (RESOLUTE). PET emission data were reconstructed with all 6 AC methods. The accuracy of the AC approaches was evaluated on a region of interest-basis for the parameters V_T, BP_P, and occupancy with respect to the results of AC CT.

Results

Variations for V_T and BP_P were found with all AC methods with bias ranging from -15 to 17%. The smallest relative errors for all regions were found with AC pseudoCT (<|5%|). Although the bias between BP_P SSRI and BP_P placebo varied markedly with AC DIXON (<|12%|) and AC UTE (<|9%|), a high correlation to AC CT was obtained (r ²∼1). The relative difference of the occupancy for all tested AC methods was small for SERT high binding regions (<|4%|).

Conclusion

The high correlation might offer a rescaling from the biased parameters V_T and BP_P to the true values. Overall, the pseudoCT approach yielded smallest errors and the best agreement with AC CT. For SERT occupancy, all AC methods showed little bias in high binding regions, indicating that errors may cancel out in longitudinal assessments.

Metal artifact correction strategies in MRI-based attenuation correction in PET/MRI

In hybrid positron emission tomography (PET) and MRI systems, attenuation correction for PET image reconstruction is commonly based on processing of dedicated MR images. The image quality of the latter is strongly affected by metallic objects inside the body, such as e.g. dental implants, endoprostheses, or surgical clips which all lead to substantial artifacts that propagate into MRI-based attenuation images. In this work, we review publications about metal artifact correction strategies in MRI-based attenuation correction in PET/MRI. Moreover, we also give an overview about publications investigating the impact of MRI-based attenuation correction metal artifacts on the reconstructed PET image quality and quantification.

UTE-mDixon-based thorax synthetic CT generation

PURPOSE

Accurate photon attenuation assessment from MR data remains an unmet challenge in the thorax due to tissue heterogeneity and the difficulty of MR lung imaging. As thoracic tissues encompass the whole physiologic range of photon absorption, large errors can occur when using, for example, a uniform, water-equivalent or a soft-tissue-only approximation. The purpose of this study was to introduce a method for voxel-wise thoracic synthetic CT (sCT) generation from MR data attenuation correction (AC) for PET/MR or for MR-only radiation treatment planning (RTP).

METHODS

Acquisition: A radial stack-of-stars combining ultra-short-echo time (UTE) and modified Dixon (mDixon) sequence was optimized for thoracic imaging. The UTE-mDixon pulse sequence collects MR signals at three TE times denoted as UTE, Echo1, and Echo2. Three-point mDixon processing was used to reconstruct water and fat images. Bias field correction was applied in order to avoid artifacts caused by inhomogeneity of the MR magnetic field.

ANALYSIS

Water fraction and R2* maps were estimated using the UTE-mDixon data to produce a total of seven MR features, that is UTE, Echo1, Echo2, Dixon water, Dixon fat, Water fraction, and R2*. A feature selection process was performed to determine the optimal feature combination for the proposed automatic, 6-tissue classification for sCT generation. Fuzzy c-means was used for the automatic classification which was followed by voxel-wise attenuation coefficient assignment as a weighted sum of those of the component tissues. Performance evaluation: MR data collected using the proposed pulse sequence were compared to those using a traditional two-point Dixon approach. Image quality measures, including image resolution and uniformity, were evaluated using an MR ACR phantom. Data collected from 25 normal volunteers were used to evaluate the accuracy of the proposed method compared to the template-based approach. Notably, the template approach is applicable here, that is normal volunteers, but may not be robust enough for patients with pathologies.

RESULTS

The free breathing UTE-mDixon pulse sequence yielded images with quality comparable to those using the traditional breath holding mDixon sequence. Furthermore, by capturing the signal before T2* decay, the UTE-mDixon image provided lung and bone information which the mDixon image did not. The combination of Dixon water, Dixon fat, and the Water fraction was the most robust for tissue clustering and supported the classification of six tissues, that is, air, lung, fat, soft tissue, low-density bone, and dense bone, used to generate the sCT. The thoracic sCT had a mean absolute difference from the template-based (reference) CT of less than 50 HU and which was better agreement with the reference CT than the results produced using the traditional Dixon-based data.

CONCLUSION

MR thoracic acquisition and analyses have been established to automatically provide six distinguishable tissue types to generate sCT for MR-based AC of PET/MR and for MR-only RTP.

Region-specific association between basal blood insulin and cerebral glucose metabolism in older adults

Background

Although previous studies have suggested that insulin plays a role in brain function, it still remains unclear whether or not insulin has a region-specific association with neuronal and synaptic activity in the living human brain. We investigated the regional pattern of association between basal blood insulin and resting-state cerebral glucose metabolism (CMglu), a proxy for neuronal and synaptic activity, in older adults.

Method

A total of 234 nondiabetic, cognitively normal (CN) older adults underwent comprehensive clinical assessment, resting-state 18F-fluodeoxyglucose (FDG)-positron emission tomography (PET) and blood sampling to determine overnight fasting blood insulin and glucose levels, as well as apolipoprotein E (APOE) genotyping.

Results

An exploratory voxel-wise analysis of FDG-PET without a priori hypothesis demonstrated a positive association between basal blood insulin levels and resting-state CMglu in specific cerebral cortices and hippocampus, rather than in non-specific overall cerebral regions, even after controlling for the effects of APOE e4 carrier status, vascular risk factor score, body mass index, fasting blood glucose, and demographic variables. Particularly, a positive association of basal blood insulin with CMglu in the right posterior hippocampus and adjacent parahippocampal region as well as in the right inferior parietal region remained significant after multiple comparison correction. Conversely, no region showed negative association between basal blood insulin and CMglu.

Conclusions

Our finding suggests that basal fasting blood insulin may have association with neuronal and synaptic activity in specific cerebral regions, particularly in the hippocampal/parahippocampal and inferior parietal regions.

•

We investigated regional pattern of association between basal blood insulin and resting-state cerebral glucose metabolism.

•

Significant clusters with positive associations were found mainly in the hippocampal and inferior parietal regions.

•

Our finding suggests a region-specific association of basal blood insulin with resting-state cerebral glucose metabolism.

•

Further studies to elucidate underlying mechanism and implication of this region-specific association will be necessary.

Impact of Tissue Classification in MRI-Guided Attenuation Correction on Whole-Body Patlak PET/MRI

PURPOSE

The aim of this work is to investigate the impact of tissue classification in magnetic resonance imaging (MRI)-guided positron emission tomography (PET) attenuation correction (AC) for whole-body (WB) Patlak net uptake rate constant (K_i) imaging in PET/MRI studies.

PROCEDURES

WB dynamic PET/CT data were acquired for 14 patients. The CT images were utilized to generate attenuation maps (μ-map_CTAC) of continuous attenuation coefficient values (A_coeff). The μ-map_CTAC were then segmented into four tissue classes (μ-map_4-classes), namely background (air), lung, fat, and soft tissue, where a predefined A_coeff was assigned to each class. To assess the impact of bone for AC, the bones in the μ-map_CTAC were then assigned a predefined soft tissue A_coeff (0.1 cm^-1) to produce an AC μ-map without bones (μ-map_no-bones). Thereafter, both WB static SUV and dynamic PET images were reconstructed using μ-map_CTAC, μ-map_4-classes, and μ-map_no-bones (PET_CTAC, PET_4-classes, and PET_no-bones), respectively. WB indirect and direct parametric K_i images were generated using Patlak graphical analysis. Malignant lesions were delineated on PET images with an automatic segmentation method that uses an active contour model (MASAC). Then, the quantitative metrics of the metabolically active tumor volume (MATV), target-to-background (TBR), contrast-to-noise ratio (CNR), peak region-of-interest (ROI_peak), maximum region-of-interest (ROI_max), mean region-of-interest (ROI_mean), and metabolic volume product (MVP) were analyzed. The Wilcoxon test was conducted to assess the difference between PET_4-classes and PET_no-bones against PET_CTAC for all images. The same test was also adopted to compare the differences between SUV, indirect K_i, and direct K_i images for each evaluated AC method.

RESULTS

No significant differences in MATV, TBR, and CNR were observed between PET_4-classes and PET_CTAC for either SUV or K_i images. PET_4-classes significantly overestimated ROI_peak, ROI_max, ROI_mean, as well as MVP scores compared with PET_CTAC in both SUV and K_i images. SUV images exhibited the highest median relative errors for PET_4-classes with respect to PET_CTAC (RE_4-classes): 6.91 %, 6.55 %, 5.90 %, and 6.56 % for ROI_peak, ROI_max, ROI_mean, and MVP, respectively. On the contrary, K_i images showed slightly reduced RE_4-classes (indirect 5.52 %, 5.95 %, 4.43 %, and 5.70 %, direct 6.61 %, 6.33 %, 5.53 %, and 4.96 %) for ROI_peak, ROI_max, ROI_mean, and MVP, respectively. A higher TBR was observed on indirect and direct K_i images relative to SUV, while direct K_i images demonstrated the highest CNR.

CONCLUSIONS

Four-tissue class AC may impact SUV and K_i parameter estimation but only to a limited extent, thereby suggesting that WB Patlak K_i imaging for dynamic WB PET/MRI studies is feasible. Patlak K_i imaging can enhance TBR, thereby facilitating lesion segmentation and quantification. However, patient-specific A_coeff for each tissue class should be used when possible to address the high inter-patient variability of A_coeff distributions.

Cardiac PET/MRI-an update

It is now about 8 years since the first whole-body integrated PET/MRI has been installed. First, reports on technical characteristics and system performance were published. Early after, reports on the first use of PET/MRI in oncological patients were released. Interestingly, the first article on the application in cardiology was a review article, which was published before the first original article was put out. Since then, researchers have gained a lot experience with the PET/MRI in various cardiovascular diseases and an increasing number on auspicious indications is appearing. In this review article, we give an overview on technical updates within these last years with potential impact on cardiac imaging and summarize those scenarios where PET/MRI plays a pivotal role in cardiovascular medicine.

Brain β-Amyloid and Atrophy in Individuals at Increased Risk of Cognitive Decline

BACKGROUND AND PURPOSE

The relationship between brain β-amyloid and regional atrophy is still incompletely understood in elderly individuals at risk of dementia. Here, we studied the associations between brain β-amyloid load and regional GM and WM volumes in older adults who were clinically evaluated as being at increased risk of cognitive decline based on cardiovascular risk factors.

MATERIALS AND METHODS

Forty subjects (63-81 years of age) were recruited as part of a larger study, the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability. Neuroimaging consisted of PET using ¹¹C Pittsburgh compound-B and T1-weighted 3D MR imaging for the measurement of brain β-amyloid and GM and WM volumes, respectively. All subjects underwent clinical, genetic, and neuropsychological evaluations for the assessment of cognitive function and the identification of cardiovascular risk factors.

RESULTS

Sixteen subjects were visually evaluated as showing cortical β-amyloid (positive for β-amyloid). In the voxel-by-voxel analyses, no significant differences were found in GM and WM volumes between the samples positive and negative for β-amyloid. However, in the sample positive for β-amyloid, increases in ¹¹C Pittsburgh compound-B uptake were associated with reductions in GM volume in the left prefrontal (P = .02) and right temporal lobes (P = .04).

CONCLUSIONS

Our results show a significant association between increases in brain β-amyloid and reductions in regional GM volume in individuals at increased risk of cognitive decline. This evidence is consistent with a model in which increases in β-amyloid incite neurodegeneration in memory systems before cognitive impairment manifests.

Emerging role of MRI in radiation therapy

Advances in multimodality imaging, providing accurate information of the irradiated target volume and the adjacent critical structures or organs at risk (OAR), has made significant improvements in delivery of the external beam radiation dose. Radiation therapy conventionally has used computed tomography (CT) imaging for treatment planning and dose delivery. However, magnetic resonance imaging (MRI) provides unique advantages: added contrast information that can improve segmentation of the areas of interest, motion information that can help to better target and deliver radiation therapy, and posttreatment outcome analysis to better understand the biologic effect of radiation. To take advantage of these and other potential advantages of MRI in radiation therapy, radiologists and MRI physicists will need to understand the current radiation therapy workflow and speak the same language as our radiation therapy colleagues. This review article highlights the emerging role of MRI in radiation dose planning and delivery, but more so for MR-only treatment planning and delivery. Some of the areas of interest and challenges in implementing MRI in radiation therapy workflow are also briefly discussed. Level of Evidence: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1468-1478.

A dual-tuned 13 C/1 H head coil for PET/MR hybrid neuroimaging: Development, attenuation correction, and first evaluation

PURPOSE

This study aims to develop, implement, and evaluate a dual-tuned ¹³ C/¹ H head coil for integrated positron emission tomography/magnetic resonance (PET/MR) neuroimaging. The radiofrequency (RF) head coil is designed for optimized MR imaging performance and PET transparency and attenuation correction (AC) is applied for accurate PET quantification.

MATERIAL AND METHODS

A dual-tuned ¹³ C/¹ H RF head coil featuring a 16-rung birdcage was designed to be used for integrated PET/MR hybrid imaging. While the open birdcage design can be considered inherently PET transparent, all further electronic RF components were placed as far as possible outside of the field-of-view (FOV) of the PET detectors. The RF coil features a rigid geometry and thin-walled casing. Attenuation correction of the RF head coil is performed by generating and applying a dedicated 3D CT-based template attenuation map (μmap). Attenuation correction was systematically evaluated in phantom experiments using a large-volume cylindrical emission phantom filled with 18-F-Fluordesoxyglucose (FDG) radiotracer. The PET/MR imaging performance and PET attenuation correction were then evaluated in a patient study including six patients.

RESULTS

The dual-tuned RF head coil causes a mean relative attenuation difference of 8.8% across the volume of the cylindrical phantom, while the local relative differences range between 1% and 25%. Applying attenuation correction, the relative difference between the two measurements with and without RF coil is reduced to mean value of 0.5%, with local differences of ±3.6%. The quantitative results of the phantom measurements were corroborated by patient PET/MR measurements. Patient scans using the RF head coil show a decrease of PET signal of 5.17% ± 0.81% when compared to the setup without RF head coil in place, which served as a reference scan. When applying attenuation correction of the RF coil in the patient measurements, the mean difference to a measurement without RF coil was reduced to -0.87% ± 0.65%.

CONCLUSION

A dual-tuned ¹³ C/¹ H RF head coil was designed and evaluated regarding its potential use in integrated PET/MR hybrid imaging. Attenuation correction was successfully applied. In conclusion, the RF head coil was successfully integrated into PET/MR hybrid imaging and can now be used for ¹³ C/¹ H multinuclear hybrid neuroimaging in future studies.

Capturing Bone Signal in MRI of Pelvis, as a Large FOV Region, Using TWIST Sequence and Generating a 5-Class Attenuation Map for Prostate PET/MRI Imaging

Purpose:

Prostate imaging is a major application of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI). Currently, MRI-based attenuation correction (MRAC) for whole-body PET/MRI in which the bony structures are ignored is the main obstacle to successful implementation of the hybrid modality in the clinical work flow. Ultrashort echo time sequence captures bone signal but needs specific hardware–software and is challenging in large field of view (FOV) regions, such as pelvis. The main aims of the work are (1) to capture a part of the bone signal in pelvis using short echo time (STE) imaging based on time-resolved angiography with interleaved stochastic trajectories (TWIST) sequence and (2) to consider the bone in pelvis attenuation map (µ-map) to MRAC for PET/MRI systems.

Procedures:

Time-resolved angiography with interleaved stochastic trajectories, which is routinely used for MR angiography with high temporal and spatial resolution, was employed for fast/STE MR imaging. Data acquisition was performed in a TE of 0.88 milliseconds (STE) and 4.86 milliseconds (long echo time [LTE]) in pelvis region. Region of interest (ROI)-based analysis was used for comparing the signal-to-noise ratio (SNR) of cortical bone in STE and LTE images. A hybrid segmentation protocol, which is comprised of image subtraction, a Fuzzy-based segmentation, and a dedicated morphologic operation, was used for generating a 5-class µ-map consisting of cortical bone, air cavity, fat, soft tissue, and background (µ-map_MR-5c). A MR-based 4-class µ-map (µ-map_MR-4c) that considered soft tissue rather than bone was generated. As such, a bilinear (µ-map_CT-ref), 5 (µ-map_CT-5c), and 4 class µ-map (µ-map_CT-4c) based on computed tomography (CT) images were generated. Finally, simulated PET data were corrected using µ-map_MR-5c (PET-MRAC5c), µ-map_MR-4c (PET-MRAC4c), µ-map_CT-5c (PET-CTAC5c), and µ-map_CT-ref (PET-CTAC).

Results:

The ratio of SNR_bone to SNR_{air cavity} in LTE images was 0.8, this factor was increased to 4.4 in STE images. The Dice, Sensitivity, and Accuracy metrics for bone segmentation in proposed method were 72.4% ± 5.5%, 69.6% ± 7.5%, and 96.5% ± 3.5%, respectively, where the segmented CT served as reference. The mean relative error in bone regions in the simulated PET images were −13.98% ± 15%, −35.59% ± 15.41%, and 1.81% ± 12.2%, respectively, in PET-MRAC5c, PET-MRAC4c, and PET-CTAC5c where PET-CTAC served as the reference. Despite poor correlation in the joint histogram of µ-map_MR-4c versus µ-map_CT-5c (R² > 0.78) and PET-MRAC4c versus PET-CTAC5c (R² = 0.83), high correlations were observed in µ-map_MR-5c versus µ-map_CT-5c (R² > 0.94) and PET-MRAC5c versus PET-CTAC5c (R² > 0.96).

Conclusions:

According to the SNR_{STE, pelvic bone}, the cortical bone can be separate from air cavity in STE imaging based on TWIST sequence. The proposed method generated an MRI-based µ-map containing bone and air cavity that led to more accurate tracer uptake estimation than MRAC4c. Uptake estimation in hybrid PET/MRI can be improved by employing the proposed method.