Evaluating a radiotherapy deep learning synthetic CT algorithm for PET-MR attenuation correction in the pelvis.

BACKGROUND: Positron emission tomography-magnetic resonance (PET-MR) attenuation correction is challenging because the MR signal does not represent tissue density and conventional MR sequences cannot image bone. A novel zero echo time (ZTE) MR sequence has been previously developed which generates signal from cortical bone with images acquired in 65 s. This has been combined with a deep learning model to generate a synthetic computed tomography (sCT) for MR-only radiotherapy. This study aimed to evaluate this algorithm for PET-MR attenuation correction in the pelvis. METHODS: Ten patients being treated with ano-rectal radiotherapy received a [Formula: see text]F-FDG-PET-MR in the radiotherapy position. Attenuation maps were generated from ZTE-based sCT (sCTAC) and the standard vendor-supplied MRAC. The radiotherapy planning CT scan was rigidly registered and cropped to generate a gold standard attenuation map (CTAC). PET images were reconstructed using each attenuation map and compared for standard uptake value (SUV) measurement, automatic thresholded gross tumour volume (GTV) delineation and GTV metabolic parameter measurement. The last was assessed for clinical equivalence to CTAC using two one-sided paired t tests with a significance level corrected for multiple testing of [Formula: see text]. Equivalence margins of [Formula: see text] were used. RESULTS: Mean whole-image SUV differences were -0.02% (sCTAC) compared to -3.0% (MRAC), with larger differences in the bone regions (-0.5% to -16.3%). There was no difference in thresholded GTVs, with Dice similarity coefficients [Formula: see text]. However, there were larger differences in GTV metabolic parameters. Mean differences to CTAC in [Formula: see text] were [Formula: see text] (± standard error, sCTAC) and [Formula: see text] (MRAC), and [Formula: see text] (sCTAC) and [Formula: see text] (MRAC) in [Formula: see text]. The sCTAC was statistically equivalent to CTAC within a [Formula: see text] equivalence margin for [Formula: see text] and [Formula: see text] ([Formula: see text] and [Formula: see text]), whereas the MRAC was not ([Formula: see text] and [Formula: see text]). CONCLUSION: Attenuation correction using this radiotherapy ZTE-based sCT algorithm was substantially more accurate than current MRAC methods with only a 40 s increase in MR acquisition time. This did not impact tumour delineation but did significantly improve the accuracy of whole-image and tumour SUV measurements, which were clinically equivalent to CTAC. This suggests PET images reconstructed with sCTAC would enable accurate quantitative PET images to be acquired on a PET-MR scanner.

The Use of Magnetic Resonance Imaging in Radiation Therapy Treatment Simulation and Planning.

Ever since its introduction as a diagnostic imaging tool the potential of magnetic resonance imaging (MRI) in radiation therapy (RT) treatment simulation and planning has been recognized. Recent technical advances have addressed many of the impediments to use of this technology and as a result have resulted in rapid and growing adoption of MRI in RT. The purpose of this article is to provide a broad review of the multiple uses of MR in the RT treatment simulation and planning process, identify several of the most used clinical scenarios in which MR is integral to the simulation and planning process, highlight existing limitations and provide multiple unmet needs thereby highlighting opportunities for the diagnostic MR imaging community to contribute and collaborate with our oncology colleagues. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 5.

Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network.

Objective. In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT via an MR image. In this work, we aim to demonstrate a method to generate accurate synthetic CT (sCT) from an MR image to suit the radiation therapy (RT) treatment planning workflow. We show the feasibility of the method and make way for a broader clinical evaluation.Approach. We present a machine learning method for sCT generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. The misestimation of bone density in the radiation path could lead to unintended dose delivery to the target volume and results in suboptimal treatment outcome. We propose a loss function that favors a spatially sparse bone region in the image. We harness the ability of the multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via segmentation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task.Main results. We have included 54 brain patient images in this study and tested the sCT images against reference CT on a subset of 20 cases. A pilot dose evaluation was performed on 9 of the 20 test cases to demonstrate the viability of the generated sCT in RT planning. The average quantitative metrics produced by the proposed method over the test set were-(a) mean absolute error (MAE) of 70 ± 8.6 HU; (b) peak signal-to-noise ratio (PSNR) of 29.4 ± 2.8 dB; structural similarity metric (SSIM) of 0.95 ± 0.02; and (d) Dice coefficient of the body region of 0.984 ± 0.Significance. We demonstrate that the proposed method generates sCT images that resemble visual characteristics of a real CT image and has a quantitative accuracy that suits RT dose planning application. We compare the dose calculation from the proposed sCT and the real CT in a radiation therapy treatment planning setup and show that sCT based planning falls within 0.5% target dose error. The method presented here with an initial dose evaluation makes an encouraging precursor to a broader clinical evaluation of sCT based RT planning on different anatomical regions.

Comprehensive dose evaluation of a Deep Learning based synthetic Computed Tomography algorithm for pelvic Magnetic Resonance-only radiotherapy.

BACKGROUND AND PURPOSE: Magnetic Resonance (MR)-only radiotherapy enables the use of MR without the uncertainty of MR-Computed Tomography (CT) registration. This requires a synthetic CT (sCT) for dose calculations, which can be facilitated by a novel Zero Echo Time (ZTE) sequence where bones are visible and images are acquired in 65 seconds. This study evaluated the dose calculation accuracy for pelvic sites of a ZTE-based Deep Learning sCT algorithm developed by GE Healthcare. MATERIALS AND METHODS: ZTE and CT images were acquired in 56 pelvic radiotherapy patients in the radiotherapy position. A 2D U-net convolutional neural network was trained using pairs of deformably registered CT and ZTE images from 36 patients. In the remaining 20 patients the dosimetric accuracy of the sCT was assessed using cylindrical dummy Planning Target Volumes (PTVs) positioned at four different central axial locations, as well as the clinical treatment plans (for prostate (n = 10), rectum (n = 4) and anus (n = 6) cancers). The sCT was rigidly and deformably registered, the plan recalculated and the doses compared using mean differences and gamma analysis. RESULTS: Mean dose differences to the PTV D98% were ≤ 0.5% for all dummy PTVs and clinical plans (rigid registration). Mean gamma pass rates at 1%/1 mm were 98.0 ± 0.4% (rigid) and 100.0 ± 0.0% (deformable), 96.5 ± 0.8% and 99.8 ± 0.1%, and 95.4 ± 0.6% and 99.4 ± 0.4% for the clinical prostate, rectum and anus plans respectively. CONCLUSIONS: A ZTE-based sCT algorithm with high dose accuracy throughout the pelvis has been developed. This suggests the algorithm is sufficiently accurate for MR-only radiotherapy for all pelvic sites.

Magnetic Resonance Imaging-Based Delineation of Organs at Risk in the Head and Neck Region.

Purpose: The aim of this article is to establish a comprehensive contouring guideline for treatment planning using only magnetic resonance images through an up-to-date set of organs at risk (OARs), recommended organ boundaries, and relevant suggestions for the magnetic resonance imaging (MRI)-based delineation of OARs in the head and neck (H&N) region. Methods and Materials: After a detailed review of the literature, MRI data were collected from the H&N region of healthy volunteers. OARs were delineated in the axial, coronal, and sagittal planes on T2-weighted sequences. Every contour defined was revised by 4 radiation oncologists and subsequently by 2 independent senior experts (H&N radiation oncologist and radiologist). After revision, the final structures were presented to the consortium partners. Results: A definitive consensus was reached after multi-institutional review. On that basis, we provided a detailed anatomic and functional description and specific MRI characteristics of the OARs. Conclusions: In the era of precision radiation therapy, the need for well-built, straightforward contouring guidelines is on the rise. Precise, uniform, delineation-based, automated OAR segmentation on MRI may lead to increased accuracy in terms of organ boundaries and analysis of dose-dependent sequelae for an adequate definition of normal tissue complication probability.

Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks.

The integrated positron emission tomography/magnetic resonance imaging (PET/MRI) scanner simultaneously acquires metabolic information via PET and morphological information using MRI. However, attenuation correction, which is necessary for quantitative PET evaluation, is difficult as it requires the generation of attenuation-correction maps from MRI, which has no direct relationship with the gamma-ray attenuation information. MRI-based bone tissue segmentation is potentially available for attenuation correction in relatively rigid and fixed organs such as the head and pelvis regions. However, this is challenging for the chest region because of respiratory and cardiac motions in the chest, its anatomically complicated structure, and the thin bone cortex. We propose a new method using unsupervised generative attentional networks with adaptive layer-instance normalisation for image-to-image translation (U-GAT-IT), which specialised in unpaired image transformation based on attention maps for image transformation. We added the modality-independent neighbourhood descriptor (MIND) to the loss of U-GAT-IT to guarantee anatomical consistency in the image transformation between different domains. Our proposed method obtained a synthesised computed tomography of the chest. Experimental results showed that our method outperforms current approaches. The study findings suggest the possibility of synthesising clinically acceptable computed tomography images from chest MRI with minimal changes in anatomical structures without human annotation.

Zero-TE MRI: principles and applications in the head and neck.

Zero echo-time (ZTE) MRI is a novel imaging technique that utilizes ultrafast readouts to capture signal from short-T2 tissues. Additional sequence advantages include rapid imaging times, silent scanning, and artifact resistance. A robust application of this technology is imaging of cortical bone without the use of ionizing radiation, thus representing a viable alternative to CT for both rapid screening and "one-stop-shop" MRI. Although ZTE is increasingly used in musculoskeletal and body imaging, neuroimaging applications have historically been limited by complex anatomy and pathology. In this article, we review the imaging physics of ZTE including pulse sequence options, practical limitations, and image reconstruction. We then discuss optimization of settings for ZTE bone neuroimaging including acquisition, processing, segmentation, synthetic CT generation, and artifacts. Finally, we examine clinical utility of ZTE in the head and neck with imaging examples including malformations, trauma, tumors, and interventional procedures.

Motion corrected silent ZTE neuroimaging.

PURPOSE: To develop self-navigated motion correction for 3D silent zero echo time (ZTE) based neuroimaging and characterize its performance for different types of head motion. METHODS: The proposed method termed MERLIN (Motion Estimation & Retrospective correction Leveraging Interleaved Navigators) achieves self-navigation by using interleaved 3D phyllotaxis k-space sampling. Low resolution navigator images are reconstructed continuously throughout the ZTE acquisition using a sliding window and co-registered in image space relative to a fixed reference position. Rigid body motion corrections are then applied retrospectively to the k-space trajectory and raw data and reconstructed into a final, high-resolution ZTE image. RESULTS: MERLIN demonstrated successful and consistent motion correction for magnetization prepared ZTE images for a range of different instructed motion paradigms. The acoustic noise response of the self-navigated phyllotaxis trajectory was found to be only slightly above ambient noise levels (<4 dBA). CONCLUSION: Silent ZTE imaging combined with MERLIN addresses two major challenges intrinsic to MRI (i.e., subject motion and acoustic noise) in a synergistic and integrated manner without increase in scan time and thereby forms a versatile and powerful framework for clinical and research MR neuroimaging applications.

Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning Pseudo-CT and Maximum-Likelihood Estimation of Activity and Attenuation.

A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of magnetic resonance imaging (MRI) artifacts (e.g., implants and motion) and uncertainties due to the limitations of MRI contrast (e.g., accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that in addition to generating an initial pseudo-CT from MR data, it also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with the maximum-likelihood estimation of activity and attenuation (MLAA) reconstruction that uses the PET emission data to improve the attenuation maps. With the proposed approach uncertainty estimation and pseudo-CT prior for robust MLAA (UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions and show recovery of metal implants. In patients without implants, UpCT-MLAA had acceptable but slightly higher root-mean-squared-error (RMSE) than Zero-echotime and Dixon Deep pseudo-CT when compared to CTAC. In patients with metal implants, MLAA recovered the metal implant; however, anatomy outside the implant region was obscured by noise and crosstalk artifacts. Attenuation coefficients from the pseudo-CT from Dixon MRI were accurate in normal anatomy; however, the metal implant region was estimated to have attenuation coefficients of air. UpCT-MLAA estimated attenuation coefficients of metal implants alongside accurate anatomic depiction outside of implant regions.

Silent zero TE MR neuroimaging: Current state-of-the-art and future directions.

Magnetic Resonance Imaging (MRI) scanners produce loud acoustic noise originating from vibrational Lorentz forces induced by rapidly changing currents in the magnetic field gradient coils. Using zero echo time (ZTE) MRI pulse sequences, gradient switching can be reduced to a minimum, which enables near silent operation.Besides silent MRI, ZTE offers further interesting characteristics, including a nominal echo time of TE = 0 (thus capturing short-lived signals from MR tissues which are otherwise MR-invisible), 3D radial sampling (providing motion robustness), and ultra-short repetition times (providing fast and efficient scanning).In this work we describe the main concepts behind ZTE imaging with a focus on conceptual understanding of the imaging sequences, relevant acquisition parameters, commonly observed image artefacts, and image contrasts. We will further describe a range of methods for anatomical and functional neuroimaging, together with recommendations for successful implementation.

Revealing the mechanisms behind novel auditory stimuli discrimination: An evaluation of silent functional MRI using looping star.

Looping Star is a near-silent, multi-echo, 3D functional magnetic resonance imaging (fMRI) technique. It reduces acoustic noise by at least 25dBA, with respect to gradient-recalled echo echo-planar imaging (GRE-EPI)-based fMRI. Looping Star has successfully demonstrated sensitivity to the cerebral blood-oxygen-level-dependent (BOLD) response during block design paradigms but has not been applied to event-related auditory perception tasks. Demonstrating Looping Star's sensitivity to such tasks could (a) provide new insights into auditory processing studies, (b) minimise the need for invasive ear protection, and (c) facilitate the translation of numerous fMRI studies to investigations in sound-averse patients. We aimed to demonstrate, for the first time, that multi-echo Looping Star has sufficient sensitivity to the BOLD response, compared to that of GRE-EPI, during a well-established event-related auditory discrimination paradigm: the "oddball" task. We also present the first quantitative evaluation of Looping Star's test-retest reliability using the intra-class correlation coefficient. Twelve participants were scanned using single-echo GRE-EPI and multi-echo Looping Star fMRI in two sessions. Random-effects analyses were performed, evaluating the overall response to tones and differential tone recognition, and intermodality analyses were computed. We found that multi-echo Looping Star exhibited consistent sensitivity to auditory stimulation relative to GRE-EPI. However, Looping Star demonstrated lower test-retest reliability in comparison with GRE-EPI. This could reflect differences in functional sensitivity between the techniques, though further study is necessary with additional cognitive paradigms as varying cognitive strategies between sessions may arise from elimination of acoustic scanner noise.

AZTEK: Adaptive zero TE k-space trajectories.

PURPOSE: Because of short signal lifetimes and respiratory motion, 3D lung MRI is still challenging today. Zero-TE (ZTE) pulse sequences offer promising solutions as they overcome the issue of short T2∗ . Nevertheless, as they rely on continuous readout gradients, the trajectories they follow in k-space are not adapted to retrospective gating and inferred motion correction. THEORY AND METHODS: We propose AZTEK (adaptive ZTE k-space trajectories), a set of 3D radial trajectories featuring three tuning parameters, to adapt the acquisition to any moving organ while keeping seamless transitions between consecutive spokes. Standard ZTE and AZTEK trajectories were compared for static and moving phantom acquisitions as well as for human thoracic imaging performed on 3 volunteers (1 healthy and 2 patients with lung cancer). RESULTS: For the static phantom, we observe comparable image qualities with standard and AZTEK trajectories. For the moving phantom, spatially coherent undersampling artifacts observed on gated images with the standard trajectory are alleviated with AZTEK. The same improvement in image quality is obtained in human, so details are more delineated in the lung with the use of the adaptive trajectory. CONCLUSION: The AZTEK technique opens the possibility for 3D dynamic ZTE lung imaging with retrospective gating. It enables us to uniformly sample the k-space for any arbitrary respiratory motion gate, while preserving static image quality, improving dynamic image quality and guaranteeing continuous readout gradient transitions between spokes, which makes it appropriate to ZTE.

Three-dimensional magnetic resonance imaging ultrashort echo-time cones for assessing lung density in pediatric patients.

BACKGROUND: MRI of lung parenchyma is challenging because of the rapid decay of signal by susceptibility effects of aerated lung on routine fast spin-echo sequences. OBJECTIVE: To assess lung signal intensity in children on ultrashort echo-time sequences in comparison to a fast spin-echo technique. MATERIALS AND METHODS: We conducted a retrospective study of lung MRI obtained in 30 patients (median age 5 years, range 2 months to 18 years) including 15 with normal lungs and 15 with cystic fibrosis. On a fast spin-echo sequence with radial readout and an ultrashort echo-time sequence, both lungs were segmented and signal intensities were extracted. We compared lung-to-background signal ratios and histogram analysis between the two patient cohorts using non-parametric tests and correlation analysis. RESULTS: On ultrashort echo-time the lung-to-background ratio was age-dependent, ranging from 3.15 to 1.33 with high negative correlation (Rs = -0.86). Signal in posterior dependent portions of the lung was 18% and 11% higher than that of the anterior lung for age groups 0-2 and 2-18 years, respectively. The fast spin-echo sequence showed no variation of signal ratios by age or location, with a median of 0.99 (0.98-1.02). Histograms of ultrashort echo-time slices between controls and children with aggravated cystic fibrosis with mucus plugging and wall thickening exhibited significant discrepancies that differentiated between normal and pathological lungs. CONCLUSION: Signal intensity of lung on ultrashort echo-time is higher than that on fast spin-echo sequences, is age-dependent and shows a gravity-dependent anterior to posterior gradient. This signal variation appears similar to lung density described on CT.

Zero Echo Time MRAC on FDG-PET/MR Maintains Diagnostic Accuracy for Alzheimer's Disease; A Simulation Study Combining ADNI-Data.

AIM: Attenuation correction using zero-echo time (ZTE) - magnetic resonance imaging (MRI) (ZTE-MRAC) has become one of the standard methods for brain-positron emission tomography (PET) on commercial PET/MR scanners. Although the accuracy of the net tracer-uptake quantification based on ZTE-MRAC has been validated, that of the diagnosis for dementia has not yet been clarified, especially in terms of automated statistical analysis. The aim of this study was to clarify the impact of ZTE-MRAC on the diagnosis of Alzheimer's disease (AD) by performing simulation study. METHODS: We recruited 27 subjects, who underwent both PET/computed tomography (CT) and PET/MR (GE SIGNA) examinations. Additionally, we extracted 107 subjects from the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset. From the PET raw data acquired on PET/MR, three FDG-PET series were generated, using two vendor-provided MRAC methods (ZTE and Atlas) and CT-based AC. Following spatial normalization to Montreal Neurological Institute (MNI) space, we calculated each patient's specific error maps, which correspond to the difference between the PET image corrected using the CTAC method and the PET images corrected using the MRAC methods. To simulate PET maps as if ADNI data had been corrected using MRAC methods, we multiplied each of these 27 error maps with each of the 107 ADNI cases in MNI space. To evaluate the probability of AD in each resulting image, we calculated a cumulative t-value using a fully automated method which had been validated not only in the original ADNI dataset but several multi-center studies. In the method, PET score = 1 is the 95% prediction limit of AD. PET score and diagnostic accuracy for the discrimination of AD were evaluated in simulated images using the original ADNI dataset as reference. RESULTS: Positron emission tomography score was slightly underestimated both in ZTE and Atlas group compared with reference CTAC (-0.0796 ± 0.0938 vs. -0.0784 ± 0.1724). The absolute error of PET score was lower in ZTE than Atlas group (0.098 ± 0.075 vs. 0.145 ± 0.122, p < 0.001). A higher correlation to the original PET score was observed in ZTE vs. Atlas group (R 2: 0.982 vs. 0.961). The accuracy for the discrimination of AD patients from normal control was maintained in ZTE and Atlas compared to CTAC (ZTE vs. Atlas. vs. original; 82.5% vs. 82.1% vs. 83.2% (CI 81.8-84.5%), respectively). CONCLUSION: For FDG-PET images on PET/MR, attenuation correction using ZTE-MRI had superior accuracy to an atlas-based method in classification for dementia. ZTE maintains the diagnostic accuracy for AD.

Silent myelin-weighted magnetic resonance imaging.

Background: Inhomogeneous Magnetization Transfer (ihMT) is an emerging, uniquely myelin-specific magnetic resonance imaging (MRI) contrast. Current ihMT acquisitions utilise fast Gradient Echo sequences which are among the most acoustically noisy MRI sequences, reducing patient comfort during acquisition. We sought to address this by modifying a near silent MRI sequence to include ihMT contrast. Methods: A Magnetization Transfer preparation module was incorporated into a radial Zero Echo-Time sequence. Repeatability of the ihMT ratio and inverse ihMT ratio were assessed in a cohort of healthy subjects. We also investigated how head orientation affects ihMT across subjects, as a previous study in a single subject suggests this as a potential confound. Results: We demonstrated that ihMT ratios comparable to existing, acoustically loud, implementations could be obtained with the silent sequence. We observed a small but significant effect of head orientation on inverse ihMTR. Conclusions: Silent ihMT imaging is a comparable alternative to conventional, noisy, alternatives. For all future ihMT studies we recommend careful positioning of the subject within the scanner.

Silent 3D MR sequence for quantitative and multicontrast T1 and proton density imaging.

This study aims to develop a silent, fast and 3D method for T1 and proton density (PD) mapping, while generating time series of T1-weighted (T1w) images with bias-field correction. Undersampled T1w images at different effective inversion times (TIs) were acquired using the inversion recovery prepared RUFIS sequence with an interleaved k-space trajectory. Unaliased images were reconstructed by constraining the signal evolution to a temporal subspace which was learned from the signal model. Parameter maps were obtained by fitting the data to the signal model, and bias-field correction was conducted on T1w images. Accuracy and repeatability of the method was accessed in repeated experiments with phantom and volunteers. For the phantom study, T1 values obtained by the proposed method were highly consistent with values from the gold standard method, R2 = 0.9976. Coefficients of variation (CVs) ranged from 0.09% to 0.83%. For the volunteer study, T1 values from gray and white matter regions were consistent with literature values, and peaks of gray and white matter can be clearly delineated on whole-brain T1 histograms. CVs ranged from 0.01% to 2.30%. The acoustic noise measured at the scanner isocenter was 2.6 dBA higher compared to the in-bore background. Rapid and with low acoustic noise, the proposed method is shown to produce accurate T1 and PD maps with high repeatability by reconstructing sparsely sampled T1w images at different TIs using temporal subspace. Our approach can greatly enhance patient comfort during examination and therefore increase the acceptance of the procedure.

The impact of atlas-based MR attenuation correction on the diagnosis of FDG-PET/MR for Alzheimer's diseases- A simulation study combining multi-center data and ADNI-data.

BACKGROUND: The purpose of this study was to assess the impact of vendor-provided atlas-based MRAC on FDG PET/MR for the evaluation of Alzheimer's disease (AD) by using simulated images. METHODS: We recruited 47 patients, from two institutions, who underwent PET/CT and PET/MR (GE SIGNA) examination for oncological staging. From the PET raw data acquired on PET/MR, two FDG-PET series were generated, using vendor-provided MRAC (atlas-based) and CTAC. The following simulation steps were performed in MNI space: After spatial normalization and smoothing of the PET datasets, we calculated the error map for each patient, PETMRAC/PETCTAC. We multiplied each of these 47 error maps with each of the 203 Alzheimer's Disease Neuroimaging Initiative (ADNI) cases after the identical normalization and smoothing. This resulted in 203*47 = 9541 datasets. To evaluate the probability of AD in each resulting image, a cumulative t-value was calculated automatically using commercially-available software (PMOD PALZ) which has been used in multiple large cohort studies. The diagnostic accuracy for the discrimination of AD and predicting progression from mild cognitive impairment (MCI) to AD were evaluated in simulated images compared with ADNI original images. RESULTS: The accuracy and specificity for the discrimination of AD-patients from normal controls were not substantially impaired, but sensitivity was slightly impaired in 5 out of 47 datasets (original vs. error; 83.2% [CI 75.0%-89.0%], 83.3% [CI 74.2%-89.8%] and 83.1% [CI 75.6%-88.3%] vs. 82.7% [range 80.4-85.0%], 78.5% [range 72.9-83.3%,] and 86.1% [range 81.4-89.8%]). The accuracy, sensitivity and specificity for predicting progression from MCI to AD during 2-year follow-up was not impaired (original vs. error; 62.5% [CI 53.3%-69.3%], 78.8% [CI 65.4%-88.6%] and 54.0% [CI 47.0%-69.1%] vs. 64.8% [range 61.5-66.7%], 75.7% [range 66.7-81.8%,] and 59.0% [range 50.8-63.5%]). The worst 3 error maps show a tendency towards underestimation of PET scores. CONCLUSION: FDG-PET/MR based on atlas-based MR attenuation correction showed similar diagnostic accuracy to the CT-based method for the diagnosis of AD and the prediction of progression of MCI to AD using commercially-available software, although with a minor reduction in sensitivity.

Looping Star fMRI in Cognitive Tasks and Resting State.

BACKGROUND: Conventional T2 *-weighted functional magnetic resonance imaging (fMRI) is performed with echo-planar imaging (EPI) sequences that create substantial acoustic noise. The loud acoustic noise not only affects the activation of the auditory cortex, but may also interfere with resting state and task fMRI experiments. PURPOSE: To demonstrate the feasibility of a novel, quiet, T2 *, whole-brain blood oxygenation level-dependent (BOLD)-fMRI method, termed Looping Star, compared to conventional multislice gradient-echo EPI. STUDY TYPE: Prospective. PHANTOM/SUBJECTS: Glover stability QA phantom; 10 healthy volunteers. FIELD STRENGTH/SEQUENCE: 3.0T: gradient echo (GE)-EPI and T2 * Looping Star fMRI. ASSESSMENT: Looping Star fMRI was presented and compared to GE-EPI with a working memory (WM) task and resting state (RS) experiments. Temporal stability and acoustic measurements were obtained for both methods. Functional maps and activation accuracy were compared to evaluate the performance of the novel sequence. STATISTICAL TESTS: Mean and standard deviation values were analyzed for temporal stability and acoustic noise tests. Activation maps were assessed with one-sample t-tests and contrast estimates (CE). Paired t-tests and receiver operator characteristic (ROC) were used to compare fMRI sensitivity and performance. RESULTS: Looping Star presented a 98% reduction in sound pressure compared with GE-EPI, with stable temporal stability (0.09% percent fluctuation), but reduced temporal signal-to-noise ratio (tSNR) (mean difference = 15.9%). The novel method yielded consistent activations for RS and WM (83.4% and 69.5% relative BOLD sensitivity), which increased with task difficulty (mean CE 2-back = 0.56 vs. 0-back = 0.08, P < 0.05). A few differences in spatial activations were found between sequences, leading to a 4-8% lower activation accuracy with Looping Star. DATA CONCLUSION: Looping Star provides a suitable approach for whole-brain coverage with sufficient spatiotemporal resolution and BOLD sensitivity, with only 0.5 dB above ambient noise. From the comparison with GE-EPI, further developments of Looping Star fMRI should target increased sensitivity and spatial specificity for both RS and task experiments. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:739-751.

Silent T1 mapping using the variable flip angle method with B1 correction.

PURPOSE: To compare the silent rotating ultrafast imaging sequence (RUFIS) to a traditional Cartesian spoiled gradient-echo (SPGR) acquisition scheme for variable flip angle (VFA) T1 mapping. METHODS: A two-point VFA measurement was performed using RUFIS and Cartesian SPGR in a quantitative phantom and healthy volunteers. To correct for B1 errors, a novel silent magnetization prepared B1 map acquisition (SIMBA) was developed, which combined with RUFIS VFA allows for a completely silent T1 mapping protocol. RESULTS: The silent protocol was found to have comparable repeatability but higher reproducibility in vivo compared to the standard SPGR protocol, and showed no increase in acoustic noise levels above background noise levels compared to a 33 dBA increase for the SPGR acquisition. CONCLUSIONS: VFA T1 mapping using RUFIS is a feasible alternative to SPGR, achieving silent T1 mapping with comparable acquisition time.

In-phase zero TE musculoskeletal imaging.

PURPOSE: To introduce a new method for in-phase zero TE (ipZTE) musculoskeletal MR imaging. METHODS: ZTE is a 3D radial imaging method, which is sensitive to chemical shift off-resonance signal interference, especially around fat-water tissue interfaces. The ipZTE method addresses this fat-water chemical shift artifact by acquiring each 3D radial spoke at least twice with varying readout gradient amplitude and hence varying effective sampling time. Using k-space-based chemical shift decomposition, the acquired data is then reconstructed into an in-phase ZTE image and an out-of-phase disturbance. RESULTS: The ipZTE method was tested for knee, pelvis, brain, and whole-body. The obtained images demonstrate exceptional soft-tissue uniformity free from out-of-phase disturbances apparent in the original ZTE images. The chemical shift decomposition was found to improve SNR at the cost of reduced image resolution. CONCLUSION: The ipZTE method can be used as an averaging mechanism to eliminate fat-water chemical shift artifacts and improve SNR. The method is expected to improve ZTE-based musculoskeletal imaging and pseudo CT conversion as required for PET/MR attenuation correction and MR-guided radiation therapy planning.