Magnetization Transfer BOOST Noncontrast Angiography Improves Pulmonary Vein Imaging in Adults With Congenital Heart Disease.

BACKGROUND: Cardiac MRI plays an important role in the diagnosis and follow-up of patients with congenital heart disease (CHD). Gadolinium-based contrast agents are often needed to overcome flow-related and off-resonance artifacts that can impair the quality of conventional noncontrast 3D imaging. As serial imaging is often required in CHD, the development of robust noncontrast 3D MRI techniques is desirable. PURPOSE: To assess the clinical utility of noncontrast enhanced magnetization transfer and inversion recovery prepared 3D free-breathing sequence (MTC-BOOST) compared to conventional 3D whole heart imaging in patients with CHD. STUDY TYPE: Prospective, image quality. POPULATION: A total of 27 adult patients (44% female, mean age 30.9 ± 14.8 years) with CHD. FIELD STRENGTH/SEQUENCE: A 1.5 T; free-breathing 3D MTC-BOOST sequence. ASSESSMENT: MTC-BOOST was compared to diaphragmatic navigator-gated, noncontrast T2 prepared 3D whole-heart imaging sequence (T2prep-3DWH) for comparison of vessel dimensions, lumen-to-myocardium contrast ratio (CR), and image quality (vessel wall sharpness and presence and type of artifacts) assessed by two experienced cardiologists on a 5-point scale. STATISTICAL TESTS: Mann-Whitney test, paired Wilcoxon signed-rank test, Bland-Altman plots. P < 0.05 was considered statistically significant. RESULTS: MTC-BOOST significantly improved image quality and CR of the right-sided pulmonary veins (PV): (CR: right upper PV 1.06 ± 0.50 vs. 0.58 ± 0.74; right lower PV 1.32 ± 0.38 vs. 0.81 ± 0.73) compared to conventional T2prep-3DWH imaging where the PVs were not visualized in some cases due to off-resonance effects. MTC-BOOST demonstrated resistance to degradation of luminal signal (assessed by CR) secondary to accelerated or turbulent flow conditions. T2prep-3DWH had higher image quality scores than MTC-BOOST for the aorta and coronary arteries; however, great vessel dimensions derived from MTC-BOOST showed excellent agreement with standard T2prep-3DWH imaging. DATA CONCLUSION: MTC-BOOST allows for improved contrast-free imaging of pulmonary veins and regions characterized by accelerated or turbulent blood flow compared to standard T2prep-3DWH imaging, with excellent agreement of great vessel dimensions. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

The relationship between glutamate, dopamine, and cortical gray matter: A simultaneous PET-MR study.

Prefrontal cortex has been shown to regulate striatal dopaminergic function via glutamatergic mechanisms in preclinical studies. Concurrent disruption of these systems is also often seen in neuropsychiatric disease. The simultaneous measurement of striatal dopamine signaling, cortical gray matter, and glutamate levels is therefore of major interest, but has not been previously reported. In the current study, twenty-eight healthy subjects underwent 2 simultaneous [11C]-( + )-PHNO PET-MRI scans, once after placebo and once after amphetamine in a double-blind randomized cross-over design, to measure striatal dopamine release, striatal dopamine receptor (D2/3R) availability, anterior cingulate glutamate+glutamine (Glx) levels, and cortical gray matter volumes at the same time. Voxel-based morphometry was used to investigate associations between neurochemical measures and gray matter volumes. Whole striatum D2/3R availability was positively associated with prefrontal cortex gray matter volume (pFWE corrected = 0.048). This relationship was mainly driven by associative receptor availability (pFWE corrected = 0.023). In addition, an interaction effect was observed between sensorimotor striatum D2/3R availability and anterior cingulate Glx, such that in individuals with greater anterior cingulate Glx concentrations, D2/3R availability was negatively associated with right frontal cortex gray matter volumes, while a positive D2/3R-gray matter association was observed in individuals with lower anterior cingulate Glx levels (pFWE corrected = 0.047). These results are consistent with the hypothesis that the prefrontal cortex is involved in regulation of striatal dopamine function. Furthermore, the observed associations raise the possibility that this regulation may be modulated by anterior cingulate glutamate concentrations.

Whole-heart T1 mapping using a 2D fat image navigator for respiratory motion compensation.

PURPOSE: To combine a 3D saturation-recovery-based myocardial T1 mapping (3D SASHA) sequence with a 2D image navigator with fat excitation (fat-iNAV) to allow 3D T1 maps with 100% respiratory scan efficiency and predictable scan time. METHODS: Data from T1 phantom and 10 subjects were acquired at 1.5T. For respiratory motion compensation, a 2D fat-iNAV was acquired before each 3D SASHA k-space segment to correct for 2D translational motion in a beat-to-beat fashion. The effect of the fat-iNAV on the 3D SASHA T1 estimation was evaluated on the T1 phantom. For 3 representative subjects, the proposed free-breathing 3D SASHA with fat-iNAV was compared to the original implementation with the diaphragmatic navigator. The 3D SASHA with fat-iNAV was compared to the breath-hold 2D SASHA sequence in terms of accuracy and precision. RESULTS: In the phantom study, the Bland-Altman plot shows that the 2D fat-iNAVs does not affect the T1 quantification of the 3D SASHA acquisition (0 ± 12.5 ms). For the in vivo study, the 2D fat-iNAV permits to estimate the respiratory motion of the heart, while allowing for 100% scan efficiency, improving the precision of the T1 measurement compared to non-motion-corrected 3D SASHA. However, the image quality achieved with the proposed 3D SASHA with fat-iNAV is lower compared to the original implementation, with reduced delineation of the myocardial borders and papillary muscles. CONCLUSIONS: We demonstrate the feasibility to combine the 3D SASHA T1 mapping imaging sequence with a 2D fat-iNAV for respiratory motion compensation, allowing 100% respiratory scan efficiency and predictable scan time.

Accelerated high-resolution free-breathing 3D whole-heart T2-prepared black-blood and bright-blood cardiovascular magnetic resonance.

BACKGROUND: The free-breathing 3D whole-heart T2-prepared Bright-blood and black-blOOd phase SensiTive inversion recovery (BOOST) cardiovascular magnetic resonance (CMR) sequence was recently proposed for simultaneous bright-blood coronary CMR angiography and black-blood late gadolinium enhancement (LGE) imaging. This sequence enables simultaneous visualization of cardiac anatomy, coronary arteries and fibrosis. However, high-resolution (< 1.4 × 1.4 × 1.4 mm3) fully-sampled BOOST requires long acquisition times of ~ 20 min. METHODS: In this work, we propose to extend a highly efficient respiratory-resolved motion-corrected reconstruction framework (XD-ORCCA) to T2-prepared BOOST to enable high-resolution 3D whole-heart coronary CMR angiography and black-blood LGE in a clinically feasible scan time. Twelve healthy subjects were imaged without contrast injection (pre-contrast BOOST) and 10 patients with suspected cardiovascular disease were imaged after contrast injection (post-contrast BOOST). A quantitative analysis software was used to compare accelerated pre-contrast BOOST against the fully-sampled counterpart (vessel sharpness and length of the left and right coronary arteries). Moreover, three cardiologists performed diagnostic image quality scoring for clinical 2D LGE and both bright- and black-blood 3D BOOST imaging using a 4-point scale (1-4, non-diagnostic-fully diagnostic). A two one-sided test of equivalence (TOST) was performed to compare the pre-contrast BOOST images. Nonparametric TOST was performed to compare post-contrast BOOST image quality scores. RESULTS: The proposed method produces images from 3.8 × accelerated non-contrast-enhanced BOOST acquisitions with comparable vessel length and sharpness to those obtained from fully- sampled scans in healthy subjects. Moreover, in terms of visual grading, the 3D BOOST LGE datasets (median 4) and the clinical 2D counterpart (median 3.5) were found to be statistically equivalent (p < 0.05). In addition, bright-blood BOOST images allowed for visualization of the proximal and middle left anterior descending and right coronary sections with high diagnostic quality (mean score > 3.5). CONCLUSIONS: The proposed framework provides high-resolution 3D whole-heart BOOST images from a single free-breathing acquisition in ~ 7 min.

Correction to: 3D SASHA myocardial T1 mapping with high accuracy and improved precision.

The original version of this article unfortunately contained a mistake. The presentation of Equation was incorrect. The corrected equation is given below.

3D SASHA myocardial T1 mapping with high accuracy and improved precision.

PURPOSE: To improve the precision of a free-breathing 3D saturation-recovery-based myocardial T1 mapping sequence using a post-processing 3D denoising technique. METHODS: A T1 phantom and 15 healthy subjects were scanned on a 1.5 T MRI scanner using 3D saturation-recovery single-shot acquisition (SASHA) for myocardial T1 mapping. A 3D denoising technique was applied to the native T1-weighted images before pixel-wise T1 fitting. The denoising technique imposes edge-preserving regularity and exploits the co-occurrence of 3D spatial gradients in the native T1-weighted images by incorporating a multi-contrast Beltrami regularization. Additionally, 2D modified Look-Locker inversion recovery (MOLLI) acquisitions were performed for comparison purposes. Accuracy and precision were measured in the myocardial septum of 2D MOLLI and 3D SASHA T1 maps and then compared. Furthermore, the accuracy and precision of the proposed approach were evaluated in a standardized phantom in comparison to an inversion-recovery spin-echo sequence (IRSE). RESULTS: For the phantom study, Bland-Altman plots showed good agreement in terms of accuracy between IRSE and 3D SASHA, both on non-denoised and denoised T1 maps (mean difference -1.4 ± 18.9 ms and -4.4 ± 21.2 ms, respectively), while 2D MOLLI generally underestimated the T1 values (69.4 ± 48.4 ms). For the in vivo study, there was a statistical difference between the precision measured on 2D MOLLI and on non-denoised 3D SASHA T1 maps (P = 0.005), while there was no statistical difference after denoising (P = 0.95). CONCLUSION: The precision of 3D SASHA myocardial T1 mapping was substantially improved using a 3D Beltrami regularization based denoising technique and was similar to that of 2D MOLLI T1 mapping, while preserving the higher accuracy and whole-heart coverage of 3D SASHA.

3D myocardial T1 mapping using saturation recovery.

PURPOSE: To propose a 3D quantitative high-resolution T1 mapping technique, called 3D SASHA (saturation-recovery single-shot acquisition), which combines a saturation recovery pulse with 1D-navigator-based-respiratory motion compensation to acquire the whole volume of the heart in free breathing. The sequence was tested and validated both in a T1 phantom and in healthy subjects. MATERIALS AND METHODS: The 3D SASHA method was implemented on a 1.5T scanner. A diaphragmatic navigator was used to allow free-breathing acquisition and the images were acquired with a resolution of 1.4 × 1.4 × 8 mm3 . For assessment of accuracy and precision the sequence was compared with the reference gold-standard inversion-recovery spin echo (IRSE) pulse sequence in a T1 phantom, while for the in vivo studies (10 healthy volunteers) 3D SASHA was compared with the clinically used 2D MOLLI (3-3-5) and 2D SASHA protocols. RESULTS: There was good agreement between the T1 values measured in a T1 phantom with 3D SASHA and the reference IRSE pulse sequences (1111.6 ± 31 msec vs. 1123.6 ± 8 msec, P = 0.9947). Mean and standard deviation of the myocardial T1 values in healthy subjects measured with 2D MOLLI, 2D SASHA, and 3D SASHA sequences were 881 ± 40 msec, 1181.3 ± 32 msec, and 1153.6 ± 28 msec respectively. CONCLUSION: The proposed 3D SASHA sequence allows for high-resolution free-breathing whole-heart T1 -mapping with T1 values in good agreement with the 2D SASHA and improved precision. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:218-227.

Coronary endothelial function assessment using self-gated cardiac cine MRI and k-t sparse SENSE.

PURPOSE: Electrocardiogram (ECG)-gated cine MRI, paired with isometric handgrip exercise, can be used to accurately, reproducibly, and noninvasively measure coronary endothelial function (CEF). Obtaining a reliable ECG signal at higher field strengths, however, can be challenging due to rapid gradient switching and an increased heart rate under stress. To address these limitations, we present a self-gated cardiac cine MRI framework for CEF measurements that operates without ECG signal. METHODS: Cross-sectional slices of the right coronary artery (RCA) were acquired using a two-dimensional golden angle radial trajectory. This sampling approach, combined with the k-t sparse SENSE algorithm, allows for the reconstruction of both real-time images for self-gating signal calculations and retrospectively reordered self-gated cine images. CEF measurements were quantitatively compared using both the self-gated and the standard ECG-gated approach. RESULTS: Self-gated cine images with high-quality, temporal, and spatial resolution were reconstructed for 18 healthy volunteers. CEF as measured in self-gated images was in good agreement (R2 = 0.60) with that measured by its standard ECG-gated counterpart. CONCLUSION: High spatial and temporal resolution cross-sectional cine images of the RCA can be obtained without ECG signal. The coronary vasomotor response to handgrip exercise compares favorably with that obtained with the standard ECG-gated method. Magn Reson Med 76:1443-1454, 2015. © 2015 International Society for Magnetic Resonance in Medicine.

Initial soil moisture and soil texture control the impact of storm surges in coastal forests.

Flooding and salinization triggered by storm surges threaten the survival of coastal forests. The forest root zone (top 40 cm of soil) is the most affected by surge flooding. Determining the effect of a storm surge on edaphic conditions is essential to estimate vegetation response. Pre-storm soil hydrology could mitigate or enhance the salinization effect, ultimately determining the resilience of the forest. Here we assess the influence of pre-storm soil water content and salinity on storm surge effects in coastal vegetated areas. A 1D model (HYDRUS-1D) is used to simulate saltwater infiltration from above through the unsaturated zone. Different water content and concentration scenarios, along with scenarios with variable storm surge salt concentration, storm surge height and storm surge flooding duration are considered. In soils characterized by silt and clay, the maximum salinized soil depth increases as water content increases, affecting the top 40 cm of soil, except for clay loam soil, for which only the top 20 cm are salinized. In sandy soils, the salinization process involves the entire soil column. The contribution of water content to salinization varies from 12 % to 30 % along the top 40 cm in fine soils. In fine soils, the storm-surge height also becomes relevant. A study case is presented to support the numerical results. Field data confirm that soil water content controls the salinization of the root zone in clay and silt soils. Overall, we conclude that the root zones of coastal forests with clay and silt soils and low water content are the most at risk during storm surges. These events have the potential to radically change edaphic conditions and affect ecosystems.

Mesostriatal Dopaminergic Circuit Dysfunction in Schizophrenia: A Multimodal Neuromelanin-Sensitive Magnetic Resonance Imaging and [18F]-DOPA Positron Emission Tomography Study.

BACKGROUND: Striatal hyperdopaminergia is implicated in the pathoetiology of schizophrenia, but how this relates to dopaminergic midbrain activity is unclear. Neuromelanin (NM)-sensitive magnetic resonance imaging provides a marker of long-term dopamine function. We examined whether midbrain NM-sensitive magnetic resonance imaging contrast-to-noise ratio (NM-CNR) was higher in people with schizophrenia than in healthy control (HC) participants and whether this correlated with dopamine synthesis capacity. METHODS: One hundred fifty-four participants (schizophrenia group: n = 74, HC group: n = 80) underwent NM-sensitive magnetic resonance imaging of the substantia nigra and ventral tegmental area (SN-VTA). A subset of the schizophrenia group (n = 38) also received [18F]-DOPA positron emission tomography to measure dopamine synthesis capacity (Kicer) in the SN-VTA and striatum. RESULTS: SN-VTA NM-CNR was significantly higher in patients with schizophrenia than in HC participants (effect size = 0.38, p = .019). This effect was greatest for voxels in the medial and ventral SN-VTA. In patients, SN-VTA Kicer positively correlated with SN-VTA NM-CNR (r = 0.44, p = .005) and striatal Kicer (r = 0.71, p < .001). Voxelwise analysis demonstrated that SN-VTA NM-CNR was positively associated with striatal Kicer (r = 0.53, p = .005) and that this relationship seemed strongest between the ventral SN-VTA and associative striatum in schizophrenia. CONCLUSIONS: Our results suggest that NM levels are higher in patients with schizophrenia than in HC individuals, particularly in midbrain regions that project to parts of the striatum that receive innervation from the limbic and association cortices. The direct relationship between measures of NM and dopamine synthesis suggests that these aspects of schizophrenia pathophysiology are linked. Our findings highlight specific mesostriatal circuits as the loci of dopamine dysfunction in schizophrenia and thus as potential therapeutic targets.

An automatic analysis framework for FDOPA PET neuroimaging.

In this study we evaluate the performance of a fully automated analytical framework for FDOPA PET neuroimaging data, and its sensitivity to demographic and experimental variables and processing parameters. An instance of XNAT imaging platform was used to store the King's College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. By re-engineering the historical Matlab-based scripts for FDOPA PET analysis, a fully automated analysis pipeline for imaging processing and data quantification was implemented in Python and integrated in XNAT. The final data repository includes 892 FDOPA PET scans organized from 23 different studies. We found good reproducibility of the data analysis by the automated pipeline (in the striatum for the Kicer: for the controls ICC = 0.71, for the psychotic patients ICC = 0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F = 10.7, p < 0.001), with women showing greater dopamine synthesis capacity than men. Our automated analysis pipeline represents a valid resourse for standardised and robust quantification of dopamine synthesis capacity using FDOPA PET data. Combining information from different neuroimaging studies has allowed us to test it comprehensively and to validate its replicability and reproducibility performances on a large sample size.

Groundwater, soil moisture, light and weather data collected in a coastal forest bordering a salt marsh in the Delmarva Peninsula (VA).

Data presented in this paper were collected in eight sites across a coastal forest in the Delmarva Peninsula, VA USA. The sites, located along transects from the marshland to the inner forest, are representative of the progressive forest retreat and the consequent marsh expansion driven by sea level rise. The sites are divided in marsh, transition zone where marsh vegetation is invading the forest, low forest, where tree dieback is widespread, intermediate forest (medium forest), where trees show signs of stress, and high forest, where trees are healthy. Sea level rise and storm surge events are the drivers of the forest conversion to salt marsh. Groundwater level and electrical conductivity were measured in a well at each site. Soil water content and electrical conductivity data were measured in the first 7-cm layer of soil. Weather and light data were collected to determine the effects of external inputs on groundwater and soil moisture datasets and to relate hydrological variables and illuminance to local ecology. Data collected are fundamental to estimate feedbacks between hydrology and ecology in the study area and to quantify forest retreat due to flooding and salinization.

Bitbox: A Cloud-based data sharing solution for medical images.

With the modernization and digitisation of the healthcare system, the need for exchanging medical data has become increasingly compelling. Biomedical imaging has been no exception, where the gathering of medical imaging acquisitions from multi-site collaborations have enabled to reach data sizes never imaginable until few years ago. Usually, medical imaging data have very large volume and diverse complexity, requiring bespoken transfer systems that protect personal information as well as data integrity. Despite many digital innovations, there are still technical and regulatory bottlenecks that make biomedical imaging data exchange challenging. Here we present Bitbox, a web-based application which provides a reliable yet straightforward service to securely exchange medical imaging data. With Bitbox, both imaging and non-imaging data of any type can be transferred from any external and independent site into a centralized server. A showcase of the system will be illustrated for the COVID-19 Clinical Neuroscience Study (COVID-CNS) project, a UK-wide experimental medicine study to investigate the neurological and neuropsychiatric effects of COVID-19 infections in hundreds of patients.

Accelerating 3D MTC-BOOST in patients with congenital heart disease using a joint multi-scale variational neural network reconstruction.

PURPOSE: Free-breathing Magnetization Transfer Contrast Bright blOOd phase SensiTive (MTC-BOOST) is a prototype balanced-Steady-State Free Precession sequence for 3D whole-heart imaging, that employs the endogenous magnetisation transfer contrast mechanism. This achieves reduction of flow and off-resonance artefacts, that often arise with the clinical T2prepared balanced-Steady-State Free Precession sequence, enabling high quality, contrast-agent free imaging of the thoracic cardiovascular anatomy. Fully-sampled MTC-BOOST acquisition requires long scan times (~10-24 min) and therefore acceleration is needed to permit its clinical incorporation. The aim of this study is to enable and clinically validate the 5-fold accelerated MTC-BOOST acquisition with joint Multi-Scale Variational Neural Network (jMS-VNN) reconstruction. METHODS: Thirty-six patients underwent free-breathing, 3D whole-heart imaging with the MTC-BOOST sequence, which is combined with variable density spiral-like Cartesian sampling and 2D image navigators for translational motion estimation. This sequence acquires two differently weighted bright-blood volumes in an interleaved fashion, which are then joined in a phase sensitive inversion recovery reconstruction to obtain a complementary fully co-registered black-blood volume. Data from eighteen patients were used for training, whereas data from the remaining eighteen patients were used for testing/evaluation. The proposed deep-learning based approach adopts a supervised multi-scale variational neural network for joint reconstruction of the two differently weighted bright-blood volumes acquired with the 5-fold accelerated MTC-BOOST. The two contrast images are stacked as different channels in the network to exploit the shared information. The proposed approach is compared to the fully-sampled MTC-BOOST and 5-fold undersampled MTC-BOOST acquisition with Compressed Sensing (CS) reconstruction in terms of scan/reconstruction time and bright-blood image quality. Comparison against conventional 2-fold undersampled T2-prepared 3D bright-blood whole-heart clinical sequence (T2prep-3DWH) is also included. RESULTS: Acquisition time was 3.0 ±â€¯1.0 min for the 5-fold accelerated MTC-BOOST versus 9.0 ±â€¯1.1 min for the fully-sampled MTC-BOOST and 11.1 ±â€¯2.6 min for the T2prep-3DWH (p < 0.001 and p < 0.001, respectively). Reconstruction time was significantly lower with the jMS-VNN method compared to CS (10 ±â€¯0.5 min vs 20 ±â€¯2 s, p < 0.001). Image quality was higher for the proposed 5-fold undersampled jMS-VNN versus conventional CS, comparable or higher to the corresponding T2prep-3DWH dataset and similar to the fully-sampled MTC-BOOST. CONCLUSION: The proposed 5-fold accelerated jMS-VNN MTC-BOOST framework provides efficient 3D whole-heart bright-blood imaging in fast acquisition and reconstruction time with concomitant reduction of flow and off-resonance artefacts, that are frequently encountered with the clinical sequence. Image quality of the cardiac anatomy and thoracic vasculature is comparable or superior to the clinical scan and 5-fold CS reconstruction in faster reconstruction time, promising potential clinical adoption.

Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning.

INTRODUCTION: With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control the data collected and the associated analysis. Attempts to use artificial intelligence (AI) to perform automated quality control (QC) for both single-site and multi-site datasets have been explored in some neuroimaging techniques (e.g. EEG or MRI), although these methods struggle to find replication in other domains. The aim of this study is to test the feasibility of an automated QC pipeline for brain [18F]-FDOPA PET imaging as a biomarker for the dopamine system. METHODS: Two different Convolutional Neural Networks (CNNs) were used and combined to assess spatial misalignment to a standard template and the signal-to-noise ratio (SNR) relative to 200 static [18F]-FDOPA PET images that had been manually quality controlled from three different PET/CT scanners. The scans were combined with an additional 400 scans, in which misalignment (200 scans) and low SNR (200 scans) were simulated. A cross-validation was performed, where 80% of the data were used for training and 20% for validation. Two additional datasets of [18F]-FDOPA PET images (50 and 100 scans respectively with at least 80% of good quality images) were used for out-of-sample validation. RESULTS: The CNN performance was excellent in the training dataset (accuracy for motion: 0.86 ± 0.01, accuracy for SNR: 0.69 ± 0.01), leading to 100% accurate QC classification when applied to the two out-of-sample datasets. Data dimensionality reduction affected the generalizability of the CNNs, especially when the classifiers were applied to the out-of-sample data from 3D to 1D datasets. CONCLUSIONS: This feasibility study shows that it is possible to perform automatic QC of [18F]-FDOPA PET imaging with CNNs. The approach has the potential to be extended to other PET tracers in both brain and non-brain applications, but it is dependent on the availability of large datasets necessary for the algorithm training.

Faster 3D saturation-recovery based myocardial T1 mapping using a reduced number of saturation points and denoising.

PURPOSE: To accelerate the acquisition of free-breathing 3D saturation-recovery-based (SASHA) myocardial T1 mapping by acquiring fewer saturation points in combination with a post-processing 3D denoising technique to maintain high accuracy and precision. METHODS: 3D SASHA T1 mapping acquires nine T1-weighted images along the saturation recovery curve, resulting in long acquisition times. In this work, we propose to accelerate conventional cardiac T1 mapping by reducing the number of saturation points. High T1 accuracy and low standard deviation (as a surrogate for precision) is maintained by applying a 3D denoising technique to the T1-weighted images prior to pixel-wise T1 fitting. The proposed approach was evaluated on a T1 phantom and 20 healthy subjects, by varying the number of T1-weighted images acquired between three and nine, both prospectively and retrospectively. Following the results from the healthy subjects, three patients with suspected cardiovascular disease were acquired using five T1-weighted images. T1 accuracy and precision was determined for all the acquisitions before and after denoising. RESULTS: In the T1 phantom, no statistical difference was found in terms of accuracy and precision for the different number of T1-weighted images before or after denoising (P = 0.99 and P = 0.99 for accuracy, P = 0.64 and P = 0.42 for precision, respectively). In vivo, both prospectively and retrospectively, the precision improved considerably with the number of T1-weighted images employed before denoising (P<0.05) but was independent on the number of T1-weighted images after denoising. CONCLUSION: We demonstrate the feasibility of accelerating 3D SASHA T1 mapping by reducing the number of acquired T1-weighted images in combination with an efficient 3D denoising, without affecting accuracy and precision of T1 values.