3D-UTE Rosette MRI and MRSI at UHF human (7T) and animal scanners (9.4T) using The (BART) toolbox

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#ismrm #ISMRM2022 #ISMRM22 3D-UTE Rosette Applications (MRI and MRSI) at UHF human (7T) and animal scanners (9.4T) using The Berkeley Advanced Reconstruction Toolbox (BART) toolbox Uzay Emir Afternoon Tea with BART Time: 15:45-16:45

Science

3D-UTE Rosette Applications (MRI and MRSI)
at UHF human (7T) and animal scanners
(9.4T) using The Berkeley Advanced
Reconstruction Toolbox (BART) toolbox
Uzay E Emir, Ali Ozen, Mark Chiew, Guy Williams, Zoe Kourtzi, Stephen Sawiak,
School of Health Sciences, Purdue University, West Lafayette, IN, United States
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States,
Department of Radiology, Medical Center, University of Freiburg, Freiburg, Germany,
Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
University of Cambridge, Wolfson Brain Imaging Centre, Cambridge, United Kingdom
University of Cambridge, The Adaptive Brain Lab, Cambridge, United Kingdom

A 3D-UTE Dual Echo Rosette MRI
and MRSI Sequence
2
Shen X, et al., doi: https://doi.org/10.1101/2021.09.18.460869
Normalized Radial UTE Reconstruction (RG) Normalized Rosette UTE Reconstruction (RG)

3D-UTE Rosette rocks
#5009
#1040
#1495 #1738
#1982 #5001 #1534

Day 1
Wednesday
Arrive at 3 pm: University of
Cambridge, Wolfson Brain Imaging
Centre, Cambridge, United
Kingdom
3:15 pm Installed the sequence
Of course, it did not work… 

Day 2 Thursday
• Arrive at 1 pm University of Cambridge, Wolfson Brain
Imaging Centre, Cambridge, United Kingdom
• Phantom Experiments
• Did it work?
• Of course, it did work…. How do I know?

The Berkeley Advanced
Reconstruction Toolbox
(BART) toolbox
• Problems
• Non-Cartesian K-space
trajectory
• Giant data size
• Limited computational power
• Solution
• The Berkeley Advanced
Reconstruction Toolbox
(BART) toolbox

Day 3 k-Space Design 3D Rosette (high-resolution)
• Siemen 7T Terra
• 32 Channel Nova Coil
• Kmax=500/m
• 𝜔1=𝜔2=0.766 kHz
• Field of view (FOV):240x240x240 mm3
• Matrix size=256x256x256,
• Readout dwell time=10 𝜇𝑠
• Flip angle=7-degree
• TR=7 ms,
• Readout duration=4.2 ms
• RF pulse duration=20 𝜇𝑠
• TE: 30 𝜇𝑠 and 2.12 ms
• Number of petals: 71442 (40%)
• Total scan time: 8.7 minutes
Sequential k-space fill-
in
Kz
(/m)
First Last

Reconstruction
Two reconstruction methods were performed: compressed sensing (as described above)
and regular regridding applying a density compensated (Pipe&Menon) adjoint NUFFT.
Adjouint NUFFT Total Reconstruction time: ~ 2 minutes
Compressed sensing ~10 minutes.

Day 3 ~ 1 mm isotropic Resolution 7T
~ 0.5 mm isotropic Resolution 7T

Day 3 How About 7T MRSI
• Siemen 7T Terra
• 32 Channel Nova Coil
• Field of view (FOV):240x240x240 mm3
• Matrix size=48x48x48,
• Readout dwell time=10 𝜇𝑠
• Flip angle=30-degree
• TR=500 ms,
• Readout duration=~460 us
• RF pulse duration=20 𝜇𝑠
• TE: 30 𝜇𝑠, Maximum
• Spectral Bandwidth ~4.4 Khz
• Number of petals: 1444
• Total scan time: ~10 minutes

How about 9.4T Bruker animal Scanner (PetalUTE, Stephen Sawiak)

High quality US imaging demand large number of measurements that can increase the cost, size and power requirements. Therefore, low-powered, portable and 3D ultrasound imaging system require reconstruction algorithms that can produce high quality images using fewer receive measurements. Number of model specific methods has been proposed which doesn't work under perturbation. For instance, compressive deconvolution ultrasound which provide a reasonable quality with limited measurements however, it has its own down-sides such as high computation cost and accurate estimation of point spread function (PSF). An other major limitation of conventional methods is that they require RF or base-band signal which is difficult to obtain from portable US systems. To deal with the aforementioned issues, in this study we designed a novel deep deconvolution model for image domain-based deconvolution. The proposed deep deconvolution (DeepDeconv) model can be trained in an unsupervised fashion, alleviate the need of paired high and low quality images. The model was evaluated on both the phantom and in-vivo scans for various sampling configurations. The proposed DeepDeconv significantly enhance the details of anatomical structures and using unsupervised learning on average it achieved 2.14dB, 4.96dB and 0.01 units gain in CR, PSNR and SSIM values respectively, which are comparable to the supervised method.

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While Phosphorous (31P) MRS (I) has been promising in experimental and clinical settings since the early 70s, it has been beset by prohibitively lower sensitivity, limited spectral-spatial resolution, and prolonged acquisition. This manuscript and proceedings of the annual scientific meeting of ISMRM in 2022 (REF1) and 2023 (REF2) demonstrate that our novel acquisition strategy, the novel Rosette Trajectory for fast and flexible MR(S)I contrast (Shen et al. 2023 (REF3), later we renamed it as PETALUTE after the translation to the preclinical scanners of 7T and 9.4T), enables operator-independent (1) rapid acquisition (~7 minutes), (2) reconstruction, and (3) processing pipeline, resulting in phosphorous metabolite ratio maps (10 x 10 x 10 mm3) of the whole brain. In response to the “Repeat it with Me” challenge organized by the Reproducible Research study group of ISMRM, we demonstrated the power of this technique in 5 healthy volunteers at three different institutions with different experimental setups (2nd Place: UTE 31P 3D Rosette MRSI Reproducibility Team, REF4). Since the proposed acquisition/reconstruction/processing pipeline was operator/scanner/coil-independent, the Reproducer sub-teams successfully replicated the findings of the original proceeding in 2022 (REF1). As part of this challenge, we provided some MATLAB scripts and k-space data to reproduce some of the results described in this manuscript. The software and data can be downloaded from https://purr.purdue.edu/projects/ismrm31pmrsi. These results will likely be of broad interest across clinical settings since the proposed acquisition strategy is not specific to any region, nuclei, or magnetic field and is operator-independent. This study's resolution and signal-to-noise ratios permit the metabolite maps in an experimentally and clinically feasible timeframe at 3 Tesla and 7T. REF1 Bozymski B, Shen X, Ozen AC, Ibey S, Chiew M, Thomas A, Dydak U, Emir UE. Ultra-Short Echo Time 31P 3D MRSI at 3T with Novel Rosette k-space Trajectory. Proceedings 30th Scientific Meeting, International Society for Magnetic Resonance in Medicine, 2022. REF2 Farley N, Bozymski B, Dydak U, Emir UE*. Fast 3D 31P MRSI Using Novel Rosette Petal Trajectory at 3T with x4 Accelerated Compressed Sensing. Proceedings 31st Scientific Meeting, International Society for Magnetic Resonance in Medicine, 2023. REF3 Shen X, Özen AC, Sunjar A, Ilbey S, Sawiak S, Shi R, Chiew M, Emir UE. Ultrashort T2 components imaging of the whole brain using 3D dual-echo UTE MRI with rosette k-space pattern. Magnetic Resonance in Medicine. 2023;89(2):508–521. REF4 https://challenge.ismrm.org/2023-24-reproducibility-challenge/results-22-23/

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Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.

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Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...

3D-UTE Rosette MRI and MRSI at UHF human (7T) and animal scanners (9.4T) using The (BART) toolbox

1. 3D-UTE Rosette Applications (MRI and MRSI) at UHF human (7T) and animal scanners (9.4T) using The Berkeley Advanced Reconstruction Toolbox (BART) toolbox Uzay E Emir, Ali Ozen, Mark Chiew, Guy Williams, Zoe Kourtzi, Stephen Sawiak, School of Health Sciences, Purdue University, West Lafayette, IN, United States Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States, Department of Radiology, Medical Center, University of Freiburg, Freiburg, Germany, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom University of Cambridge, Wolfson Brain Imaging Centre, Cambridge, United Kingdom University of Cambridge, The Adaptive Brain Lab, Cambridge, United Kingdom

2. A 3D-UTE Dual Echo Rosette MRI and MRSI Sequence 2 Shen X, et al., doi: https://doi.org/10.1101/2021.09.18.460869 Normalized Radial UTE Reconstruction (RG) Normalized Rosette UTE Reconstruction (RG)

3. 3D-UTE Rosette rocks #5009 #1040 #1495 #1738 #1982 #5001 #1534

4. Day 1 Wednesday Arrive at 3 pm: University of Cambridge, Wolfson Brain Imaging Centre, Cambridge, United Kingdom 3:15 pm Installed the sequence Of course, it did not work… 

5. Day 2 Thursday • Arrive at 1 pm University of Cambridge, Wolfson Brain Imaging Centre, Cambridge, United Kingdom • Phantom Experiments • Did it work? • Of course, it did work…. How do I know?

6. The Berkeley Advanced Reconstruction Toolbox (BART) toolbox • Problems • Non-Cartesian K-space trajectory • Giant data size • Limited computational power • Solution • The Berkeley Advanced Reconstruction Toolbox (BART) toolbox

7. Day 3 k-Space Design 3D Rosette (high-resolution) • Siemen 7T Terra • 32 Channel Nova Coil • Kmax=500/m • 𝜔1=𝜔2=0.766 kHz • Field of view (FOV):240x240x240 mm3 • Matrix size=256x256x256, • Readout dwell time=10 𝜇𝑠 • Flip angle=7-degree • TR=7 ms, • Readout duration=4.2 ms • RF pulse duration=20 𝜇𝑠 • TE: 30 𝜇𝑠 and 2.12 ms • Number of petals: 71442 (40%) • Total scan time: 8.7 minutes Sequential k-space fill- in Kz (/m) First Last

8. Reconstruction Two reconstruction methods were performed: compressed sensing (as described above) and regular regridding applying a density compensated (Pipe&Menon) adjoint NUFFT. Adjouint NUFFT Total Reconstruction time: ~ 2 minutes Compressed sensing ~10 minutes.

9. Day 3 ~ 1 mm isotropic Resolution 7T ~ 0.5 mm isotropic Resolution 7T

10. Day 3 How About 7T MRSI • Siemen 7T Terra • 32 Channel Nova Coil • Field of view (FOV):240x240x240 mm3 • Matrix size=48x48x48, • Readout dwell time=10 𝜇𝑠 • Flip angle=30-degree • TR=500 ms, • Readout duration=~460 us • RF pulse duration=20 𝜇𝑠 • TE: 30 𝜇𝑠, Maximum • Spectral Bandwidth ~4.4 Khz • Number of petals: 1444 • Total scan time: ~10 minutes

11. How about 9.4T Bruker animal Scanner (PetalUTE, Stephen Sawiak)

12. 3D-UTE Rosette rocks #5009 #1040 #1495 #1738 #1982 #5001 #1534

13. Thank you so much

Editor's Notes

With this in mind, we sought out to study the validity of a novel UTE 3D MRSI sequence with a rosette k-space trajectory for use in phosphorus-31 spectroscopic imaging. This sequence has been successfully tested in proton phantoms and phosphorus localization phantoms. The rosette trajectory’s multiple crossings of k-space origin theoretically lead to smooth transitions in temporal repetitions for spectra. Additionally, the unprecedently short UTE intrinsically avoids all baseline and phasing issues. More details can be found in the linked pre-printed paper below. To study this sequence, we recruited 5 healthy volunteers for uninterrupted, back-to-back leg muscle scanning with the novel MRSI sequence and an existing conventional MRSI sequence. We hypothesize that the novel UTE rosette sequence can simplify first-order phase and baseline corrections while providing competitive SNR relative to the conventional sequence.

3D-UTE Rosette MRI and MRSI at UHF human (7T) and animal scanners (9.4T) using The (BART) toolbox

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3D-UTE Rosette MRI and MRSI at UHF human (7T) and animal scanners (9.4T) using The (BART) toolbox

Editor's Notes