Virtual Scanner: MRI on a Browser

Tong, Gehua; Geethanath, Sairam; Jimeno, Marina Manso; Qian, Enlin; Ravi, Keerthi Sravan; Girish, Nishika; Vaughan, J. Thomas

doi:10.21105/joss.01637

Cited by 6 publications

(12 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, this can be observed in the T 2 FLAIR contrast in Figure 4. Potentially, Virtual Scanner (Tong et al ., 2019) and its digital twinning capability (Tong, Vaughan and Geethanath, 2021) can be leveraged to design a physics-informed LUT optimization approach.…”

Section: Discussionmentioning

confidence: 99%

Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer’s disease imaging

Ravi

Nandakumar

Thomas

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Magnetic Resonance Imaging (MRI) is expensive and time-consuming. Protocol optimization to accelerate MRI requires local expertise since each MR sequence involves multiple configurable parameters that need optimization for contrast, acquisition time, and signal-to-noise ratio (SNR). The availability and access to technical training are limited in under-served regions, resulting in a scarcity of local expertise required to operate the hardware and perform MR examinations. Along with other cultural and temporal constraints, these factors contribute to the highly inefficient utilization of MRI services diminishing their clinical value. In this work, we extend our previous effort and demonstrate accelerated MRI via intelligent protocolling of the modified brain screen protocol, referred to as the Gold Standard (GS) protocol. We leverage deep learning-based contrast-specific image-denoising to improve the image quality of data acquired using the accelerated protocol. Since the SNR of MR acquisitions depends on the volume of the object being imaged, we demonstrate subject-specific (SS) image-denoising. Utilizing the accelerated protocol resulted in a 1.94x gain in imaging throughput over the GS protocol. The minimum /maximum PSNR gains (measured in dB) were 1.18/11.68 and 1.04/13.15, from the baseline and SS image-denoising models, respectively. Alzheimers Disease (AD) accounts for up to 60-80% of dementia cases and a global trend of longer lifespans has resulted in an increase in the prevalence of dementia/AD. Therefore, an accurate differential diagnosis of AD is crucial to determine the right course of treatment. The GS protocol constitutes 44.44% of the comprehensive AD imaging protocol defined by the European Prevention of Alzheimers Disease project. Therefore, we also demonstrate the potential for AD-imaging via automated volumetry of relevant brain anatomies whose atrophies have been shown to be reliable indicators of the onset of the disease. The volumetric measurements of the hippocampus and amygdala from the GS and accelerated protocols were in excellent agreement, as measured by the intra-class correlation coefficient. In conclusion, accelerated brain imaging with the potential for AD imaging was demonstrated, and image quality was recovered post-acquisition using DL-based image denoising models.

show abstract

Section: Discussionmentioning

confidence: 99%

Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer’s disease imaging

Ravi

Nandakumar

Thomas

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…All games operate in "virtual mode", where the image acquisition part is performed through Virtual Scanner simulation. 9,10 In addition, the games were designed for communicating with an educational scanner (Figure S1). The games are unified in their structure, composed of three parts: (i) user input fields; (ii) visualizations; and (iii) the laboratory manual.…”

Section: Game Designmentioning

confidence: 99%

“…The tool leveraged the virtual scanner software 10 to provide the required simulations. The informal feedback at the Uganda workshop helped us map the minimum requirement to play the games at the level of a US high schooler.…”

Section: Development Of the Gamesmentioning

confidence: 99%

“…3 Many existing open-source magnetic resonance (MR) simulation programs provide functionality with educational goals or simulation capabilities that are helpful for teaching MRI. [4][5][6][7][8][9][10][11][12][13] These tools are summarized in Table S1. On the other hand, custom MR hardware systems for education have also been developed to help students gain real scanner operating experience and an understanding of the MR hardware components that work together.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Expanding access to magnetic resonance education through open‐source web tutorials

Tong,

Ananth,

Vaughan

et al. 2024

NMR in Biomedicine

Self Cite

View full text Add to dashboard Cite

As of 2018, two-thirds of the world did not have access to magnetic resonance imaging (MRI), 1 with most of this population living in low-resource settings. While having more scanners is necessary for expanding access, scanner numbers alone do not help sustain operations: local expertise is required to maintain the scanner's productivity in the long run. 2 Sustaining the accessibility of MRI requires the training of students in these settings. Open-source and free educational software can help teach future generations of MRI technicians and scientists and is recognized as a critical need. 2 Nonprofit organizations such as RAD-AID support expanding access to medical imaging through local assessment of readiness, installation of imaging equipment, and education of personnel. 3 Many existing open-source magnetic resonance (MR) simulation programs provide functionality with educational goals or simulation capabilities that are helpful for teaching MRI. [4][5][6][7][8][9][10][11][12][13] These tools are summarized in Table S1. On the other hand, custom MR hardware systems for education have also been developed to help students gain real scanner operating experience and an understanding of the MR hardware components that work together. [14][15][16][17][18] They often have associated educational materials and console software. A selection of these systems is summarized in Table S2.Each of these platforms assumes that MR education starts at the college level. However, access to advanced degrees and curricula in lowresource settings is a challenge. 1 Therefore, a gap exists in younger populations or those needing more access to formal courses provided by an advanced educational infrastructure. MR is a multifaceted field and includes many aspects of mathematical, biological, physical, and spatial thinking. Many of its concepts and processes need visualization. The accessibility of open-source software and open hardware in MR enables the development of new educational possibilities. 19 Therefore, a platform aimed at students at or above the educational background of a US high

show abstract

“…We have leveraged the PyPulseq library to implement acquisition oriented components of the Autonomous MRI (AMRI) package (Ravi et al, 2018a(Ravi et al, , 2019a(Ravi et al, , 2019b, Virtual Scanner (Tong et al, 2019), and the non-Cartesian acquisition library (Ravi et al, 2018b). Also, the PyPulseq-gpi branch integrates a previous version of PyPulseq with GPI to enable GUI-based pulse sequence design.…”

Section: About Pypulseqmentioning

confidence: 99%

PyPulseq: A Python Package for MRI Pulse Sequence Design

Ravi¹,

Geethanath²,

Vaughan³

2019

JOSS

Self Cite

View full text Add to dashboard Cite

Magnetic Resonance Imaging (MRI) is a critical component of healthcare. MRI data is acquired by playing a series of radio-frequency and magnetic field gradient pulses. Designing these pulse sequences requires knowledge of specific programming environments depending on the vendor hardware (generations) and software (revisions) intended for implementation. This impedes the pace of prototyping. Pulseq (Layton et al., 2017) introduced an open source file standard for pulse sequences that can be deployed on Siemens/GE via TOPPE (Nielsen & Noll, 2018)/Bruker platforms. In this work, we introduce PyPulseq, which enables pulse sequence programming in Python. Its advantages are zero licensing fees and easy integration with deep learning methods developed in Python. PyPulseq is aimed at MRI researchers, faculty, students, and other allied field researchers such as those in neuroscience. We have leveraged this tool for several published research works (Poojar, Geethanath, Reddy, & Venkatesan, n.d.;

show abstract

Virtual Scanner: MRI on a Browser

Cited by 6 publications

References 13 publications

Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer’s disease imaging

Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer’s disease imaging

Expanding access to magnetic resonance education through open‐source web tutorials

PyPulseq: A Python Package for MRI Pulse Sequence Design

Contact Info

Product

Resources

About