Dataset Information

Open-source magnetic resonance imaging acquisition: Data and documentation for two validated pulse sequences.

ABSTRACT: Raw data, simulated and acquired phantom images, and quantitative longitudinal and transverse relaxation times (T₁/T₂) maps from two open-source Magnetic Resonance Imaging (MRI) pulse sequences are presented in this dataset along with corresponding ".seq" files, sequence implementation scripts, and reconstruction/analysis scripts [1]. Real MRI data were collected from a 3T Siemens Prisma Fit and a 1.5T Siemens Aera via the Pulseq open-source MR sequence platform, and corresponding in silico data were generated using the simulation module of Virtual Scanner [2]. This dataset and its associated code can be used to validate the pipeline for using the same pulse sequences at other research sites using Pulseq, to provide guidelines for documenting and sharing open-source pulse sequences in general, and to demonstrate practical, customizable acquisition scripts using the PyPulseq library.

SUBMITTER: Tong G

PROVIDER: S-EPMC9011005 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Open-source magnetic resonance imaging acquisition: Data and documentation for two validated pulse sequences.

Tong Gehua G Gaspar Andreia S AS Qian Enlin E Ravi Keerthi Sravan KS Vaughan John Thomas JT Nunes Rita G RG Geethanath Sairam S

Data in brief 20220328

Raw data, simulated and acquired phantom images, and quantitative longitudinal and transverse relaxation times (T<sub>1</sub>/T<sub>2</sub>) maps from two open-source Magnetic Resonance Imaging (MRI) pulse sequences are presented in this dataset along with corresponding ".seq" files, sequence implementation scripts, and reconstruction/analysis scripts [1]. Real MRI data were collected from a 3T Siemens Prisma Fit and a 1.5T Siemens Aera via the Pulseq open-source MR sequence platform, and corres ...[more]

PMID: 35434217

Similar Datasets

Project description:ObjectiveTo perform a meta-analysis to quantitatively assess functional magnetic resonance imaging (MRI) in the diagnosis of locally recurrent prostate cancer.Materials and methodsA comprehensive search of the PubMed, Embase, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews was conducted from January 1, 1995 to December 31, 2016. Diagnostic accuracy was quantitatively pooled for all studies by using hierarchical logistic regression modeling, including bivariate modeling and hierarchical summary receiver operating characteristic (HSROC) curves (AUCs). The Z test was used to determine whether adding functional MRI to T2-weighted imaging (T2WI) results in significantly increased diagnostic sensitivity and specificity.ResultsMeta-analysis of 13 studies involving 826 patients who underwent radical prostatectomy showed a pooled sensitivity and specificity of 91%, and the AUC was 0.96. Meta-analysis of 7 studies involving 329 patients who underwent radiotherapy showed a pooled sensitivity of 80% and specificity of 81%, and the AUC was 0.88. Meta-analysis of 11 studies reporting 1669 sextant biopsies from patients who underwent radiotherapy showed a pooled sensitivity of 54% and specificity of 91%, and the AUC was 0.85. Sensitivity after radiotherapy was significantly higher when diffusion-weighted MRI data were combined with T2WI than when only T2WI results were used. This was true when meta-analysis was performed on a per-patient basis (p = 0.027) or per sextant biopsy (p = 0.046). A similar result was found when 1H-magnetic resonance spectroscopy (1H-MRS) data were combined with T2WI and sextant biopsy was the unit of analysis (p = 0.036).ConclusionFunctional MRI data may not strengthen the ability of T2WI to detect locally recurrent prostate cancer in patients who have undergone radical prostatectomy. By contrast, diffusion-weight MRI and 1H-MRS data may improve the sensitivity of T2WI for patients who have undergone radiotherapy.

Project description:BACKGROUND:MR-guided focused ultrasound or high-intensity focused ultrasound (MRgFUS/MRgHIFU) is a non-invasive therapeutic modality with many potential applications in areas such as cancer therapy, drug delivery, and blood-brain barrier opening. However, the large financial costs involved in developing preclinical MRgFUS systems represent a barrier to research groups interested in developing new techniques and applications. We aim to mitigate these challenges by detailing a validated, open-source preclinical MRgFUS system capable of delivering thermal and mechanical FUS in a quantifiable and repeatable manner under real-time MRI guidance. METHODS:A hardware and software package was developed that includes closed-loop feedback controlled thermometry code and CAD drawings for a therapy table designed for a preclinical MRI scanner. For thermal treatments, the modular software uses a proportional integral derivative controller to maintain a precise focal temperature rise in the target given input from MR phase images obtained concurrently. The software computes the required voltage output and transmits it to a FUS transducer that is embedded in the delivery table within the magnet bore. The delivery table holds the FUS transducer, a small animal and its monitoring equipment, and a transmit/receive RF coil. The transducer is coupled to the animal via a water bath and is translatable in two dimensions from outside the magnet. The transducer is driven by a waveform generator and amplifier controlled by real-time software in Matlab. MR acoustic radiation force imaging is also implemented to confirm the position of the focus for mechanical and thermal treatments. RESULTS:The system was validated in tissue-mimicking phantoms and in vivo during murine tumor hyperthermia treatments. Sonications were successfully controlled over a range of temperatures and thermal doses for up to 20 min with minimal temperature overshoot. MR thermometry was validated with an optical temperature probe, and focus visualization was achieved with acoustic radiation force imaging. CONCLUSIONS:We developed an MRgFUS platform for small-animal treatments that robustly delivers accurate, precise, and controllable sonications over extended time periods. This system is an open source and could increase the availability of low-cost small-animal systems to interdisciplinary researchers seeking to develop new MRgFUS applications and technology.

Dataset Information

Open-source magnetic resonance imaging acquisition: Data and documentation for two validated pulse sequences.

Publications

Open-source magnetic resonance imaging acquisition: Data and documentation for two validated pulse sequences.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets