A standard system phantom for magnetic resonance imaging.

PURPOSE: A standard MRI system phantom has been designed and fabricated to assess scanner performance, stability, comparability and assess the accuracy of quantitative relaxation time imaging. The phantom is unique in having traceability to the International System of Units, a high level of precision, and monitoring by a national metrology institute. Here, we describe the phantom design, construction, imaging protocols, and measurement of geometric distortion, resolution, slice profile, signal-to-noise ratio (SNR), proton-spin relaxation times, image uniformity and proton density. METHODS: The system phantom, designed by the International Society of Magnetic Resonance in Medicine ad hoc committee on Standards for Quantitative MR, is a 200 mm spherical structure that contains a 57-element fiducial array; two relaxation time arrays; a proton density/SNR array; resolution and slice-profile insets. Standard imaging protocols are presented, which provide rapid assessment of geometric distortion, image uniformity, T1 and T2 mapping, image resolution, slice profile, and SNR. RESULTS: Fiducial array analysis gives assessment of intrinsic geometric distortions, which can vary considerably between scanners and correction techniques. This analysis also measures scanner/coil image uniformity, spatial calibration accuracy, and local volume distortion. An advanced resolution analysis gives both scanner and protocol contributions. SNR analysis gives both temporal and spatial contributions. CONCLUSIONS: A standard system phantom is useful for characterization of scanner performance, monitoring a scanner over time, and to compare different scanners. This type of calibration structure is useful for quality assurance, benchmarking quantitative MRI protocols, and to transition MRI from a qualitative imaging technique to a precise metrology with documented accuracy and uncertainty.

Development of advanced signal processing and source imaging methods for superparamagnetic relaxometry.

Superparamagnetic relaxometry (SPMR) is a highly sensitive technique for the in vivo detection of tumor cells and may improve early stage detection of cancers. SPMR employs superparamagnetic iron oxide nanoparticles (SPION). After a brief magnetizing pulse is used to align the SPION, SPMR measures the time decay of SPION using super-conducting quantum interference device (SQUID) sensors. Substantial research has been carried out in developing the SQUID hardware and in improving the properties of the SPION. However, little research has been done in the pre-processing of sensor signals and post-processing source modeling in SPMR. In the present study, we illustrate new pre-processing tools that were developed to: (1) remove trials contaminated with artifacts, (2) evaluate and ensure that a single decay process associated with bounded SPION exists in the data, (3) automatically detect and correct flux jumps, and (4) accurately fit the sensor signals with different decay models. Furthermore, we developed an automated approach based on multi-start dipole imaging technique to obtain the locations and magnitudes of multiple magnetic sources, without initial guesses from the users. A regularization process was implemented to solve the ambiguity issue related to the SPMR source variables. A procedure based on reduced chi-square cost-function was introduced to objectively obtain the adequate number of dipoles that describe the data. The new pre-processing tools and multi-start source imaging approach have been successfully evaluated using phantom data. In conclusion, these tools and multi-start source modeling approach substantially enhance the accuracy and sensitivity in detecting and localizing sources from the SPMR signals. Furthermore, multi-start approach with regularization provided robust and accurate solutions for a poor SNR condition similar to the SPMR detection sensitivity in the order of 1000 cells. We believe such algorithms will help establishing the industrial standards for SPMR when applying the technique in pre-clinical and clinical settings.

Magnetic relaxometry as applied to sensitive cancer detection and localization.

BACKGROUND: Here we describe superparamagnetic relaxometry (SPMR), a technology that utilizes highly sensitive magnetic sensors and superparamagnetic nanoparticles for cancer detection. Using SPMR, we sensitively and specifically detect nanoparticles conjugated to biomarkers for various types of cancer. SPMR offers high contrast in vivo, as there is no superparamagnetic background, and bones and tissue are transparent to the magnetic fields. METHODS: In SPMR measurements, a brief magnetizing pulse is used to align superparamagnetic nanoparticles of a discrete size. Following the pulse, an array of superconducting quantum interference detectors (SQUID) sensors detect the decaying magnetization field. NP size is chosen so that, when bound, the induced field decays in seconds. They are functionalized with specific biomarkers and incubated with cancer cells in vitro to determine specificity and cell binding. For in vivo experiments, functionalized NPs are injected into mice with xenograft tumors, and field maps are generated to localize tumor sites. RESULTS: Superparamagnetic NPs developed here have small size dispersion. Cell incubation studies measure specificity for different cell lines and antibodies with very high contrast. In vivo animal measurements verify SPMR localization of tumors. Our results indicate that SPMR possesses sensitivity more than 2 orders of magnitude better than previously reported.

Multi-flux-transformer MRI detection with an atomic magnetometer.

Recently, anatomical ultra-low field (ULF) MRI has been demonstrated with an atomic magnetometer (AM). A flux-transformer (FT) has been used for decoupling MRI fields and gradients to avoid their negative effects on AM performance. The field of view (FOV) was limited because of the need to compromise between the size of the FT input coil and MRI sensitivity per voxel. Multi-channel acquisition is a well-known solution to increase FOV without significantly reducing sensitivity. In this paper, we demonstrate twofold FOV increase with the use of three FT input coils. We also show that it is possible to use a single atomic magnetometer and single acquisition channel to acquire three independent MRI signals by applying a frequency-encoding gradient along the direction of the detection array span. The approach can be generalized to more channels and can be critical for imaging applications of non-cryogenic ULF MRI where FOV needs to be large, including head, hand, spine, and whole-body imaging.