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Magnetic Resonance Imaging Physical Principles And Sequence Design

Purpose: To investigate a measurement method for evaluating the resolution properties of CTimaging systems across reconstruction algorithms, dose, and contrast. Methods: An algorithm was developed to extract the task-based modulation transfer function(MTF) from disk images generated from the rod inserts in the ACR phantom (model 464 Gammex, WI). These inserts are conventionally employed for HU accuracy assessment. The edge of the disk objects was analyzed to determine the edge-spread function, which was differentiated to yield the line-spread function and Fourier-transformed to generate the object-specific MTF for task-based assessment, denoted MTFTask. The proposed MTFmeasurement method was validated against the conventional wire technique and further applied to measure the MTF of CTimagesreconstructed with an adaptive statistical iterative algorithm (ASIR) and a model-based iterative (MBIR) algorithm. Results were further compared to the standard filtered back projection (FBP) algorithm. Measurements were performed and compared across different doses and contrast levels to ascertain the MTFTask dependencies on those factors. Results: For the FBP reconstructed images, the MTFTaskmeasured with the inserts were the same as the MTFmeasured from the wire-based method. For the ASIR and MBIR data, the MTFTask using the high contrast insert was similar to the wire-based MTF and equal or superior to that of FBP. However, results for the MTFTaskmeasured using the low-contrast inserts, the MTFTask for ASIR and MBIR data was lower than for the FBP, which was constant throughout all measurements. Similarly, as a function of mA, the MTFTask for ASIR and MBIR varied as a function of noise–-with MTFTask being proportional to mA. Overall greater variability of MTFTask across dose and contrast was observed for MBIR than for ASIR. Conclusions: This approach provides a method for assessing the task-based MTF of a CT system using conventional and iterative reconstructions. Results demonstrated that the object-specific MTF can vary as a function of dose and contrast. The analysis highlighted the paradigm shift for iterative reconstructions when compared to FBP, where iterative reconstructions generally offer superior noise performance but with varying resolution as a function of dose and contrast. The MTFTask generated by this method is expected to provide a more comprehensive assessment of image resolution across different reconstruction algorithms and imaging tasks.

Towards task-based assessment of CT performance: System and object MTF across different reconstruction algorithms

In recent years, significant progress was achieved in the field of nanomedicine and bioimaging, but the development of new biomarkers for reliable detection of diseases at an early stage, molecular imaging, targeting and therapy remains crucial. The disadvantages of commonly used organic dyes include photobleaching, autofluorescence, phototoxicity and scattering when UV (ultraviolet) or visible light is used for excitation. The limited penetration depth of the excitation light and the visible emission into and from the biological tissue is a further drawback with regard to in vivo bioimaging. Lanthanide containing inorganic nanostructures emitting in the near-infrared (NIR) range under NIR excitation may overcome those problems. Due to the outstanding optical and magnetic properties of lanthanide ions (Ln(3+)), nanoscopic host materials doped with Ln(3+), e.g. Y2O3:Er(3+),Yb(3+), are promising candidates for NIR-NIR bioimaging. Ln(3+)-doped gadolinium-based inorganic nanostructures, such as Gd2O3:Er(3+),Yb(3+), have a high potential as opto-magnetic markers allowing the combination of time-resolved optical imaging and magnetic resonance imaging (MRI) of high spatial resolution. Recent progress in our research on over-1000 nm NIR fluorescent nanoprobes for in vivo NIR-NIR bioimaging will be discussed in this review.

/pdf/upconverting-and-nir-emitting-rare-earth-based-4x25nqoixs.pdf

Upconverting and NIR emitting rare earth based nanostructures for NIR-bioimaging

Purpose
To introduce a two-dimensional MR fingerprinting (MRF) technique for quantification of T1, T2, and M0 in myocardium.

Methods
An electrocardiograph-triggered MRF method is introduced for mapping myocardial T1, T2, and M0 during a single breath-hold in as short as four heartbeats. The pulse sequence uses variable flip angles, repetition times, inversion recovery times, and T2 preparation dephasing times. A dictionary of possible signal evolutions is simulated for each scan that incorporates the subject's unique variations in heart rate. Aspects of the sequence design were explored in simulations, and the accuracy and precision of cardiac MRF were assessed in a phantom study. In vivo imaging was performed at 3 Tesla in 11 volunteers to generate native parametric maps.

Results
T1 and T2 measurements from the proposed cardiac MRF sequence correlated well with standard spin echo measurements in the phantom study (R2 > 0.99). A Bland-Altman analysis revealed good agreement for myocardial T1 measurements between MRF and MOLLI (bias 1 ms, 95% limits of agreement −72 to 72 ms) and T2 measurements between MRF and T2-prepared balanced steady-state free precession (bias, −2.6 ms; 95% limits of agreement, −8.5 to 3.3 ms).

Conclusion
MRF can provide quantitative single slice T1, T2, and M0 maps in the heart within a single breath-hold. Magn Reson Med 77:1446–1458, 2017. © 2016 International Society for Magnetic Resonance in Medicine

/pdf/mr-fingerprinting-for-rapid-quantification-of-myocardial-t1-31ibcljw3v.pdf

MR fingerprinting for rapid quantification of myocardial T1 , T2 , and proton spin density.

White matter injury in ischemic stroke.

When too few photons reach detector elements, strong streaks appear through paths of high X-ray attenuation and an image becomes completely useless. This photon starvation artifact phenomenon occurs frequently when a pelvis or shoulder is scanned with thin slices. The common understanding regarding photon starvation streaks is that they are a manifestation of irregularities caused by noise in the raw data profile. Therefore, the common countermeasure is local raw-data filtering, which detects and smoothes out the highly noisy part of the raw data. However, the photon starvation artifact can be solved only partly with such a method and a more effective solution is necessary. Here, we examined the mean level shift of raw data attributable to the nonlinear nature of logarithmic conversion, which is the process required for generating raw data from detected X-ray data. We judge that the real culprit of the photon starvation artifact is this mean level shift. When the noise level is very high or the photon level is very low, this mean level shift can become prominent and can become manifest as thick streaks against which the conventional local raw data filtering has no power. To solve this problem, we propose a new scheme of local raw data filtering, which consists of reverting log-converted raw data to a form that is equivalent to pre-log detector data. With this method, not only fine streaks, but also thick streaks are removed effectively. A better image quality with lower X-ray doses is possible with this method.

Photon starvation artifacts of X-ray CT: their true cause and a solution

The point spread function (PSF) method is currently the one predominantly used to determine the modulation transfer function (MTF) of an X-ray CT system. However, the image examined with the PSF method must have a very high contrast-to-noise ratio (CNR); it must also be reconstructed with a fine pixel pitch using a zooming reconstruction. Therefore, the PSF method is often inappropriate for describing the MTF of clinical operating conditions when image linearity is not guaranteed. The edge spread function (ESF) method requires no zooming reconstruction, but its susceptibility to image noise is no better than that of the PSF method. We describe a technique for rendering the ESF method robust to image noise. We smooth out the noisy ESF through multiple stages of filtering. Invariably, the line spread function (LSF) obtained from the smoothed ESF is blurred, and the MTF obtained from the LSF is incorrect. However, because the filtering that has been applied is known, much of the LSF blurring can be corrected. An estimate of the true LSF is obtainable from the blurred LSF, assuming that the true LSF is not very different from either a Gaussian or a composite of multiple Gaussians. For an image reconstructed with a kernel for soft-tissue imaging, the MTF obtained by our method is sufficiently consistent with the theoretical MTF, even when the CNR is as low as 2.

Deriving the modulation transfer function of CT from extremely noisy edge profiles.

Gd2O3 nanoparticles were synthesized by using our original method for application as MRI contrast agents. The sample diameters were controlled in the range 18–66 nm by adjusting the annealing temperature between 773 and 1273 K in air or in an Ar atmosphere. Magnetization measurements were performed at 300 K, and the effective magnetic moment of each sample was calculated. They showed paramagnetism at 300 K and they had large effective magnetic moment μeff of 7.15–8.05 μB. MRI measurements were performed in 0.8 wt% agarose solution, and the Gd2O3 nanoparticles were found to work as effective T1-shortening MRI contrast agents.

https://iopscience.iop.org/article/10.1088/1742-6596/352/1/012008/pdf

Synthesis of Gd2O3 nanoparticles for MRI contrast agents

Parallel magnetic resonance imaging (MRI) with arrays of receiver coils such as those of sensitivity encoding (SENSE) are being widely used. However, conventional methods of image signal-to-noise ratio (SNR) determination cannot be used for parallel MRI, and a novel method has been reported in JSRT. However, this method of SNR determination is for phantom images not for clinical images. Therefore, we researched accurate measurement of the image noise of parallel MRI reconstruction including the unfolding process and uniformity filters. The possibility of a subtraction method using clinical images and the accuracy of standard deviation (SD) of clinical images for optimum ROI were studied because it was not possible to use the air-signal method. The results indicated that the position of the ROI was selected for uniformity of signal intensity area and that the size of the ROI was about 50 pixels. However, under these conditions, the noise value of SNR was higher than that using the phantom-subtraction method. In addition, the tissue-subtraction method was useful when the two scanning images were in agreement.

Method of SNR determination using clinical images

When measuring the signal-to-noise ratio (SNR) of an image the used parallel magnetic resonance imaging, it was confirmed that there was a problem in the application of past SNR measurement. With the method of measuring the noise from the background signal, SNR with parallel imaging was higher than that without parallel imaging. In the subtraction method (NEMA standard), which sets a wide region of interest, the white noise was not evaluated correctly although SNR was close to the theoretical value. We proposed two techniques because SNR in parallel imaging was not uniform according to inhomogeneity of the coil sensitivity distribution and geometry factor. Using the first method (subtraction mapping), two images were scanned with identical parameters. The SNR in each pixel divided the running mean (7 by 7 pixels in neighborhood) by standard deviation/radical2 in the same region of interest. Using the second (consecutive) method, more than fifty consecutive scans of the uniform phantom were obtained with identical scan parameters. Then the SNR was calculated from the ratio of mean signal intensity to the standard deviation in each pixel on a series of images. Moreover, geometry factors were calculated from SNRs with and without parallel imaging. The SNR and geometry factor using parallel imaging in the subtraction mapping method agreed with those of the consecutive method. Both methods make it possible to obtain a more detailed determination of SNR in parallel imaging and to calculate the geometry factor.

https://www.jstage.jst.go.jp/article/jjrt/64/8/64_8_930/_pdf

Yoshio Machida

Papers

Photon starvation artifacts of X-ray CT: their true cause and a solution

Deriving the modulation transfer function of CT from extremely noisy edge profiles.

Synthesis of Gd2O3 nanoparticles for MRI contrast agents

Method of SNR determination using clinical images

[Signal-to-noise ratio measurement in parallel MRI with subtraction mapping and consecutive methods].