Radiative MRI coil design using parasitic scatterers: MRI Yagi

Conventionally, radiofrequency (RF) coils used for magnetic resonance imaging (MRI) are electrically small and designed for nearfield operation. Therefore, existing antenna design techniques are mostly irrelevant for RF coils. However, the use of higher frequencies in ultrahigh field (UHF) MRI allows for antenna design techniques to be adapted to RF coil designs. This study proposes the use of parasitic scatterers to improve the performance of an existing 7T MRI coil called the single-sided adapted dipole (SSAD) antenna. The results reveal that scatterers arranged in a Yagi fashion can be applied to reduce local specific absorption rate (SAR) maxima of a reference SSAD by 40% with only a 6% decrease in the propagated B1+ field at the tissue depth of 15 cm. The higher directivity of the proposed design also decreasing the coupling with additional elements, making this antenna suitable for use in high density arrays. These findings show the potential of parasitic scatterers as an effective method to improve the performance of existing radiative MRI coils

(68)Ga-labeled superparamagnetic iron oxide nanoparticles (SPIONs) for multi-modality PET/MR/Cherenkov luminescence imaging of sentinel lymph nodes.

The aim of this study was to develop (68)Ga-SPIONs for use as a single contrast agent for dynamic, quantitative and high resolution PET/MR imaging of Sentinel Lymph Node (SLN). In addition (68)Ga enables Cherenkov light emission which can be used for optical guidance during resection of SLN. SPIONs were labeled with (68)Ga in ammonium acetate buffer, pH 5.5. The labeling yield and stability in human serum were determined using instant thin layer chromatography. An amount of 0.07-0.1 mL (~5-10 MBq, 0.13 mg Fe) of (68)Ga-SPIONs was subcutaneously injected in the hind paw of rats. The animals were imaged at 0-3 h and 25 h post injection with PET/CT, 9.4 T MR and CCDbased Cherenkov optical systems. A biodistribution study was performed by dissecting and measuring the radioactivity in lymph nodes, kidneys, spleen, liver and the injection site. The labeling yield was 97.3 ± 0.05% after 15 min and the (68)Ga-SPIONs were stable in human serum. PET, MR and Cherenkov luminescence imaging clearly visualized the SLN. Biodistribution confirmed a high uptake of the (68)Ga-SPIONs within the SLN. We conclude that generator produced (68)Ga can be labeled to SPIONs. Subcutaneously injected (68)Ga-SPIONs can enhance the identification of the SLNs by combining sensitive PET and high resolution MR imaging. Clinically, hybrid PET/MR cameras are already in use and (68)Ga-SPIONs have a great potential as a single-dose, tri-modality agent for diagnostic imaging and potential Cherenkov luminescent guided resection of SLN

Toward accurate cerebral blood flow estimation in mice after accounting for anesthesia

Purpose: To improve the accuracy of cerebral blood flow (CBF) measurement in mice by accounting for the anesthesia effects.Methods: The dependence of CBF on anesthesia dose and time was investigated by simultaneously measuring respiration rate (RR) and heart rate (HR) under four different anesthetic regimens. Quantitative CBF was measured by a phase-contrast (PC) MRI technique. RR was evaluated with a mouse monitoring system (MouseOX) while HR was determined using an ultrashort-TE MRI sequence. CBF, RR, and HR were recorded dynamically with a temporal resolution of 1 min in a total of 19 mice. Linear regression models were used to investigate the relationships among CBF, anesthesia dose, RR, and HR.Results: CBF, RR, and HR all showed a significant dependence on anesthesia dose (p < 0.0001). However, the dose in itself was insufficient to account for the variations in physiological parameters, in that they showed a time-dependent change even for a constant dose. RR and HR together can explain 52.6% of the variations in CBF measurements, which is greater than the amount of variance explained by anesthesia dose (32.4%). Based on the multi-parametric regression results, a model was proposed to correct the anesthesia effects in mouse CBF measurements, specifically CBFcorrected=CBF+0.58RR−0.41HR−32.66Dose. We also reported awake-state CBF in mice to be 142.0 ± 8.8 mL/100 g/min, which is consistent with the model-predicted value.Conclusion: The accuracy of CBF measurement in mice can be improved by using a correction model that accounts for respiration rate, heart rate, and anesthesia dose

WAVELET NOISE REDUCTION AND VASCULAR WATER TRANSPORT MODELLING : APPLICATIONS TO DIFFUSION AND PERFUSION MRI

Magnetic resonance imaging (MRI) is a powerful medical imaging technique, used to detect and characterise a range of diseases and conditions. It is based on the use of a strong static magnetic field in combination with magnetic field gradients and pulsed radiofrequency electromagnetic fields to visualise various organs and structures in the body according to their morphology or function.Diffusion and perfusion MRI are established methods for quantitative measurements, often used in neurological and neurovascular clinical applications. Although these techniques are often used separately to investigate a number of diseases, combined diffusion and perfusion information can provide unique information, e.g. for assessment of whether stroke patients in the acute stage are likely to benefit from reperfusion therapy. This may be accomplished by identification of the so-called ischemic penumbra (i.e. the area surrounding the core of an infarct, exhibiting disturbed microcirculation but still viable and salvageable if the local blood supply is efficiently restored). This identification concept is often referred to as the diffusion–perfusion mismatch. In oncological applications, a combination of diffusion and perfusion MRI is sometimes used in tumour characterisation and in attempts to monitor early treatment response.Quantitative diffusion MRI may be hampered by a bias induced by the so-called rectified noise floor in areas with low signal-to-noise ratio (SNR), and to address this issue, a wavelet-based filtering method was presented and used for noise reduction in diffusion MRI.Perfusion images acquired by arterial spin labelling (ASL), which is the technique investigated in the present work, suffer from inherently low SNR, and this is commonly addressed by averaging multiple repetitions, which leads to a prolonged acquisition time. As an alternative approach, wavelet-domain filtering for noise reduction was applied to ASL data, and the performance of the proposed filtering technique was investigated (in terms of accuracy, precision and structural degradation), and a comparison with conventional Gaussian smoothing was also included. Additionally, a quantitative non-compartment modelling approach for assessment of blood water transit time through the microvasculature and the blood–brain barrier (BBB) was investigated. In one study, the model was adapted to a clinical setup and applied to test–retest data from healthy volunteers, and the effects of noise on the model were examined by simulations. In an animal study, the model was further developed by introducing a bolus- tracking ASL solution that included a measured arterial input function (AIF) instead of a theoretical rectangular input function. Furthermore, it was explored whether effects of mildly damaged red blood cells on microvascular parameters were detectable using the proposed modelling approach and by ASL-based CBF quantification.The extracted water transit time parameters can be used separately or in combination with conventional perfusion and diffusion estimates. Changes in the blood water transit time in the microvasculature may be related to alterations in capillary water permeability, and may thus be useful in the assessment of BBB integrity. Disturbed BBB permeability has been attributed to a number of disease states, and may be relevant to, for example, early diagnosis of Alzheimer's disease, inflammation, tumour grading and ischaemic stroke

Denoising of Complex MRI Data by Wiener-like Filtering in the Wavelet Domain - Application to High b-value Diffusion Weighted Imaging

Noise is a well-known problem in many imaging modalities. In magnitude magnetic resonance images, obtained by quadrature detection, the associated Rician distribution of noise constitutes a further complication. This type of noise is especially problematic at low signal-to-noise ratio (SNR) regions. The Rician noise distribution causes a non-zero minimum signal in the image, often referred to as the rectified noise floor. True low signal is likely to be concealed in the noise, and quantification is severely hampered in low-SNR regions. To reduce this problem, real and imaginary Magnetic Resonance Imaging (MRI) data in the image domain were filtered, before construction of the magnitude image. The noise-reduction filtering (or denoising) was accomplished by Wiener-like filtering in the wavelet domain. The advantage of denoising the complex MRI data in the image domain is that the noise in the real and imaginary channels is Gaussian and most denoising tools in the wavelet domain are adapted to such a distribution. A further advantage, compared with filtering the complex k-space data, is that image artefacts caused by phase errors are minimized. The proposed noise-removal scheme efficiently reduced the standard deviation and significantly lowered the rectified noise floor. The contrast of the images, especially in the low-SNR regions was increased accordingly. Denoising was tested on simulated diffusion-weighted (DW) images with slow and fast apparent diffusion coefficients (ADC) regions as well as on a dataset showing bi-exponential signal decay. Experimentally, the method was applied to diffusion-weighted images from a homogenous n-decane (C10H22) phantom and to data from a healthy volunteer. The proposed de-noising algorithm is useful not only in DW-MRI but also for others kinds of MR images where SNR and/or image contrast is low.Kvaliteten på kliniska bilder har stor betydelse för korrekt medicinsk diagnostik. I konventionella morfologiska MR-bilder är signalen normalt relativt hög i förhållande till bruset. Vissa nya MR-metoder för s.k. funktionsdiagnostik bygger emellertid på en avsiktlig degradering av signalen för att vissa kvantitativa parametrar skall kunna beräknas. I sådana studier kan följaktligen signal-till-brus-förhållandet (signal-to-noise ratio, SNR) vara mycket lågt. Ett exempel är diffusionskänslig MR-teknik som används t.ex. för påvisande av en ischemisk strokeskada redan under den första timmen efter slaganfallet. Den metodologiska bakgrunden är att vattenmolekyler i det skadade området får minskad termisk rörlighet (diffusion) jämfört med frisk vävnad. Tekniken utnyttjar diffusionsviktade bilder som tas med olika grad av diffusionskodning och erhållna signalvärden för varje pixel i bilden används för kvantifiering av t.ex. diffusionskoefficient (apparent diffusion coefficient, ADC) som är ett direkt mått på vattenmolekylernas rörlighet i det givna volymselementet. För mycket kraftiga diffusionskodningar blir signalen så kraftigt degraderad att den nästan helt drunknar i bruset. För dessa bilder kan brusreduktion vara ett intressant alternativ för förbättrat SNR. Brusreduktionen genomfördes på MR-data i det komplexa bildrummet, vilket är ett mellansteg i MR-bildrekonstruktionen. I detta rum är bruset vitt (Gaussiskt fördelat), vilket är en förutsättning för att metoden ska fungera väl. De komplexa bilderna transformeras med ett matematiskt verktyg – den s.k. diskreta wavelet-transformen – som separerar signalen i olika frekvenser, men till skillnad från Fourier-transformen så tappar man inte informationen om rumsupplösningen. I wavelet-rummet motsvarar de låga frekvenserna grova drag hos objektet i bildrummet medan höga frekvenser motsvarar fina detaljer. Eftersom bruset återspeglas som höga frekvenser i wavelet-rummet så kan man genom att ta bort de allra högsta frekvenserna i wavelet-rummet med ett så kallat ”hard-threshold filter” kombinerat med ett Wiener-liknande filter minska bruset i det vanliga bildrummet. Fördelen med att filtrera i wavelet-rummet jämfört med att filtrera i Fourier-rummet är att man kan undvika att filtrera bort de fina detaljerna från objektet. I simulerade bilder reducerade det föreslagna filtrerings schemat standard avvikelsen i signalen upp till 85-90%, sänkte medelsignalen i bakgrunden (brusmattan) med en faktor 6 och ökade kontrasten i områden med lågt SNR områden med en faktorn 10. Detta innebär att signalen med dubbelt så hög diffusionskodning kan utnyttjas för korrekta kvantitativa beräkningar utan interferens med brusmattan. Effekten var något lägre i in vivo bilder pga. att bilddata påverkats av andra normalt förekommande bildartefakter. Filtrering med Wiener-liknande filter i wavelet-rummet är en metod som tidigare använts framgångsrikt för filtrering av t.ex. gamla biograffilmer, satellitbilder och fotografiska bilder samt i andra applikationer där bruset är Gaussiskt fördelat. Kravet på Gaussiskt brus göra att det är viktigt att filtrera i det komplexa bildrummet, där bruset är av sådan karaktär. Filtrering av data i k-data rummet är också genomförbart men kan medföra vissa bildartefakter och kan göra bilderna suddiga. Metoden lämpar sig inte för magnitud bilderna (de rekonstruerade bilderna) pga. att bruset i den domänen är signalberoende (Rice-fördelade)

Denoising of arterial spin labeling data: wavelet-domain filtering compared with Gaussian smoothing.

PURPOSE: To investigate a wavelet-based filtering scheme for denoising of arterial spin labeling (ASL) data, potentially enabling reduction of the required number of averages and the acquisition time. METHODS: ASL magnetic resonance imaging (MRI) provides quantitative perfusion maps by using arterial water as an endogenous tracer. The signal difference between a labeled image, where inflowing arterial spins are inverted, and a control image is proportional to blood perfusion. ASL perfusion maps suffer from low SNR, and the experiment must be repeated a number of times (typically more than 40) to achieve adequate image quality. In this study, systematic errors introduced by the proposed wavelet-domain filtering approach were investigated in simulated and experimental image datasets and compared with conventional Gaussian smoothing. RESULTS: Application of the proposed method enabled a reduction of the number of averages and the acquisition time by at least 50% with retained standard deviation, but with effects on absolute CBF values close to borders and edges. CONCLUSIONS: When the ASL perfusion maps showed moderate-to-high SNRs, wavelet-domain filtering was superior to Gaussian smoothing in the vicinity of borders between gray and white matter, while Gaussian smoothing was a better choice for larger homogeneous areas, irrespective of SNR

Denoising of complex MRI data by wavelet-domain filtering: Application to high-b-value diffusion-weighted imaging.

The Rician distribution of noise in magnitude magnetic resonance (MR) images is particularly problematic in low signal-to-noise ratio (SNR) regions. The Rician noise distribution causes a nonzero minimum signal in the image, which is often referred to as the rectified noise floor. True low signal is likely to be concealed in the noise, and quantification is severely hampered in low-SNR regions. To address this problem we performed noise reduction (or denoising) by Wiener-like filtering in the wavelet domain. The filtering was applied to complex MRI data before construction of the magnitude image. The noise-reduction algorithm was applied to simulated and experimental diffusion-weighted (DW) images. Denoising considerably reduced the signal standard deviation (SD, by up to 87% in simulated images) and decreased the background noise floor (by approximately a factor of 6 in simulated and experimental images)

Fast dynamic MPI cytometry

We developed in vivo fast dynamic MPI “cytometry” to quantify and localize the accumulation of SPIO-labeled stem cells in different organs after intra-arterial injection on a time scale of minutes. Bone marrow-derived mesenchymal stem cells (MSCs) and superoxide dismutase 1 gene-corrected neural precursor cells (NPCs) were labeled with Resovist and injected into Rag2 mice using four separate injections. Whole body standard 2D/3D MPI scans were obtained, quantified and co-registered with CT. Using cell calibration fiducials, cells could be clearly visualized and quantified by MPI in vivo in the brain, liver, and lung. The cytometric ratio of the number of cells in the liver/lung vs. the brain was 1.5 for MSCs and 15.6 for NPCs, respectively, at 24 min post-injection. Fast dynamic MPI cytometry may find applications for optimizing the dose, volume, speed and route of administration when performing interventional cell therapy procedures in real-time