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Background: A power injector is typically employed to deliver a contrast agent to a subject (e.g., humans, animals, phantoms) during MRI, but it is costly and cannot inject more than one subject at a time. Conventional syringe pumps housing multiple syringes are typically non-MRI compatible and, thus, should be placed outside the MRI room., Purpose: To develop the prototype of a cost-effective, easy-to-use, reliable syringe pump that could be deployed inside a clinical MRI bore and operated on during a scan., Methods: The MRI-compatible syringe pump (MSP) was designed using Solidworks 3D modeling software and fabricated using a Raise3D Pro2 Printer. The MSP was designed to infuse up to three syringes simultaneously. The injection speed was mainly controlled with different gear sets in the escapement unit and further adjusted by changing the effective hairspring length via a pinch pin. The MSP was evaluated with three gear sets (gear ratios: 0.20:1, 0.56:1, and 1.09:1) and 10 different effective hairspring lengths at each gear ratio. A video was recorded while operating MSP at each injection speed to calculate the volume injection rate of a 5-mL syringe (mL/s). The MSP was operated five times repeatedly at each injection speed, and the mean and standard deviation of the volume injection rate were calculated., Results: The volume injection rates produced by three gear ratios (0.20:1, 0.56:1, and 1.09:1) were 0.209 ± 0.003 mL/s, 0.411 ± 0.002 mL/s, and 0.625 ± 0.006 mL/s, respectively, at the full hairspring length. The injection rates of gear set 1 (gear ratio: 0.20:1) decreased from 0.273 ± 0.001 mL/s to 0.245 ± 0.001 mL/s with a decrement of 0.003 mL/s for 10 different effective hairspring lengths (r = 0.997, p < 0.001). The injection rates of gear set 2 (gear ratio: 0.56:1) decreased from 0.519 ± 0.003 mL/s to 0.469 ± 0.003 mL/s with a decrement of 0.006 mL/s (r = 0.987, p < 0.001), and that of gear set 3 (gear ratio: 1.09:1) decreased from 0.779 ± 0.012 mL/s to 0.709 ± 0.005 mL/s with a decrement of 0.007 mL/s (r = 0.963, p < 0.001). The coefficient of variation in the injection rate measurement was 0.727 ± 0.346%., Conclusions: The MSP is a portable device that can reliably deliver a liquid agent to multiple subjects inside a clinical MRI bore during a scan., (© 2023 American Association of Physicists in Medicine.)

Objectives: The aim of this study was to test the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with concurrent perfusion phantom for monitoring therapeutic response in patients with pancreatic ductal adenocarcinoma (PDAC)., Materials and Methods: A prospective pilot study was conducted with 8 patients (7 men and 1 woman) aged 46 to 78 years (mean age, 66 years). Participants had either locally advanced (n = 7) or metastatic (n = 1) PDAC, and had 2 DCE-MRI examinations: one before and one 8 ± 1 weeks after starting first-line chemotherapy. A small triplicate perfusion phantom was imaged with each patient, serving as an internal reference for accurate quantitative image analysis. Tumor perfusion was measured with K using extended Tofts model before and after phantom-based data correction. Results are presented as mean ± SD and 95% confidence intervals (CIs). Statistical difference was evaluated with 1-way analysis of variance., Results: Tumor-size change of responding group (n = 4) was -12% ± 4% at 8 weeks of therapy, while that of nonresponding group (n = 4) was 18% ± 15% (P = 0.0100). Before phantom-based data correction, the K change of responding tumors was 69% ± 23% (95% CI, 32% to 106%) at 8 weeks, whereas that of nonresponding tumors was -1% ± 41% (95% CI, -65% to 64%) (P = 0.0247). After correction, the data variation in each group was significantly reduced; the K change of responding tumors was 73% ± 6% (95% CI, 64% to 82%) compared with nonresponding tumors of -0% ± 5% (95% CI, -7% to 8%) (P < 0.0001)., Conclusions: Quantitative DCE-MRI measured the significant perfusion increase of PDAC tumors responding favorably to chemotherapy, with decreased variability after correction using a perfusion phantom.

This technical note describes how to modify a population-based arterial input function to incorporate variation among the individuals. In DCE-MRI, an arterial input function (AIF) is often distorted by pulsated inflow effect and noise. A population-based AIF (pAIF) has high signal-to-noise ratio (SNR), but cannot incorporate the individual variation. AIF variation is mainly induced by variation in cardiac output and blood volume of the individuals, which can be detected by the full width at half maximum (FWHM) during the first passage and the amplitude of AIF, respectively. Thus pAIF scaled in time and amplitude fitting to the individual AIF may serve as a high SNR AIF incorporating the individual variation. The proposed method was validated using DCE-MRI images of 18 prostate cancer patients. Root mean square error (RMSE) of pAIF from individual AIFs was 0.88±0.48mM (mean±SD), but it was reduced to 0.25±0.11mM after pAIF modification using the proposed method (p<0.0001)., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Purpose: The aim of this study was to develop a portable perfusion phantom and validate its utility in quantitative dynamic contrast-enhanced magnetic resonance imaging of the abdomen., Methods: A portable perfusion phantom yielding a reproducible contrast enhancement curve (CEC) was developed. A phantom package including perfusion and static phantoms were imaged simultaneously with each of three healthy human volunteers in two different 3T MR scanners. Look-up tables correlating reference (known) contrast concentrations with measured ones were created using either the static or perfusion phantom. Contrast maps of image slices showing four organs (liver, spleen, pancreas, and paravertebral muscle) were generated before and after data correction using the look-up tables. The contrast concentrations at 4.5 min after dosing in each of the four organs were averaged for each volunteer. The mean contrast concentrations (4 organs × 3 volunteers = 12) were compared for the two scanners, and the intra-class correlation coefficient (ICC) was calculated. Also, the ICC of the mean K trans values between the two scanners was calculated before and after data correction., Results: The repeatability coefficient of CECs of perfusion phantom was higher than 0.997 in all measurements. The ICC of the tissue contrast concentrations between the two scanners was 0.693 before correction, but increased to 0.974 after correction using the look-up tables (LUTs) of perfusion phantom. However, the ICC was not increased after correction using static phantom (ICC: 0.617). Similarly, the ICC of the K trans values was 0.899 before correction, but increased to 0.996 after correction using perfusion phantom LUTs. The ICC of the K trans values, however, was not increased when static phantom LUTs were used (ICC: 0.866)., Conclusions: The perfusion phantom reduced variability in quantitating contrast concentration and K trans values of human abdominal tissues across different MR units, but static phantom did not. The perfusion phantom has the potential to facilitate multi-institutional clinical trials employing quantitative DCE-MRI to evaluate various abdominal malignancies., (© 2017 American Association of Physicists in Medicine.)

Measurement of total kidney volume (TKV) using magnetic resonance imaging (MRI) is a valuable approach for monitoring disease progression in autosomal dominant polycystic kidney disease (PKD) and is becoming more common in preclinical studies using animal models. Manual contouring of kidney MRI areas [i.e., manual method (MM)] is a conventional, but time-consuming, way to determine TKV. We developed a template-based semiautomatic image segmentation method (SAM) and validated it in three commonly used PKD models: Cys1cpk/cpk mice, Pkd1RC/RC mice, and Pkhd1pck/pck rats (n = 10 per model). We compared SAM-based TKV with that obtained by clinical alternatives including the ellipsoid formula-based method (EM) using three kidney dimensions, the longest kidney length method (LM), and MM, which is considered the gold standard. Both SAM and EM presented high accuracy in TKV assessment in Cys1cpk/cpk mice [interclass correlation coefficient (ICC) ≥ 0.94]. SAM was superior to EM and LM in Pkd1RC/RC mice (ICC = 0.87, 0.74, and <0.10 for SAM, EM, and LM, respectively) and Pkhd1pck/pck rats (ICC = 0.59, <0.10, and <0.10, respectively). Also, SAM outperformed EM in processing time in Cys1cpk/cpk mice (3.6 ± 0.6 vs. 4.4 ± 0.7 min/kidney) and Pkd1RC/RC mice (3.1 ± 0.4 vs. 7.1 ± 2.6 min/kidney, both P < 0.001) but not in Pkhd1PCK/PCK rats (3.7 ± 0.8 vs. 3.2 ± 0.5 min/kidney). LM was the fastest ~1 min) but correlated most poorly with MM-based TKV in all studied models. Processing times by MM were longer for Cys1cpk/cpk mice, Pkd1RC/RC mice, and Pkhd1pck.pck rats (66.1 ± 7.3, 38.3 ± 7.5, and 29.2 ± 3.5 min). In summary, SAM is a fast and accurate method to determine TKV in mouse and rat PKD models. [ABSTRACT FROM AUTHOR]

Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion-weighted imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101-e121. [ABSTRACT FROM AUTHOR]

Electrical properties tomography (EPT) is an imaging method that uses a magnetic resonance (MR) system to non-invasively determine the spatial distribution of the conductivity and permittivity of the imaged object. This manuscript starts by providing clear definitions about the data required for, and acquired in, EPT, followed by comprehensively formulating the physical equations underlying a large number of analytical EPT techniques. This thorough mathematical overview of EPT harmonizes several EPT techniques in a single type of formulation and gives insight into how they act on the data and what their data requirements are. Furthermore, the review describes machine learning-based algorithms. Matlab code of several differential and iterative integral methods is available upon request. [ABSTRACT FROM AUTHOR]