Principles of Quantitative MR Imaging with Illustrated Review of Applicable Modular Pulse Diagrams

Conceptualizing psychological resilience through resting-state functional MRI in a mentally healthy population: a systematic review

Conceptualizations and operational definitions of psychological resilience vary across resilience neuroimaging studies. Data on the neural features of resilience among healthy individuals has been scarce. Furthermore, findings from resting-state functional magnetic resonance imaging (fMRI) studies were inconsistent across studies. This systematic review summarized resting-state fMRI findings in different modalities from various operationally defined resilience in a mentally healthy population. The PubMed and MEDLINE databases were searched. Articles that focused on resting-state fMRI in relation to resilience, and published before 2022, were targeted. Orbitofrontal cortex, anterior cingulate cortex, insula and amygdala, were reported the most from the 19 included studies. Regions in emotional network was reported the most from the included studies. The involvement of regions like amygdala and orbitofrontal cortex indicated the relationships between emotional processing and resilience. No common brain regions or neural pathways were identified across studies. The emotional network appears to be studied the most in association with resilience. Matching fMRI modalities and operational definitions of resilience across studies are essential for meta-analysis.

Quantitative MRI to Characterize Hypoxic Tumors in Comparison to FMISO PET/CT for Radiotherapy in Oropharynx Cancers

Intratumoral hypoxia is associated with a poor prognosis and poor response to treatment in head and neck cancers. Its identification would allow for increasing the radiation dose to hypoxic tumor subvolumes. 18F-FMISO PET imaging is the gold standard; however, quantitative multiparametric MRI could show the presence of intratumoral hypoxia. Thus, 16 patients were prospectively included and underwent 18F-FDG PET/CT, 18F-FMISO PET/CT, and multiparametric quantitative MRI (DCE, diffusion and relaxometry T1 and T2 techniques) in the same position before treatment. PET and MRI sub-volumes were segmented and classified as hypoxic or non-hypoxic volumes to compare quantitative MRI parameters between normoxic and hypoxic volumes. In total, 13 patients had hypoxic lesions. The Dice, Jaccard, and overlap fraction similarity indices were 0.43, 0.28, and 0.71, respectively, between the FDG PET and MRI-measured lesion volumes, showing that the FDG PET tumor volume is partially contained within the MRI tumor volume. The results showed significant differences in the parameters of SUV in FDG and FMISO PET between patients with and without measurable hypoxic lesions. The quantitative MRI parameters of ADC, T1 max mapping and T2 max mapping were different between hypoxic and normoxic subvolumes. Quantitative MRI, based on free water diffusion and T1 and T2 mapping, seems to be able to identify intra-tumoral hypoxic sub-volumes for additional radiotherapy doses.

Advances in PET and MRI imaging of tumor hypoxia

Tumor hypoxia is a complex and evolving phenomenon both in time and space. Molecular imaging allows to approach these variations, but the tracers used have their own limitations. PET imaging has the disadvantage of low resolution and must take into account molecular biodistribution, but has the advantage of high targeting accuracy. The relationship between the signal in MRI imaging and oxygen is complex but hopefully it would lead to the detection of truly oxygen-depleted tissue. Different ways of imaging hypoxia are discussed in this review, with nuclear medicine tracers such as [¹⁸F]-FMISO, [¹⁸F]-FAZA, or [⁶⁴Cu]-ATSM but also with MRI techniques such as perfusion imaging, diffusion MRI or oxygen-enhanced MRI. Hypoxia is a pejorative factor regarding aggressiveness, tumor dissemination and resistance to treatments. Therefore, having accurate tools is particularly important.

Heavily Gd-Doped Non-Toxic Cerium Oxide Nanoparticles for MRI Labelling of Stem Cells

Recently, human mesenchymal stem cells (hMSc) have attracted a great deal of attention as potential therapeutic agents in the treatment of socially significant diseases. Despite substantial advances in stem-cell therapy, the biological mechanisms of hMSc action after transplantation remain unclear. The use of magnetic resonance imaging (MRI) as a non-invasive method for tracking stem cells in the body is very important for analysing their distribution in tissues and organs, as well as for ensuring control of their lifetime after injection. Herein, detailed experimental data are reported on the biocompatibility towards hMSc of heavily gadolinium-doped cerium oxide nanoparticles (Ce_0.8Gd_0.2O_2-x) synthesised using two synthetic protocols. The relaxivity of the nanoparticles was measured in a magnetic field range from 1 mT to 16.4 T. The relaxivity values (r₁ = 11 ± 1.2 mM^-1 s^-1 and r₁ = 7 ± 1.2 mM^-1 s^-1 in magnetic fields typical of 1.5 and 3 T MRI scanners, respectively) are considerably higher than those of the commercial Omniscan MRI contrast agent. The low toxicity of gadolinium-doped ceria nanoparticles to hMSc enables their use as an effective theranostic tool with improved MRI-contrasting properties.

T2* quantification using multi-echo gradient echo sequences: a comparative study of different readout gradients

To quantify T2*, multiple echoes are typically acquired with a multi-echo gradient echo sequence using either monopolar or bipolar readout gradients. The use of bipolar readout gradients achieves a shorter echo spacing time, enabling the acquisition of a larger number of echoes in the same scan time. However, despite their relative time efficiency and the potential for more accurate quantification, a comparative investigation of these readout gradients has not yet been addressed. This work aims to compare the performance of monopolar and bipolar readout gradients for T2* quantification. The differences in readout gradients were theoretically investigated with a Cramér-Rao lower bound and validated with computer simulations with respect to the various imaging parameters (e.g., flip angle, TR, TE, TE range, and BW). The readout gradients were then compared at 3 T using phantom and in vivo experiments. The bipolar readout gradients provided higher precision than monopolar readout gradients in both computer simulations and experimental results. The difference between the two readout gradients increased for a lower SNR and smaller TE range, consistent with the prediction made using Cramér-Rao lower bound. The use of bipolar readout gradients is advantageous for regions or situations where a lower SNR is expected or a shorter acquisition time is required.

Tissue Characteristics of Endometrial Carcinoma Analyzed by Quantitative Synthetic MRI and Diffusion-Weighted Imaging

BACKGROUND

This study investigates the association of T1, T2, proton density (PD) and the apparent diffusion coefficient (ADC) with histopathologic features of endometrial carcinoma (EC).

METHODS

One hundred and nine EC patients were prospectively enrolled from August 2019 to December 2020. Synthetic magnetic resonance imaging (MRI) was acquired through one acquisition, in addition to diffusion-weighted imaging (DWI) and other conventional sequences using 1.5T MRI. T1, T2, PD derived from synthetic MRI and ADC derived from DWI were compared among different histopathologic features, namely the depth of myometrial invasion (MI), tumor grade, cervical stromal invasion (CSI) and lymphovascular invasion (LVSI) of EC by the Mann-Whitney U test. Classification models based on the significant MRI metrics were constructed with their respective receiver operating characteristic (ROC) curves, and their micro-averaged ROC was used to evaluate the overall performance of these significant MRI metrics in determining aggressive histopathologic features of EC.

RESULTS

EC with MI had significantly lower T2, PD and ADC than those without MI (p = 0.007, 0.006 and 0.043, respectively). Grade 2-3 EC and EC with LVSI had significantly lower ADC than grade 1 EC and EC without LVSI, respectively (p = 0.005, p = 0.020). There were no differences in the MRI metrics in EC with or without CSI. Micro-averaged ROC of the three models had an area under the curve of 0.83.

CONCLUSIONS

Synthetic MRI provided quantitative metrics to characterize EC with one single acquisition. Low T2, PD and ADC were associated with aggressive histopathologic features of EC, offering excellent performance in determining aggressive histopathologic features of EC.

Application of a novel T1 retrospective quantification using internal references (T1-REQUIRE) algorithm to derive quantitative T1 relaxation maps of the brain

Most MRI sequences used clinically are qualitative or weighted. While such images provide useful information for clinicians to diagnose and monitor disease progression, they lack the ability to quantify tissue damage for more objective assessment. In this study, an algorithm referred to as the T1-REQUIRE is presented as a proof-of-concept which uses nonlinear transformations to retrospectively estimate T1 relaxation times in the brain using T1-weighted MRIs, the appropriate signal equation, and internal, healthy tissues as references. T1-REQUIRE was applied to two T1-weighted MR sequences, a spin-echo and a MPRAGE, and validated with a reference standard T1 mapping algorithm in vivo. In addition, a multiscanner study was run using MPRAGE images to determine the effectiveness of T1-REQUIRE in conforming the data from different scanners into a more uniform way of analyzing T1-relaxation maps. The T1-REQUIRE algorithm shows good agreement with the reference standard (Lin's concordance correlation coefficients of 0.884 for the spin-echo and 0.838 for the MPRAGE) and with each other (Lin's concordance correlation coefficient of 0.887). The interscanner studies showed improved alignment of cumulative distribution functions after T1-REQUIRE was performed. T1-REQUIRE was validated with a reference standard and shown to be an effective estimate of T1 over a clinically relevant range of T1 values. In addition, T1-REQUIRE showed excellent data conformity across different scanners, providing evidence that T1-REQUIRE could be a useful addition to big data pipelines.

Synthetic MRI for Radiotherapy Planning for Brain and Prostate Cancers: Phantom Validation and Patient Evaluation

Purpose

We aimed to evaluate the accuracy of T₁ and T₂ mappings derived from a multispectral pulse sequence (magnetic resonance image compilation, MAGiC^®) on 1.5-T MRI and with conventional sequences [gradient echo with variable flip angle (GRE-VFA) and multi-echo spin echo (ME-SE)] compared to the reference values for the purpose of radiotherapy treatment planning.

Methods

The accuracy of T₁ and T₂ measurements was evaluated with 2 coils [head and neck unit (HNU) and BODY coils] on phantoms using descriptive statistics and Bland–Altman analysis. The reproducibility and repeatability of T₁ and T₂ measurements were performed on 15 sessions with the HNU coil. The T₁ and T₂ synthetic sequences obtained by both methods were evaluated according to quality assurance (QA) requirements for radiotherapy. T₁ and T₂in vivo measurements of the brain or prostate tissues of two groups of five subjects were also compared.

Results

The phantom results showed good agreement (mean bias, 8.4%) between the two measurement methods for T₁ values between 490 and 2,385 ms and T₂ values between 25 and 400 ms. MAGiC^® gave discordant results for T₁ values below 220 ms (bias with the reference values, from 38% to 1,620%). T₂ measurements were accurately estimated below 400 ms (mean bias, 8.5%) by both methods. The QA assessments are in agreement with the recommendations of imaging for contouring purposes for radiotherapy planning. On patient data of the brain and prostate, the measurements of T₁ and T₂ by the two quantitative MRI (qMRI) methods were comparable (max difference, <7%).

Conclusion

This study shows that the accuracy, reproducibility, and repeatability of the multispectral pulse sequence (MAGiC^®) were compatible with its use for radiotherapy treatment planning in a range of values corresponding to soft tissues. Even validated for brain imaging, MAGiC^® could potentially be used for prostate qMRI.

Review and Prospect: Artificial Intelligence in Advanced Medical Imaging

Artificial intelligence (AI) as an emerging technology is gaining momentum in medical imaging. Recently, deep learning-based AI techniques have been actively investigated in medical imaging, and its potential applications range from data acquisition and image reconstruction to image analysis and understanding. In this review, we focus on the use of deep learning in image reconstruction for advanced medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). Particularly, recent deep learning-based methods for image reconstruction will be emphasized, in accordance with their methodology designs and performances in handling volumetric imaging data. It is expected that this review can help relevant researchers understand how to adapt AI for medical imaging and which advantages can be achieved with the assistance of AI.

Evaluation of liver T1 using MOLLI gradient echo readout under the influence of fat

BACKGROUND

The effect of hepatic steatosis on the gradient-echo (GRE) based Modified Look-Locker Inversion Recovery (MOLLI) technique for T1 mapping has not been evaluated. The purpose of this study was to evaluate a GRE based MOLLI technique for hepatic T1 mapping and determine the relationship of T1 differences (ΔT1) on in-phase (IP) and out-of-phase (OP) to fat fraction (FF) measurement.

MATERIALS AND METHODS

3 T MRI included MOLLI T1 mapping with TE = 1.3 (OP), 2.4 (IP), and 1.8 ms, and chemical-shift-encoded sequence with spectral modeling of fat to generate FF map as a reference. Bloch simulations and oil/water phantoms were used to characterize the response of the MOLLI T1 in various FF < 30% since MOLLI T1 estimation was erratic beyond this limit. Curve fit between ΔT1 and FF from simulation was applied to validate the phantom and the in-vivo results. Thirty-eight normal volunteers were included (16 women, Age 44 ± 12 years, BMI 27 ± 5.3 kg/m²). MOLLI water images were reconstructed by the average of OP and IP images, and the T1 values on water images served as the reference for T1 bias calculation defined as the percent difference between OP, IP, TE = 1.8 ms and the referenced water T1. Linear regression was performed to correlate the FF quantified by the reference and MOLLI methods.

RESULTS

Phantom results were consistent with the Bloch simulations. The simulated relationship between FF (0-30%) and ΔT1 could be modeled precisely by a cubic equation with R2 = 1. In-vivo MOLLI ΔT1 and estimated FF were correlated to the reference FF (both R2 ≥ 0.96 and P < 0.001). TE = 1.8 ms demonstrated less T1 bias (-1.34%) compared to TE = OP (5.32%) or IP (-3.8%, both P < 0.001).

CONCLUSION

At 3 T, TE of 1.8 ms can be used to reduce the T1 bias and deliver consistent T1 values when FF is <30%.

A Radio-Nano-Platform for T1/T2 Dual-Mode PET-MR Imaging

Purpose

This study aimed to develop a chelate-free radiolabeled nanoparticle platform for simultaneous positron emission tomography (PET) and magnetic resonance (MR) imaging that provides contrast-enhanced diagnostic imaging and significant image quality gain by integrating the high spatial resolution of MR with the high sensitivity of PET.

Methods

A commercially available super-paramagnetic iron oxide nanoparticle (SPION) (Feraheme^®, FH) was labeled with the [⁸⁹Zr]Zr using a novel chelate-free radiolabeling technique, heat-induced radiolabeling (HIR). Radiochemical yield (RCY) and purity (RCP) were measured using size exclusion chromatography (SEC) and radio-thin layer chromatography (radio-TLC). Characterization of the non-radioactive isotope ⁹⁰Zr-labeled FH was performed by transmission electron microscopy (TEM). Simultaneous PET-MR phantom imaging was performed with different ⁸⁹Zr-FH concentrations. The MR quantitative image analysis determined the contrast-enhancing properties of FH. The signal-to-noise ratio (SNR) and full-width half-maximum (FWHM) of the line spread function (LSF) were calculated before and after co-registering the PET and MR image data.

Results

High RCY (92%) and RCP (98%) of the [⁸⁹Zr]Zr-FH product was achieved. TEM analysis confirmed the ⁹⁰Zr atoms adsorption onto the SPION surface (≈ 10% average radial increase). Simultaneous PET-MR scans confirmed the capability of the [⁸⁹Zr]Zr-FH nano-platform for this multi-modal imaging technique. Relative contrast image analysis showed that [⁸⁹Zr]Zr-FH can act as a dual-mode T1/T2 contrast agent. For co-registered PET-MR images, higher spatial resolution (FWHM enhancement ≈ 3) and SNR (enhancement ≈ 8) was achieved at a clinical dose of radio-isotope and Fe.

Conclusion

Our results demonstrate FH is a highly suitable SPION-based platform for chelate-free labeling of PET tracers for hybrid PET-MR. The high RCY and RCP confirmed the robustness of the chelate-free HIR technique. An overall image quality gain was achieved compared to PET- or MR-alone imaging with a relatively low dosage of [⁸⁹Zr]Zr-FH. Additionally, FH is suitable as a dual-mode T1/T2 MR image contrast agent.

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Magnetic Resonance Imaging for Translational Research in Oncology

The translation of results from the preclinical to the clinical setting is often anything other than straightforward. Indeed, ideas and even very intriguing results obtained at all levels of preclinical research, i.e., in vitro, on animal models, or even in clinical trials, often require much effort to validate, and sometimes, even useful data are lost or are demonstrated to be inapplicable in the clinic. In vivo, small-animal, preclinical imaging uses almost the same technologies in terms of hardware and software settings as for human patients, and hence, might result in a more rapid translation. In this perspective, magnetic resonance imaging might be the most translatable technique, since only in rare cases does it require the use of contrast agents, and when not, sequences developed in the lab can be readily applied to patients, thanks to their non-invasiveness. The wide range of sequences can give much useful information on the anatomy and pathophysiology of oncologic lesions in different body districts. This review aims to underline the versatility of this imaging technique and its various approaches, reporting the latest preclinical studies on thyroid, breast, and prostate cancers, both on small laboratory animals and on human patients, according to our previous and ongoing research lines.

Gastric cancer and imaging biomarkers: Part 1 - a critical review of DW-MRI and CE-MDCT findings

Abstract

The current standard of care for gastric cancer imaging includes heterogeneity in image acquisition techniques and qualitative image interpretation. In addition to qualitative assessment, several imaging techniques, including diffusion-weighted magnetic resonance imaging (DW-MRI), contrast-enhanced multidetector computed tomography (CE-MDCT), dynamic-contrast enhanced MRI and 18F-fluorodeoxyglucose positron emission tomography, can allow quantitative analysis. However, so far there is no consensus regarding the application of functional imaging in the management of gastric cancer. The aim of this article is to specifically review two promising biomarkers for gastric cancer with reasonable spatial resolution: the apparent diffusion coefficient (ADC) from DW-MRI and textural features from CE-MDCT. We searched MEDLINE/ PubMed for manuscripts published from inception to 6 February 2018. Initially, we searched for (gastric cancer OR gastric tumour) AND diffusion weighted magnetic resonance imaging. Then, we searched for (gastric cancer OR gastric tumour) AND texture analysis AND computed tomography. We collated the results from the studies related to this query. There is evidence that: (1) the ADC is a promising biomarker for the evaluation of the aggressiveness (T and N stage), treatment response and prognosis of gastric cancer; (2) textural features are related to the degree of differentiation, Lauren classification, treatment response and prognosis of gastric cancer. We conclude that these imaging biomarkers hold promise as effective additional tools in the diagnostic pathway of gastric cancer and may facilitate the multidisciplinary work between the radiologist and clinician, and across different institutions, to provide a greater biological understanding of gastric cancer.

Key Points

• Quantitative imaging is the extraction of quantifiable features from medical images for the assessment of normal or pathological conditions and represents a promising area for gastric cancer.

• Quantitative analysis from CE-MDCT and DW-MRI allows the extrapolation of multiple imaging biomarkers.

• ADC from DW-MRI and CE- MDCT-based texture features are non-invasive, quantitative imaging biomarkers that hold promise in the evaluation of the aggressiveness, treatment response and prognosis of gastric cancer.

Synthetic MRI of the Knee: Phantom Validation and Comparison with Conventional MRI

Purpose To test the hypothesis that synthetic MRI of the knee generates accurate and repeatable quantitative maps and produces morphologic MR images with similar quality and detection rates of structural abnormalities than does conventional MRI. Materials and Methods Data were collected prospectively between January 2017 and April 2018 and were retrospectively analyzed. An International Society for Magnetic Resonance in Medicine-National Institute of Standards and Technology phantom was used to determine the accuracy of T1, T2, and proton density (PD) quantification. Statistical models were applied for correction. Fifty-four participants (24 men, 30 women; mean age, 40 years; range, 18-62 years) underwent synthetic and conventional 3-T MRI twice on the same day. Fifteen of 54 participants (28%) repeated the protocol within 9 days. The intra- and interday agreements of quantitative cartilage measurements were assessed. Contrast-to-noise (CNR) ratios, image quality, and structural abnormalities were assessed on corresponding synthetic and conventional images. Statistical analyses included the Wilcoxon test, χ² test, and Cohen Kappa. P values less than or equal to .01 were considered to indicate a statistically significant difference. Results Synthetic MRI quantification of T1, T2, and PD values had an overall model-corrected error margin of 0.8%. The synthetic MRI interday repeatability of articular cartilage quantification had native and model-corrected error margins of 3.3% and 3.5%, respectively. The cartilage-to-fluid CNR and menisci-to-fluid CNR was higher on synthetic than conventional MR images (P ≤ .001, respectively). Synthetic MRI improved short-tau inversion recovery fat suppression (P ˂ .01). Intermethod agreements of structural abnormalities were good (kappa, 0.621-0.739). Conclusion Synthetic MRI of the knee is accurate for T1, T2, and proton density quantification, and simultaneously generated morphologic MR images have detection rates of structural abnormalities similar to those of conventional MR images, with similar acquisition time. © RSNA, 2018.