Enhancing quantitative imaging to study DNA damage response: A guide to automated liquid handling and imaging.

Laboratory automation and quantitative high-content imaging are pivotal in advancing diverse scientific fields. These innovative techniques alleviate the burden of manual labour, facilitating large-scale experiments characterized by exceptional reproducibility. Nonetheless, the seamless integration of such systems continues to pose a constant challenge in many laboratories. Here, we present a meticulously designed workflow that automates the immunofluorescence staining process, coupled with quantitative high-content imaging to study DNA damage signalling as an example. This is achieved by using an automatic liquid handling system for sample preparation. Additionally, we also offer practical recommendations aimed at ensuring the reproducibility and scalability of experimental outcomes. We illustrate the high level of efficiency and reproducibility achieved through the implementation of the liquid handling system but also addresses the associated challenges. Furthermore, we extend the discussion into critical aspects such as microscope selection, optimal objective choices, and considerations for high-content image acquisition. Our study streamlines the image analysis process, offering valuable recommendations for efficient computing resources and the integration of cutting-edge deep learning techniques. Emphasizing the paramount importance of robust data management systems aligned with the FAIR data principles, we provide practical insights into suitable storage options and effective data visualization techniques. Together, our work serves as a comprehensive guide for life science laboratories seeking to elevate their high-content quantitative imaging capabilities through the seamless integration of advanced laboratory automation.

Rapid and quantitative CEST-MRI sequence using water presaturation.

PURPOSE: Despite the significant potential for in vivo metabolic imaging in preclinical and clinical applications, CEST MRI suffers from long scan time and inaccurate quantification. This study aims to suppress the contaminations among signals under different frequencies, which could shorten the TR and thereby facilitate CEST imaging acceleration and quantification. METHODS: A novel sequence is proposed by applying a water-presaturation (WPS) module at the beginning of each TR. WPS CEST quickly knocks down the residual signal from previous TRs so that the magnetization of all TRs recovers from zero, which aligns well with the formula of quasi-steady-state theorem and enables accurate quantification within shorter TR. WPS CEST was assessed by simulations, creatine phantom, and healthy human brain scans at 3 T. RESULTS: In simulation and phantom experiment, WPS CEST allows accurate estimation of exchange rate (ksw) using omega plot and using shorter delay time (Td) and saturation time (Ts) (e.g., 1 s/1 s) compared with the conventional CEST. Simulations further showed that WPS CEST could obtain consistent spin-lock relaxation (R1ρ) values over varied Tds and Tss. Six human scans indicated that R1ρ collected from conventional sequences showed significant differences between two groups with Td and Ts of (1 s/1 s) and (2 s/2 s) (amide: 1.721 ± 0.051 s-1 vs. 1.622 ± 0.050 s-1, p = 0.001; nuclear Overhauser enhancement: 1.792 ± 0.046 s-1 vs. 1.687 ± 0.053 s-1, p = 0.004), whereas WPS CEST scans using these 2 Td/Ts values obtained the same mean R1ρ (amide: 1.616 ± 0.053 s-1 vs. 1.616 ± 0.048 s-1, p = 0.862; nuclear Overhauser enhancement: 1.688 ± 0.064 s-1 vs. 1.684 ± 0.054 s-1, p = 0.544). CONCLUSION: WPS CEST demonstrated accurate quantitation within shorter TR compared with conventional sequences, and thereby may allow rapid quantitative CEST scans in various situations.

Feasibility of using contrast-free quantitative magnetic resonance imaging for liver sparing stereotactic ablative body radiotherapy.

Background and purpose: Tumours in the liver often develop on a background of liver cirrhosis and impaired liver function. As a result, radiotherapy treatments are limited by radiation-induced liver disease, parameterised by the liver mean dose (LMD). Liver function is highly heterogeneous, especially in liver cancer, but the use of LMD does not take this into account. One possible way to improve liver treatments is to use quantitative imaging techniques to assess liver health and prioritise the sparing of healthy liver tissue. Materials and methods: Anatomical T2 and quantitative iron-corrected T1 (cT1) images were made available for 10 patients with liver metastases. Functional liver volumes were automatically segmented on the quantitative images using a threshold. Liver stereotactic ablative body radiotherapy (SABR) plans were made using a departmental protocol. Liver-sparing plans were then made by reducing the dose to the functional sub-volume. Results: The sparing plans achieved a statistically significant ( p = 0.002 ) reduction in the functional liver mean dose, with a mean reduction of 1.4 Gy. The LMD was also significantly different ( p = 0.002 ) but had a smaller magnitude with a mean reduction of 0.7 Gy. There were some differences in the planning target volume D99% ( p = 0.04 ) but the sparing plans remained within the optimal tolerance and the D95% was not significantly different ( p = 0.2 ). Conclusions: This study has, for the first time, demonstrated the use of cT1 maps in radiotherapy showing significant reductions in dose to the healthy liver. Further work is needed to validate this in liver cancer patients, who would likely benefit most.

Deep-learning-based attenuation map generation in kidney single photon emission computed tomography.

BACKGROUND: Accurate attenuation correction (AC) is vital in nuclear medicine, particularly for quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) imaging. This study aimed to establish a CT-free quantification technology in kidney SPECT imaging using deep learning to generate synthetic attenuation maps (µ-maps) from SPECT data, thereby reducing radiation exposure and eliminating the need for CT scans. RESULTS: A dataset of 1000 Tc-99m DTPA SPECT/CT scans was analyzed for training (n = 800), validation (n = 100), and testing (n = 100) using a modified 3D U-Net for deep learning. The study investigated the use of primary emission and scattering SPECT data, normalization methods, loss function optimization, and up-sampling techniques for optimal µ-map generation. The problem of checkerboard artifacts, unique to µ-map generation from SPECT signals, and the effects of iodine contrast media were evaluated. The addition of scattering SPECT to primary emission SPECT imaging, logarithmic maximum normalization, the combination of absolute difference loss (L1) and three times the absolute gradient difference loss (3 × LGDL), and the nearest-neighbor interpolation significantly enhanced AI performance in µ-map generation (p < 0.00001). Checkerboard artifacts were effectively eliminated using the nearest-neighbor interpolation technique. The developed AI algorithm produced µ-maps neutral to the presence of iodine contrast and showed negligible contrast effects on quantitative SPECT measurement, such as glomerular filtration rate (GFR). The potential reduction in radiation exposure by transitioning to AI-based CT-free SPECT imaging ranges from 45.3 to 78.8%. CONCLUSION: The study successfully developed and optimized a deep learning algorithm for generating synthetic µ-maps in kidney SPECT images, demonstrating the potential to transition from conventional SPECT/CT to CT-free SPECT imaging for GFR measurement. This advancement represents a significant step towards enhancing patient safety and efficiency in nuclear medicine.

Targeted K-Edge Nanoprobes From Praseodymium and Hafnium for Ratiometric Tracking of Dual Biomarkers using Spectral Photon Counting CT.

Utilizing metal nanoprobes with unique K-edge identities to visualize complementary biological activities simultaneously can provide valuable information about complex biological processes. This study describes the design and preparation of an innovative pair of K-edge metal nanoprobes and demonstrates the feasibility of their simultaneous quantitative detection using spectral photon-counting computed tomography (SPCCT). Glycosaminoglycan (GAG) capped nanoparticles (ca. 15-20 nm) targeting two distinct components of the cartilage tissue, namely, aggrecan (acan) and aggrecanase (acanase) are designed and synthesized. These targeted nanoparticles comprised of praseodymium (Pr) and hafnium (Hf), with well-separated K-edge energies, enable simultaneous and ratiometric imaging of dual biomarkers in cartilage tissue. Following extensive physico-chemical characterization of the ligand-targeted particles, the feasibility of homing dual biomarkers in vitro is demonstrated. The material discrimination and simultaneous quantification of these targeted particles are also achieved and corroborated with inductively coupled plasmon spectroscopy. For the first time, the use of praseodymium is reported as a contrast agent for SPCCT imaging and demonstrates the ability to pair it with hafnium nanoprobes for multicontrast imaging of diseases. Importantly, the potential for ratiometric molecular imaging and tracking of osteoarthritis (OA) progression is shown with SPCCT K-edge based imaging approach.

Quantitative in Vivo Molecular MRI.

Molecular magnetic resonance imaging (MRI) combines chemistry, chemical biology, and imaging techniques to track molecular events non-invasively. Quantitative molecular MRI aims to provide meaningful, reproducible numerical measurements of molecular processes or biochemical targets within the body. In this review, the classifications of molecular MRI probes based on their signal-generating mechanism and functionality are first described. From there, the primary considerations for in vitro characterization and in vivo validation of molecular MRI probes, including how to avoid pitfalls and biases are discussed. Then, recommendations on imaging acquisition protocols and analysis methods to establish quantitative relationships between MRI signal change induced by the probes and the molecular processes of interest are provided. Finally, several representative case studies are highlighted that incorporate these features. Quantitative molecular MRI is a multidisciplinary research area incorporating expertise in chemical biology, inorganic chemistry, molecular probes, imaging physics, drug development, pathobiology, and medicine. The purpose of this review is to provide guidance to chemists developing MR imaging probes and methods in terms of in vitro and in vivo validation to accelerate the translation of these new quantitative tools for non-invasive imaging of biological processes.

An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.

Current imaging methods for diagnosing breast cancer (BC) are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to improve BC diagnosis and subtype differentiation. In this case, novel quantitative computational methods, such as radiomics, have been developed to enhance the sensitivity and specificity of early BC diagnosis and classification. The potential of radiomics in improving the diagnostic efficacy of imaging studies has been shown in several studies. In this review article, we discuss the radiomics workflow and current handcrafted radiomics methods in the diagnosis and classification of BC based on the most recent studies on different imaging modalities, e.g., MRI, mammography, contrast-enhanced spectral mammography (CESM), ultrasound imaging, and digital breast tumosynthesis (DBT). We also discuss current challenges and potential strategies to improve the specificity and sensitivity of radiomics in breast cancer to help achieve a higher level of BC classification and diagnosis in the clinical setting. The growing field of AI incorporation with imaging information has opened a great opportunity to provide a higher level of care for BC patients.

Measurable imaging-based changes in enhancement of intrahepatic cholangiocarcinoma after radiotherapy reflect physical mechanisms of response.

Background: Although escalated doses of radiation therapy (RT) for intrahepatic cholangiocarcinoma (iCCA) are associated with durable local control (LC) and prolonged survival, uncertainties persist regarding personalized RT based on biological factors. Compounding this knowledge gap, the assessment of RT response using traditional size-based criteria via computed tomography (CT) imaging correlates poorly with outcomes. We hypothesized that quantitative measures of enhancement would more accurately predict clinical outcomes than size-based assessment alone and developed a model to optimize RT. Methods: Pre-RT and post-RT CT scans of 154 patients with iCCA were analyzed retrospectively for measurements of tumor dimensions (for RECIST) and viable tumor volume using quantitative European Association for Study of Liver (qEASL) measurements. Binary classification and survival analyses were performed to evaluate the ability of qEASL to predict treatment outcomes, and mathematical modeling was performed to identify the mechanistic determinants of treatment outcomes and to predict optimal RT protocols. Results: Multivariable analysis accounting for traditional prognostic covariates revealed that percentage change in viable volume following RT was significantly associated with OS, outperforming stratification by RECIST. Binary classification identified ≥33% decrease in viable volume to optimally correspond to response to RT. The model-derived, patient-specific tumor enhancement growth rate emerged as the dominant mechanistic determinant of treatment outcome and yielded high accuracy of patient stratification (80.5%), strongly correlating with the qEASL-based classifier. Conclusion: Following RT for iCCA, changes in viable volume outperformed radiographic size-based assessment using RECIST for OS prediction. CT-derived tumor-specific mathematical parameters may help optimize RT for resistant tumors.

Role of quantitative imaging biomarkers in an early FDG-PET/CT for detection of immune-related adverse events in melanoma patients: a prospective study.

BACKGROUND: To evaluate the role of the novel quantitative imaging biomarker (QIB) SUVX% of 18F-FDG uptake extracted from early 18F-FDG-PET/CT scan at 4 weeks for the detection of immune-related adverse events (rAE) in a cohort of patients with metastatic melanoma (mM) patients receiving immune-checkpoint inhibitors (ICI). PATIENTS AND METHODS: In this prospective non-interventional, one-centre clinical study, patients with mM, receiving ICI treatment, were regularly followed by 18F-FDG PET/CT. Patients were scanned at baseline, early point at week four (W4), week sixteen (W16) and week thirty-two (W32) after ICI initiation. A convolutional neural network (CNN) was used to segment three organs: lung, bowel, thyroid. QIB of irAE - SUVX% - was analyzed within the target organs and correlated with the clinical irAE status. Area under the receiver-operating characteristic curve (AUROC) was used to quantify irAE detection performance. RESULTS: A total of 242 18F-FDG PET/CT images of 71 mM patients were prospectively collected and analysed. The early W4 scan showed improved detection only for the thyroid gland compared to W32 scan (p=0.047). The AUROC for detection of irAE in the three target organs was highest when SUVX% was extracted from W16 scan and was 0.76 for lung, 0.53 for bowel and 0.81 for thyroid. SUVX% extracted from W4 scan did not improve detection of irAE compared to W16 scan (lung: p = 0.54, bowel: p = 0.75, thyroid: p = 0.3, DeLong test), as well as compared to W32 scan in lungs (p = 0.32) and bowel (p = 0.3). CONCLUSIONS: Early time point 18F-FDG PET/CT at W4 did not lead to statistically significant earlier detection of irAE. However, organ 18F-FDG uptake as quantified by SUVX% proved to be a consistent QIB of irAE. To better assess the role of 18F-FDG PET/CT in irAE detection, the time evolution of 18F-FDG PET/CT quantifiable inflammation would be of essence, only achievable in multi centric studies.

Comparison of Hounsfield Unit variability between two same model photon counting CT detector systems. A phantom study applied to lung CT.

Accurate quantification of lung density, in Hounsfield Units (HU), is of high importance to monitor progression of diseases such as emphysema using chest CT imaging. Reproducibility of HU quantification on independent photon counting detector CT (PCD-CT) systems with a focus on lung imaging have not yet been evaluated. We thus aimed to evaluate HU reproducibility on 2 independent PCD-CT systems using a repeatable phantom setup with identical acquisition and image reconstruction settings. A COPDGene phantom comprising densities of air, water and lung was scanned on 2 independent PCCT systems using 3 different radiation exposures, 2 medium-sharpness reconstruction kernels (Br40 and Qr36), with and without iterative reconstruction (levels 0 vs 3). Our results demonstrate that acquisitions performed with full dose (3.2 mGy), half dose (1.6 mGy), and one-eighth dose (0.4 mGy) had minimal influence on HU accuracy (<6 HU) when using Br40 and Qr36 kernels. The level of iterative reconstruction also has a minimal impact (<6 HU) with the same kernels. Between the 2 PCD-CT systems evaluated, reproducible HU quantification was achieved for changes to CTDIvol, iterative reconstruction level and reconstruction kernel.

Technical Development and In Silico Implementation of SyntheticMR in Head and Neck Adaptive Radiation Therapy: A Prospective R-IDEAL Stage 0/1 Technology Development Report.

Objective: The purpose of this study was to investigate the technical feasibility of integrating the quantitative maps available from SyntheticMR into the head and neck adaptive radiation oncology workflow. While SyntheticMR has been investigated for diagnostic applications, no studies have investigated its feasibility and potential for MR-Simulation or MR-Linac workflow. Demonstrating the feasibility of using this technique will facilitate rapid quantitative biomarker extraction which can be leveraged to guide adaptive radiation therapy decision making. Approach: Two phantoms, two healthy volunteers, and one patient were scanned using SyntheticMR on the MR-Simulation and MR-Linac devices with scan times between four to six minutes. Images in phantoms and volunteers were conducted in a test/retest protocol. The correlation between measured and reference quantitative T1, T2, and PD values were determined across clinical ranges in the phantom. Distortion was also studied. Contours of head and neck organs-at-risk (OAR) were drawn and applied to extract T1, T2, and PD. These values were plotted against each other, clusters were computed, and their separability significance was determined to evaluate SyntheticMR for differentiating tumor and normal tissue. Main Results: The Lin's Concordance Correlation Coefficient between the measured and phantom reference values was above 0.98 for both the MR-Sim and MR-Linac. No significant levels of distortion were measured. The mean bias between the measured and phantom reference values across repeated scans was below 4% for T1, 7% for T2, and 4% for PD for both the MR-Sim and MR-Linac. For T1 vs. T2 and T1 vs. PD, the GTV contour exhibited perfect purity against neighboring OARs while being 0.7 for T2 vs. PD. All cluster significance levels between the GTV and the nearest OAR, the tongue, using the SigClust method was p < 0.001. Significance: The technical feasibility of SyntheticMR was confirmed. Application of this technique to the head and neck adaptive radiation therapy workflow can enrich the current quantitative biomarker landscape.

POSE: POSition Encoding for accelerated quantitative MRI.

Quantitative MRI utilizes multiple acquisitions with varying sequence parameters to sufficiently characterize a biophysical model of interest, resulting in undesirable scan times. Here we propose, validate and demonstrate a new general strategy for accelerating MRI using subvoxel shifting as a source of encoding called POSition Encoding (POSE). The POSE framework applies unique subvoxel shifts along the acquisition parameter dimension, thereby creating an extra source of encoding. Combining with a biophysical signal model of interest, accelerated and enhanced resolution maps of biophysical parameters are obtained. This has been validated and demonstrated through numerical Bloch equation simulations, phantom experiments and in vivo experiments using the variable flip angle signal model in 3D acquisitions as an application example. Monte Carlo simulations were performed using in vivo data to investigate our method's noise performance. POSE quantification results from numerical Bloch equation simulations of both a numerical phantom and realistic digital brain phantom concur well with the reference method, validating our method both theoretically and for realistic situations. NIST phantom experiment results show excellent overall agreement with the reference method, confirming our method's applicability for a wide range of T1 values. In vivo results not only exhibit good agreement with the reference method, but also show g-factors that significantly outperforms conventional parallel imaging methods with identical acceleration. Furthermore, our results show that POSE can be combined with parallel imaging to further accelerate while maintaining superior noise performance over parallel imaging that uses lower acceleration factors.

Neurological prognosis prediction for cardiac arrest patients using quantitative imaging biomarkers from brain computed tomography.

PURPOSE: We aimed to predict the neurological prognosis of cardiac arrest (CA) patients using quantitative imaging biomarkers extracted from brain computed tomography images. METHODS: We retrospectively enrolled 86 CA patients (good prognosis, 32; poor prognosis, 54) who were treated at three hospitals between 2017 and 2019. We then extracted 1131 quantitative imaging biomarkers from whole-brain and local volumes of interest in the computed tomography images of the patients. The data were split into training and test sets containing 60 and 26 samples, respectively, and the training set was used to select representative quantitative imaging biomarkers for classification. In univariate analysis, the classification was evaluated using the p-value of the Brunner-Munzel test and area under the receiver operating characteristic curve (AUC) for the test set. In multivariate analysis, machine learning models reflecting nonlinear and complex relations were trained, and they were evaluated using the AUC on the test set. RESULTS: The best performance provided p = 0.009 (<0.01) and an AUC of 0.775 (95% confidence interval, 0.590-0.960) for the univariate analysis and an AUCof0.813 (95% confidence interval, 0.640-0.985) for the multivariate analysis. Overall, the gray level with the maximum gradient in the histogram of the three-dimensionally low-pass-filtered image was an important feature for prediction across the analyses. CONCLUSIONS: Quantitative imaging biomarkers can be used in neurological prognosis prediction for CA patients. Relevant biomarkers may contribute to protocolized computed tomography image acquisition to ensure proper decision support in acute care.

Impact of radiomics features, pulmonary emphysema score and muscle mass on the rate of pneumothorax and chest tube insertion in CT-guided lung biopsies.

Iatrogenic pneumothorax is a relevant complication of computed tomography (CT)-guided percutaneous lung biopsy. The aim of the present study was to analyze the prognostic significance of texture analysis, emphysema score and muscle mass derived from CT-imaging to predict postinterventional pneumothorax after CT-guided lung biopsy. Consecutive patients undergoing CT-guided percutaneous lung biopsy between 2012 and 2021 were analyzed. Multivariate logistic regression analysis included clinical risk factors and CT-imaging features to detect associations with pneumothorax development. Overall, 479 patients (178 females, mean age 65 ± 11.7 years) underwent CT-guided percutaneous lung biopsy of which 180 patients (37.5%) developed pneumothorax including 55 patients (11.5%) requiring chest tube placement. Risk factors associated with pneumothorax were chronic-obstructive pulmonary disease (COPD) (p = 0.03), age (p = 0.02), total lung capacity (p < 0.01) and residual volume (p = 0.01) as well as interventional parameters needle length inside the lung (p < 0.001), target lesion attached to pleura (p = 0.04), and intervention duration (p < 0.001). The combined model demonstrated a prediction accuracy of the occurrence of pneumothorax with an AUC of 0.78 [95%CI: 0.70-0.86] with a resulting sensitivity 0.80 and a specificity of 0.66. In conclusion, radiomics features of the target lesion and the lung lobe CT-emphysema score are predictive for the occurrence of pneumothorax and need for chest insertion after CT-guided lung biopsy.

Comparative Analysis of Repeatability in CT Radiomics and Dosiomics Features under Image Perturbation: A Study in Cervical Cancer Patients.

This study aims to evaluate the repeatability of radiomics and dosiomics features via image perturbation of patients with cervical cancer. A total of 304 cervical cancer patients with planning CT images and dose maps were retrospectively included. Random translation, rotation, and contour randomization were applied to CT images and dose maps before radiomics feature extraction. The repeatability of radiomics and dosiomics features was assessed using intra-class correlation of coefficient (ICC). Pearson correlation coefficient (r) was adopted to quantify the correlation between the image characteristics and feature repeatability. In general, the repeatability of dosiomics features was lower compared with CT radiomics features, especially after small-sigma Laplacian-of-Gaussian (LoG) and wavelet filtering. More repeatable features (ICC > 0.9) were observed when extracted from the original, Large-sigma LoG filtered, and LLL-/LLH-wavelet filtered images. Positive correlations were found between image entropy and high-repeatable feature number in both CT and dose (r = 0.56, 0.68). Radiomics features showed higher repeatability compared to dosiomics features. These findings highlight the potential of radiomics features for robust quantitative imaging analysis in cervical cancer patients, while suggesting the need for further refinement of dosiomics approaches to enhance their repeatability.

Lung imaging in COPD and asthma.

Chronic obstructive pulmonary disease (COPD) and asthma are common lung diseases with heterogeneous clinical presentations. Lung imaging allows evaluations of underlying pathophysiological changes and provides additional personalized approaches for disease management. This narrative review provides an overview of recent advances in chest imaging analysis using various modalities, such as computed tomography (CT), dynamic chest radiography, and magnetic resonance imaging (MRI). Visual CT assessment localizes emphysema subtypes and mucus plugging in the airways. Dedicated software quantifies the severity and spatial distribution of emphysema and the airway tree structure, including the central airway wall thickness, branch count and fractal dimension of the tree, and airway-to-lung size ratio. Nonrigid registration of inspiratory and expiratory CT scans quantifies small airway dysfunction, local volume changes and shape deformations in specific regions. Lung ventilation and diaphragm movement are also evaluated on dynamic chest radiography. Functional MRI detects regional oxygen transfer across the alveolus using inhaled oxygen and ventilation defects and gas diffusion into the alveolar-capillary barrier tissue and red blood cells using inhaled hyperpolarized 129Xe gas. These methods have the potential to determine local functional properties in the lungs that cannot be detected by lung function tests in patients with COPD and asthma. Further studies are needed to apply these technologies in clinical practice, particularly for early disease detection and tailor-made interventions, such as the efficient selection of patients likely to respond to biologics. Moreover, research should focus on the extension of healthy life expectancy in patients at higher risk and with established diseases.

Quantitative MRI in children with Crohn's disease - where do we stand?

Crohn's disease (CD) is a chronic inflammatory condition that affects the gastrointestinal tract, particularly the ileum and colon. This disease is characterized by recurrent bouts of intestinal inflammation with subsequent bowel wall damage, including scarring (i.e., fibrosis) and abnormal smooth muscle proliferation. MR enterography, an MRI examination tailored to assess the small bowel, is a first-line diagnostic tool for diagnosing CD in children, characterization and monitoring of disease severity and extent, and assessment of disease-related complications. To date, such MRI evaluations have been mostly qualitative, which can adversely impact diagnostic performance and inter-radiologist agreement. Quantitative MRI methods have been shown to aid in the evaluation of a variety of medical conditions and have been increasingly investigated in children and adults with CD. In CD, such objective techniques have been used to assist with diagnosis, assess treatment response, and characterize bowel wall histologic abnormalities. In the current work, we will review quantitative MRI methods for detecting and measuring intestinal active inflammation (MRI-based scoring systems, T1 relaxation mapping, diffusion-weighted imaging, intra-voxel incoherent motion, mesenteric phase contrast), bowel wall damage (magnetization transfer), and motility (quantitative cine imaging) in small bowel CD, with an emphasis on the pediatric population.

Quantitative 99mTc-pyrophosphate myocardial uptake: Changes on transthyretin stabilization therapy.

BACKGROUND: Quantitative technetium-99m-pyrophosphate cardiac single-photon emission computed tomography (99mTc-PYP SPECT/CT) is an emerging method for estimating myocardial burden of transthyretin cardiac amyloidosis (ATTR-CA), but its efficacy in monitoring longitudinal changes remains uncertain. We aimed to investigate longitudinal changes in cardiac ATTR amyloid burden following transthyretin stabilization therapy using visual and quantitative 99mTc-PYP SPECT/CT and to relate these with changes in cardiac biomarkers and function. METHODS: This prospective longitudinal cohort study investigated changes in 99mTc-PYP SPECT/CT in 23 participants with ATTR-CA on transthyretin stabilization therapy (median: 2.6 years). Quantitative analysis included left ventricular (LV) standardized uptake values (SUVs) (SUVmax, SUVmean), cardiac amyloid activity (CAA; SUVmean∗LV activity volume), and percent injected dose (%ID) (mean activity concentration∗LV activity volume/injected activity), calculated using a threshold of >1.5 times left atrial blood pool activity concentration on SPECT/CT. Longitudinal changes of paired continuous and ordinal variables were analyzed using Wilcoxon signed-rank test. RESULTS: Following therapy, visual grade decreased significantly (P = 0.003). Several quantitative 99mTc-PYP metrics also decreased significantly: SUVmax (median -0.75, P = 0.011), CAA (median: -406.6, P < 0.001), and %ID (median: -0.45, P < 0.001). Serum transthyretin levels improved (median: +6.5 mg/dL, P = 0.008). Echocardiographic parameters (global longitudinal strain, LV mass index, and LV wall thickness), N-terminal pro-B-type natriuretic peptide, and estimated glomerular filtration rate remained stable. CONCLUSIONS: Favorable changes in 99mTc-PYP myocardial uptake were observed in participants on transthyretin stabilization therapy, whereas echocardiographic parameters and biomarkers remained stable. These results likely signify myocardial ATTR amyloid stabilization rather than amyloid burden regression. Further investigation is needed to understand the implications of these findings.

Fat-signal suppression in breast diffusion-weighted imaging: the Good, the Bad, and the Ugly.

OBJECTIVES: Fat-signal suppression is essential for breast diffusion magnetic resonance imaging (or diffusion-weighted MRI, DWI) as the very low diffusion coefficient of fat tends to decrease absolute diffusion coefficient (ADC) values. Among several methods, the STIR (short-tau inversion recovery) method is a popular approach, but signal suppression/attenuation is not specific to fat contrary to other methods such as SPAIR (spectral adiabatic (or attenuated) inversion recovery). This article focuses on those two techniques to illustrate the importance of appropriate fat suppression in breast DWI, briefly presenting the pros and cons of both approaches. METHODS AND RESULTS: We show here through simulation and data acquired in a dedicated breast DWI phantom made of vials with water and various concentrations of polyvinylpyrrolidone (PVP) how ADC values obtained with STIR DWI may be biased toward tissue components with the longest T1 values: ADC values obtained with STIR fat suppression may be over/underestimated depending on the T1 and ADC profile within tissues. This bias is also illustrated in two clinical examples. CONCLUSION: Fat-specific methods should be preferred over STIR for fat-signal suppression in breast DWI, such as SPAIR which also provides a higher sensitivity than STIR for lesion detection. One should remain aware, however, that efficient fat-signal suppression with SPAIR requires good B0 shimming to avoid ADC underestimation from residual fat contamination. CLINICAL RELEVANCE STATEMENT: The spectral adiabatic (or attenuated) inversion recovery (SPAIR) method should be preferred over short-tau inversion recovery (STIR) for fat suppression in breast DWI. KEY POINTS: Fat-signal suppression is essential for breast DWI; the SPAIR method is recommended. Short-tau inversion recovery (STIR) is not specific to fat; as a result, SNR is decreased and ADC values may be over- or underestimated. The STIR fat-suppression method must not be used after the injection of gadolinium-based contrast agents.

Optimization and validation of low-field MP2RAGE T1 mapping on 0.35T MR-Linac: Toward adaptive dose painting with hypoxia biomarkers.

BACKGROUND: Stereotactic MR-guided Adaptive Radiation Therapy (SMART) dose painting for hypoxia has potential to improve treatment outcomes, but clinical implementation on low-field MR-Linac faces substantial challenges due to dramatically lower signal-to-noise ratio (SNR) characteristics. While quantitative MRI and T1 mapping of hypoxia biomarkers show promise, T1-to-noise ratio (T1NR) optimization at low fields is paramount, particularly for the clinical implementation of oxygen-enhanced (OE)-MRI. The 3D Magnetization Prepared (2) Rapid Gradient Echo (MP2RAGE) sequence stands out for its ability to acquire homogeneous T1-weighted contrast images with simultaneous T1 mapping. PURPOSE: To optimize MP2RAGE for low-field T1 mapping; conduct experimental validation in a ground-truth phantom; establish feasibility and reproducibility of low-field MP2RAGE acquisition and T1 mapping in healthy volunteers. METHODS: The MP2RAGE optimization was performed to maximize the contrast-to-noise ratio (CNR) of T1 values in white matter (WM) and gray matter (GM) brain tissues at 0.35T. Low-field MP2RAGE images were acquired on a 0.35T MR-Linac (ViewRay MRIdian) using a multi-channel head coil. Validation of T1 mapping was performed with a ground-truth Eurospin phantom, containing inserts of known T1 values (400-850 ms), with one and two average (1A and 2A) MP2RAGE scans across four acquisition sessions, resulting in eight T1 maps. Mean (± SD) T1 relative error, T1NR, and intersession coefficient of variation (CV) were determined. Whole-brain MP2RAGE scans were acquired in 5 healthy volunteers across two sessions (A and B) and T1 maps were generated. Mean (± SD) T1 values for WM and GM were determined. Whole-brain T1 histogram analysis was performed, and reproducibility was determined with the CV between sessions. Voxel-by-voxel T1 difference maps were generated to evaluate 3D spatial variation. RESULTS: Low-field MP2RAGE optimization resulted in parameters: MP2RAGETR of 3250 ms, inversion times (TI1/TI2) of 500/1200 ms, and flip angles (α1/α2) of 7/5°. Eurospin T1 maps exhibited a mean (± SD) relative error of 3.45% ± 1.30%, T1NR of 20.13 ± 5.31, and CV of 2.22% ± 0.67% across all inserts. Whole-brain MP2RAGE images showed high anatomical quality with clear tissue differentiation, resulting in mean (± SD) T1 values: 435.36 ± 10.01 ms for WM and 623.29 ± 14.64 ms for GM across subjects, showing excellent concordance with literature. Whole-brain T1 histograms showed high intrapatient and intersession reproducibility with characteristic intensity peaks consistent with voxel-level WM and GM T1 values. Reproducibility analysis revealed a CV of 0.46% ± 0.31% and 0.35% ± 0.18% for WM and GM, respectively. Voxel-by-voxel T1 difference maps show a normal 3D spatial distribution of noise in WM and GM. CONCLUSIONS: Low-field MP2RAGE proved effective in generating accurate, reliable, and reproducible T1 maps with high T1NR in phantom studies and in vivo feasibility established in healthy volunteers. While current work is focused on refining the MP2RAGE protocol to enable clinically efficient OE-MRI, this study establishes a foundation for TOLD T1 mapping for hypoxia biomarkers. This advancement holds the potential to facilitate a paradigm shift toward MR-guided biological adaptation and dose painting by leveraging 3D hypoxic spatial distributions and improving outcomes in conventionally challenging-to-treat cancers.