Quantifying in vivo collagen reorganization during immunotherapy in murine melanoma with second harmonic generation imaging.

Significance: Increased collagen linearization and deposition during tumorigenesis can impede immune cell infiltration and lead to tumor metastasis. Although melanoma is well studied in immunotherapy research, studies that quantify collagen changes during melanoma progression and treatment are lacking. Aim: We aim to image in vivo collagen in preclinical melanoma models during immunotherapy and quantify the collagen phenotype in treated and control mice. Approach: Second-harmonic generation imaging of collagen was performed in mouse melanoma tumors in vivo over a treatment time course. Animals were treated with a curative radiation and immunotherapy combination. Collagen morphology was quantified over time at an image and single-fiber level using CurveAlign and CT-FIRE software. Results: In immunotherapy-treated mice, collagen was reorganized toward a healthy phenotype, including shorter, wider, curlier collagen fibers, with modestly higher collagen density. Temporally, collagen fiber straightness and length changed late in treatment (days 9 and 12), while width and density changed early (day 6) compared with control mice. Single-fiber collagen features calculated in CT-FIRE were the most sensitive to the changes among treatment groups compared with bulk collagen features. Conclusions: Quantitative second-harmonic generation imaging can provide insight into collagen dynamics in vivo during immunotherapy, with key implications in improving immunotherapy response in melanoma and other cancers.

Three-dimensional simultaneous T1 and T2* relaxation times and quantitative susceptibility mapping at 3 T: A multicenter validation study.

We aimed to determine the intra-site repeatability and cross-site reproducibility of T1 and T2* relaxation times and quantitative susceptibility (χ) values obtained through quantitative parameter mapping (QPM) at 3 T. This prospective study included three 3-T scanners with the same hardware and software platform at three sites. The brains of twelve healthy volunteers were scanned three times using QPM at three sites. Intra-site repeatability and cross-site reproducibility were evaluated based on voxel-wise and region-of-interest analyses. The within-subject coefficient of variation (wCV), within-subject standard deviation (wSD), linear regression, Bland-Altman plot, and intraclass correlation coefficient (ICC) were used for evaluation. The intra-site repeatability wCV was 11.9 ± 6.86% for T1 and 3.15 ± 0.03% for T2*, and wSD of χ at 3.35 ± 0.10 parts per billion (ppb). Intra-site ICC(1,k) values for T1, T2*, and χ were 0.878-0.904, 0.972-0.976, and 0.966-0.972, respectively, indicating high consistency within the same scanner. Linear regression analysis revealed a strong agreement between measurements from each site and the site-average measurement, with R-squared values ranging from 0.79 to 0.83 for T1, 0.94-0.95 for T2*, and 0.95-0.96 for χ. The cross-site wCV was 13.4 ± 5.47% for T1 and 3.69 ± 2.25% for T2*, and cross-site wSD of χ at 4.08 ± 3.22 ppb. The cross-site ICC(2,1) was 0.707, 0.913, and 0.902 for T1, T2*, and χ, respectively. QPM provides T1, T2*, and χ values with an intra-site repeatability of <12% and cross-site reproducibility of <14%. These findings may contribute to the development of multisite studies.

Quantitative ultrasound imaging parameters in patients with cancerous thyroid nodules: development of a diagnostic model.

OBJECTIVE: This study aimed to develop a diagnostic model utilizing quantitative ultrasound parameters to accurately differentiate benign from malignant thyroid nodules. METHODS: A retrospective analysis of 194 patients with thyroid nodules, encompassing 65 malignant and 129 benign cases, was performed. Clinical data, ultrasound characteristics, and hemodynamic indicators were compared. Receiver operating characteristic (ROC) curves and logistic regression analysis identified independent diagnostic markers. RESULTS: No significant differences in clinical data were observed between the groups (P>0.05). Malignant nodules, however, were more likely to exhibit solid composition, hypoechoicity, irregular shapes, calcifications, central blood flow, and unclear margins (P<0.05). Hemodynamic parameters showed that malignant nodules had lower end-diastolic volume (EDV) but higher peak systolic velocity (PSV), resistive index (RI), and vascularization flow index (VFI) (P<0.001). Independent diagnostic factors identified included calcification, margin definition, RI, and VFI. A risk prediction model was formulated, demonstrating significantly lower scores for benign nodules (P<0.0001), achieving an ROC area of 0.964. CONCLUSION: Color Doppler ultrasound effectively distinguishes malignant from benign thyroid nodules. The diagnostic model emphasizes the importance of calcification, margin clarity, RI, and VFI as critical elements, enhancing the accuracy of thyroid nodule characterization and facilitating informed clinical decisions.

Physics-guided self-supervised learning for retrospective T1 and T2 mapping from conventional weighted brain MRI: Technical developments and initial validation in glioblastoma.

PURPOSE: To develop a self-supervised learning method to retrospectively estimate T1 and T2 values from clinical weighted MRI. METHODS: A self-supervised learning approach was constructed to estimate T1, T2, and proton density maps from conventional T1- and T2-weighted images. MR physics models were employed to regenerate the weighted images from the network outputs, and the network was optimized based on loss calculated between the synthesized and input weighted images, alongside additional constraints based on prior information. The method was evaluated on healthy volunteer data, with conventional mapping as references. The reproducibility was examined on two 3.0T scanners. Performance in tumor characterization was inspected by applying the method to a public glioblastoma dataset. RESULTS: For T1 and T2 estimation from three weighted images (T1 MPRAGE, T1 gradient echo sequences, and T2 turbo spin echo), the deep learning method achieved global voxel-wise error ≤9% in brain parenchyma and regional error ≤12.2% in six types of brain tissues. The regional measurements obtained from two scanners showed mean differences ≤2.4% and correlation coefficients >0.98, demonstrating excellent reproducibility. In the 50 glioblastoma patients, the retrospective quantification results were in line with literature reports from prospective methods, and the T2 values were found to be higher in tumor regions, with sensitivity of 0.90 and specificity of 0.92 in a voxel-wise classification task between normal and abnormal regions. CONCLUSION: The self-supervised learning method is promising for retrospective T1 and T2 quantification from clinical MR images, with the potential to improve the availability of quantitative MRI and facilitate brain tumor characterization.

Cartilage compositional MRI-a narrative review of technical development and clinical applications over the past three decades.

Articular cartilage damage and degeneration are among hallmark manifestations of joint injuries and arthritis, classically osteoarthritis. Cartilage compositional MRI (Cart-C MRI), a quantitative technique, which aims to detect early-stage cartilage matrix changes that precede macroscopic alterations, began development in the 1990s. However, despite the significant advancements over the past three decades, Cart-C MRI remains predominantly a research tool, hindered by various technical and clinical hurdles. This paper will review the technical evolution of Cart-C MRI, delve into its clinical applications, and conclude by identifying the existing gaps and challenges that need to be addressed to enable even broader clinical application of Cart-C MRI.

Magnetic resonance imaging-based prognostic model for subsequent distant metastasis in patients with ipsilateral breast tumor recurrence following breast-conserving surgery.

Background: Ipsilateral breast tumor recurrence (IBTR) following breast-conserving surgery (BCS) has been considered a risk factor for distant metastasis (DM). Limited data are available regarding the subsequent outcomes after IBTR. Therefore, this study aimed to determine the clinical course after IBTR and develop a magnetic resonance imaging (MRI)-based predictive model for subsequent DM. Methods: We retrospectively extracted quantitative features from MRI to construct a radiomics cohort, with all eligible patients undergoing preoperative MRI at time of primary tumor and IBTR between 2010 and 2018. Multivariate Cox analysis was performed to identify factors associated with DM. Three models were constructed using different sets of clinicopathological, qualitative, and quantitative MRI features and compared. Additionally, Kaplan-Meier analysis was performed to assess the prognostic value of the optimal model. Results: Among the 183 patients who experienced IBTR, 47 who underwent MRI for both primary and recurrent tumors were enrolled. Multivariate analysis demonstrated that the independent prognostic factors were human epidermal growth factor receptor 2 (HER2) status [hazard ratio (HR) =5.40] and background parenchymal enhancement (BPE) (HR =7.94) (all P values <0.01). Furthermore, four quantitative MRI features of recurrent tumors were selected through the least absolute shrinkage and selection operator (LASSO) method. The combined model exhibited superior performance [concordance index (C-index) 0.77] compared to the clinicoradiological model (C-index 0.71; P=0.006) and radiomics model (C-index 0.70; and P=0.01). Furthermore, the combined model successfully categorized patients into low- and high-risk subgroups with distinct prognoses (P<0.001). Conclusions: The clinicopathological and MRI features of IBTR were associated with secondary events following surgery. Additionally, the MRI-based combined model exhibited the highest predictive efficacy. These findings could be helpful in risk stratification and tailoring follow-up strategies in patients with IBTR.

Photon-Counting Computed Tomography for Microstructural Imaging of Bone and Joints.

PURPOSE OF REVIEW: Recently, photon-counting computed tomography (PCCT) has been introduced in clinical research and diagnostics. This review describes the technological advances and provides an overview of recent applications with a focus on imaging of bone. RECENT FINDINGS: PCCT is a full-body scanner with short scanning times that provides better spatial and spectral resolution than conventional energy-integrating-detector CT (EID-CT), along with an up to 50% reduced radiation dose. It can be used to quantify bone mineral density, to perform bone microstructural analyses and to assess cartilage quality with adequate precision and accuracy. Using a virtual monoenergetic image reconstruction, metal artefacts can be greatly reduced when imaging bone-implant interfaces. Current PCCT systems do not allow spectral imaging in ultra-high-resolution (UHR) mode. Given its improved resolution, reduced noise and spectral imaging capabilities PCCT has diagnostic capacities in both qualitative and quantitative imaging that outperform those of conventional CT. Clinical use in monitoring bone health has already been demonstrated. The full potential of PCCT systems will be unlocked when UHR spectral imaging becomes available.

Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry.

Significance: Photoacoustic imaging (PAI) promises to measure spatially resolved blood oxygen saturation but suffers from a lack of accurate and robust spectral unmixing methods to deliver on this promise. Accurate blood oxygenation estimation could have important clinical applications from cancer detection to quantifying inflammation. Aim: We address the inflexibility of existing data-driven methods for estimating blood oxygenation in PAI by introducing a recurrent neural network architecture. Approach: We created 25 simulated training dataset variations to assess neural network performance. We used a long short-term memory network to implement a wavelength-flexible network architecture and proposed the Jensen-Shannon divergence to predict the most suitable training dataset. Results: The network architecture can flexibly handle the input wavelengths and outperforms linear unmixing and the previously proposed learned spectral decoloring method. Small changes in the training data significantly affect the accuracy of our method, but we find that the Jensen-Shannon divergence correlates with the estimation error and is thus suitable for predicting the most appropriate training datasets for any given application. Conclusions: A flexible data-driven network architecture combined with the Jensen-Shannon divergence to predict the best training data set provides a promising direction that might enable robust data-driven photoacoustic oximetry for clinical use cases.

Comparison of Vendor-Independent Software Tools for Liver Proton Density Fat Fraction Estimation at 1.5 T.

(1) Background: Open-source software tools are available to estimate proton density fat fraction (PDFF). (2) Methods: We compared four algorithms: complex-based with graph cut (GC), magnitude-based (MAG), magnitude-only estimation with Rician noise modeling (MAG-R), and multi-scale quadratic pseudo-Boolean optimization with graph cut (QPBO). The accuracy and reliability of the methods were evaluated in phantoms with known fat/water ratios and a patient cohort with various grades (S0-S3) of steatosis. Image acquisitions were performed at 1.5 Tesla (T). (3) Results: The PDFF estimates showed a nearly perfect correlation (Pearson r = 0.999, p < 0.001) and inter-rater agreement (ICC = from 0.995 to 0.999, p < 0.001) with true fat fractions. The absolute bias was low with all methods (0.001-1%), and an ANCOVA detected no significant difference between the algorithms in vitro. The agreement across the methods was very good in the patient cohort (ICC = 0.891, p < 0.001). However, MAG estimates (-2.30% ± 6.11%, p = 0.005) were lower than MAG-R. The field inhomogeneity artifacts were most frequent in MAG-R (70%) and GC (39%) and absent in QPBO images. (4) Conclusions: The tested algorithms all accurately estimate PDFF in vitro. Meanwhile, QPBO is the least affected by field inhomogeneity artifacts in vivo.

Real-Time Synthetic Aperture Radar Imaging with Random Sampling Employing Scattered Power Mapping.

A novel image-reconstruction method is proposed for the processing of data acquired at random spatial positions. The images are reconstructed and updated in real time concurrently with the measurements to produce an evolving image, the quality of which is continuously improving and converging as the number of data points increases with the stream of additional measurements. It is shown that the images converge to those obtained with data acquired on a uniformly sampled surface, where the sampling density satisfies the Nyquist limit. The image reconstruction employs a new formulation of the method of scattered power mapping (SPM), which first maps the data into a three-dimensional (3D) preliminary image of the target on a uniform spatial grid, followed by fast Fourier space image deconvolution that provides the high-quality 3D image.

Contrast agent-free functional magnetic resonance imaging with matrix pencil decomposition to quantify abnormalities in lung perfusion and ventilation in patients with cystic fibrosis.

Background: Previous studies showed that contrast-enhanced (CE) morpho-functional magnetic resonance imaging (MRI) detects abnormalities in lung morphology and perfusion in patients with cystic fibrosis (CF). Novel matrix pencil decomposition MRI (MP-MRI) enables quantification of lung perfusion and ventilation without intravenous contrast agent administration. Objectives: To compare MP-MRI with established morpho-functional MRI and spirometry in patients with CF. Methods: Thirty-nine clinically stable patients with CF (mean age 21.6 ± 10.7 years, range 8-45 years) prospectively underwent morpho-functional MRI including CE perfusion MRI, MP-MRI and spirometry. Two blinded chest radiologists assessed morpho-functional MRI and MP-MRI employing the validated chest MRI score. In addition, MP-MRI data were processed by automated software calculating perfusion defect percentage (QDP) and ventilation defect percentage (VDP). Results: MP perfusion score and QDP correlated strongly with the CE perfusion score (both r = 0.81; p < 0.01). MP ventilation score and VDP showed strong inverse correlations with percent predicted FEV1 (r = -0.75 and r = -0.83; p < 0.01). The comparison of visual and automated parameters showed that both MP perfusion score and QDP, and MP ventilation score and VDP were strongly correlated (r = 0.74 and r = 0.78; both p < 0.01). Further, the MP perfusion score and MP ventilation score, as well as QDP and VDP were strongly correlated (r = 0.88 and r = 0.86; both p < 0.01). Conclusion: MP-MRI detects abnormalities in lung perfusion and ventilation in patients with CF without intravenous or inhaled contrast agent application, and correlates strongly with the well-established CE perfusion MRI score and spirometry. Automated analysis of MP-MRI may serve as quantitative noninvasive outcome measure for diagnostic monitoring and clinical trials.

Dual-source photon-counting CT: impact of residual cross-scatter on quantitative spectral results.

Dual-source photon-counting CT combines the high temporal resolution and high pitch of dual-source CT with the material quantification capabilities of photon-counting CT. It, however, results in cross-scatter that increases in severity with increased patient size and collimation. This cross-scatter must be corrected to ensure the removal of scatter artifacts and improve quantitative accuracy. To evaluate residual cross-scatter of a first-generation dual-source photon-counting CT and the effect of phantom size, collimation, and radiation dose, a phantom was scanned in single- and dual-source modes with and without its extension ring at three collimations and three radiation doses. Virtual monoenergetic images (VMI) at 50 keV, VMI 150 keV, and iodine density maps were reconstructed to determine variation between acquisition parameters in single- and dual-source modes. Additionally, differences relative to single-source acquisitions and to single-source and small collimation acquisitions were calculated to reflect residual cross-scatter with and without matched collimation. At VMI 50 keV, inserts exhibited accuracy and similar variation between single- and dual-source modes, averaging 5.4 ± 2.6 and 6.2 ± 2.5 HU, respectively, across phantom size, collimation, and radiation dose. Differences relative to single-source measured 5.1 ± 8.5 and 0.4 ± 4.2 HU while differences relative to single-source and small collimation acquisitions were 6.4 ± 10.8 HU and -0.5 ± 3.9 HU for VMI 50 and 150 keV, respectively. This minimal residual cross-scatter increases confidence in the quantitative accuracy of spectral results necessary for clinical applications of dual-source photon-counting CT with motion, such as cardiac imaging.

T1 and T2-mapping in pancreatic MRI: Current evidence and future perspectives.

Conventional T1- and T2-weighted magnetic resonance imaging (MRI) of the pancreas can vary significantly due to factors such as scanner differences and pulse sequence variations. This review explores T1 and T2 mapping techniques, modern MRI methods providing quantitative information about tissue relaxation times. Various T1 and T2 mapping pulse sequences are currently under investigation. Clinical and research applications of T1 and T2 mapping in the pancreas include their correlation with fibrosis, inflammation, and neoplasms. In chronic pancreatitis, T1 mapping and extracellular volume (ECV) quantification demonstrate potential as biomarkers, aiding in early diagnosis and classification. T1 mapping also shows promise in evaluating pancreatic exocrine function and detecting glucose metabolism disorders. T2* mapping is valuable in quantifying pancreatic iron, offering insights into conditions like thalassemia major. However, challenges persist, such as the lack of consensus on optimal sequences and normal values for healthy pancreas relaxometry. Large-scale studies are needed for validation, and improvements in mapping sequences are essential for widespread clinical integration. The future holds potential for mixed qualitative and quantitative models, extending the applications of relaxometry techniques to various pancreatic lesions and enhancing routine MRI protocols for pancreatic pathology diagnosis and prognosis.

Automated quantitative assessment of bone contusions and overlying articular cartilage following anterior cruciate ligament injury.

Quantitative methods to characterize bone contusions and associated cartilage injury remain limited. We combined standardized voxelwise normalization and 3D mapping to automate bone contusion segmentation post-anterior cruciate ligament (ACL) injury and evaluate anomalies in articular cartilage overlying bone contusions. Forty-five patients (54% female, 26.4 ± 11.8 days post-injury) with an ACL tear underwent 3T magnetic resonance imaging of their involved and uninvolved knees. A novel method for voxelwise normalization and 3D anatomical mapping was used to automate segmentation, labeling, and localization of bone contusions in the involved knee. The same mapping system was used to identify the associated articular cartilage overlying bone lesions. Mean regional T1ρ was extracted from articular cartilage regions in both the involved and uninvolved knees for quantitative paired analysis against ipsilateral cartilage within the same compartment outside of the localized bone contusion. At least one bone contusion lesion was detected in the involved knee within the femur and/or tibia following ACL injury in 42 participants. Elevated T1ρ (p = 0.033) signal were documented within the articular cartilage overlying the bone contusions resulting from ACL injury. In contrast, the same cartilaginous regions deprojected onto the uninvolved knees showed no ipsilateral differences (p = 0.795). Automated bone contusion segmentation using standardized voxelwise normalization and 3D mapping deprojection identified altered cartilage overlying bone contusions in the setting of knee ACL injury.

The textures of sarcoidosis: quantifying lung disease through variograms.

Objective: Sarcoidosis is a granulomatous disease affecting the lungs in over 90% of patients. Qualitative assessment of chest CT by radiologists is standard clinical practice and reliable quantification of disease from CT would support ongoing efforts to identify sarcoidosis phenotypes. Standard imaging feature engineering techniques such as radiomics suffer from extreme sensitivity to image acquisition and processing, potentially impeding generalizability of research to clinical populations. In this work, we instead investigate approaches to engineering variogram-based features with the intent to identify a robust, generalizable pipeline for image quantification in the study of sarcoidosis. Approach: For a cohort of more than 300 individuals with sarcoidosis, we investigated 24 feature engineering pipelines differing by decisions for image registration to a template lung, empirical and model variogram estimation methods, and feature harmonization for CT scanner model, and subsequently 48 sets of phenotypes produced through unsupervised clustering. We then assessed sensitivity of engineered features, phenotypes produced through unsupervised clustering, and sarcoidosis disease signal strength to pipeline. Main results: We found that variogram features had low to mild association with scanner model and associations were reduced by image registration. For each feature type, features were also typically robust to all pipeline decisions except image registration. Strength of disease signal as measured by association with pulmonary function testing and some radiologist visual assessments was strong (optimistic AUC ≈ 0.9, p ≪ 0.0001 in models for architectural distortion, conglomerate mass, fibrotic abnormality, and traction bronchiectasis) and fairly consistent across engineering approaches regardless of registration and harmonization for CT scanner. Significance: Variogram-based features appear to be a suitable approach to image quantification in support of generalizable research in pulmonary sarcoidosis.

Evaluating Chromosome Instability and Genotoxicity Through Single Cell Quantitative Imaging Microscopy.

Across eukaryotes, genome stability is essential for normal cell function, physiology, and species survival. Aberrant expression of key genes or exposure to genotoxic agents can have detrimental effects on genome stability and contribute to the development of various diseases, including cancer. Chromosome instability (CIN), or ongoing changes in chromosome complements, is a frequent form of genome instability observed in cancer and is a driver of genetic and cell-to-cell heterogeneity that can be rapidly detected and quantitatively assessed using surrogate markers of CIN. For example, single cell quantitative imaging microscopy (QuantIM) can be used to simultaneously identify changes in nuclear areas and micronucleus formation. While changes in nuclear areas are often associated with large-scale changes in chromosome complements (i.e., ploidy), micronuclei are small extra-nuclear bodies found outside the primary nucleus that have previously been employed as a measure of genotoxicity of test compounds. Here, we present a facile QuantIM approach that allows for the rapid assessment and quantification of CIN associated phenotypes and genotoxicity. First, we provide protocols to optimize and execute CIN and genotoxicity assays. Secondly, we present the critical imaging settings, optimization steps, downstream statistical analyses, and data visualization strategies employed to obtain high quality and robust data. These approaches can be easily applied to assess the prevalence of CIN associated phenotypes and genotoxic stress for a myriad of experimental and clinical contexts ranging from direct tests to large-scale screens of various genetic contexts (i.e., aberrant gene expression) or chemical compounds. In summary, this QuantIM approach facilitates the identification of novel CIN genes and/or genotoxic agents that will provide greater insight into the aberrant genes and pathways underlying CIN and genotoxicity.

Assessment of Treatment Response in Patients With Severe Asthma Using Visual and Quantitative Analysis of Chest CT.

OBJECTIVE: To evaluate the role of visual and quantitative chest CT parameters in assessing treatment response in patients with severe asthma. MATERIALS AND METHODS: Korean participants enrolled in a prospective multicenter study, named the Precision Medicine Intervention in Severe Asthma study, from May 2020 to August 2021, underwent baseline and follow-up chest CT scans (inspiration/expiration) 10-12 months apart, before and after biologic treatment. Two radiologists scored bronchiectasis severity and mucus plugging extent. Quantitative parameters were obtained from each CT scan as follows: normal lung area (normal), air trapping without emphysema (AT without emph), air trapping with emphysema (AT with emph), and airway (total branch count, Pi10). Clinical parameters, including pulmonary function tests (forced expiratory volume in 1 s [FEV1] and FEV1/forced vital capacity [FVC]), sputum and blood eosinophil count, were assessed at initial and follow-up stages. Changes in CT parameters were correlated with changes in clinical parameters using Pearson or Spearman correlation. RESULTS: Thirty-four participants (female:male, 20:14; median age, 50.5 years) diagnosed with severe asthma from three centers were included. Changes in the bronchiectasis and mucus plugging extent scores were negatively correlated with changes in FEV1 and FEV1/FVC (ρ = from -0.544 to -0.368, all P < 0.05). Changes in quantitative CT parameters were correlated with changes in FEV1 (normal, r = 0.373 [P = 0.030], AT without emph, r = -0.351 [P = 0.042]), FEV1/FVC (normal, r = 0.390 [P = 0.022], AT without emph, r = -0.370 [P = 0.031]). Changes in total branch count were positively correlated with changes in FEV1 (r = 0.349 [P = 0.043]). There was no correlation between changes in Pi10 and the clinical parameters (P > 0.05). CONCLUSION: Visual and quantitative CT parameters of normal, AT without emph, and total branch count may be effective for evaluating treatment response in patients with severe asthma.

Value of MRI - T2 Mapping to Differentiate Clinically Significant Prostate Cancer.

Standardized reporting of multiparametric prostate MRI (mpMRI) is widespread and follows international standards (Pi-RADS). However, quantitative measurements from mpMRI are not widely comparable. Although T2 mapping sequences can provide repeatable quantitative image measurements and extract reliable imaging biomarkers from mpMRI, they are often time-consuming. We therefore investigated the value of quantitative measurements on a highly accelerated T2 mapping sequence, in order to establish a threshold to differentiate benign from malignant lesions. For this purpose, we evaluated a novel, highly accelerated T2 mapping research sequence that enables high-resolution image acquisition with short acquisition times in everyday clinical practice. In this retrospective single-center study, we included 54 patients with clinically indicated MRI of the prostate and biopsy-confirmed carcinoma (n = 37) or exclusion of carcinoma (n = 17). All patients had received a standard of care biopsy of the prostate, results of which were used to confirm or exclude presence of malignant lesions. We used the linear mixed-effects model-fit by REML to determine the difference between mean values of cancerous tissue and healthy tissue. We found good differentiation between malignant lesions and normal appearing tissue in the peripheral zone based on the mean T2 value. Specifically, the mean T2 value for tissue without malignant lesions was (151.7 ms [95% CI: 146.9-156.5 ms] compared to 80.9 ms for malignant lesions [95% CI: 67.9-79.1 ms]; p < 0.001). Based on this assessment, a limit of 109.2 ms is suggested. Aditionally, a significant correlation was observed between T2 values of the peripheral zone and PI-RADS scores (p = 0.0194). However, no correlation was found between the Gleason Score and the T2 relaxation time. Using REML, we found a difference of -82.7 ms in mean values between cancerous tissue and healthy tissue. We established a cut-off-value of 109.2 ms to accurately differentiate between malignant and non-malignant prostate regions. The addition of T2 mapping sequences to routine imaging could benefit automated lesion detection and facilitate contrast-free multiparametric MRI of the prostate.

The dynamics and regulation of PARP1 and PARP2 in response to DNA damage and during replication.

DNA strand breaks activate Poly(ADP-ribose) polymerase (PARP) 1 and 2, which use NAD+ as the substrate to covalently conjugate ADP-ribose on themselves and other proteins (e.g., Histone) to promote chromatin relaxation and recruit additional DNA repair factors. Enzymatic inhibitors of PARP1 and PARP2 (PARPi) are promising cancer therapy agents that selectively target BRCA1- or BRCA2- deficient cancers. As immediate early responders to DNA strand breaks with robust activities, PARP1 and PARP2 normally form transient foci (<10 minutes) at the micro-irradiation-induced DNA lesions. In addition to enzymatic inhibition, PARPi also extend the presence of PARP1 and PARP2 at DNA lesions, including at replication forks, where they may post a physical block for subsequent repair and DNA replication. The dynamic nature of PARP1 and PARP2 foci made live cell imaging a unique platform to detect subtle changes and the functional interaction among PARP1, PARP2, and their regulators. Recent imaging studies have provided new understandings of the biological consequence of PARP inhibition and uncovered functional interactions between PARP1 and PARP2 and new regulators (e.g., histone poly(ADP-ribosylation) factor). Here, we review recent advances in dissecting the temporal and spatial Regulation of PARP1 and PARP2 at DNA lesions and discuss their physiological implications on both cancer and normal cells.

Model-based quantitative photoacoustic tomography with directional total variation.

In photoacoustic tomography (PAT), acoustic inversion aims to recover the spatial distribution of light energy deposition within the imaging object from the signals captured by detectors. To achieve quantitative imaging, optical inversion is further employed to derive absorption coefficient (AC) images. However, limitations such as restricted detection angles and inherent noise lead to substantial artifacts and degradation in the quality of PAT images, consequently affecting the accuracy of optical inversion results. In this study, we propose a directional total variation constrained optical inversion model to reconstruct the AC image. By incorporating anatomy prior information into the optical inversion process, our method can effectively suppress artifacts in AC images while maintaining structural integrity. Simulation, phantom, and in vivo experimental results demonstrate that our method significantly improves the reconstructed AC image quality. Our method provides a reliable foundation for achieving high-quality quantitative PAT imaging.