A large normative connectome for exploring the tractographic correlates of focal brain interventions.

Diffusion-weighted MRI (dMRI) is a widely used neuroimaging modality that permits the in vivo exploration of white matter connections in the human brain. Normative structural connectomics - the application of large-scale, group-derived dMRI datasets to out-of-sample cohorts - have increasingly been leveraged to study the network correlates of focal brain interventions, insults, and other regions-of-interest (ROIs). Here, we provide a normative, whole-brain connectome in MNI space that enables researchers to interrogate fiber streamlines that are likely perturbed by given ROIs, even in the absence of subject-specific dMRI data. Assembled from multi-shell dMRI data of 985 healthy Human Connectome Project subjects using generalized Q-sampling imaging and multispectral normalization techniques, this connectome comprises ~12 million unique streamlines, the largest to date. It has already been utilized in at least 18 peer-reviewed publications, most frequently in the context of neuromodulatory interventions like deep brain stimulation and focused ultrasound. Now publicly available, this connectome will constitute a useful tool for understanding the wider impact of focal brain perturbations on white matter architecture going forward.

Prognostic value of preoperative diffusion restriction in glioblastoma.

INTRODUCTION: Although glioblastoma (GBM) has a very poor prognosis, overall survival (OS) in treated patients shows great difference varying from few days to several months. Identifying factors explaining this difference would improve management of patient treatment. AIM: To determine the relevance of diffusion restriction in newly diagnosed treatment-naïve GBM patients. METHODS: Preoperative magnetic resonance scans of 33 patients with GBM were reviewed. Regions of interest including all the T2 hyperintense lesion were drawn on diffusion weighted B0 images and transferred to the apparent diffusion coefficient (ADC) map. For each patient, a histogram displaying the ADC values within in the regions of interest was generated. Volumetric parameters including tumor regions with restricted diffusion, parameters derived from histogram and mean ADC value of the tumor were calculated. Their relationship with OS was analyzed. RESULTS: Patients with mean ADC value < 1415x10-6 mm2/s had a significantly shorter OS (p=0.021). Among volumetric parameters, the percentage of volume within T2 lesion with a normalized ADC value <1.5 times that in white matter was significantly associated with OS (p=0.0045). Patients with a percentage>23.92% had a shorter OS. Among parameters derived from histogram, the 50th percentile showed a trend towards significance for OS (p=0.055) with patients living longer when having higher values of 50th percentile. A difference in OS was observed between patients according to ADC peak of histogram but this difference did not reach statistical significance (p=0.0959). CONCLUSION: Diffusion magnetic resonance imaging may provide useful information for predicting GBM prognosis.

Renal interstitial fibrotic assessment using non-Gaussian diffusion kurtosis imaging in a rat model of hyperuricemia.

BACKGROUND: To investigate the feasibility of Diffusion Kurtosis Imaging (DKI) in assessing renal interstitial fibrosis induced by hyperuricemia. METHODS: A hyperuricemia rat model was established, and the rats were randomly split into the hyperuricemia (HUA), allopurinol (AP), and AP + empagliflozin (AP + EM) groups (n = 19 per group). Also, the normal rats were selected as controls (CON, n = 19). DKI was performed before treatment (baseline) and on days 1, 3, 5, 7, and 9 days after treatment. The DKI indicators, including mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) of the cortex (CO), outer stripe of the outer medulla (OS), and inner stripe of the outer medulla (IS) were acquired. Additionally, hematoxylin and eosin (H&E) staining, Masson trichrome staining, and nuclear factor kappa B (NF-κB) immunostaining were used to reveal renal histopathological changes at baseline, 1, 5, and 9 days after treatment. RESULTS: The HUA, AP, and AP + EM group MKOS and MKIS values gradually increased during this study. The HUA group exhibited the highest MK value in outer medulla. Except for the CON group, all the groups showed a decreasing trend in the FA and MD values of outer medulla. The HUA group exhibited the lowest FA and MD values. The MKOS and MKIS values were positively correlated with Masson's trichrome staining results (r = 0.687, P < 0.001 and r = 0.604, P = 0.001, respectively). The MDOS and FAIS were negatively correlated with Masson's trichrome staining (r = -626, P < 0.0014 and r = -0.468, P = 0.01, respectively). CONCLUSION: DKI may be a non-invasive method for monitoring renal interstitial fibrosis induced by hyperuricemia.

Conduction velocity, G-ratio, and extracellular water as microstructural characteristics of autism spectrum disorder.

The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a new approach to calculating axonal conduction velocity termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel formulation for calculating aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.

[Functional MRI assessment of microstructural and perfusion changes in the kidneys of rats with intrauterine growth restriction]. / åŠŸèƒ½ç£å ±æŒ¯è¯„ä¼°å®«å† å‘è‚²è¿Ÿç¼“ä»”é¼ è‚¾è„å¾®è§‚ç»“æž„åŠçŒæ³¨æ”¹å˜.

OBJECTIVES: To explore the value of functional magnetic resonance imaging (MRI) techniques, including intravoxel incoherent motion (IVIM), T1 mapping, and T2 mapping, in assessing the microstructural and perfusion changes in the kidneys of rats with intrauterine growth restriction (IUGR). METHODS: An IUGR rat model was established through a low-protein diet during pregnancy. Offspring from pregnant rats on a low-protein diet were randomly divided into an IUGR 8-week group and an IUGR 12-week group, while offspring from pregnant rats on a normal diet were divided into a normal 8-week group and a normal 12-week group (n=8 for each group). The apparent diffusion coefficient (ADC), true diffusion coefficient (Dt), pseudo-diffusion coefficient (D*), perfusion fraction (f), T1 value, and T2 value of the renal cortex and medulla were compared, along with serum creatinine and blood urea nitrogen levels among the groups. RESULTS: The Dt value in the renal medulla was higher in the IUGR 12-week group than in the IUGR 8-week group, and the D* value in the renal medulla was lower in the IUGR 12-week group than in both the normal 12-week group and the IUGR 8-week group (P<0.05). The T1 value in the renal medulla was higher than in the cortex in the IUGR 8-week group, and the T1 value in the renal medulla was higher in the IUGR 12-week group than in both the IUGR 8-week group and the normal 12-week group, with the cortical T1 value in the IUGR 12-week group also being higher than that in the normal 12-week group (P<0.05). The T2 values in the renal medulla were higher than those in the cortex across all groups (P<0.05). There were no significant differences in the T2 values of either the cortex or medulla among the groups (P>0.05). There were no significant differences in serum creatinine and blood urea nitrogen levels among the groups (P>0.05). Glomerular hyperplasia and hypertrophy without significant fibrotic changes were observed in the IUGR 8-week group, whereas glomerular atrophy, cystic stenosis, and interstitial inflammatory infiltration and fibrosis were seen in the IUGR 12-week group. CONCLUSIONS: IVIM MRI can be used to assess and dynamically observe the microstructural and perfusion damage in the kidneys of IUGR rats. MRI T1 mapping can be used to evaluate kidney damage in IUGR rats, and the combination of MRI T1 mapping and T2 mapping can further differentiate renal fibrosis in IUGR rats.

IVIM and DCE-MRI in Predicting Phenotypic Subtypes and Nottingham Prognostic Index of Breast Cancer.

OBJECTIVE: To explore the value of intravoxel incoherent motion (IVIM) and dynamic contrast enhanced MRI (DCE-MRI) for predicting phenotypic subtypes and Nottingham prognostic index (NPI) of breast cancer. STUDY DESIGN: Descriptive study. Place and Duration of the Study: Department of Radiology, Affiliated Hospital of Jining Medical University, Jining, Shandong, China, from March 2020 to January 2022. METHODOLOGY: One hundred and forty-one breast cancer patients with preoperative IVIM and DCE imaging were collected. IVIM parameters of D, D*, f, and DCE-MRI parameters of Ktrans, Kep, and Ve were measured. Receiver operating characteristic curves were conducted to assess the diagnostic efficacies. Additionally, 40 patients collected from February 2022 to July 2022 were enrolled as validation cohort. RESULTS: The D value in HER2-enriched (HER2-E) was lower than that in non-HER-E, while D*, Ktrans, and Ve values were higher than that in non-HER-E (p < 0.001, 0.046, < 0.001, and < 0.001, respectively). D + Ktrans + Ve showed an optimal diagnostic efficiency (AUC = 0.868). Meanwhile, D* and f values of triple-negative breast cancer (TNBC) were higher than those of non-TNBC, and Ve value of TNBC was lower than that of non-TNBC (p = 0.013, 0.006, and < 0.001, respectively). D* + f + Ve showed the best prediction performance (AUC = 0.849). Additionally, D and Kep were independent predictors of NPI (p < 0.001, and 0.002, respectively). D + Kep showed a good diagnostic efficiency (AUC = 0.818). CONCLUSION: The combined IVIM and DCE-MRI model showed enhanced diagnostic efficiency in predicting phenotypic subtypes and NPI of breast cancer, and might thus be considered efficient in therapy decision-making for patients. KEY WORDS: Breast neoplasms, Intravoxel incoherent motion, Dynamic contrast enhanced magnetic resonance imaging, Phenotypic subtypes, Nottingham prognostic index.

Staging liver fibrosis with various diffusion-weighted magnetic resonance imaging models.

BACKGROUND: Diffusion-weighted imaging (DWI) has been developed to stage liver fibrosis. However, its diagnostic performance is inconsistent among studies. Therefore, it is worth studying the diagnostic value of various diffusion models for liver fibrosis in one cohort. AIM: To evaluate the clinical potential of six diffusion-weighted models in liver fibrosis staging and compare their diagnostic performances. METHODS: This prospective study enrolled 59 patients suspected of liver disease and scheduled for liver biopsy and 17 healthy participants. All participants underwent multi-b value DWI. The main DWI-derived parameters included Mono-apparent diffusion coefficient (ADC) from mono-exponential DWI, intravoxel incoherent motion model-derived true diffusion coefficient (IVIM-D), diffusion kurtosis imaging-derived apparent diffusivity (DKI-MD), stretched exponential model-derived distributed diffusion coefficient (SEM-DDC), fractional order calculus (FROC) model-derived diffusion coefficient (FROC-D) and FROC model-derived microstructural quantity (FROC-µ), and continuous-time random-walk (CTRW) model-derived anomalous diffusion coefficient (CTRW-D) and CTRW model-derived temporal diffusion heterogeneity index (CTRW-α). The correlations between DWI-derived parameters and fibrosis stages and the parameters' diagnostic efficacy in detecting significant fibrosis (SF) were assessed and compared. RESULTS: CTRW-D (r = -0.356), CTRW-α (r = -0.297), DKI-MD (r = -0.297), FROC-D (r = -0.350), FROC-µ (r = -0.321), IVIM-D (r = -0.251), Mono-ADC (r = -0.362), and SEM-DDC (r = -0.263) were significantly correlated with fibrosis stages. The areas under the ROC curves (AUCs) of the combined index of the six models for distinguishing SF (0.697-0.747) were higher than each of the parameters alone (0.524-0.719). The DWI models' ability to detect SF was similar. The combined index of CTRW model parameters had the highest AUC (0.747). CONCLUSION: The DWI models were similarly valuable in distinguishing SF in patients with liver disease. The combined index of CTRW parameters had the highest AUC.

A novel method for PET connectomics guided by fibre-tracking MRI: Application to Alzheimer's disease.

This study introduces a novel brain connectome matrix, track-weighted PET connectivity (twPC) matrix, which combines positron emission tomography (PET) and diffusion magnetic resonance imaging data to compute a PET-weighted connectome at the individual subject level. The new method is applied to characterise connectivity changes in the Alzheimer's disease (AD) continuum. The proposed twPC samples PET tracer uptake guided by the underlying white matter fibre-tracking streamline point-to-point connectivity calculated from diffusion MRI (dMRI). Using tau-PET, dMRI and T1-weighted MRI from the Alzheimer's Disease Neuroimaging Initiative database, structural connectivity (SC) and twPC matrices were computed and analysed using the network-based statistic (NBS) technique to examine topological alterations in early mild cognitive impairment (MCI), late MCI and AD participants. Correlation analysis was also performed to explore the coupling between SC and twPC. The NBS analysis revealed progressive topological alterations in both SC and twPC as cognitive decline progressed along the continuum. Compared to healthy controls, networks with decreased SC were identified in late MCI and AD, and networks with increased twPC were identified in early MCI, late MCI and AD. The altered network topologies were mostly different between twPC and SC, although with several common edges largely involving the bilateral hippocampus, fusiform gyrus and entorhinal cortex. Negative correlations were observed between twPC and SC across all subject groups, although displaying an overall reduction in the strength of anti-correlation with disease progression. twPC provides a new means for analysing subject-specific PET and MRI-derived information within a hybrid connectome using established network analysis methods, providing valuable insights into the relationship between structural connections and molecular distributions. PRACTITIONER POINTS: New method is proposed to compute patient-specific PET connectome guided by MRI fibre-tracking. Track-weighted PET connectivity (twPC) matrix allows to leverage PET and structural connectivity information. twPC was applied to dementia, to characterise the PET nework abnormalities in Alzheimer's disease and mild cognitive impairment.

Correlation between MRI (DWI and DCE) and cellularity of parotid gland pleomorphic adenomas.

PURPOSE: Parotid pleomorphic adenomas present a risk of recurrence, higher when the tumour is a hypocellular subtype. The aim of the study was to determine whether it is possible to characterize this histological subtype with diffusion and perfusion sequences of the preoperative MRI. METHODS: This retrospective study included 97 patients operated between 2010 and 2020. Histologic slides review was performed to classify tumours into three histologic subtypes: hypocellular, classical and hypercellular. Univariate and multivariate analyses studied the correlation between histology and diffusion and perfusion MRI parameters obtained with OleaSphere® software. RESULTS: The hypocellular subtype had higher apparent diffusion coefficient values than the other two subtypes: 2.13 ± 0.23, 1.83 ± 0.42, and 1.61 ± 0.4 × 10-3 mm2/s for hypocellular, classical and hypercellular subtype respectively (p < 0.0001). Multivariate analysis showed that an ADCmean > 1.88 × 10-3 mm2/s was suggestive of a hypocellular pleomorphic adenoma in 79% of the cases, with a specificity and PPV of 94 and 96% (p < 0.001), respectively. CONCLUSION: The histological subtype of a pleomorphic adenoma can be predicted preoperatively with ADC values. A prospective and multicentric study on a larger cohort is needed to confirm our results.

Preoperative 11 C-Methionine PET-MRI in Pediatric Infratentorial Tumors.

PURPOSE: MRI is the main imaging modality for pediatric brain tumors, but amino acid PET can provide additional information. Simultaneous PET-MRI acquisition allows to fully assess the tumor and lower the radiation exposure. Although symptomatic posterior fossa tumors are typically resected, the patient management is evolving and will benefit from an improved preoperative tumor characterization. We aimed to explore, in children with newly diagnosed posterior fossa tumor, the complementarity of the information provided by amino acid PET and MRI parameters and the correlation to histopathological results. PATIENTS AND METHODS: Children with a newly diagnosed posterior fossa tumor prospectively underwent a preoperative 11 C-methionine (MET) PET-MRI. Images were assessed visually and semiquantitatively. Using correlation, minimum apparent diffusion coefficient (ADC min ) and contrast enhancement were compared with MET SUV max . The diameter of the enhancing lesions was compared with metabolic tumoral volume. Lesions were classified according to the 2021 World Health Organization (WHO) classification. RESULTS: Ten children were included 4 pilocytic astrocytomas, 2 medulloblastomas, 1 ganglioglioma, 1 central nervous system embryonal tumor, and 1 schwannoma. All lesions showed visually increased MET uptake. A negative moderate correlation was found between ADC min and SUV max values ( r = -0.39). Mean SUV max was 3.8 (range, 3.3-4.2) in WHO grade 4 versus 2.5 (range, 1.7-3.0) in WHO grade 1 lesions. A positive moderate correlation was found between metabolic tumoral volume and diameter values ( r = 0.34). There was no correlation between SUV max and contrast enhancement intensity ( r = -0.15). CONCLUSIONS: Preoperative 11 C-MET PET and MRI could provide complementary information to characterize pediatric infratentorial tumors.

Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning.

Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters. In vivo and synthetic brain 5D-Diffusion-T1-T2∗-weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cramér-Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions.

MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline.

Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments. It fully automates either probabilistic or deterministic tractography, starting from a subject's magnetic resonance imaging (MRI) data, including structural and diffusion MRI images, to the edge density image (EDI) of their structural connectomes. Dependencies are containerized with Singularity (now called Apptainer) and decoupled from code to enable rapid prototyping and modification. Data derivatives are organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so it can accelerate brain connectome research for a broader user community. MaPPeRTrac is available at: https://github.com/LLNL/mappertrac .

Dynamic Contrast Enhanced-MRI and Apparent Diffusion Coefficient Quantitation for Differentiate Hepatocellular Carcinoma from Hepatocellular Adenoma.

BACKGROUND: Due to their overlapping radiological characteristics, hepatic lesions, such as hepatocellular carcinoma (HCC) and hepatocellular adenoma (HCA), present a substantial diagnostic challenge. Accurate differentiation between HCC and HCA is essential for the best clinical treatment and therapeutic decision-making. This study aims to assess the potential role of DCE-MRI and Apparent Diffusion Coefficient (ADC) quantitation in the diagnosis of hepatocellular carcinoma (HCC) from hepatocellular adenoma (HCA). METHODS: 103 patients (56 HCC, 47 HCA) with histopathologically proven hepatocellular lesions were the subjects of a cross-sectional investigation. A standardized imaging technique was used for DCE-MRI on all patients. Diffusion-weighted imaging (DWI) provided the ADC values. The diagnostic efficacy of DCE-MRI and ADC in differentiation was evaluated using statistical analyses, such as t-tests and receiver operating characteristic (ROC) curve analysis. SPSS VER 16 was used for the analysis of the collected data. RESULTS: A total of 103 patients (female: male= 52:51, 57.14±3.09 years) were included in the study. The study revealed significant differences in DCE-MRI parameters and ADC values between HCC and HCA lesions. ADC value was significantly lower in HCC than in HCA (p < 0.001). The area under the curve (AUC) was 0.78 (95% CI: 0.69-0.87) for ADC, 0.84 (95% CI: 0.76-0.91) for Ktrans, and 0.72 (95% CI: 0.62-0.82) for Ve. Sensitivity and specificity for ADC were 76.59% and 71.42%, respectively. Also, PPV and NPV of ADC were 69.23% and 78.43%, respectively. Sensitivity and specificity for Ktrans were 82.14% and 76.59%, respectively. Also, PPV and NPV of Ktrans were 80.7% and 78.26%, respectively. CONCLUSION: In conclusion, DCE-MRI-derived parameters, along with ADC values, exhibit promise as non-invasive tools for differentiating HCC from HCA.

High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance.

PURPOSE: Majority of men with low-risk prostate cancer can be managed with active surveillance (AS). This study evaluates a high-resolution diffusion-weighted imaging (HR-DWI) technique to predict adverse biopsy histology (AH), defined as Gleason score ≥7 on any biopsy or ≥3 increase in number of positive biopsy cores on systematic biopsies. We test the hypothesis that high-grade disease and progressing disease undergo subtle changes during even short intervals that can be detected by HR-DWI. EXPERIMENTAL DESIGN: In a prospective clinical trial, serial multiparametric MRIs, incorporating HR-DWI and standard DWI (S-DWI) were performed approximately 12 months apart prior to prostate biopsy (n = 59). HR-DWI, which uses reduced field-of-view and motion compensation techniques, was compared with S-DWI. RESULTS: HR-DWI had a 3-fold improvement in spacial resolution compared with S-DWI as confirmed using imaging phantoms. For detecting AH, multiparametric MRI using HR-DWI had a sensitivity of 75% and specificity of 83.9%, and MRI using S-DWI had a sensitivity of 71.4% and specificity of 54.8%. The AUC for HR-DWI was significantly higher (0.794 vs. 0.631, P = 0.014). Secondary analyses of univariable predictors of AH showed tumor size increase [OR 16.8; 95% confidence interval (CI): 4.06-69.48; P < 0.001] and apparent diffusion coefficient (ADC) decrease (OR 5.06; 95% CI: 1.39-18.38; P = 0.014) on HR-DWI were significant predictors of AH. CONCLUSION: HR-DWI outperforms S-DWI in predicting AH. Patient with AH have tumors that change in size and ADC that could be detected using HR-DWI. Future studies with longer follow-up should assess HR-DWI for predicting disease progression during AS. SIGNIFICANCE: We report on a prospective clinical trial using a MRI that has three times the resolution of standard MRI. During AS for prostate cancer, two high-resolution MRIs performed approximately a year apart can detect tumor changes that predict the presence of aggressive cancers that should be considered for curative therapy such as prostatectomy or radiation.

Interpretation of Complete Tumor Response on MRI Following Chemoradiotherapy of Rectal Cancer: Inter-Reader Agreement and Associated Factors in Multi-Center Clinical Practice.

OBJECTIVE: To measure inter-reader agreement and identify associated factors in interpreting complete response (CR) on magnetic resonance imaging (MRI) following chemoradiotherapy (CRT) for rectal cancer. MATERIALS AND METHODS: This retrospective study involved 10 readers from seven hospitals with experience of 80-10210 cases, and 149 patients who underwent surgery after CRT for rectal cancer. Using MRI-based tumor regression grading (mrTRG) and methods employed in daily practice, the readers independently assessed mrTRG, CR on T2-weighted images (T2WI) denoted as mrCRT2W, and CR on all images including diffusion-weighted images (DWI) denoted as mrCRoverall. The readers described their interpretation patterns and how they utilized DWI. Inter-reader agreement was measured using multi-rater kappa, and associated factors were analyzed using multivariable regression. Correlation between sensitivity and specificity of each reader was analyzed using Spearman coefficient. RESULTS: The mrCRT2W and mrCRoverall rates varied widely among the readers, ranging 18.8%-40.3% and 18.1%-34.9%, respectively. Nine readers used DWI as a supplement sequence, which modified interpretations on T2WI in 2.7% of cases (36/1341 [149 patients × 9 readers]) and mostly (33/36) changed mrCRT2W to non-mrCRoverall. The kappa values for mrTRG, mrCRT2W, and mrCRoverall were 0.56 (95% confidence interval: 0.49, 0.62), 0.55 (0.52, 0.57), and 0.54 (0.51, 0.57), respectively. No use of rectal gel, larger initial tumor size, and higher initial cT stage exhibited significant association with a higher inter-reader agreement for assessing mrCRoverall (P ≤ 0.042). Strong negative correlations were observed between the sensitivity and specificity of individual readers (coefficient, -0.718 to -0.963; P ≤ 0.019). CONCLUSION: Inter-reader agreement was moderate for assessing CR on post-CRT MRI. Readers' varying standards on MRI interpretation (i.e., threshold effect), along with the use of rectal gel, initial tumor size, and initial cT stage, were significant factors associated with inter-reader agreement.

Detection of abdominopelvic lymph nodes in multi-parametric MRI.

Reliable localization of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Radiologists routinely measure the nodal size in order to distinguish benign from malignant nodes, which require subsequent cancer staging. However, identification of lymph nodes is a cumbersome task due to their myriad appearances in mpMRI studies. Multiple sequences are acquired in mpMRI studies, including T2 fat suppressed (T2FS) and diffusion weighted imaging (DWI) sequences among others; consequently, the sizing of LNs is rendered challenging due to the variety of signal intensities in these sequences. Furthermore, radiologists can miss potentially metastatic LNs during a busy clinical day. To lighten these imaging and workflow challenges, we propose a computer-aided detection (CAD) pipeline to detect both benign and malignant LNs in the body for their subsequent measurement. We employed the recently proposed Dynamic Head (DyHead) neural network to detect LNs in mpMRI studies that were acquired using a variety of scanners and exam protocols. The T2FS and DWI series were co-registered, and a selective augmentation technique called Intra-Label LISA (ILL) was used to blend the two volumes with the interpolation factor drawn from a Beta distribution. In this way, ILL diversified the samples that the model encountered during the training phase, while the requirement for both sequences to be present at test time was nullified. Our results showed a mean average precision (mAP) of 53.5% and a sensitivity of ∼78% with ILL at 4 FP/vol. This corresponded to an improvement of ≥10% in mAP and ≥12% in sensitivity at 4FP (p ¡ 0.05) respectively over current LN detection approaches evaluated on the same dataset. We also established the out-of-distribution robustness of the DyHead model by training it on data acquired by a Siemens Aera scanner and testing it on data from the Siemens Verio, Siemens Biograph mMR, and Philips Achieva scanners. Our pilot work represents an important first step towards automated detection, segmentation, and classification of lymph nodes in mpMRI.

A comparative study of mono-exponential and advanced diffusion-weighted imaging in differentiating stage IA endometrial carcinoma from benign endometrial lesions.

PURPOSE: The purpose of the current investigation is to compare the efficacy of different diffusion models and diffusion kurtosis imaging (DKI) in differentiating stage IA endometrial carcinoma (IAEC) from benign endometrial lesions (BELs). METHODS: Patients with IAEC, endometrial hyperplasia (EH), or a thickened endometrium confirmed between May 2016 and August 2022 were retrospectively enrolled. All of the patients underwent a preoperative pelvic magnetic resonance imaging (MRI) examination. The apparent diffusion coefficient (ADC) from the mono-exponential model, pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) from the bi-exponential model, distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index from the stretched-exponential model, diffusion coefficient (Dk) and diffusion kurtosis (K) from the DKI model were calculated. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficiency. RESULTS: A total of 90 patients with IAEC and 91 patients with BELs were enrolled. The values of ADC, D, DDC and Dk were significantly lower and D* and K were significantly higher in cases of IAEC (p < 0.05). Multivariate analysis showed that K was the only predictor. The area under the ROC curve of K was 0.864, significantly higher compared with the ADC (0.601), D (0.811), D* (0.638), DDC (0.743) and Dk (0.675). The sensitivity, specificity and accuracy of K were 78.89%, 85.71% and 80.66%, respectively. CONCLUSION: Advanced diffusion-weighted imaging models have good performance for differentiating IAEC from EH and endometrial thickening. Among all of the diffusion parameters, K showed the best performance and was the only independent predictor. Diffusion kurtosis imaging was defined as the most valuable model in the current context.

Apparent diffusion coefficient values predict response to brachytherapy in bulky cervical cancer.

BACKGROUND: Diffusion-weighted magnetic resonance imaging (DWI) provides a measurement of tumor cellularity. We evaluated the potential of apparent diffusion coefficient (ADC) values obtained from post-external beam radiation therapy (EBRT) DWI and prior to brachytherapy (BT) to predict for complete metabolic response (CMR) in bulky cervical cancer. METHODS: Clinical and DWI (b value = 500 s/mm2) data were obtained from patients undergoing interstitial BT with high-risk clinical target volumes (HR-CTVs) > 30 cc. Volumes were contoured on co-registered T2 weighted images and 90th percentile ADC values were calculated. Patients were stratified by CMR (defined by PET-CT at three months post-BT). Relation of CMR with 90th percentile ADC values and other clinical factors (International Federation of Gynecology and Obstetrics (FIGO) stage, histology, tumor and HR-CTV size, pre-treatment hemoglobin, and age) was assessed both in univariate and multivariate logistic regression analyses. Youden's J statistic was used to identify a threshold value. RESULTS: Among 45 patients, twenty-eight (62%) achieved a CMR. On univariate analysis for CMR, only 90th percentile ADC value was significant (p = 0.029) while other imaging and clinical factors were not. Borderline significant factors were HR-CTV size (p = 0.054) and number of chemotherapy cycles (p = 0.078). On multivariate analysis 90th percentile ADC (p < 0.0001) and HR-CTV size (p < 0.003) were highly significant. Patients with 90th percentile ADC values above 2.10 × 10- 3 mm2/s were 5.33 (95% CI, 1.35-24.4) times more likely to achieve CMR. CONCLUSIONS: Clinical DWI may serve to risk-stratify patients undergoing interstitial BT for bulky cervical cancer.

Accelerated IVIM-corrected DTI in acute hamstring injury: towards a clinically feasible acquisition time.

BACKGROUND: Intravoxel incoherent motion (IVIM)-corrected diffusion tensor imaging (DTI) potentially enhances return-to-play (RTP) prediction after hamstring injuries. However, the long scan times hamper clinical implementation. We assessed accelerated IVIM-corrected DTI approaches in acute hamstring injuries and explore the sensitivity of the perfusion fraction (f) to acute muscle damage. METHODS: Athletes with acute hamstring injury received DTI scans of both thighs < 7 days after injury and at RTP. For a subset, DTI scans were repeated with multiband (MB) acceleration. Data from standard and MB-accelerated scans were fitted with standard and accelerated IVIM-corrected DTI approach using high b-values only. Segmentations of the injury and contralateral healthy muscles were contoured. The fitting methods as well as the standard and MB-accelerated scan were compared using linear regression analysis. For sensitivity to injury, Δ(injured minus healthy) DTI parameters between the methods and the differences between injured and healthy muscles were compared (Wilcoxon signed-rank test). RESULTS: The baseline dataset consisted of 109 athletes (16 with MB acceleration); 64 of them received an RTP scan (8 with MB acceleration). Linear regression of the standard and high-b DTI fitting showed excellent agreement. With both fitting methods, standard and MB-accelerated scans were comparable. Δ(injured minus healthy) was similar between standard and accelerated methods. For all methods, all IVIM-DTI parameters except f were significantly different between injured and healthy muscles. CONCLUSIONS: High-b DTI fitting with MB acceleration reduced the scan time from 11:08 to 3:40 min:s while maintaining sensitivity to hamstring injuries; f was not different between healthy and injured muscles. RELEVANCE STATEMENT: The accelerated IVIM-corrected DTI protocol, using fewer b-values and MB acceleration, reduced the scan time to under 4 min without affecting the sensitivity of the quantitative outcome parameters to hamstring injuries. This allows for routine clinical monitoring of hamstring injuries, which could directly benefit injury treatment and monitoring. KEY POINTS: • Combining high-b DTI-fitting and multiband-acceleration dramatically reduced by two thirds the scan time. • The accelerated IVIM-corrected DTI approaches maintained the sensitivity to hamstring injuries. • The IVIM-derived perfusion fraction was not sensitive to hamstring injuries.