Effects of semaglutide, empagliflozin and their combination on renal diffusion-weighted MRI and total kidney volume in patients with type 2 diabetes: a post hoc analysis from a 32 week randomised trial.

AIMS/HYPOTHESIS: The apparent diffusion coefficient (ADC) derived from diffusion-weighted MRI (DWI-MRI) has been proposed as a measure of changes in kidney microstructure, including kidney fibrosis. In advanced kidney disease, the kidneys often become atrophic; however, in the initial phase of type 2 diabetes, there is an increase in renal size. Glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter 2 inhibitors both provide protection against progression of kidney disease in diabetes. However, the mechanisms are incompletely understood. To explore this, we examined the effects of semaglutide, empagliflozin and their combination on renal ADC and total kidney volume (TKV). METHODS: This was a substudy of a randomised clinical trial on the effects of semaglutide and empagliflozin alone or in combination. Eighty patients with type 2 diabetes and high risk of CVD were randomised into four groups (n=20 in each) receiving either tablet placebo, empagliflozin, a combination of semaglutide and tablet placebo (herein referred to as the 'semaglutide' group), or the combination of semaglutide and empagliflozin (referred to as the 'combination-therapy' group). The semaglutide and the combination-therapy group had semaglutide treatment for 16 weeks and then had either tablet placebo or empagliflozin added to the treatment, respectively, for a further 16 weeks; the placebo and empagliflozin groups were treated with the respective monotherapy for 32 weeks. We analysed the effects of treatment on changes in ADC (cortical, medullary and the cortico-medullary difference [ΔADC; medullary ADC subtracted from cortical ADC]), as well as TKV measured by MRI. RESULTS: Both semaglutide and empagliflozin decreased cortical ADC significantly compared with placebo (semaglutide: -0.20×10-3 mm2/s [95% CI -0.30, -0.10], p<0.001; empagliflozin: -0.15×10-3 mm2/s [95% CI -0.26, -0.04], p=0.01). No significant change was observed in the combination-therapy group (-0.05×10-3 mm2/s [95%CI -0.15, 0.05]; p=0.29 vs placebo). The changes in cortical ADC were not associated with changes in GFR, albuminuria, TKV or markers of inflammation. Further, there were no changes in medullary ADC in any of the groups compared with placebo. Only treatment with semaglutide changed ΔADC significantly from placebo, showing a decrease of -0.13×10-3 mm2/s (95% CI -0.22, -0.04; p=0.01). Compared with placebo, TKV decreased by -3% (95% CI -5%, -0.3%; p=0.04), -3% (95% CI -5%, -0.4%; p=0.02) and -5% (95% CI -8%, -2%; p<0.001) in the semaglutide, empagliflozin and combination-therapy group, respectively. The changes in TKV were associated with changes in GFR, albuminuria and HbA1c. CONCLUSIONS/INTERPRETATION: In a population with type 2 diabetes and high risk of CVD, semaglutide and empagliflozin significantly reduced cortical ADC compared with placebo, indicating microstructural changes in the kidneys. These changes were not associated with changes in GFR, albuminuria or inflammation. Further, we found a decrease in TKV in all active treatment groups, which was possibly mediated by a reduction in hyperfiltration. Our findings suggest that DWI-MRI may serve as a promising tool for investigating the underlying mechanisms of medical interventions in individuals with type 2 diabetes but may reflect effects not related to fibrosis. TRIAL REGISTRATION: European Union Drug Regulating Authorities Clinical Trials Database (EudraCT) 2019-000781-38.

Calculation of intravoxel incoherent motion parameter maps using a kernelized total difference-based method.

Quantitative analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for many clinical applications since its development. In particular, the intravoxel incoherent motion (IVIM) model for DW-MRI has been commonly utilized in various organs. However, because of the presence of excessive noise, the IVIM parameter maps obtained from pixel-wise fitting are often unreliable. In this study, we propose a kernelized total difference-based curve-fitting method to estimate the IVIM parameters. Simulated DW-MRI data at five signal-to-noise ratios (i.e., 10, 20, 30, 50, and 100) and real abdominal DW-MRI data acquired on a 1.5-T MRI scanner with nine b-values (i.e., 0, 10, 25, 50, 100, 200, 300, 400, and 500 s/mm2) and six diffusion-encoding gradient directions were used to evaluate the performance of the proposed method. The results were compared with those obtained by three existing methods: trust-region reflective (TRR) algorithm, Bayesian probability (BP), and deep neural network (DNN). Our simulation results showed that the proposed method outperformed the other three comparing methods in terms of root-mean-square error. Moreover, the proposed method could preserve small details in the estimated IVIM parameter maps. The experimental results showed that, compared with the TRR method, the proposed method as well as the BP (and DNN) method could reduce the overestimation of the pseudodiffusion coefficient and improve the quality of IVIM parameter maps. For all studied abdominal organs except the pancreas, both the proposed method and the BP method could provide IVIM parameter estimates close to the reference values; the former had higher precision. The kernelized total difference-based curve-fitting method has the potential to improve the reliability of IVIM parametric imaging.

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.

Ischemic core detection threshold of computed tomography perfusion (CTP) in acute stroke.

PURPOSE: This study aimed to determine the accuracy of detecting ischemic core volume using computed tomography perfusion (CTP) in patients with suspected acute ischemic stroke compared to diffusion-weighted magnetic resonance imaging (DW-MRI) as the reference standard. METHODS: This retrospective monocentric study included patients who underwent CTP and DW-MRI for suspected acute ischemic stroke. The ischemic core size was measured at DW-MRI. The detectability threshold volume was defined as the lowest volume detected by each method. Clinical data on revascularization therapy, along with the clinical decision that influenced the choice, were collected. Volumes of the ischemic cores were compared using the Mann-Whitney U test. RESULTS: Of 83 patients who underwent CTP, 52 patients (median age 73 years, IQR 63-80, 36 men) also had DW-MRI and were included, with a total of 70 ischemic cores. Regarding ischemic cores, only 18/70 (26%) were detected by both CTP and DW-MRI, while 52/70 (74%) were detected only by DW-MRI. The median volume of the 52 ischemic cores undetected on CTP (0.6 mL, IQR 0.2-1.3 mL) was significantly lower (p < 0.001) than that of the 18 ischemic cores detected on CTP (14.2 mL, IQR 7.0-18.4 mL). The smallest ischemic core detected on CTP had a volume of 5.0 mL. Among the 20 patients with undetected ischemic core on CTP, only 10% (2/20) received thrombolysis treatment. CONCLUSIONS: CTP maps failed in detecting ischemic cores smaller than 5 mL. DW-MRI remains essential for suspected small ischemic brain lesions to guide a correct treatment decision-making.

Utility of diffusion weighted Magnetic Resonance Imagining to detect non-palpable undescended testis.

Objective: To assess the effectiveness of diffusion-weighted magnetic resonance imaging (DW-MRI) in the detection of non-palpable undescended testis (NPUT) and to compare DW-MRI results with surgical findings. Methods: This descriptive, cross-sectional study was carried out at Dow Institute of Radiology, Dow University of Health Sciences. Karachi on a cohort of patients who underwent DW-MRI for suspected NPUT between 15th September, 2022 to 16th March, 2023. The study included 175 boys below the age of 16 years with history of clinically non-palpable testes. MRI scans were acquired using additional DWI sequence. Two radiology faculty of more than five years of experience independently evaluated the DW-MRI images for the presence or absence of testes in its normal anatomical position, abnormal location, side and size of testes. Results: Out of the 175 patients, DW-MRI successfully detected 128 (73.1%) non-palpable undescended testes. Statistical analysis in clinically detected cases of showed sensitivity of NPTU 0.8%, specificity of 92.5%, diagnostic accuracy 94.29%, positive predictive value 97.7%, and negative predictive value 84.1% for localizing undescended testes with Diffusion Weighted DW-MRI taking surgical findings as gold standard. Conclusion: DWI sequences complement the conventional MRI, increasing its sensitivity and diagnostic accuracy. By facilitating early and accurate diagnosis, DW-MRI has the potential to streamline patient management, reduce unnecessary surgical exploration, and ultimately improve the long-term reproductive health and quality of life for individuals with undescended testes.

Impact of high-power short-duration atrial fibrillation ablation technique on the incidence of silent cerebral embolism: a prospective randomized controlled study.

BACKGROUND: High-power short-duration (HPSD) ablation strategy has emerged as a popular approach for treating atrial fibrillation (AF), with shorter ablation time. The utilized Smart Touch Surround Flow (STSF) catheter, with 56 holes around the electrode, lowers electrode-tissue temperature and thrombus risk. Thus, we conducted this prospective, randomized study to investigate if the HPSD strategy with STSF catheter in AF ablation procedures reduces the silent cerebral embolism (SCE) risk compared to the conventional approach with the Smart Touch (ST) catheter. METHODS: From June 2020 to September 2021, 100 AF patients were randomized 1:1 to the HPSD group using the STSF catheter (power set at 50 W) or the conventional group using the ST catheter (power set at 30 to 35 W). Pulmonary vein isolation was performed in all patients, with additional lesions at operator's discretion. High-resolution cerebral diffusion-weighted magnetic resonance imaging (hDWI) with slice thickness of 1 mm was performed before and 24-72 h after ablation. The incidence of new periprocedural SCE was defined as the primary outcome. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA) test. RESULTS: All enrolled AF patients (median age 63, 60% male, 59% paroxysmal AF) underwent successful ablation. Post-procedural hDWI identified 106 lesions in 42 enrolled patients (42%), with 55 lesions in 22 patients (44%) in the HPSD group and 51 lesions in 20 patients (40%) in the conventional group (p = 0.685). No significant differences were observed between two groups regarding the average number of lesions (p = 0.751), maximum lesion diameter (p = 0.405), and total lesion volume per patient (p = 0.669). Persistent AF and CHA2DS2-VASc score were identified as SCE determinants during AF ablation procedure by multivariable regression analysis. No significant differences in MoCA scores were observed between patients with SCE and those without, both immediately post-procedure (p = 0.572) and at the 3-month follow-up (p = 0.743). CONCLUSIONS: Involving a small sample size of 100 AF patients, this study reveals a similar incidence of SCE in AF ablation procedures, comparing the HPSD strategy using the STSF catheter to the conventional approach with the ST catheter. TRIAL REGISTRATION: Clinicaltrials.gov: NCT04408716. AF = Atrial fibrillation, DWI = Diffusion-weighted magnetic resonance imaging, HPSD = High-power short-duration, ST = Smart Touch, STSF = Smart Touch Surround Flow.

An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma.

BACKGROUND AND PURPOSE: Ki-67 labeling index (LI) is an important indicator of tumor cell proliferation in glioma, which can only be obtained by postoperative biopsy at present. This study aimed to explore the correlation between Ki-67 LI and apparent diffusion coefficient (ADC) parameters and to predict the level of Ki-67 LI noninvasively before surgery by multiple MRI characteristics. METHODS: Preoperative MRI data of 166 patients with pathologically confirmed glioma in our hospital from 2016 to 2020 were retrospectively analyzed. The cut-off point of Ki-67 LI for glioma grading was defined. The differences in MRI characteristics were compared between the low and high Ki-67 LI groups. The receiver operating characteristic (ROC) curve was used to estimate the accuracy of each ADC parameter in predicting the Ki-67 level, and finally a multivariate logistic regression model was constructed based on the results of ROC analysis. RESULTS: ADCmin, ADCmean, rADCmin, rADCmean and Ki-67 LI showed a negative correlation (r = - 0.478, r = - 0.369, r = - 0.488, r = - 0.388, all P < 0.001). The Ki-67 LI of low-grade gliomas (LGGs) was different from that of high-grade gliomas (HGGs), and the cut-off point of Ki-67 LI for distinguishing LGGs from HGGs was 9.5%, with an area under the ROC curve (AUROC) of 0.962 (95%CI 0.933-0.990). The ADC parameters in the high Ki-67 group were significantly lower than those in the low Ki-67 group (all P < 0.05). The peritumoral edema (PTE) of gliomas in the high Ki-67 LI group was higher than that in the low Ki-67 LI group (P < 0.05). The AUROC of Ki-67 LI level assessed by the multivariate logistic regression model was 0.800 (95%CI 0.721-0.879). CONCLUSIONS: There was a negative correlation between ADC parameters and Ki-67 LI, and the multivariate logistic regression model combined with peritumoral edema and ADC parameters could improve the prediction ability of Ki-67 LI.

Optimized flip angle schemes for the split acquisition of fast spin-echo signals (SPLICE) sequence and application to diffusion-weighted imaging.

PURPOSE: The diffusion-weighted SPLICE (split acquisition of fast spin-echo signals) sequence employs split-echo rapid acquisition with relaxation enhancement (RARE) readout to provide images almost free of geometric distortions. However, due to the varying T 2 $$ {}_2 $$ -weighting during k-space traversal, SPLICE suffers from blurring. This work extends a method for controlling the spatial point spread function (PSF) while optimizing the signal-to-noise ratio (SNR) achieved by adjusting the flip angles in the refocusing pulse train of SPLICE. METHODS: An algorithm based on extended phase graph (EPG) simulations optimizes the flip angles by maximizing SNR for a flexibly chosen predefined target PSF that describes the desired k-space density weighting and spatial resolution. An optimized flip angle scheme and a corresponding post-processing correction filter which together achieve the target PSF was tested by healthy subject brain imaging using a clinical 1.5 T scanner. RESULTS: Brain images showed a clear and consistent improvement over those obtained with a standard constant flip angle scheme. SNR was increased and apparent diffusion coefficient estimates were more accurate. For a modified Hann k-space weighting example, considerable benefits resulted from acquisition weighting by flip angle control. CONCLUSION: The presented flexible method for optimizing SPLICE flip angle schemes offers improved MR image quality of geometrically accurate diffusion-weighted images that makes the sequence a strong candidate for radiotherapy planning or stereotactic surgery.

Deep learning intravoxel incoherent motion modeling: Exploring the impact of training features and learning strategies.

PURPOSE: The development of advanced estimators for intravoxel incoherent motion (IVIM) modeling is often motivated by a desire to produce smoother parameter maps than least squares (LSQ). Deep neural networks show promise to this end, yet performance may be conditional on a myriad of choices regarding the learning strategy. In this work, we have explored potential impacts of key training features in unsupervised and supervised learning for IVIM model fitting. METHODS: Two synthetic data sets and one in-vivo data set from glioma patients were used in training of unsupervised and supervised networks for assessing generalizability. Network stability for different learning rates and network sizes was assessed in terms of loss convergence. Accuracy, precision, and bias were assessed by comparing estimations against ground truth after using different training data (synthetic and in vivo). RESULTS: A high learning rate, small network size, and early stopping resulted in sub-optimal solutions and correlations in fitted IVIM parameters. Extending training beyond early stopping resolved these correlations and reduced parameter error. However, extensive training resulted in increased noise sensitivity, where unsupervised estimates displayed variability similar to LSQ. In contrast, supervised estimates demonstrated improved precision but were strongly biased toward the mean of the training distribution, resulting in relatively smooth, yet possibly deceptive parameter maps. Extensive training also reduced the impact of individual hyperparameters. CONCLUSION: Voxel-wise deep learning for IVIM fitting demands sufficiently extensive training to minimize parameter correlation and bias for unsupervised learning, or demands a close correspondence between the training and test sets for supervised learning.

Multiparametric magnetic resonance imaging for detection of pathological changes in the central nervous system of a mouse model of multiple sclerosis in vivo.

Multiple sclerosis (MS) is an autoimmune disease involving demyelination and axonal damage in the central nervous system (CNS). In this study, we investigated pathological changes in the lumbar spinal cord of C57BL/6 mice induced with progressive experimental autoimmune encephalomyelitis (EAE) disease using 9.4-T magnetic resonance imaging (MRI). Multiparametric MRI measurements including MR spectroscopy, diffusion tensor imaging (DTI) and volumetric analyses were applied to detect metabolic changes in the CNS of EAE mice. Compared with healthy mice, EAE mice showed a significant reduction in N-acetyl aspartate and increases in choline, glycine, taurine and lactate. DTI revealed a significant reduction in fractional anisotropy and axial diffusivity and an increase in radial diffusivity in the lumbar spinal cord white matter (WM), while in the grey matter (GM), fractional anisotropy increased. High-resolution structural imaging also revealed lumbar spinal cord WM hypertrophy and GM atrophy. Importantly, these MRI changes were strongly correlated with EAE disease scoring and pathological changes in the lumbar (L2-L6), particularly WM demyelination lesions and aggregation of immune cells (microglia/macrophages and astrocytes) in this region. This study identified changes in MRI biomarker signatures that can be useful for evaluating the efficacy of novel drugs using EAE models in vivo.

Shared and distinct white matter abnormalities in adolescent-onset schizophrenia and adolescent-onset psychotic bipolar disorder.

BACKGROUND: While adolescent-onset schizophrenia (ADO-SCZ) and adolescent-onset bipolar disorder with psychosis (psychotic ADO-BPD) present a more severe clinical course than their adult forms, their pathophysiology is poorly understood. Here, we study potentially state- and trait-related white matter diffusion-weighted magnetic resonance imaging (dMRI) abnormalities along the adolescent-onset psychosis continuum to address this need. METHODS: Forty-eight individuals with ADO-SCZ (20 female/28 male), 15 individuals with psychotic ADO-BPD (7 female/8 male), and 35 healthy controls (HCs, 18 female/17 male) underwent dMRI and clinical assessments. Maps of extracellular free-water (FW) and fractional anisotropy of cellular tissue (FAT) were compared between individuals with psychosis and HCs using tract-based spatial statistics and FSL's Randomise. FAT and FW values were extracted, averaged across all voxels that demonstrated group differences, and then utilized to test for the influence of age, medication, age of onset, duration of illness, symptom severity, and intelligence. RESULTS: Individuals with adolescent-onset psychosis exhibited pronounced FW and FAT abnormalities compared to HCs. FAT reductions were spatially more widespread in ADO-SCZ. FW increases, however, were only present in psychotic ADO-BPD. In HCs, but not in individuals with adolescent-onset psychosis, FAT was positively related to age. CONCLUSIONS: We observe evidence for cellular (FAT) and extracellular (FW) white matter abnormalities in adolescent-onset psychosis. Although cellular white matter abnormalities were more prominent in ADO-SCZ, such alterations may reflect a shared trait, i.e. neurodevelopmental pathology, present across the psychosis spectrum. Extracellular abnormalities were evident in psychotic ADO-BPD, potentially indicating a more dynamic, state-dependent brain reaction to psychosis.

Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology.

Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state-of-the-art of DW-MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW-MRI method is essential. In this article, we review the current landscape of DW-MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b-tensor diffusion encoding or joint diffusion-relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter-/intra-scanner robustness, and on assessing histological validity in broader contexts. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.

Plasma neurofilament light admission levels and development of axonal pathology in mild traumatic brain injury.

BACKGROUND: It is known that blood levels of neurofilament light (NF-L) and diffusion-weighted magnetic resonance imaging (DW-MRI) are both associated with outcome of patients with mild traumatic brain injury (mTBI). Here, we sought to examine the association between admission levels of plasma NF-L and white matter (WM) integrity in post-acute stage DW-MRI in patients with mTBI. METHODS: Ninety-three patients with mTBI (GCS ≥ 13), blood sample for NF-L within 24 h of admission, and DW-MRI ≥ 90 days post-injury (median = 229) were included. Mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated from the skeletonized WM tracts of the whole brain. Outcome was assessed using the Extended Glasgow Outcome Scale (GOSE) at the time of imaging. Patients were divided into CT-positive and -negative, and complete (GOSE = 8) and incomplete recovery (GOSE < 8) groups. RESULTS: The levels of NF-L and FA correlated negatively in the whole cohort (p = 0.002), in CT-positive patients (p = 0.016), and in those with incomplete recovery (p = 0.005). The same groups showed a positive correlation with mean MD, AD, and RD (p < 0.001-p = 0.011). In CT-negative patients or in patients with full recovery, significant correlations were not found. CONCLUSION: In patients with mTBI, the significant correlation between NF-L levels at admission and diffusion tensor imaging (DTI) measurements of diffuse axonal injury (DAI) over more than 3 months suggests that the early levels of plasma NF-L may associate with the presence of DAI at a later phase of TBI.

Chemotherapy response evaluation using diffusion weighted MRI in Ewing Sarcoma: A single center experience.

BACKGROUND: Non-invasive biomarkers for early chemotherapeutic response in Ewing sarcoma family of tumors (ESFT) are useful for optimizing existing treatment protocol. PURPOSE: To assess the role of diffusion-weighted magnetic resonance imaging (MRI) in the early evaluation of chemotherapeutic response in ESFT. MATERIAL AND METHODS: A total of 28 patients (mean age = 17.2 ± 5.6 years) with biopsy proven ESFT were analyzed prospectively. Patients underwent MRI acquisition on a 1.5-T scanner at three time points: before starting neoadjuvant chemotherapy (baseline), after first cycle chemotherapy (early time point), and after completion of chemotherapy (last time point). RECIST 1.1 criteria was used to evaluate the response to chemotherapy and patients were categorized as responders (complete and partial response) and non-responders (stable and progressive disease). Tumor diameter, absolute apparent diffusion coefficient (ADC), and normalized ADC (nADC) values in the tumor were measured. Baseline parameters and relative percentage change of parameters after first cycle chemotherapy were assessed for early detection of chemotherapy response. RESULTS: The responder:non-responder ratio was 21:7. At baseline, ADC ([0.864 ± 0.266 vs. 0.977 ± 0.246]) × 10-3mm2/s; P = 0.205) and nADC ([0.740 ± 0.254 vs. 0.925 ± 0.262] × 10-3mm2/s; P = 0.033) among responders was lower than the non-responders and predicted response to chemotherapy with AUCs of 0.6 and 0.735, respectively. At the early time point, tumor diameter (27% ± 14% vs. 4.6% ± 10%; P = 0.002) showed a higher reduction and ADC (75% ± 44% vs. 52% ± 72%; P = 0.039) and nADC (81% ± 44% vs. 48% ± 67%; P = 0.008) showed a higher increase in mean values among responders than the non-responders and identified chemotherapy response with AUC of 0.890, 0.723, and 0.756, respectively. CONCLUSION: Baseline nADC and its change after the first cycle of chemotherapy can be used as non-invasive surrogate markers of early chemotherapeutic response in patients with ESFT.

Different hemodynamics of basal ganglia between moyamoya and non-moyamoya diseases using intravoxel incoherent motion imaging and single-photon emission computed tomography.

BACKGROUND: Moyamoya disease (MMD) and non-MMD have different pathogenesis, clinical presentation, and treatment policy. PURPOSE: To identify differences in hemodynamics between MMD and non-MMD using intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) and single-photon emission computed tomography (SPECT). MATERIAL AND METHODS: Patients who had undergone 99mTc-ECD or 123I-IMP SPECT, and IVIM imaging were retrospectively studied. IVIM imaging was acquired using six different b-values. Cerebral blood flow ratio (CBFR) in the basal ganglia was calculated using a standardized volume-of-interest template. The cerebellum was used as a reference region. IVIM perfusion fraction (f) was obtained using a two-step fitting algorithm. Elliptical regions of interest were placed in bilateral basal ganglia on the IVIM f map. Patients were classified into MMD and non-MMD groups. The correlation between CBFR and mean IVIM f (fmean) in the basal ganglia was evaluated using Spearman's rank correlation coefficient. RESULTS: In total, 20 patients with MMD and 28 non-MMD patients were analyzed. No significant differences in fmean were observed among MMD, affected hemisphere with non-MMD (non-MMDaff), and unaffected hemispheres with non-MMD (non-MMDunaff). A negative correlation was seen between fmean and CBFR in the MMD group (r = -0.40, P = 0.0108), but not in the non-MMD group (non-MMDaff, r = 0.07, P = 0.69; non-MMDunaff, r = -0.22, P = 0.29). No significant differences were found among MMD and non-MMD patients, irrespective of SPECT tracers. CONCLUSION: The combination of IVIM MRI and SPECT appears to allow non-invasive identification of differences in hemodynamics between MMD and non-MMD.

H-Scan Ultrasound Monitoring of Breast Cancer Response to Chemotherapy and Validation With Diffusion-Weighted Magnetic Resonance Imaging.

OBJECTIVES: H-scan ultrasound (US) imaging is a novel tissue characterization technique to detect apoptosis-induced changes in cancer cells after the initiation of effective drug treatment. The objective of the proposed research was to assess the sensitivity of 3-dimensional (3D) H-scan US technique for monitoring the response of breast cancer-bearing animals to neoadjuvant chemotherapy and correlate results to diffusion-weighted magnetic resonance imaging (DW-MRI) measurements of programmed cancer cell death. METHODS: Experimental studies used female mice (N = 18) implanted with human breast cancer cells. Animals underwent H-scan US and DW-MRI imaging on days 0, 1, 3, 7, and 10. After imaging at day 0, breast tumor-bearing nude mice were treated biweekly with an apoptosis-inducing drug. Texture analysis of H-scan US images explored spatial relationships between local US scattering. At day 10, H-scan measurements were compared with DW-MRI-derived apparent diffusion coefficient (ADC) values and histological findings. RESULTS: H-scan US imaging of low and high dose cisplatin-treated cancer-bearing animals revealed changes in image intensity suggesting a progressive decrease in aggregate US scatterer size that was not observed in control animals. Longitudinal trends discovered in H-scan US result matched with texture analysis and DW-MRI (P < .01). Further, analysis of the H-scan US image intensity and corresponding DW-MRI-derived ADC values revealed a strong linear correlation (R2  = .93, P < .001). These changes were due to cancer cell apoptotic activity and consider as early detectable biomarker during treatment. CONCLUSIONS: The 3D H-scan technique holds promise for assisting clinicians in monitoring the early response of breast cancer tumor to neoadjuvant chemotherapy and adding value to traditional diagnostic ultrasound examinations.

MR-Guided Transurethral Ultrasound Ablation of Prostate Cancer: Initial Experience of Monitoring Tumor Response by Dynamic Apparent Diffusion Coefficient Measurements at 3.0 T.

INTRODUCTION: Diffusion-weighted imaging (DWI) as part of multiparametric magnetic resonance imaging (mpMRI) is an important sequence for the detection of prostate cancer (PCa). The objective of this retrospective analysis was to evaluate changes in apparent diffusion coefficient (ADC) measurements in biopsy-proven PCa undergoing TULSA-PRO (MR-guided transurethral ultrasound ablation of the prostate) at 3.0 T after 1, 3, and 6-12 months posttreatment. METHODS: Nineteen patients underwent follow-up examinations after 1, 3, and 6-12 months including mpMRI at 3.0 T and urological-clinical examinations with quantitative analysis of ADCs. RESULTS: In PCa, a significant increase of ADC values after 6-12 months was measured after TULSA-PRO treatment by 29.1% (pre-TULSA: 0.79 ± 0.16 × 10-3 mm2/s, 6-12 months: 1.02 ± 0.35 × 10-3 mm2/s), while the corresponding value in the reference tissue decreased by 48.5% (pre-TULSA: 1.20 ± 0.15 × 10-3 mm2/s, 6-12 months: 0.91 ± 0.29 × 10-3 mm2/s). The mean ADC values in the early follow-up groups at 1 and 3 months did not change significantly. CONCLUSION: DWI with ADC as part of mpMRI can serve as a biomarker to dynamically monitor the follow-up after TULSA after 6-12 months. For early posttreatment progression, it is not suitable due to too many confounding variables.

PROPELLER diffusion weighted imaging for diagnosis of cholesteatoma in comparison with surgical and histopathological results: emphasis on false positivity and false negativity.

PURPOSE: To investigate the role of non-echo planar diffusion weighted imaging (DWI) using "periodically rotated overlapping parallel lines with enhanced reconstruction" (PROPELLER) sequence for the diagnosis of cholesteatoma compared to surgical and histopathological results in an attempt to determine the factors causing false negative and false positive diagnoses. METHODS: Patients who had PROPELLER DWI before ear surgery were retrospectively reviewed. The presence of a lesion with diffusion restriction on PROPELLER DWI was accepted as positive for cholesteatoma, and the results were compared to the intraoperative and histopathological findings. RESULTS: A total of 112 ears in 109 patients were reviewed. On PROPELLER DWI, a lesion with diffusion restriction was found in 101 (90.2%) ears, while in 11 (9.8%) of the patients, no diffusion restriction was found. Surgery and histopathological analysis revealed a cholesteatoma in 100 (89.3%) ears, while in 12 (10.7%) ears, no cholesteatoma was found surgically. There were 96 (85.7%) true positives, 7 (6.2%) true negatives, 5 (4.5%) false positives and 4 (3.6%) false negatives. The accuracy, sensitivity, specificity, positive predictive and negative predictive values of non-echo planar DWI were calculated to be 91.96%, 96%, 58.33%, 95.05%, and 63.64%, respectively. CONCLUSION: Non-echo planar DWI using PROPELLER sequence has high accuracy, sensitivity and positive predictive value and can be used for the detection of cholesteatoma. The external auditory canal, postoperative ears and small lesions should be evaluated with caution to avoid false results.

A supervised deep neural network approach with standardized targets for enhanced accuracy of IVIM parameter estimation from multi-SNR images.

Extraction of intravoxel incoherent motion (IVIM) parameters from noisy diffusion-weighted (DW) images using a biexponential fitting model is computationally challenging, and the reliability of the estimated perfusion-related quantities represents a limitation of this technique. Artificial intelligence can overcome the current limitations and be a suitable solution to advance use of this technique in both preclinical and clinical settings. The purpose of this work was to develop a deep neural network (DNN) approach, trained on numerical simulated phantoms with different signal to noise ratios (SNRs), to improve IVIM parameter estimation. The proposed approach is based on a supervised fully connected DNN having 3 hidden layers, 18 inputs and 3 targets with standardized values. 14 × 103 simulated DW images, based on a Shepp-Logan phantom, were randomly generated with varying SNRs (ranging from 10 to 100). 7 × 103 images (1000 for each SNR) were used for training. Performance accuracy was assessed in simulated images and the proposed approach was compared with the state-of-the-art Bayesian approach and other DNN algorithms. The DNN approach was also evaluated in vivo on a high-field MRI preclinical scanner. Our DNN approach showed an overall improvement in accuracy when compared with the Bayesian approach and other DNN methods in most of the simulated conditions. The in vivo results demonstrated the feasibility of the proposed approach in real settings and generated quantitative results comparable to those obtained using the Bayesian and unsupervised approaches, especially for D and f, and with lower variability in homogeneous regions. The DNN architecture proposed in this work outlines two innovative features as compared with other studies: (1) the use of standardized targets to improve the estimation of parameters, and (2) the implementation of a single DNN to enhance the IVIM fitting at different SNRs, providing a valuable alternative tool to compute IVIM parameters in conditions of high background noise.

MRI-based automatic segmentation of rectal cancer using 2D U-Net on two independent cohorts.

BACKGROUND: Tumor delineation is time- and labor-intensive and prone to inter- and intraobserver variations. Magnetic resonance imaging (MRI) provides good soft tissue contrast, and functional MRI captures tissue properties that may be valuable for tumor delineation. We explored MRI-based automatic segmentation of rectal cancer using a deep learning (DL) approach. We first investigated potential improvements when including both anatomical T2-weighted (T2w) MRI and diffusion-weighted MR images (DWI). Secondly, we investigated generalizability by including a second, independent cohort. MATERIAL AND METHODS: Two cohorts of rectal cancer patients (C1 and C2) from different hospitals with 109 and 83 patients, respectively, were subject to 1.5 T MRI at baseline. T2w images were acquired for both cohorts and DWI (b-value of 500 s/mm2) for patients in C1. Tumors were manually delineated by three radiologists (two in C1, one in C2). A 2D U-Net was trained on T2w and T2w + DWI. Optimal parameters for image pre-processing and training were identified on C1 using five-fold cross-validation and patient Dice similarity coefficient (DSCp) as performance measure. The optimized models were evaluated on a C1 hold-out test set and the generalizability was investigated using C2. RESULTS: For cohort C1, the T2w model resulted in a median DSCp of 0.77 on the test set. Inclusion of DWI did not further improve the performance (DSCp 0.76). The T2w-based model trained on C1 and applied to C2 achieved a DSCp of 0.59. CONCLUSION: T2w MR-based DL models demonstrated high performance for automatic tumor segmentation, at the same level as published data on interobserver variation. DWI did not improve results further. Using DL models on unseen cohorts requires caution, and one cannot expect the same performance.