BONBID-HIE 2023: Lesion Segmentation Challenge in BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy.

Cerebral surface extraction plays a fundamental role of computer aided diagnosis (CAD) for neonatal brain magnetic resonance (MR) images. However, cerebral sulci of the neonatal brains is complexity folded, and it is difficult to extract complete cerebral contour from MR images due to the limitation of spatial resolution and partial volume effect (PVE). This paper proposes a novel method to extract the cerebral contour based on fuzzy thick rubber model (TRM). The TRM is deformed by using fuzzy control schemes so that the digitally synthesized MR images from the deforming TRM are identical to the given MR images. By synthesizing the MR images with respect to PVE, the proposed method is able to extract the cerebral contour with sub-voxel accuracy. The proposed method was applied to 7 subjects whose revised ages were from -17 days to 34 days. The root-mean-squared-error between the extracted contour and the manually delineated contour by two physicians was 1.09 plusmn 0.48 mm from the truth contour. And, to demonstrate the clinical effective, gyral index was calculated using the extracted cerebral contour.

Objective To explore the correlation between apparent diffusion coefficient(ADC)and neonatal behavioral neurological assessment(NBNA) of newborns with hypoxic ischemic encephalopathy (HIE), and to evaluate ADC in the early diagnosis of HIE from the imaging perspective. Method One hundred and nine neonates aged 0—7 days with HIE underwent conventional MRI and DWI. According to HIE grading standards, there were 43 neonates in the mild group, 38 in the moderate group, 28 in the severe group, and meanwhile 24 normal neonates with the same ages were selected as the control group. All cases were assessed with NBNA, and ADC values of bilateral frontal white matter, parietal white matter, periventricular white matter, posterior limb of the internal capsule (PLIC), ventral lateral nucleus of thalamus, lenticular nucleus, splenium of the corpus callosum, brainstem were measured. ADC values of different groups were compared by analysis of variance, and the correlations between ADC values of all ROIs and NBNA were analyzed by Pearson correlation. Results Except lenticular nucleus and the brainstem, ADC values of frontal white matter, parietal white matter, periventricular white matter and PLIC, ventral lateral nucleus of thalamus, splenium of the corpus callosum were decreased in the mild, moderate, severe group. In the mild, moderate, and severe group, the ADC values of frontal deep white matter were (1.82±0.33)×10-3, (1.77±0.34)×10-3 and (1.62±0.31)×10-3 mm2/s, while they were (1.81±0.34)×10-3, (1.79±0.27)× 10-3 and (1.72±0.31)×10-3 mm2/s for the parietal deep white matter, (1.27±0.15)×10-3, (1.23±0.12)×10-3 and (1.15±0.17)×10-3 mm2/s for the periventricular white matter, (1.08±0.09)×10-3, (0.97±0.07)×10-3 and (0.84±0.06)×10-3 mm2/s for the PLIC, (1.13±0.07)×10-3, (1.08±0.13)×10-3 and (1.00±0.13)×10-3 mm2/s for the ventral lateral nucleus of thalamus, (1.27±0.22)×10-3, (1.18±0.16)×10-3 and (1.00±0.23)×10-3 mm2/s for the splenium of the corpus callosum. There were statistically significant differences between the 3 groups (F=61.27, 16.27, 23.26, 72.70, 26.73, 66.09, all P<0.05). In the mild, modreate and severe group, NBNA were (36.8±1.4) in the mild group, (33.5±1.6) in the moderate, and (29.3±2.6) in the severe group. There was positive correlation between ADC values of frontal white matter, parietal white matter, periventricular white matter and PLIC, ventral lateral nucleus of thalamus, splenium of the corpus callosum and NBNA scores (r=0.60, 0.49, 0.54, 0.67, 0.56, 0.65, all P<0.05). Conclusions There are correlations between ADC values of the related ROIs of HIE and NBNA scores. Combining two aspects might diagnose the brain injury of HIE more accurately and objectively. Key words: Diffusion magnetic resonance imaging; Infant, newborn; Hypoxia-ischemia, brain

A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images 1

Objective Magnetic-resonance-imaging (MRI) after brain injury is crucial for providing therapeutic strategies and prognosis. Serial MRI increases the prognostic value by revealing changes present at different time points after the initial injury and aids to study pathology and potential therapeutic strategies. Mobile ultralow-field MRI provides a unique opportunity to offer both bed-side and serial MRI in seriously sick patients. Design We investigated whether serial bedside MRI, including quantitative Apparent Diffusion Coefficient (ADC) measurements are feasible in an infant with hypoxic-ischemic brain injury and how the appearance of brain injury evolves on MRI sequences. Setting/patients/interventions We performed serial bed-side ultralow-field MRI with ADC measurements in an infant with hypoxic-ischemic brain injury over 11 days. Main results We demonstrated that morphological changes associated with hypoxic-ischemic brain injury detected using standard MRI could be verified in an ultralow-field scan. We documented how hypoxic-ischemic brain injury develops in T1, T2, FLAIR and DWI sequences over a time span of 11 days and how signal intensity changes over this period. We show that ultralow-field imaging visualizes the pathophysiology known by experimental data. We report quantitative serial ADC-measurement in lesions. Conclusion Ultralow-field MRI enables the detection of hypoxic-ischemic brain injury in infants and has the potential to significantly broaden access to MRI. Its application may provide more timely, comprehensive diagnostic information related to hypoxic-ischemic brain injury. Serial ultralow-field MRI enables quantitative assessment of ADC values, which may support the establishment of clinically relevant cutoff values. This not only enhances diagnostic and prognosis precision in hypoxic-ischemic brain injury, but also provides a framework for evaluating the efficacy of emerging therapeutic strategies.

This study aimed to evaluate the effectiveness of apparent diffusion coefficient (ADC) parameters in distinguishing between Prostate Cancer Research International Active Surveillance (PRIAS) non-reclassification and reclassification groups during active surveillance (AS) of prostate cancer. We included 55 patients who fulfilled the PRIAS criteria and underwent ≥ 2 magnetic resonance imaging (MRI) including diffusion-weighted imaging with an interval of ≤ 3years between baseline and second MRI. A mono-exponential fitting model was used to automatically create ADC maps with minimum b-values of 0 and maximum of 2000s/mm2. For detectable lesions on ADC maps, the lesions were manually segmented on each slice of the ADC maps. For undetectable lesions, the corresponding normal-appearing zone of the lobe on each slice of ADC maps was segmented. The ADC data for each slice were summed to obtain the 25th, 50th, and 75th percentile ADC values of the histogram at baseline and second MRI. These ADC parameters at baseline and second MRI, and the changes of ADC parameters from baseline to second MRI were compared between PRIAS non-reclassification and reclassification groups. The PRIAS reclassification group had significantly lower 25th, 50th, and 75th percentile ADC values at second MRI compared to the non-reclassification group. The non-reclassification group had significantly lower changes in ADC values in these percentiles compared to the reclassification group. The ADC parameters at second MRI and the changes from baseline to second MRI may be effective distinguishing factors between PRIAS non-reclassification and reclassification groups.

The aim of this study was to evaluate an advanced magnetic resonance imaging (MRI) method, apparent diffusion coefficient (ADC) mapping, in the functional assessment of carious teeth. 38 extracted human teeth with scores of 0, 3 and 6 according to International Caries Detection and Assessment System (ICDAS) criteria were screened and subsequently analyzed by MRI at 2.35 T. Histology sectioning of teeth was used for the gold standard by analyzing two extreme cases (intact and severely decayed). ADC maps of the same teeth were calculated from corresponding diffusion-weighted images and used to obtain ADC distributions along dental pulp as functions of the relative pulp length measured from the occlusal pulp side. The measured distributions were analyzed for the best fit by a four-parameter three-segment linear regression model for ADC distribution along the pulp. MRI results were in good agreement with findings in histological sections of identical teeth. The best fit model parameters, relative decayed region depth, relative transition region width and ADC values of intact and decayed pulp tissue, showed statistically significant differences between the ADC values of intact and decayed pulp tissue (1.0 × 10^-9 m²/s vs. 0.74-0.89 × 10^-9 m²/s) and the relative decayed region depth progressing with ICDAS score (3 vs. 46% with ICDAS 3 vs. ICDAS 6). The results of this feasibility study confirmed relevance of ADC mapping for the discrimination and localization of intact and decayed regions in dental pulps of carious teeth.

The Segmentation is a fundamental technique used in image processing to extract suspicious regions from a given image. Quantitative assessment of Magnetic Resonance Imaging (MRI) lesion load of patients with multiple sclerosis (MS) is the most objective approach for a better understanding of the history of the pathology, either natural or modified by therapies. Recently, numerous methods have been introduced for the segmentation of MS lesions in MR images. In this work, we propose to segment the images based on a constrained Gaussian Mixture Model (GMM) and using Genetic Algorithm to determine the optimal parameters of this highly non-linear model. We use this method for automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) of magnetic resonance images. Moreover, a preprocessing step is also performed to suppress artifacts and to remove the unwanted skull portions from brain MRI. The proposed method has been tested on real patients' MR images. The experimental results show that the proposed method can effectively segment MS lesions in the brain MR images.

Background: The brain energy metabolism of severely birth asphyxiated human neonates, measured by 31P-Magnetic Resonance Spectroscopy(31P-MRS), shows a secondary energy failure (SEF) 2-4 days after hypoxic-ischemic (HI) insult. The prognostic value of neonatal 31P-MRS, given by trough values of energy metabolites, critically depends on the correct time point and placement of measurement within the brain. Quantitative maps of the apparent diffusion coefficient (ADC) are faster to obtain on routine MR Systems compared to 31P-MRS and provide spatially higher resolved information about the postischemic brain. Aim: To test if ADC maps, obtainable early after HI, are a reliable and predictive parameter of post HI brain outcome in rats. Methods: Right sided HI brain damage was produced in two groups of 7d rats (unilateral carotid artery ligation and exposure to 8% 02 for 1.5h). Group 1 (n = 8) was kept normothermic (37 °C) and group 2(n = 8) hypothermic (30 °C) for 24h after HI. 31P-MRS, DWI and T2-WI were acquired and quantitative ADC maps were processed continuously up to 4 days .Results and Discussion: The evolution of Pcr/Pi revealed partial recovery within 10h after HI, followed by a SEF at 24h, which was profound in normothermic but mild in hypothermic animals. The final extent of brain damage was correctly predicted by the ratio Pcr/Pi. During HI both groups showed declined ADC in the affected region (primary cytotoxic edema), which normalized within 2h after HI. After a second decline, trough ADC was measured between 10h and 25h post HI. At 15h 80% and at 24h of the maximal extent of the cytotoxic edema was visible. The ADC values were comparable in the core region in both groups, whereas they were 20% higher in the penumbra of hypothermic animals. The maximal edema occupied 45% of the brain in normothermic and 22% in the hypothermic group. Finally, the ADC increased in the core regions saturating at values of free water at 4 to 6 days. The sizes of the final infarcts were 35% in the normothermic compared to 15% in the hypothermic group. Conclusions: 1. In all animals primary cytotoxic edema marked brain area at risk. 2. The spatial distribution of trough values of ADC at 15h became necrotic after 4 to 6 d. 3. At equal primary lesions, the size of the final infarcts were 50% smaller in hypothermic compared to normothermic animals. 4. The ADC maps predicted the final infarct size 10 to 15h earlier, could be detected longer and permitted higher spatial resolution compared to 31P-MRS.

This study aims for targeted biopsy validation of magnetic resonance fingerprinting (MRF) and diffusion mapping for characterizing peripheral zone (PZ) prostate cancer and noncancers. One hundred four PZ lesions in 85 patients who underwent magnetic resonance imaging were retrospectively analyzed with apparent diffusion coefficient (ADC) mapping, MRF, and targeted biopsy (cognitive or in-gantry). A radiologist blinded to pathology drew regions of interest on targeted lesions and visually normal peripheral zone on MRF and ADC maps. Mean T1, T2, and ADC were analyzed using linear mixed models. Generalized estimating equations logistic regression analyses were used to evaluate T1 and T2 relaxometry combined with ADC in differentiating pathologic groups. Targeted biopsy revealed 63 cancers (low-grade cancer/Gleason score 6 = 10, clinically significant cancer/Gleason score ≥7 = 53), 15 prostatitis, and 26 negative biopsies. Prostate cancer T1, T2, and ADC (mean ± SD, 1660 ± 270 milliseconds, 56 ± 20 milliseconds, 0.70 × 10 ± 0.24 × 10 mm/s) were significantly lower than prostatitis (mean ± SD, 1730 ± 350 milliseconds, 77 ± 36 milliseconds, 1.00 × 10 ± 0.30 × 10 mm/s) and negative biopsies (mean ± SD, 1810 ± 250 milliseconds, 71 ± 37 milliseconds, 1.00 × 10 ± 0.33 × 10 mm/s). For cancer versus prostatitis, ADC was sensitive and T2 specific with comparable area under curve (AUC; (AUCT2 = 0.71, AUCADC = 0.79, difference between AUCs not significant P = 0.37). T1 + ADC (AUCT1 + ADC = 0.83) provided the best separation between cancer and negative biopsies. Low-grade cancer T2 and ADC (mean ± SD, 75 ± 29 milliseconds, 0.96 × 10 ± 0.34 × 10 mm/s) were significantly higher than clinically significant cancers (mean ± SD, 52 ± 16 milliseconds, 0.65 ± 0.18 × 10 mm/s), and T2 + ADC (AUCT2 + ADC = 0.91) provided the best separation. T1 and T2 relaxometry combined with ADC mapping may be useful for quantitative characterization of prostate cancer grades and differentiating cancer from noncancers for PZ lesions seen on T2-weighted images.

In this dissertation, I describe development of novel deep learning (DL)-based methodologies for the detection and segmentation of clinical abnormalities including renal lesions, left ventricle (LV) scar, and prostate cancer (PCa) in 3D computed tomography (CT) scans and magnetic resonance (MR) images. In the first phase, I developed a decision fusion of patch-based convolutional neural network (CNN) for renal masses classification into cyst versus solid. The solid renal masses were then categorized into benign and malignant using an image-based CNN. These approaches were selected to capture local and global features of the renal masses including intensity, texture, shape, and size, which are key features used by radiologists to classify renal masses. Results demonstrated that automated assessment of renal mass with moderate-to-high degrees of accuracy is feasible. In the second phase, I designed a novel algorithm that comprehensively learns and integrates inter-and intra-slice features from 3D late gadolinium enhancement (LGE)-MR images and allows to accurately and efficiently delineate LV scar fully automatically. In the proposed method, three U-Nets were trained using LGE-MR images extracted from transversal, coronal, and sagittal directions to learn the description of LV scar from different views and the predicted results were combined through majority voting system for the final segmentation. This algorithm benefited from isotropic property of the voxels in 3D MRI that permits for multiplanar reformation. In the third phase, I described a U-Net-based methodology to segment prostate zones from T2-weighted (T2W) and apparent diffusion coefficient (ADC) map prostate MR images as a fundamental requirement for automated diagnosis of PCa. This work is the first attempt for prostate zonal segmentation using ADC map MR images. Furthermore, I presented an ensemble learning system for fully automated localization of peripheral zone PCa from the ADC map. The ensemble learning model allowed iii accurate PCa detection, which was not possible using one network due to the complexity of the decision boundary. Our results confirmed that automated PCa detection and segmentation using ADC map MR image are feasible, highly sensitive and can be performed rapidly.

Malignant peripheral nerve sheath tumors (MPNSTs) are rare neoplasms requiring differentiation from benign neurogenic tumors on diagnostic imaging. We evaluated the diagnostic utility of texture analysis (TA) in distinguishing seven pathologically confirmed MPNSTs from eight schwannomas using magnetic resonance imaging (MRI), including T1- (T1WI) and T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) maps. TA's performance was compared with that of conventional approaches using ADC values alone. Tumors were segmented, and 90 texture features were extracted using LIFEx software. Significantly different features (p < 0.05) were identified using the Mann-Whitney U test and evaluated through receiver operating characteristic (ROC) analysis. ADC maps were used to measure the minimum, mean, and maximum ADC values, followed by ROC analysis. Two T2WI-based texture features (neighborhood grey-tone difference matrix Contrast and grey-level size zone matrix Grey Level Variance) demonstrated the highest diagnostic performance (area under the curve = 0.911 [95% confidence interval (CI):0.755-1.000]), comparable to the minimum and mean ADC values (area under the curve = 0.898 [95% CI:0.691-1.000]). TA may help differentiate MPNSTs from schwannomas. T2WI-based TA offers a viable alternative when ADC maps are limited by magnetic susceptibility artifacts. J. Med. Invest. 72 : 367-374, August, 2025.

Introduction: The conventional magnetic resonance imaging (MRI) method is widely considered ‘with limited success’ in differentiating the meningioma types but may fail to localize the tumor occupation of white-matter fiber bundles accurately. Diffusion tensor imaging (DTI) is considered as an imaging modality that may elucidate the microstructure of brain tumors. We provide characteristics of meningioma using DTI-based-three-dimensional tracing of white matter to portray meningioma in a noninvasive approach and its structural contact to contiguous tumors and elucidate the influence of occupying lesions on white-matter fiber bundles. Case Presentation: A 28-year-old female presented with visuospatial disturbances and persistent headaches for 2 years. Conventional and advanced MRI studies were performed. Diffusion-weighted Images (DWI) and Apparent Diffusion Coefficient (ADC) values were measured in the lesion using routine MRI sequences. Advanced MRI using DTI was also performed. Conventional MRI outcomes showed tumor parenchyma, peritumoral edema, and compression on the circumnavigated brain tissue. There was hyperintense on DW trace image and isointense on ADC map. On T2-weighted image (T2WI) and Fluid-Attenuated Inversion Recovery (FLAIR) images, there was an increased signal intensity that demonstrated an extra-axial lesion, while T1-weighted imaging signals showed hypointensity. DTI fractional anisotropy (FA) marker is an unclear optic radiation in the concerned area, indicating the shift or destruction of the optic radiation. The mean FA values of solid-enhancing areas of meningioma were 0.28 ± 0.17. Mean ADC values (103 mm2/s) were 0.764 ± 0.172. Conclusions: Classic meningioma in this case has low intratumoral FA and high ADC. DTI displayed that intratumoral microscopic water motion is disorganized.

To examine the clinicopathological features and identify the predictors of pathological upgrading in low-risk prostate cancer (PCa) patients without hypointense lesions on the apparent diffusion coefficient (ADC) map calculated from multiparametric magnetic resonance imaging. We reviewed the medical records of 1905 PCa patients who underwent radical prostatectomy between 2007 and 2015. All ADC images were graded using the five-grade Likert scale; the positive hypointense lesions were graded 4-5. We analyzed 256 patients with low-risk classifications according to D'Amico criteria. Patients were classified into two groups according to the pathologic upgrading in the surgical specimens. The predictive factors for pathologic upgrading were evaluated using amultivariate logistic regression analysis. In 256 patients with low-risk PCa, the percentage of positive cores [odds ratio (OR) 1.09; 95% confidence interval (CI) 1.02-1.16], the percentage of cancer in the positive cores (OR 1.07, 95% CI 1.03-1.12), and the presence of hypointensity on an ADC map (OR 2.28; 95% CI 1.23-4.22) were independent predictors of pathologic upgrading. Notably, 138 of low-risk patients (53.9%) had no hypointense lesions on an ADC map. Of these 138 patients, the percentage of positive cores (OR 1.09; 95% CI 1.01-1.18) and the percentage of cancer in the positive cores (OR 1.06; 95% CI 1.01-1.12) remained independent predictors of pathologic upgrading. In low-risk PCa patients without hypointense lesions on an ADC map, biopsy-related parameters such as the percentage of positive cores and the percentage of cancer in the positive cores were independent predictors of pathological upgrading following radical prostatectomy.

Prostate cancer (PCa) is a highly heterogeneous disease, making tailored treatment approaches challenging. Magnetic resonance imaging (MRI), notably diffusion-weighted imaging (DWI) and the derived Apparent Diffusion Coefficient (ADC) maps, plays a crucial role in PCa characterization. In this context, radiomics is a very promising approach able to disclose insights from MRI data. However, the sensitivity of radiomic features to MRI settings, encompassing DWI protocols and multicenter variations, requires the development of robust and generalizable models. To develop a comprehensive radiomics framework for noninvasive PCa characterization using ADC maps, focusing on identifying reliable imaging biomarkers against intra- and inter-institution variations. Two patient cohorts, including an internal cohort (118 PCa patients) used for both training (75%) and hold-out testing (25%), and an external cohort (50 PCa patients) for independent testing, were employed in the study. DWI images were acquired with three different DWI protocols on two different MRI scanners: two DWI protocols acquired on a 1.5-T scanner for the internal cohort, and one DWI protocol acquired on a 3-T scanner for the external cohort. One hundred and seven radiomics features (i.e., shape, first order, texture) were extracted from ADC maps of the whole prostate gland. To address variations in DWI protocols and multicenter variability, a dedicated pipeline, including two-way ANOVA, sequential-feature-selection (SFS), and ComBat features harmonization was implemented. Mann-Whitney U-tests (α=0.05) were performed to find statistically significant features dividing patients with different tumor characteristics in terms of Gleason score (GS) and T-stage. Support-Vector-Machine models were then developed to predict GS and T-stage, and the performance was assessed through the area under the curve (AUC) of receiver-operating-characteristic curves. Downstream of ANOVA, two subsets of 38 and 41 features stable against DWI protocol were identified for GS and T-stage, respectively. Among these, SFS revealed the most predictive features, yielding an AUC of 0.75 (GS) and 0.70 (T-stage) in the hold-out test. Employing ComBat harmonization improved the external-test performance of the GS model, raising AUC from 0.72 to 0.78. By incorporating stable features with a harmonization procedure and validating the model on an external dataset, model robustness, and generalizability were assessed, highlighting the potential of ADC and radiomics for PCa characterization.

Significance of diffusion-weighted imaging and apparent diffusion coefficient maps for the evaluation of pyogenic ventriculitis