Automatic Detection of Prostate Tumor Habitats using Diffusion MRI.
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ABSTRACT:
SUBMITTER: Tschudi Y
PROVIDER: S-EPMC6235961 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:As an alternative tool to identify cortical motor areas for planning surgical resection in children with focal epilepsy, the present study proposed a maximum a posteriori probability (MAP) classification of corticospinal tract (CST) visualized by diffusion MR tractography.Diffusion-weighted imaging (DWI) was performed in 17 normally developing children and 20 children with focal epilepsy. An independent component analysis tractography combined with ball-stick model was performed to identify unique CST pathways originating from mouth/lip, finger, and leg areas determined by functional magnetic resonance imaging (fMRI) in healthy children and electrical stimulation mapping (ESM) in children with epilepsy. Group analyses were performed to construct stereotaxic probability maps of primary motor pathways connecting precentral gyrus and posterior limb of internal capsule, and then utilized to design a novel MAP classifier that can sort individual CST fibers associated with three classes of interest: mouth/lip, fingers, and leg. A systematic leave-one-out approach was applied to train an optimal classifier. A match was considered to occur if classified fibers contacted or surrounded true areas localized by fMRI and ESM.It was found that the DWI-MAP provided high accuracy for the CST fibers terminating in proximity to the localization of fMRI/ESM: 78%/77% for mouth/lip, 77%/76% for fingers, 78%/86% for leg (contact), and 93%/89% for mouth/lip, 91%/89% for fingers, and 92%/88% for leg (surrounded within 2 cm).This study provides preliminary evidence that in the absence of fMRI and ESM data, the DWI-MAP approach can effectively retrieve the locations of cortical motor areas and underlying CST courses for planning epilepsy surgery.
| S-EPMC3731426 | biostudies-literature
Project description:PurposeTo develop a robust and clinically applicable automated method for analyzing Dynamic Contrast Enhanced (DCE-) MRI of the prostate as a guide for targeted biopsies and treatments.Materials and methodsAn unsupervised pattern recognition (PR) method was used to analyze prostate DCE-MRI from 71 sequential radiotherapy patients. Identified regions of interest (ROIs) with increased perfusion were assigned either to the peripheral (PZ) or transition zone (TZ). Six quantitative features, associated with the washin and washout part of the weighted average DCE curve from the ROI, were calculated. The associations between the assigned DCE-scores and Gleason Score (GS) were investigated. A heatmap of tumor aggressiveness covering the entire prostate was generated and validated with histopathology from MRI-ultrasound fused (MRI-US) targeted biopsies.ResultsThe volumes of the PR-identified ROI's were significantly correlated with the highest GS from the biopsy session for each patient. Following normalization (and only after normalization) with gluteus maximus muscle's DCE signal, the quantitative features in PZ were significantly correlated with GS. These correlations straightened in subset of patients with available MRI-US biopsies when GS from the individual biopsies were used. Area under the receiver operating characteristics curve for discrimination between indolent vs aggressive cancer for the significant quantitative features reached 0.88-0.95. When DCE-scores were calculated in normal appearing tissues, the features were highly discriminative for cancer vs no cancer both in PZ and TZ. The generated heatmap of tumor aggressiveness coincided with the location and GS of the MRI-US biopsies.ConclusionA quantitative approach for DCE-MRI analysis was developed. The resultant map of aggressiveness correlated well with tumor location and GS and is applicable for integration in radiotherapy/radiology imaging software for clinical translation.
| S-EPMC5686056 | biostudies-literature
Project description:Diffusion MRI fiber tracking provides a non-invasive method for mapping the trajectories of human brain connections, but its false connection problem has been a major challenge. This study introduces topology-informed pruning (TIP), a method that automatically identifies singular tracts and eliminates them to improve the tracking accuracy. The accuracy of the tractography with and without TIP was evaluated by a team of 6 neuroanatomists in a blinded setting to examine whether TIP could improve the accuracy. The results showed that TIP improved the tracking accuracy by 11.93% in the single-shell scheme and by 3.47% in the grid scheme. The improvement is significantly different from a random pruning (p value < 0.001). The diagnostic agreement between TIP and neuroanatomists was comparable to the agreement between neuroanatomists. The proposed TIP algorithm can be used to automatically clean-up noisy fibers in deterministic tractography, with a potential to confirm the existence of a fiber connection in basic neuroanatomical studies or clinical neurosurgical planning.
| S-EPMC6361061 | biostudies-literature
Project description:Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides functional information on the microcirculation in tissues by analyzing the enhancement kinetics which can be used as biomarkers for prostate lesions detection and characterization.The purpose of this study is to investigate spatiotemporal patterns of tumors by extracting semi-quantitative as well as wavelet-based features, both extracted from pixel-based time-signal intensity curves to segment prostate lesions on prostate DCE-MRI.Quantitative dynamic contrast-enhanced MRI data were acquired on 22 patients. Optimal features selected by forward selection are used for the segmentation of prostate lesions by applying fuzzy c-means (FCM) clustering. The images were reviewed by an expert radiologist and manual segmentation performed as the ground truth.Empirical results indicate that fuzzy c-mean classifier can achieve better results in terms of sensitivity, speci?city when semi-quantitative features were considered versus wavelet kinetic features for lesion segmentation (Sensitivity of 87.58% and 75.62%, respectively) and (Specificity of 89.85% and 68.89 %, respectively).The proposed segmentation algorithm in this work can potentially be implemented for automatic prostate lesion detection in a computer aided diagnosis scheme and combined with morphologic features to increase diagnostic credibility.
| S-EPMC5928300 | biostudies-literature
Project description:PurposeWith current gold standard treatment, which associates maximum safe surgery and chemo-radiation, the large majority of glioblastoma patients relapse within a year in the peritumoral non contrast-enhanced region (NCE). A subpopulation of glioblastoma stem-like cells (GSC) are known to be particularly radio-resistant and aggressive, and are thus suspected to be the cause of these relapses. Previous studies have shown that their distribution is heterogeneous in the NCE compartment, but no study exists on the sensitivity of medical imaging for localizing these cells. In this work, we propose to study the magnetic resonance (MR) signature of these infiltrative cells.MethodsIn the context of a clinical trial on 16 glioblastoma patients, relative Cerebral Blood Volume (rCBV) and Apparent Diffusion Coefficient (ADC) were measured in a preoperative diffusion and perfusion MRI examination. During surgery, two biopsies were extracted using image-guidance in the hyperintensities-FLAIR region. GSC subpopulation was quantified within the biopsies and then cultivated in selective conditions to determine their density and aggressiveness.ResultsLow ADC was found to be a good predictor of the time to GSC neurospheres formation in vitro. In addition, GSCs were found in higher concentrations in areas with high rCBV.ConclusionsThis study confirms that GSCs have a critical role for glioblastoma aggressiveness and supports the idea that peritumoral sites with low ADC or high rCBV should be preferably removed when possible during surgery and targeted by radiotherapy.
| S-EPMC9179449 | biostudies-literature
Project description:Background and purposeDetecting incidence and enlargement of lesions is essential in monitoring the progression of MS. In clinical trials, lesion load is observed by manually segmenting and comparing serial MR images, which is time consuming, costly, and prone to inter- and intraobserver variability. Subtracting images from consecutive time points nulls stable lesions, leaving only new lesion activity. We propose SuBLIME, an automated method for segmenting incident lesion voxels.Materials and methodsWe used logistic regression models incorporating multiple MR imaging sequences and subtraction images from consecutive longitudinal studies to estimate voxel-level probabilities of lesion incidence. We used T1-weighted, T2-weighted, FLAIR, and PD volumes from a total of 110 MR imaging studies from 10 subjects.ResultsTo assess the performance of the model, we assigned 5 subjects to a training set and the remaining 5 to a validation set. With SuBLIME, lesion incidence is detected and delineated in the validation set with an AUC of 99% (95% CI [97%, 100%]) at the voxel level.ConclusionsThis fully automated and computationally fast method allows sensitive and specific detection of lesion incidence that can be applied to large collections of images. Using the explicit form of the statistical model, SuBLIME can easily be adapted to cases when more or fewer imaging sequences are available.
| S-EPMC3554794 | biostudies-literature
Project description:Prostate cancer identification and assessment of clinical significance continues to be a challenge. Routine multiparametric magnetic resonance imaging has shown to be useful in assessing disease progression. Although dynamic contrast-enhanced imaging (DCE) has the ability to characterize perfusion across time and has shown enormous utility, radiological assessment (Prostate Imaging-Reporting and Data System or PIRADS version 2) has limited its use owing to lack of consistency and nonquantitative nature. In our work, we propose a systematic methodology to quantify perfusion dynamics for the DCE imaging. Using these metrics, 7 different subregions or perfusion habitats of the targeted lesions are localized and related to clinical significance. We found that quantitative features describing the habitat based on the late area under the DCE time-activity curve was a good predictor of clinical significance disease. The best predictive feature in the habitat had an AUC of 0.82, CI [0.81-0.83].
| S-EPMC6403034 | biostudies-literature
Project description:Diffusion-MRI (dMRI) measures molecular diffusion, which allows to characterize microstructural properties of the human brain. Gliomas strongly alter these microstructural properties. Delineation of brain tumors currently mainly relies on conventional MRI-techniques, which are, however, known to underestimate tumor volumes in diffusely infiltrating glioma. We hypothesized that dMRI is well suited for tumor delineation, and developed two different deep-learning approaches. The first diffusion-anomaly detection architecture is a denoising autoencoder, the second consists of a reconstruction and a discrimination network. Each model was exclusively trained on non-annotated dMRI of healthy subjects, and then applied on glioma patients' data. To validate these models, a state-of-the-art supervised tumor segmentation network was modified to generate groundtruth tumor volumes based on structural MRI. Compared to groundtruth segmentations, a dice score of 0.67 ± 0.2 was obtained. Further inspecting mismatches between diffusion-anomalous regions and groundtruth segmentations revealed, that these colocalized with lesions delineated only later on in structural MRI follow-up data, which were not visible at the initial time of recording. Anomaly-detection methods are suitable for tumor delineation in dMRI acquisitions, and may further enhance brain-imaging analysis by detection of occult tumor infiltration in glioma patients, which could improve prognostication of disease evolution and tumor treatment strategies.
| S-EPMC10663596 | biostudies-literature
Project description:PurposeTo investigate whether a novel signal derived from tumor motion allows more precise sorting of 4D-magnetic resonance (4D-MR) image data than do signals based on normal anatomy, reducing levels of stitching artifacts within sorted lung tumor volumes.Methods(4D-MRI) scans were collected for 10 lung cancer patients using a 2D T2-weighted single-shot turbo spin echo sequence, obtaining 25 repeat frames per image slice. For each slice, a tumor-motion signal was generated using the first principal component of movement in the tumor neighborhood (TumorPC1). Signals were also generated from displacements of the diaphragm (DIA) and upper and lower chest wall (UCW/LCW) and from slice body area changes (BA). Pearson r coefficients of correlations between observed tumor movement and respiratory signals were determined. TumorPC1, DIA, and UCW signals were used to compile image stacks showing each patient's tumor volume in a respiratory phase. Unsorted image stacks were also built for comparison. For each image stack, the presence of stitching artifacts was assessed by measuring the roughness of the compiled tumor surface according to a roughness metric (Rg). Statistical differences in weighted means of Rg between any two signals were determined using an exact permutation test.ResultsThe TumorPC1 signal was most strongly correlated with superior-inferior tumor motion, and had significantly higher Pearson r values (median 0.86) than those determined for correlations of UCW, LCW, and BA with superior-inferior tumor motion (p < 0.05). Weighted means of ratios of Rg values in TumorPC1 image stacks to those in unsorted, UCW, and DIA stacks were 0.67, 0.69, and 0.71, all significantly favoring TumorPC1 (p = 0.02-0.05). For other pairs of signals, weighted mean ratios did not differ significantly from one.ConclusionTumor volumes were smoother in 3D image stacks compiled using the first principal component of tumor motion than in stacks compiled with signals based on normal anatomy.
| S-EPMC11005973 | biostudies-literature
Project description:PurposeTo investigate the feasibility of accelerating prostate diffusion-weighted imaging (DWI) by reducing the number of acquired averages and denoising the resulting image using a proposed guided denoising convolutional neural network (DnCNN).Materials and methodsRaw data from the prostate DWI scans were retrospectively gathered between July 2018 and July 2019 from six single-vendor MRI scanners. There were 103 datasets used for training (median age, 64 years; interquartile range [IQR], 11), 15 for validation (median age, 68 years; IQR, 12), and 37 for testing (median age, 64 years; IQR, 12). High b-value diffusion-weighted (hb DW) data were reconstructed into noisy images using two averages and reference images using all 16 averages. A conventional DnCNN was modified into a guided DnCNN, which uses the low b-value DW image as a guidance input. Quantitative and qualitative reader evaluations were performed on the denoised hb DW images. A cumulative link mixed regression model was used to compare the readers' scores. The agreement between the apparent diffusion coefficient (ADC) maps (denoised vs reference) was analyzed using Bland-Altman analysis.ResultsCompared with the original DnCNN, the guided DnCNN produced denoised hb DW images with higher peak signal-to-noise ratio (32.79 ± 3.64 [standard deviation] vs 33.74 ± 3.64), higher structural similarity index (0.92 ± 0.05 vs 0.93 ± 0.04), and lower normalized mean square error (3.9% ± 10 vs 1.6% ± 1.5) (P < .001 for all). Compared with the reference images, the denoised images received higher image quality scores from the readers (P < .0001). The ADC values based on the denoised hb DW images were in good agreement with the reference ADC values (mean ADC difference ranged from -0.04 to 0.02 × 10-3 mm2/sec).ConclusionAccelerating prostate DWI by reducing the number of acquired averages and denoising the resulting image using the proposed guided DnCNN is technically feasible. Supplemental material is available for this article. © RSNA, 2020.
| S-EPMC7529434 | biostudies-literature