Intensity Standardization of Skeleton in Follow-Up Whole-Body MRI

Ceranka, Jakub; Verga, Sabrina; Lecouvet, Frédéric; Metens, Thierry; Mey, Johan De; Vandemeulebroucke, Jef

doi:10.1007/978-3-030-13736-6_7

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Another limitation of the present work is reliance on data from a single institution and scanner manufacturer. To establish calibration across scanners from different manufacturers and accommodate changes in imaging parameters such as field strength and b-values, more sophisticated techniques like histogram matching [24, 25] or the incorporation of machine learning methodologies [26] would be necessary. The present study is instructive, though, as it reveals that even minor variations of 15ms in TE can result in significant differences in quantitative measurements that require careful calibration and demonstrates physics-based correction for these differences.…”

Section: Discussionmentioning

confidence: 99%

Quantitative MRI biomarker for classification of clinically significant prostate cancer: calibration for reproducibility across echo times

Kallis,

Conlin,

Ollison

et al. 2024

Preprint

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Background: Restriction Spectrum Imaging restriction score (RSIrs) is a quantitative biomarker for detecting clinically significant prostate cancer (csPCa). However, the quantitative value of the RSIrs is affected by imaging parameters such as echo time (TE). Purpose: The purpose of the present study is to develop a calibration method to account for differences in echo times and facilitate use of RSIrs as a quantitative biomarker for the detection of csPCa. Methods: This study included 197 consecutive patients who underwent MRI and biopsy examination; 97 were diagnosed with csPCa (grade group ≥ 2). RSI data were acquired three times during the same session: twice at minimum TE~75ms and once at TE=90ms (TEmin</1>, TEmin</2>, and TE90, respectively). A proposed calibration method, trained on patients without csPCa, estimated a linear scaling factor (f) for each of the four diffusion compartments (C) of the RSI signal model. A linear regression model was determined to match C-maps of TE90 to the reference C-maps of TEmin1 within the interval ranging from 95th to 99th percentile of signal intensity within the prostate. RSIrs comparisons were made at 98th percentile within the prostate. We compared RSIrs from calibrated TE90 (RSIrs</TE90corr>) and uncorrected TE90 (RSIrs</TE90>) to RSIrs from reference TEmin1 (RSIrs</TEmin1>) and repeated TEmin2 (RSIrs</TEmin2>). Calibration performance was evaluated with sensitivity, specificity, area under the ROC curve, positive predicted value, negative predicted value, and F1-score. Results: Scaling factors for C</1>, C</2>, C</3>, and C</4> were estimated as 1.70, 1.38, 1.03, and 1.19, respectively. In non-csPCa cases, the 98th percentile of RSIrs</TEmin2> and RSIrs</TEmin1> differed by 0.27±0.86SI (mean±standard deviation), whereas RSIrs</TE90> differed from RSIrs</TEmin1> by 1.81±1.20SI. After calibration, this bias was reduced to -0.41±1.20SI, representing a 78% reduction in absolute error. For patients with csPCa, the difference was 0.54±1.98SI between RSIrs</TEmin2> and RSIrs</TEmin1> and 2.28±2.06SI between RSIrs</TE90> and RSIrs</TEmin1>. After calibration, the mean difference decreased to -0.86SI, a 38% reduction in absolute error. At the Youden index for patient-level classification of csPCa (8.94SI), RSIrs</TEmin1> has a sensitivity of 66% and a specificity of 72%. Prior to calibration, RSIrs</TE90> at the same threshold tended to over-diagnose benign cases (sensitivity 44%, specificity 88%). Post-calibration, RSIrs</TE90corr> performs more similarly to the reference (sensitivity 71%, specificity 62%). Conclusion: The proposed linear calibration method produces similar quantitative biomarker values for acquisitions with different TE, reducing TE-induced error by 78% and 38% for non-csPCa and csPCa, respectively.

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Section: Discussionmentioning

confidence: 99%

Quantitative MRI biomarker for classification of clinically significant prostate cancer: calibration for reproducibility across echo times

Kallis,

Conlin,

Ollison

et al. 2024

Preprint

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“…The authors demonstrated initial results in a cohort of 16 patients with advanced prostate cancer (APC) but noted the limitation that protocols must include at least three b-values, which can make the approach difficult to adopt at some centres. Ceranka et al [19] proposed a new method for signal intensity normalisation between baseline and follow-up wholebody MRI. The method involved two steps.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Delineation of the Spinal Canal in Whole-Body Diffusion-Weighted Imaging: Normalising Inter- and Intra-Patient Intensity Signal in Multi-Centre Datasets

Candito,

Holbrey,

Ribeiro

et al. 2024

Bioengineering

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Background: Whole-Body Diffusion-Weighted Imaging (WBDWI) is an established technique for staging and evaluating treatment response in patients with multiple myeloma (MM) and advanced prostate cancer (APC). However, WBDWI scans show inter- and intra-patient intensity signal variability. This variability poses challenges in accurately quantifying bone disease, tracking changes over follow-up scans, and developing automated tools for bone lesion delineation. Here, we propose a novel automated pipeline for inter-station, inter-scan image signal standardisation on WBDWI that utilizes robust segmentation of the spinal canal through deep learning. Methods: We trained and validated a supervised 2D U-Net model to automatically delineate the spinal canal (both the spinal cord and surrounding cerebrospinal fluid, CSF) in an initial cohort of 40 patients who underwent WBDWI for treatment response evaluation (80 scans in total). Expert-validated contours were used as the target standard. The algorithm was further semi-quantitatively validated on four additional datasets (three internal, one external, 207 scans total) by comparing the distributions of average apparent diffusion coefficient (ADC) and volume of the spinal cord derived from a two-component Gaussian mixture model of segmented regions. Our pipeline subsequently standardises WBDWI signal intensity through two stages: (i) normalisation of signal between imaging stations within each patient through histogram equalisation of slices acquired on either side of the station gap, and (ii) inter-scan normalisation through histogram equalisation of the signal derived within segmented spinal canal regions. This approach was semi-quantitatively validated in all scans available to the study (N = 287). Results: The test dice score, precision, and recall of the spinal canal segmentation model were all above 0.87 when compared to manual delineation. The average ADC for the spinal cord (1.7 × 10−3 mm2/s) showed no significant difference from the manual contours. Furthermore, no significant differences were found between the average ADC values of the spinal cord across the additional four datasets. The signal-normalised, high-b-value images were visualised using a fixed contrast window level and demonstrated qualitatively better signal homogeneity across scans than scans that were not signal-normalised. Conclusion: Our proposed intensity signal WBDWI normalisation pipeline successfully harmonises intensity values across multi-centre cohorts. The computational time required is less than 10 s, preserving contrast-to-noise and signal-to-noise ratios in axial diffusion-weighted images. Importantly, no changes to the clinical MRI protocol are expected, and there is no need for additional reference MRI data or follow-up scans.

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Comparison of intra- and inter-patient intensity standardization methods for multi-parametric whole-body MRI

Ceranka

Lecouvet

Michoux

et al. 2023

Biomed. Phys. Eng. Express

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Objective: To test and compare different intensity standardization approaches for whole-body multi-parametric MRI, allowing for the compensation of univocal representation of image intensities between scans, which pose problems in image quantification, assessment of changes between a baseline and follow-up scan and hinder performance of image processing and machine learning algorithms. Approach: In this work, we present a comparison on the accuracy of intensity standardization approaches with increasing complexity, for intra- and inter-patient multi-parametric whole-body MRI. Several approaches were used: z-scoring of the intensities, piecewise linear mapping and deformable mapping of intensity distributions into established reference intensity space. Additionally, the use reference image and average population distribution reference; as well as, whole image and region of interest standardization approaches were investigated. All methods were validated on a data set of 18 whole-body anatomical and diffusion-weighted MR scans consisting of baseline and follow-up examinations acquired from advanced prostate cancer patients and healthy volunteers. Main results: The piecewise linear intensity standardisation approach provided the best compromise between standardization accuracy and method stability, with average deviations in intensity profile of 0.011-0.027 and mean absolute difference of 0.29-0.37 standard score (intra-patient) and 0.014-0.056 (inter-patient), depending on the type of used MR modality. Significance: Linear piecewise approaches showed the overall best performance across multiple validation metrics, mostly because of its robustness. The inter-patient standardization proved to perform better when using population average reference image; in contrary to intra-patient approach, where the best results were achieved by standardizing towards a reference image taken as the baseline scan.

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Intensity Standardization of Skeleton in Follow-Up Whole-Body MRI

Cited by 3 publications

References 19 publications

Quantitative MRI biomarker for classification of clinically significant prostate cancer: calibration for reproducibility across echo times

Quantitative MRI biomarker for classification of clinically significant prostate cancer: calibration for reproducibility across echo times

Deep Learning for Delineation of the Spinal Canal in Whole-Body Diffusion-Weighted Imaging: Normalising Inter- and Intra-Patient Intensity Signal in Multi-Centre Datasets

Comparison of intra- and inter-patient intensity standardization methods for multi-parametric whole-body MRI

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