Deep‐learning‐based direct inversion for material decomposition

Gong, Hao; Tao, Shengzhen; Rajendran, Kishore; Zhou, Wentao; McCollough, Cynthia H.; Leng, Shuai

doi:10.1002/mp.14523

Cited by 31 publications

(29 citation statements)

References 55 publications

(133 reference statements)

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“…Other denoising or advanced reconstruction methods may also achieve similar noise reduction effect for material decomposition. 4,5,22,32,33 There are 2 major limitations in this exploratory animal study. First, this study aims to demonstrate using one single PCD-CT scan to image 2 liver phases.…”

Section: Discussionmentioning

confidence: 99%

Dual-Contrast Biphasic Liver Imaging With Iodine and Gadolinium Using Photon-Counting Detector Computed Tomography

et al. 2021

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The aims of this study were to develop a single-scan dual-contrast protocol for biphasic liver imaging with 2 intravenous contrast agents (iodine and gadolinium) and to evaluate its effectiveness in an exploratory swine study using a photon-counting detector computed tomography (PCD-CT) system. Materials and Methods: A dual-contrast CT protocol was developed for PCD-CT to simultaneously acquire 2 phases of liver contrast enhancement, with the late arterial phase enhanced by 1 contrast agent (iodine-based) and the portal venous phase enhanced by the other (gadolinium-based). A gadolinium contrast bolus (gadobutrol: 64 mL, 8 mL/s) and an iodine contrast bolus (iohexol: 40 mL, 5 mL/s) were intravenously injected in the femoral vein of a healthy domestic swine, with the second injection initiated after 17 seconds from the beginning of the first injection; PCD-CT image acquisition was performed 12 seconds after the beginning of the iodine contrast injection. A convolutional neural network (CNN)-based denoising technique was applied to PCD-CT images to overcome the inherent noise magnification issue in iodine/gadolinium decomposition task. Iodine and gadolinium material maps were generated using a 3-material decomposition method in image space. A set of contrast samples (mixed iodine and gadolinium) was attached to the swine belly; quantitative accuracy of material decomposition in these inserts between measured and true concentrations was calculated using root mean square error. An abdominal radiologist qualitatively evaluated the delineation of arterial and venous vasculatures in the swine liver using iodine and gadolinium maps obtained using the dual-contrast PCD-CT protocol. Results: The iodine and gadolinium samples attached to the swine were quantified with root mean square error values of 0.75 mg/mL for iodine and 0.45 mg/mL for gadolinium from the contrast material maps derived from the denoised PCD-CT images. Hepatic arteries containing iodine and veins containing gadolinium in the swine liver could be clearly visualized. Compared with the original images, better distinctions between 2 liver phases were achieved using CNN denoising, with approximately 60% to 80% noise reduction in contrast material maps acquired with the denoised PCD-CT images compared with the original images. Conclusions: Simultaneous biphasic liver imaging in a single multienergy PCD-CT acquisition using a dual-contrast (iodine and gadolinium) injection protocol and CNN denoising was demonstrated in a swine study, where the enhanced hepatic arteries (containing iodine) and the enhanced hepatic veins (containing gadolinium) could be clearly visualized and delineated in the swine liver.

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Section: Discussionmentioning

confidence: 99%

Dual-Contrast Biphasic Liver Imaging With Iodine and Gadolinium Using Photon-Counting Detector Computed Tomography

et al. 2021

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“…AGATE uses a custom dual‐task CNN architecture consisting of a stem CNN and two parallel branches that enabled simultaneous material quantification and classification (Figure 2). The stem CNN uses the custom Incept‐Boost and Incept‐Reduction modules from our recent work, 28 to identify the common features for quantification and classification tasks. Two unidirectional cross connections were used to propagate tissue‐type relevant information from classification branch to material quantification branch by applying the element‐wise product in the feature maps.…”

Section: Methodsmentioning

confidence: 99%

“…Hyperparameters were all empirically determined from prior studies. Briefly, the setup of stem CNN was determined using our prior setup in references, 28,31 and the setup of bifurcated branches was determined in our recent study. 35 The hyper-parameters (e.g., weighting factors) in loss function were also similarly set up.…”

Section: Cnn Training and Inferencementioning

confidence: 99%

Deep learning‐based virtual noncalcium imaging in multiple myeloma using dual‐energy CT

et al. 2022

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Background: Dual-energy CT with virtual noncalcium (VNCa) images allows the evaluation of focal intramedullary bone marrow involvement in patients with multiple myeloma. However, current commercial VNCa techniques suffer from excessive image noise and artifacts due to material decomposition used in synthesizing VNCa images. Objectives: In this work, we aim to improve VNCa image quality for the assessment of focal multiple myeloma, using an Artificial intelligence based Generalizable Algorithm for mulTi-Energy CT (AGATE) method. Materials and methods: AGATE method used a custom dual-task convolutional neural network (CNN) that concurrently carries out material classification and quantification. The material classification task provided an auxiliary regularization to the material quantification task. CNN parameters were optimized using custom loss functions that involved cross-entropy, physics-informed constraints, structural redundancy in spectral and material images, and texture information in spectral images. For training data, CT phantoms (diameters 30 to 45 cm) with tissue-mimicking inserts were scanned on a third generation dual-source CT system. Scans were performed at routine dose and half of the routine dose. Small image patches (i.e., 40 × 40 pixels) of tissue-mimicking inserts with known basis material densities were extracted for training samples. Numerically simulated insert materials with various shapes increased diversity of training samples. Generalizability of AGATE was evaluated using CT images from phantoms and patients. In phantoms, material decomposition accuracy was estimated using mean-absolute-percent-error (MAPE), using physical inserts that were not used during the training. Noise power spectrum (NPS) and modulation transfer function (MTF) were compared across phantom sizes and radiation dose levels. Five patients with multiple myeloma underwent dual-energy CT, with VNCa images generated using a commercial method and AGATE. Two fellowship-trained musculoskeletal radiologists reviewed the VNCa images (commercial and AGATE) side-by-side using a dual-monitor display, blinded to VNCa type, rating the image quality for focal multiple myeloma lesion visualization using a 5-level Likert comparison scale (−2 = worse visualization and diagnostic confidence, −1 = worse visualization but equivalent diagnostic confidence, 0 = equivalent visualization and diagnostic confidence, 1 = improved visualization but equivalent diagnostic confidence, 2 = improved visualization and diagnostic confidence). A post hoc assignment of comparison ratings was performed to rank AGATE images in comparison to commercial ones.

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“…Still, degraded spatial resolution is a concern, 17,21 and the regularization may affect the accuracy when the noise level is substantial 22 . Recently, learning‐based material decomposition has been an active research field, showing promising results with superior performance compared to conventional methods 23–26 . However, the robustness of the data mismatch needs to be addressed, and a sufficient number of training data is not always available.…”

Section: Introductionmentioning

confidence: 99%

Likelihood‐based bilateral filters for pre‐estimated basis sinograms using photon‐counting CT

Lee

2023

Medical Physics

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Background Noise amplification in material decomposition is an issue for exploiting photon‐counting computed tomography (PCCT). Regularization techniques and neighborhood filters have been widely used, but degraded spatial resolution and bias are concerns. Purpose This paper proposes likelihood‐based bilateral filters that can be applied to pre‐estimated basis sinograms to reduce the noise while minimally affecting spatial resolution and accuracy. Methods The proposed method needs system models (e.g., incident spectrum, detector response) to calculate the likelihood. First, it performs maximum likelihood (ML)‐based estimation in the projection domain to obtain basis sinograms. The estimated basis sinograms suffer from severe noise but are asymptotically unbiased without degrading spatial resolution. Then it calculates the neighborhood likelihoods for a given measurement at the center pixel using the neighborhood estimates and designs the weights based on the distance of likelihoods. It is also analyzed in terms of statistical inference, and then two variations of the filter are introduced: one that requires the significance level instead of the empirical hyperparameter. The other is a measurement‐based filter, which can be applied when accurate estimates are given without the system models. The proposed methods were validated by analyzing the local property of noise and spatial resolution and the global trend of noise and bias using numerical thorax and abdominal phantoms for a two‐material decomposition (water and bone). They were compared to the conventional neighborhood filters and the model‐based iterative reconstruction with an edge‐preserving penalty applied in the basis images. Results The proposed method showed comparable or superior performance for the local and global properties to conventional methods in many cases. The thorax phantom: The full width at half maximum (FWHM) decreased by −2%–31% (−2 indicates that it increased by 2% compared to the best performance from conventional methods), and the global bias was reduced by 2%–19% compared to other methods for similar noise levels (local: 51% of the ML, global: 49%) in the water basis image. The FWHM decreased by 8%–31%, and the global bias was reduced by 9%–44% for similar noise levels (local: 44% of the ML, global: 36%) in the CT image at 65 keV. The abdominal phantom: The FWHM decreased by 10%–32%, and the global bias was reduced by 3%–35% compared to other methods for similar noise levels (local: 66% of the ML, global: 67%) in the water basis image. The FWHM decreased by up to −11%–47%, and the global bias was reduced by 13%–35% for similar noise levels (local: 71% of the ML, global: 70%) in the CT image at 60 keV. Conclusions This paper introduced the likelihood‐based bilateral filters as a post‐processing method applied to the ML‐based estimates of basis sinograms. The proposed filters effectively reduced the noise in the basis images and the synthesized monochromatic CT images. It showed the potential of using likelihood‐based filters ...

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Deep‐learning‐based direct inversion for material decomposition

Cited by 31 publications

References 55 publications

Dual-Contrast Biphasic Liver Imaging With Iodine and Gadolinium Using Photon-Counting Detector Computed Tomography

Dual-Contrast Biphasic Liver Imaging With Iodine and Gadolinium Using Photon-Counting Detector Computed Tomography

Deep learning‐based virtual noncalcium imaging in multiple myeloma using dual‐energy CT

Likelihood‐based bilateral filters for pre‐estimated basis sinograms using photon‐counting CT

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