Evaluation of a similarity anisotropic diffusion denoising approach for improving in vivo CEST-MRI tumor pH imaging

Enhancing SNR in CEST imaging: A deep learning approach with a denoising convolutional autoencoder

PURPOSE

To develop a SNR enhancement method for CEST imaging using a denoising convolutional autoencoder (DCAE) and compare its performance with state-of-the-art denoising methods.

METHOD

The DCAE-CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z-spectrum via a series of one-dimensional convolutions, nonlinearity applications, and pooling. Subsequently, the decoder reconstructs an output denoised Z-spectrum using a series of up-sampling and convolution layers. The DCAE-CEST model underwent multistage training in an environment constrained by Kullback-Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis-processed Z-spectrum as a reference. The model was trained using simulated Z-spectra, and its performance was evaluated using both simulated data and in vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple-pool Lorentzian fit, along with an apparent exchange-dependent relaxation metric.

RESULTS

In digital phantom experiments, the DCAE-CEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirm the effectiveness of the DCAE-CEST in denoising the APT and NOE maps when compared with other methods. Although no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings.

CONCLUSION

The DCAE-CEST can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution compared with other methods.

Development and Validation of Four Different Methods to Improve MRI-CEST Tumor pH Mapping in Presence of Fat

CEST-MRI is an emerging imaging technique suitable for various in vivo applications, including the quantification of tumor acidosis. Traditionally, CEST contrast is calculated by asymmetry analysis, but the presence of fat signals leads to wrong contrast quantification and hence to inaccurate pH measurements. In this study, we investigated four post-processing approaches to overcome fat signal influences and enable correct CEST contrast calculations and tumor pH measurements using iopamidol. The proposed methods involve replacing the Z-spectrum region affected by fat peaks by (i) using a linear interpolation of the fat frequencies, (ii) applying water pool Lorentzian fitting, (iii) considering only the positive part of the Z-spectrum, or (iv) calculating a correction factor for the ratiometric value. In vitro and in vivo studies demonstrated the possibility of using these approaches to calculate CEST contrast and then to measure tumor pH, even in the presence of moderate to high fat fraction values. However, only the method based on the water pool Lorentzian fitting produced highly accurate results in terms of pH measurement in tumor-bearing mice with low and high fat contents.

A Denoising Convolutional Autoencoder for SNR Enhancement in Chemical Exchange Saturation Transfer imaging: (DCAE-CEST)

Purpose

To develop a SNR enhancement method for chemical exchange saturation transfer (CEST) imaging using a denoising convolutional autoencoder (DCAE), and compare its performance with state-of-the-art denoising methods.

Method

The DCAE-CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z-spectrum via a series of 1D convolutions, nonlinearity applications and pooling. Subsequently, the decoder reconstructs an output denoised Z-spectrum using a series of up-sampling and convolution layers. The DCAE-CEST model underwent multistage training in an environment constrained by Kullback-Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis processed Z-spectrum as a reference. The model was trained using simulated Z-spectra, and its performance was evaluated using both simulated data and in-vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple-pool Lorentzian fit, along with an apparent exchange-dependent relaxation metric.

Results

In digital phantom experiments, the DCAE-CEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirms the effectiveness of the DCAE-CEST in denoising the APT and NOE maps when compared to other methods. While no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings.

Conclusion

The DCAE-CEST can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution compared to other methods.

Boosting quantification accuracy of chemical exchange saturation transfer MRI with a spatial-spectral redundancy-based denoising method

Chemical exchange saturation transfer (CEST) is a versatile technique that enables noninvasive detections of endogenous metabolites present in low concentrations in living tissue. However, CEST imaging suffers from an inherently low signal-to-noise ratio (SNR) due to the decreased water signal caused by the transfer of saturated spins. This limitation challenges the accuracy and reliability of quantification in CEST imaging. In this study, a novel spatial-spectral denoising method, called BOOST (suBspace denoising with nOnlocal lOw-rank constraint and Spectral local-smooThness regularization), was proposed to enhance the SNR of CEST images and boost quantification accuracy. More precisely, our method initially decomposes the noisy CEST images into a low-dimensional subspace by leveraging the global spectral low-rank prior. Subsequently, a spatial nonlocal self-similarity prior is applied to the subspace-based images. Simultaneously, the spectral local-smoothness property of Z-spectra is incorporated by imposing a weighted spectral total variation constraint. The efficiency and robustness of BOOST were validated in various scenarios, including numerical simulations and preclinical and clinical conditions, spanning magnetic field strengths from 3.0 to 11.7 T. The results demonstrated that BOOST outperforms state-of-the-art algorithms in terms of noise elimination. As a cost-effective and widely available post-processing method, BOOST can be easily integrated into existing CEST protocols, consequently promoting accuracy and reliability in detecting subtle CEST effects.

Learned spatiotemporal correlation priors for CEST image denoising using incorporated global-spectral convolution neural network

PURPOSE

To develop a deep learning-based method, dubbed Denoising CEST Network (DECENT), to fully exploit the spatiotemporal correlation prior to CEST image denoising.

METHODS

DECENT is composed of two parallel pathways with different convolution kernel sizes aiming to extract the global and spectral features embedded in CEST images. Each pathway consists of a modified U-Net with residual Encoder-Decoder network and 3D convolution. Fusion pathway with 1 × 1 × 1 convolution kernel is utilized to concatenate two parallel pathways, and the output of DECENT is noise-reduced CEST images. The performance of DECENT was validated in numerical simulations, egg white phantom experiments, and ischemic mouse brain and human skeletal muscle experiments in comparison with existing state-of-the-art denoising methods.

RESULTS

Rician noise was added to CEST images to mimic a low SNR situation for numerical simulation, egg white phantom experiment, and mouse brain experiments, while human skeletal muscle experiments were of inherently low SNR. From the denoising results evaluated by peak SNR (PSNR) and structural similarity index (SSIM), the proposed deep learning-based denoising method (DECENT) can achieve better performance compared to existing CEST denoising methods such as NLmCED, MLSVD, and BM4D, avoiding complicated parameter tuning or time-consuming iterative processes.

CONCLUSIONS

DECENT can well exploit the prior spatiotemporal correlation knowledge of CEST images and restore the noise-free images from their noisy observations, outperforming state-of-the-art denoising methods.

Deep Learning-Based Denoising of CEST MR Data: A Feasibility Study on Applying Synthetic Phantoms in Medical Imaging

Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) provides a novel method for analyzing biomolecule concentrations in tissues without exogenous contrast agents. Despite its potential, achieving a high signal-to-noise ratio (SNR) is imperative for detecting small CEST effects. Traditional metrics such as Magnetization Transfer Ratio Asymmetry (MTRasym) and Lorentzian analyses are vulnerable to image noise, hampering their precision in quantitative concentration estimations. Recent noise-reduction algorithms like principal component analysis (PCA), nonlocal mean filtering (NLM), and block matching combined with 3D filtering (BM3D) have shown promise, as there is a burgeoning interest in the utilization of neural networks (NNs), particularly autoencoders, for imaging denoising. This study uses the Bloch-McConnell equations, which allow for the synthetic generation of CEST images and explores NNs efficacy in denoising these images. Using synthetically generated phantoms, autoencoders were created, and their performance was compared with traditional denoising methods using various datasets. The results underscored the superior performance of NNs, notably the ResUNet architectures, in noise identification and abatement compared to analytical approaches across a wide noise gamut. This superiority was particularly pronounced at elevated noise intensities in the in vitro data. Notably, the neural architectures significantly improved the PSNR values, achieving up to 35.0, while some traditional methods struggled, especially in low-noise reduction scenarios. However, the application to the in vivo data presented challenges due to varying noise profiles. This study accentuates the potential of NNs as robust denoising tools, but their translation to clinical settings warrants further investigation.

Intraperitoneal Delivery of Iopamidol to Assess Extracellular pH of Orthotopic Pancreatic Tumor Model by CEST-MRI

The extracellular pH (pHe) of solid tumors is often acidic, as a consequence of the Warburg effect, and an altered metabolic state is often associated with malignancy. It has been shown that acidosis can promote tumor progression; thus, many therapeutic strategies have been adopted against tumor metabolism; one of these involves alkalinization therapies to raise tumor pH to inhibit tumor progression, improve immune surveillance, and overcome resistance to chemotherapies. Chemical exchange saturation transfer-magnetic resonance imaging (CEST-MRI) is a noninvasive technique that can measure pH in vivo using pH-sensitive contrast agents. Iopamidol, an iodinated contrast agent, clinically used for computed tomography (CT), contains amide group protons with pH-dependent exchange rates that can reveal the pHe of the tumor microenvironment. In this study, we optimized intraperitoneal (IP) delivery of iopamidol to facilitate longitudinal assessments of orthotopic pancreatic tumor pHe by CEST-MRI. Following IV-infusion and IP-bolus injections, we compared the two protocols for assessing tumor pH. Time-resolved CT imaging was used to evaluate the uptake of iopamidol in the tumor, revealing that IP-bolus delivered a high amount of contrast agent 40 min postinjection, which was similar to the amounts reached with the IV-infusion protocol. As expected, both IP and IV injection protocols produced comparable measurements of tumor pHe, showing no statistically significant difference between groups (p=0.16). In addition, we showed the ability to conduct longitudinal monitoring of tumor pHe using CEST-MRI with the IP injection protocol, revealing a statistically significant increase in tumor pHe following bicarbonate administration (p < 0.001). In conclusion, this study shows the capability to measure pHe using an IP delivery of iopamidol into orthotopic pancreatic tumors, which is important to conduct longitudinal studies.

Tumor pH Imaging Using Chemical Exchange Saturation Transfer (CEST)-MRI

Magnetic resonance imaging (MRI) is a noninvasive imaging technique that allows for physiological and functional studies of the tumor microenvironment. Within MRI, the emerging field of chemical exchange saturation transfer (CEST) has been largely exploited for assessing a salient feature of all solid tumors, extracellular acidosis. Iopamidol-based tumor pH imaging has been demonstrated to provide accurate and high spatial resolution extracellular tumor pH maps to elucidate tumor aggressiveness and for assessing response to therapy, with a high potential for clinical translation. Here, we describe the overall setup and steps for measuring tumor extracellular pH of tumor models in mice by exploiting MRI-CEST pH imaging with a preclinical MRI scanner following the administration of iopamidol. We address issues of pH calibration curve setup, animal handling, pH-responsive contrast agent injection, acquisition protocol, and image processing for accurate quantification and visualization of tumor acidosis.

Dual-Mode Tumor Imaging Using Probes That Are Responsive to Hypoxia-Induced Pathological Conditions

Hypoxia in solid tumors is associated with poor prognosis, increased aggressiveness, and strong resistance to therapeutics, making accurate monitoring of hypoxia important. Several imaging modalities have been used to study hypoxia, but each modality has inherent limitations. The use of a second modality can compensate for the limitations and validate the results of any single imaging modality. In this review, we describe dual-mode imaging systems for the detection of hypoxia that have been reported since the start of the 21st century. First, we provide a brief overview of the hallmarks of hypoxia used for imaging and the imaging modalities used to detect hypoxia, including optical imaging, ultrasound imaging, photoacoustic imaging, single-photon emission tomography, X-ray computed tomography, positron emission tomography, Cerenkov radiation energy transfer imaging, magnetic resonance imaging, electron paramagnetic resonance imaging, magnetic particle imaging, and surface-enhanced Raman spectroscopy, and mass spectrometric imaging. These overviews are followed by examples of hypoxia-relevant imaging using a mixture of probes for complementary single-mode imaging techniques. Then, we describe dual-mode molecular switches that are responsive in multiple imaging modalities to at least one hypoxia-induced pathological change. Finally, we offer future perspectives toward dual-mode imaging of hypoxia and hypoxia-induced pathophysiological changes in tumor microenvironments.

A Brief History and Future Prospects of CEST MRI in Clinical Non-Brain Tumor Imaging

Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging tool which allows the specific detection of metabolites that contain exchangeable amide, amine, and hydroxyl protons. Decades of development have progressed CEST imaging from an initial concept to a clinical imaging tool that is used to assess tumor metabolism. The first translation efforts involved brain imaging, but this has now progressed to imaging other body tissues. In this review, we summarize studies using CEST MRI to image a range of tumor types, including breast cancer, pelvic tumors, digestive tumors, and lung cancer. Approximately two thirds of the published studies involved breast or pelvic tumors which are sites that are less affected by body motion. Most studies conclude that CEST shows good potential for the differentiation of malignant from benign lesions with a number of reports now extending to compare different histological classifications along with the effects of anti-cancer treatments. Despite CEST being a unique 'label-free' approach with a higher sensitivity than MR spectroscopy, there are still some obstacles for implementing its clinical use. Future research is now focused on overcoming these challenges. Vigorous ongoing development and further clinical trials are expected to see CEST technology become more widely implemented as a mainstream imaging technology.

GlucoCEST MRI for the Evaluation Response to Chemotherapeutic and Metabolic Treatments in a Murine Triple-Negative Breast Cancer: A Comparison with[18F]F-FDG-PET

PURPOSE

Triple-negative breast cancer (TNBC) patients have usually poor outcome after chemotherapy and early prediction of therapeutic response would be helpful. [¹⁸F]F-FDG-PET/CT acquisitions are often carried out to monitor variation in metabolic activity associated with response to the therapy, despite moderate accuracy and radiation exposure limit its application. The glucoCEST technique relies on the use of unlabelled D-glucose to assess glucose uptake with conventional MRI scanners and is currently under active investigations at clinical level. This work aims at validating the potential of MRI-glucoCEST in monitoring the therapeutic responses in a TNBC tumor murine model.

PROCEDURES

Breast tumor (4T1)-bearing mice were treated with doxorubicin or dichloroacetate for 1 week. PET/CT with [¹⁸F]F-FDG and MRI-glucoCEST were performed at baseline and after 3 cycles of treatment. Metabolic changes measured with [¹⁸F]F-FDG-PET and glucoCEST were compared and evaluated with changes in tumor volumes.

RESULTS

Doxorubicin-treated mice showed a significant decrease in tumor growth when compared to the control group. GlucoCEST imaging provided metabolic response after three cycles of treatment. Conversely, no variations were detected in [¹⁸F]F-FDG uptake. Dichloroacetate-treated mice did not show any decrease either in tumor volume or in tumor metabolic activity as assessed by both glucoCEST and [¹⁸F]F-FDG-PET.

CONCLUSIONS

Metabolic changes during doxorubicin treatment can be predicted by glucoCEST imaging that appears more sensitive than [¹⁸F]F-FDG-PET in reporting on therapeutic response. These findings support the view that glucoCEST may be a sensitive technique for monitoring metabolic response, but future studies are needed to explore the accuracy of this approach in other tumor types and treatments.

Acid Microenvironment in Bone Sarcomas

Simple Summary

Although rare, malignant bone sarcomas have devastating clinical implications for the health and survival of young adults and children. To date, efforts to identify the molecular drivers and targets have focused on cancer cells or on the interplay between cancer cells and stromal cells in the tumour microenvironment. On the contrary, in the current literature, the role of the chemical-physical conditions of the tumour microenvironment that may be implicated in sarcoma aggressiveness and progression are poorly reported and discussed. Among these, extracellular acidosis is a well-recognized hallmark of bone sarcomas and promotes cancer growth and dissemination but data presented on this topic are fragmented. Hence, we intended to provide a general and comprehensive overview of the causes and implications of acidosis in bone sarcoma.

Abstract

In bone sarcomas, extracellular proton accumulation is an intrinsic driver of malignancy. Extracellular acidosis increases stemness, invasion, angiogenesis, metastasis, and resistance to therapy of cancer cells. It reprograms tumour-associated stroma into a protumour phenotype through the release of inflammatory cytokines. It affects bone homeostasis, as extracellular proton accumulation is perceived by acid-sensing ion channels located at the cell membrane of normal bone cells. In bone, acidosis results from the altered glycolytic metabolism of bone cancer cells and the resorption activity of tumour-induced osteoclasts that share the same ecosystem. Proton extrusion activity is mediated by extruders and transporters located at the cell membrane of normal and transformed cells, including vacuolar ATPase and carbonic anhydrase IX, or by the release of highly acidic lysosomes by exocytosis. To date, a number of investigations have focused on the effects of acidosis and its inhibition in bone sarcomas, including studies evaluating the use of photodynamic therapy. In this review, we will discuss the current status of all findings on extracellular acidosis in bone sarcomas, with a specific focus on the characteristics of the bone microenvironment and the acid-targeting therapeutic approaches that are currently being evaluated.

Accelerated multi-target chemical exchange saturation transfer magnetic resonance imaging of the mouse heart

Cardiac chemical exchange saturation transfer-magnetic resonance imaging (CEST-MRI) has been used to probe levels of various metabolites that provide insight into myocardial structure and function. However, imaging of the heart using CEST-MRI is prolonged by the need to repeatedly acquire multiple images for a full Z-spectrum and to perform saturation and acquisition around cardiac and respiratory cycles. Compressed sensing (CS) reconstruction of sparse data enables accelerated acquisition, but reconstruction artifacts may bias subsequently derived measures of CEST contrast. In this study, we examine the impact of CS reconstruction of increasingly under-sampled cardiac CEST-MRI data on subsequent CEST contrasts of amine-containing metabolites and amide-containing proteins. Cardiac CEST-MRI data sets were acquired in six mice using low and high RF saturation for single and dual contrast generation, respectively. CEST-weighted images were reconstructed using CS methods at 2-5× levels of under-sampling. CEST contrasts were derived from corresponding Z-spectra and the impact of accelerated imaging on accuracy was assessed via analysis of variance. CS reconstruction preserved myocardial signal to noise ratio as compared to conventional reconstruction. However, greater absolute error and distribution of derived contrasts was observed with increasing acceleration factors. The results from this study indicate that acquisition of radial cardiac CEST-MRI data can be modestly, but meaningfully, accelerated via CS reconstructions with little error in CEST contrast quantification.

Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System

In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.