Self-supervised multicontrast super-resolution for diffusion-weighted prostate MRI

PURPOSE

This study addresses the challenge of low resolution and signal-to-noise ratio (SNR) in diffusion-weighted images (DWI), which are pivotal for cancer detection. Traditional methods increase SNR at high b-values through multiple acquisitions, but this results in diminished image resolution due to motion-induced variations. Our research aims to enhance spatial resolution by exploiting the global structure within multicontrast DWI scans and millimetric motion between acquisitions.

METHODS

We introduce a novel approach employing a "Perturbation Network" to learn subvoxel-size motions between scans, trained jointly with an implicit neural representation (INR) network. INR encodes the DWI as a continuous volumetric function, treating voxel intensities of low-resolution acquisitions as discrete samples. By evaluating this function with a finer grid, our model predicts higher-resolution signal intensities for intermediate voxel locations. The Perturbation Network's motion-correction efficacy was validated through experiments on biological phantoms and in vivo prostate scans.

RESULTS

Quantitative analyses revealed significantly higher structural similarity measures of super-resolution images to ground truth high-resolution images compared to high-order interpolation (p< $$ < $$ 0.005). In blind qualitative experiments,96 . 1 % $$ 96.1\% $$ of super-resolution images were assessed to have superior diagnostic quality compared to interpolated images.

CONCLUSION

High-resolution details in DWI can be obtained without the need for high-resolution training data. One notable advantage of the proposed method is that it does not require a super-resolution training set. This is important in clinical practice because the proposed method can easily be adapted to images with different scanner settings or body parts, whereas the supervised methods do not offer such an option.

Tensor denoising of quantitative multi-parameter mapping

PURPOSE

Quantitative multi-parameter mapping (MPM) provides maps of physical quantities representing physiologically meaningful tissue characteristics, which allows to investigate microstructure-function relationships reflecting normal or pathologic processes in the brain. However, the achievable spatial resolution and stability of MPM for basic research or clinical applications is severely constrained by SNR limits of the MR acquisition process, resulting in relatively long acquisition times. To increase SNR, we denoise MPM acquisitions using principal component analysis along tensors exploiting the Marchenko-Pastur law (tMPPCA).

METHODS

tMPPCA denoising was applied to three sets of MPM raw data before the quantification of maps of proton density, magnetization transfer saturation, R1, and R2*. The regional SNR gain for high-resolution MPM was investigated as well as reproducibility gains for clinically optimized protocols with moderate and high acceleration factors at different image resolutions.

RESULTS

Substantial noise reduction in raw data was achieved, resulting in reduced noise for quantitative mapping up to sixfold without introducing bias of mean values (below 1%). Scan-rescan fluctuations were reduced up to threefold. Denoising allowed to decrease the voxel volume fourfold at the same scan time or reduce the scan time twofold at same voxel volume without loss of sensitivity.

CONCLUSIONS

tMPPCA denoising can (a) improve of fine spatial and temporal patterns, (b) considerably reduce scan time for clinical applications, or (c) increase resolution to potentially push cutting-edge MPM protocols from the upper to the lower limit of the mesoscopic scale.

High SNR full brain relaxometry at 7T by accelerated MR-STAT

PURPOSE

To demonstrate the feasibility and robustness of the Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) framework for fast, high SNR relaxometry at 7T.

METHODS

To deploy MR-STAT on 7T-systems, we designed optimized flip-angles using the BLAKJac-framework that incorporates the SAR-constraints. Transmit RF-inhomogeneities were mitigated by including a measuredB 1 + $$ {B}_1^{+} $$ -map in the reconstruction. Experiments were performed on a gel-phantom and on five volunteers to explore the robustness of the sequence and its sensitivity toB 1 + $$ {B}_1^{+} $$ inhomogeneities. The SNR-gain at 7T was explored by comparing phantom and in vivo results to MR-STAT at 3T in terms of SNR-efficiency.

RESULTS

The higher SNR at 7T enabled two-fold acceleration with respect to current 2D MR-STAT protocols at lower field strengths. The resulting scan had whole-brain coverage, with 1 x 1 x 3 mm3 resolution (1.5 mm slice-gap) and was acquired within 3 min including theB 1 + $$ {B}_1^{+} $$ -mapping. AfterB 1 + $$ {B}_1^{+} $$ -correction, the estimated T1 and T2 in a phantom showed a mean relative error of, respectively, 1.7% and 4.4%. In vivo, the estimated T1 and T2 in gray and white matter corresponded to the range of values reported in literature with a variation over the subjects of 1.0%-2.1% (WM-GM) for T1 and 4.3%-5.3% (WM-GM) for T2. We measured a higher SNR-efficiency at 7T (R = 2) than at 3T for both T1 and T2 with, respectively, a 4.1 and 2.3 times increase in SNR-efficiency.

CONCLUSION

We presented an accelerated version of MR-STAT tailored to high field (7T) MRI using a low-SAR flip-angle train and showed high quality parameter maps with an increased SNR-efficiency compared to MR-STAT at 3T.

Increasing the scan-efficiency of pulmonary imaging at 0.55 T using iterative concomitant field and motion-corrected reconstruction

PURPOSE

To develop an iterative concomitant field and motion corrected (iCoMoCo) reconstruction for isotropic high-resolution UTE pulmonary imaging at 0.55 T.

METHODS

A free-breathing golden-angle stack-of-spirals UTE sequence was used to acquire data for 8 min with prototype and commercial 0.55 T MRI scanners. The data was binned into 12 respiratory phases based on superior-inferior navigator readouts. The previously published iterative motion corrected (iMoCo) reconstruction was extended to include concomitant field correction directly in the cost function. The reconstruction was implemented within the Gadgetron framework for inline reconstruction. Data were retrospectively reconstructed to simulate scan times of 2, 4, 6, and 8 min. Image quality was assessed using apparent SNR and image sharpness. The technique was evaluated in healthy volunteers and patients with known lung pathology including coronavirus disease 2019 infection, chronic granulomatous disease, lymphangioleiomyomatosis, and lung nodules.

RESULTS

The technique provided diagnostic-quality images, and image quality was maintained with a slight loss in SNR for simulated scan times down to 4 min. Parenchymal apparent SNR was 4.33 ± 0.57, 5.96 ± 0.65, 7.36 ± 0.64, and 7.87 ± 0.65 using iCoMoCo with scan times of 2, 4, 6, and 8 min, respectively. Image sharpness at the diaphragm was comparable between iCoMoCo and reference images. Concomitant field corrections visibly improved the sharpness of anatomical structures away from the isocenter. Inline image reconstruction and artifact correction were achieved in <5 min.

CONCLUSION

The proposed iCoMoCo pulmonary imaging technique can generate diagnostic quality images with 1.75 mm isotropic resolution in less than 5 min using a 6-min acquisition, on a 0.55 T scanner.

Investigating apparent differences between standard DKI and axisymmetric DKI and its consequences for biophysical parameter estimates

PURPOSE

The purpose of the study is to identify differences between axisymmetric diffusion kurtosis imaging (DKI) and standard DKI, their consequences for biophysical parameter estimates, and the protocol choice influence on parameter estimation.

METHODS

Noise-free and noisy, synthetic diffusion MRI human brain data is simulated using standard DKI for a standard and the fast "199" acquisition protocol. First the noise-free "baseline" difference between both DKI models is estimated and the influence of fiber complexity is investigated. Noisy data is used to establish the signal-to-noise ratio at which the baseline difference exceeds noise variability. The influence of protocol choices and denoising is investigated. The five axisymmetric DKI tensor metrics (AxTM), the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor are used to compare both DKI models. Additionally, the baseline difference is also estimated for the five parameters of the WMTI-Watson model.

RESULTS

The parallel and perpendicular kurtosis and all of the WMTI-Watson parameters had large baseline differences. Using a Westin or FA mask reduced the number of voxels with large baseline difference, that is, by selecting voxels with less complex fibers. For the noisy data, precision was worsened by the fast "199" protocol but adaptive denoising can help counteract these effects.

CONCLUSION

For the diffusivities and mean of the kurtosis tensor, axisymmetric DKI with a standard protocol delivers similar results as standard DKI. Fiber complexity is one main driver of the baseline differences. Using the "199" protocol worsens precision in noisy data but adaptive denoising mitigates these effects.

A theoretical framework to investigate the effect of high permittivity materials in MRI using anatomy-mimicking cylinders

PURPOSE

Recent numerical and empirical results proved that high permittivity materials (HPM) used in pads placed near the subject or directly integrated with coils can increase the SNR and reduce the specific absorption rate (SAR) in MRI. In this paper, we propose an analytical investigation of the effect on the magnetic field distribution of a layer of HPM surrounding an anatomy-mimicking cylindrical sample.

METHODS

The study is based on a reformulation of the Mie scattering for cylindrical geometry, following an approach recently introduced for spherical samples. The total field in each medium is decomposed in terms of inward and outward electromagnetic waves, and the fields are expressed as series of cylindrical harmonics, whose coefficients can be interpreted as classical reflection and transmission coefficients.

RESULTS

Our new formulation allows a quantitative evaluation of the effect of the HPM layer for varying permittivity and thickness, and it provides an intuitive understanding of such effect in terms of propagation and scattering of the RF field.

CONCLUSION

We show how HPM can filter out the modes that only contribute to the noise or RF power deposition, resulting in higher SNR or lower SAR, respectively. Our proposed framework provides physical insight on how to properly design HPM for MRI applications.

Submillimeter balanced SSFP BOLD-functional MRI accelerated with 3D stack-of-spirals at 9.4 T

PURPOSE

This work aims to improve the speed of balanced SSFP (bSSFP) acquisition with segmented 3D stack-of-spirals for functional brain studies at ultrahigh field.

METHODS

Functional experiments were performed with an accelerated 3D stack-of-spirals sequence with water excitation for fat suppression. The resulting data were reconstructed using an iterative algorithm with corrections for system imperfections such as trajectory deviations and B0 inhomogeneity. In the first set of experiments, we evaluated the signal change and stability with respect to echo and TR for a full-field checkerboard stimulus. To demonstrate the high spatio-temporal resolution of the developed method, the results of three optimized protocols at submillimeter resolution (0.6-mm isotropic and 0.8-mm isotropic) and at 1.2 mm isotropic resolution for whole-brain coverage were shown.

RESULTS

Water excitation and the model-based iterative reconstruction improved image quality. The BOLD-related signal changes increased with longer TE and longer TR. We observed an increase in thermal noise performance at lower TE and higher TR. However, signal stability deteriorates at higher TE and TR. Therefore, optimized protocols used shorter TE and moderately long TR to maximize the sensitivity and speed. Reproducible activations were detected along the gray-matter gyri in the submillimeter protocols with a median signal change of approximately 4% across subjects.

CONCLUSIONS

Three-dimensional stack-of-spirals enables passband balanced SSFP functional imaging at a much higher spatial and temporal scale, compared with conventional spoiled gradient-echo train sequences.

Referenceless Nyquist ghost correction outperforms standard navigator-based method and improves efficiency of in vivo diffusion tensor cardiovascular magnetic resonance

PURPOSE

The study aims to assess the potential of referenceless methods of EPI ghost correction to accelerate the acquisition of in vivo diffusion tensor cardiovascular magnetic resonance (DT-CMR) data using both computational simulations and data from in vivo experiments.

METHODS

Three referenceless EPI ghost correction methods were evaluated on mid-ventricular short axis DT-CMR spin echo and STEAM datasets from 20 healthy subjects at 3T. The reduced field of view excitation technique was used to automatically quantify the Nyquist ghosts, and DT-CMR images were fit to a linear ghost model for correction.

RESULTS

Numerical simulation showed the singular value decomposition (SVD) method is the least sensitive to noise, followed by Ghost/Object method and entropy-based method. In vivo experiments showed significant ghost reduction for all correction methods, with referenceless methods outperforming navigator methods for both spin echo and STEAM sequences at b = 32, 150, 450, and 600  smm - 2 $$ {\mathrm{smm}}^{-2} $$ . It is worth noting that as the strength of the diffusion encoding increases, the performance gap between the referenceless method and the navigator-based method diminishes.

CONCLUSION

Referenceless ghost correction effectively reduces Nyquist ghost in DT-CMR data, showing promise for enhancing the accuracy and efficiency of measurements in clinical practice without the need for any additional reference scans.

Design and analysis of self-priming extension DNA hairpin probe for miRNA detection based on a unified dynamic programming framework

MicroRNAs (miRNAs) are potential biomarkers for cancer diagnosis and prognosis, methods for detecting miRNAs with high sensitivity, selectivity, and stability are urgently needed. Various nucleic acid probes that have traditionally been for this purpose suffer several drawbacks, including inefficient signal-to-noise ratios and intensities, high cost, and time-consuming method establishment. Computing tools used for investigating the thermodynamics of DNA hybridization reactions can accurately predict the secondary structure of DNA and the interactions between DNA molecules. Herein, NUPACK was used to design a series of nucleic acid probes and develop a phosphorothioated-terminal hairpin formation and self-priming extension (PS-THSP) signal amplification strategy, which enabled the ultrasensitive detection of miR-200a in serum samples. The free and binding energies of the DNA detection probes calculated using NUPACK, as well as the biological experimental results, were considered synthetically to select the best sequence and experimental conditions. A unified dynamic programming framework, NUPACK analysis and the experimental data, were complementary and improved the designed model in all respects. Our study demonstrates the feasibility of using computer technology such as NUPACK to simplify the experimental process and provide intuitive results.

A practical chromogenic and fluorogenic dual-mode sensing platform for rapid quantification of sulfite in food

Sulfur dioxide (SO2) and its derivatives (HSO3- and SO32-) are widely used in food-processing. Whereas excessive consumption of sulfur dioxide and its derivatives (>0.70 mg·kg-1day-1) severely endangers human health. In this work, we rationally constructed a practical dual-mode probe (dicyanomethylene)-1-methyl-1,4-dihydroquinolin-2-yl)vinyl)-1-methylquinolinium (QMN), which underwent a specific 1, 4-Michael addition with sulfite to afford a noticeable color change from pale yellow to red along with a high-contrast fluorescence turn-on response at 598 nm. QMN has the advantages of rapid response, high signal-to-noise ratio, excellent selectivity, good water-solubility, large Stokes shift and low detection limit (LOD = 31.9 nM). QMN has been successfully used to on-site visually determine sulfite in a diversity of foods with satisfactory recoveries (91.33-111.33 %) and high accuracy (93.74-98.71 %). Furthermore, a portable smartphone-based fluorescence sensing platform was fabricated for on-site determination of sulfite in food with good performance.

Improved image quality in contrast-enhanced 3D-T1 weighted sequence by compressed sensing-based deep-learning reconstruction for the evaluation of head and neck

PURPOSE

To assess the utility of deep learning (DL)-based image reconstruction with the combination of compressed sensing (CS) denoising cycle by comparing images reconstructed by conventional CS-based method without DL in fat-suppressed (Fs)-contrast enhanced (CE) three-dimensional (3D) T1-weighted images (T1WIs) of the head and neck.

MATERIALS AND METHODS

We retrospectively analyzed the cases of 39 patients who had undergone head and neck Fs-CE 3D T1WI applying reconstructions based on conventional CS and CS augmented by DL, respectively. In the qualitative assessment, we evaluated overall image quality, visualization of anatomical structures, degree of artifacts, lesion conspicuity, and lesion edge sharpness based on a five-point system. In the quantitative assessment, we calculated the signal-to-noise ratios (SNRs) of the lesion and the posterior neck muscle and the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle.

RESULTS

For all items of the qualitative analysis, significantly higher scores were awarded to images with DL-based reconstruction (p < 0.001). In the quantitative analysis, DL-based reconstruction resulted in significantly higher values for both the SNR of lesions (p < 0.001) and posterior neck muscles (p < 0.001). Significantly higher CNRs were also observed in images with DL-based reconstruction (p < 0.001).

CONCLUSION

DL-based image reconstruction integrating into the CS-based denoising cycle offered superior image quality compared to the conventional CS method. This technique will be useful for the assessment of patients with head and neck disease.

FSformer: A combined frequency separation network and transformer for LDCT denoising

Low-dose computed tomography (LDCT) has been widely concerned in the field of medical imaging because of its low radiation hazard to humans. However, under low-dose radiation scenarios, a large amount of noise/artifacts are present in the reconstructed image, which reduces the clarity of the image and is not conducive to diagnosis. To improve the LDCT image quality, we proposed a combined frequency separation network and Transformer (FSformer) for LDCT denoising. Firstly, FSformer decomposes the LDCT images into low-frequency images and multi-layer high-frequency images by frequency separation blocks. Then, the low-frequency components are fused with the high-frequency components of different layers to remove the noise in the high-frequency components with the help of the potential texture of low-frequency parts. Next, the estimated noise images can be obtained by using Transformer stage in the frequency aggregation denoising block. Finally, they are fed into the reconstruction prediction block to obtain improved quality images. In addition, a compound loss function with frequency loss and Charbonnier loss is used to guide the training of the network. The performance of FSformer has been validated and evaluated on AAPM Mayo dataset, real Piglet dataset and clinical dataset. Compared with previous representative models in different architectures, FSformer achieves the optimal metrics with PSNR of 33.7714 dB and SSIM of 0.9254 on Mayo dataset, the testing time is 1.825 s. The experimental results show that FSformer is a state-of-the-art (SOTA) model with noise/artifact suppression and texture/organization preservation. Moreover, the model has certain robustness and can effectively improve LDCT image quality.

Recent developments in denoising medical images using deep learning: An overview of models, techniques, and challenges

Medical imaging plays a critical role in diagnosing and treating various medical conditions. However, interpreting medical images can be challenging even for expert clinicians, as they are often degraded by noise and artifacts that can hinder the accurate identification and analysis of diseases, leading to severe consequences such as patient misdiagnosis or mortality. Various types of noise, including Gaussian, Rician, and Salt-pepper noise, can corrupt the area of interest, limiting the precision and accuracy of algorithms. Denoising algorithms have shown the potential in improving the quality of medical images by removing noise and other artifacts that obscure essential information. Deep learning has emerged as a powerful tool for image analysis and has demonstrated promising results in denoising different medical images such as MRIs, CT scans, PET scans, etc. This review paper provides a comprehensive overview of state-of-the-art deep learning algorithms used for denoising medical images. A total of 120 relevant papers were reviewed, and after screening with specific inclusion and exclusion criteria, 104 papers were selected for analysis. This study aims to provide a thorough understanding for researchers in the field of intelligent denoising by presenting an extensive survey of current techniques and highlighting significant challenges that remain to be addressed. The findings of this review are expected to contribute to the development of intelligent models that enable timely and accurate diagnoses of medical disorders. It was found that 40% of the researchers used models based on Deep convolutional neural networks to denoise the images, followed by encoder-decoder (18%) and other artificial intelligence-based techniques (15%) (Like DIP, etc.). Generative adversarial network was used by 12%, transformer-based approaches (13%) and multilayer perceptron was used by 2% of the researchers. Moreover, Gaussian noise was present in 35% of the images, followed by speckle noise (16%), poisson noise (14%), artifacts (10%), rician noise (7%), Salt-pepper noise (6%), Impulse noise (3%) and other types of noise (9%). While the progress in developing novel models for the denoising of medical images is evident, significant work remains to be done in creating standardized denoising models that perform well across a wide spectrum of medical images. Overall, this review highlights the importance of denoising medical images and provides a comprehensive understanding of the current state-of-the-art deep learning algorithms in this field.

Normalized Spatial Autocorrelation in Ultrasound B-Mode Imaging for Point-Scatterer Detection

OBJECTIVE

Point-scatterer detection plays a key role in medical ultrasound B-mode imaging. Speckle noise and insufficient spatial resolution are important factors affecting point-scatterer detection. To address this issue, normalized spatial autocorrelation in ultrasound B-mode imaging (NSACB) is proposed.

METHODS

First, the acquired data are pre-processed by adding Gaussian white noise (GWN) with a certain signal-to-Gaussian white noise ratio (SGWNR). Next, normalized spatial autocorrelation is applied to the pre-processed data, and the data are divided into several new signals with different spatial lags. Then, the new signals are performed unsigned delay multiply and sum. Finally, the NSACB beamformed data are bandpass filtered by extracting the frequency component around twice the center frequency. Simulated and in vitro experiments were designed for validation.

RESULTS

Simulations revealed that the lateral resolution of NSACB measured by the -6-dB mainlobe width can reach as high as 11.11% of delay and sum (DAS), 25.01% of filtered delay multiply and sum (F-DMAS) and 50% of LAG-FDMAS-SCF. The sidelobe level of the NSACB can be reduced at most by 28 dB. Experimental results of simple and complex scatterer phantoms indicate the image resolution of the proposed NSACB can even reach up to 18.76% of DAS, 27.28% of F-DMAS and 14.29% of LAG-FDMAS-SCF. Compared with these methods, the proposed NSACB can reduce the sidelobe level at least by 18 dB.

CONCLUSION

Although the proposed method causes loss of the ability to observe hypo-echoic structures, these results suggest future work to determine the ability to detect breast microcalcifications, kidney stones, biopsy needle tracking and other scenarios requiring scatterer detection.

Complex multi-dimensional integration for T2* and R2* mapping

A dual Multi-Dimensional Integration (dMDI) method was proposed and demonstrated for T2* and R2* mapping. By constructing and jointly using both the original MDI term and an inversed MDI term, T2* and R2* mapping can be performed independently with intrinsic background noise suppression and spike elimination, allowing for high quantitative accuracy and robustness over a wide range of T2*. dMDI was compared to original MDI and curve fitting methods in terms of quantitative specificity, accuracy, reliability and computational efficiency. All methods were tested and compared via simulation and in vivo data. With high signal-to-noise-ratio (SNR), the proposed dMDI method yielded T2*and R2* values similar to curve fitting methods. For low SNR and background noise signals, the dMDI yielded low T2* and R2* values, thus effectively suppressing all background noise. Virtually zero spikes were observed in dMDI T2* and R2* maps in all simulation and imaging results. The dMDI method has the potential to provide improved and reliable T2* and R2* mapping results in routine and SNR-challenging scenarios.

A stable, highly concentrated fluorous nanoemulsion formulation for in vivo cancer imaging via 19F-MRI

Magnetic resonance imaging (MRI) is a routine diagnostic modality in oncology that produces excellent imaging resolution and tumor contrast without the use of ionizing radiation. However, improved contrast agents are still needed to further increase detection sensitivity and avoid toxicity/allergic reactions associated with paramagnetic metal contrast agents, which may be seen in a small percentage of the human population. Fluorine-19 (19F)-MRI is at the forefront of the developing MRI methodologies due to near-zero background signal, high natural abundance of 100%, and unambiguous signal specificity. In this study, we have developed a colloidal nanoemulsion (NE) formulation that can encapsulate high volumes of the fluorous MRI tracer, perfluoro-[15-crown-5]-ether (PFCE) (35% v/v). These nanoparticles exhibit long-term (at least 100 days) stability and high PFCE loading capacity in formulation with our semifluorinated triblock copolymer, M2F8H18. With sizes of approximately 200 nm, these NEs enable in vivo delivery and passive targeting to tumors. Our diagnostic formulation, M2F8H18/PFCE NE, yielded in vivo 19F-MR images with a high signal-to-noise ratio up to 100 in a tumor-bearing mouse model at clinically relevant scan times. M2F8H18/PFCE NE circulated stably in the vasculature, accumulated in high concentration of an estimated 4-9 × 1017 19F spins/voxel at the tumor site, and cleared from most organs over the span of 2 weeks. Uptake by the mononuclear phagocyte system to the liver and spleen was also observed, most likely due to particle size. These promising results suggest that M2F8H18/PFCE NE is a favorable 19F-MR diagnostic tracer for further development in oncological studies and potential clinical translation.

Dual-TranSpeckle: Dual-pathway transformer based encoder-decoder network for medical ultrasound image despeckling

The majority of existing deep learning-based image denoising algorithms mainly focus on processing the overall image features, ignoring the fine differences between the semantic and pixel features. Hence, we propose Dual-TranSpeckle (DTS), a medical ultrasound image despeckling network built on a dual-path Transformer. The DTS introduces two different paths, named "semantic path" and "pixel path," to facilitate the parallel transfer of feature information within the image. The semantic path passes a global view of the input semantic features, and the image features are passed through a Semantic Block to extract global semantic information from pixel-level features. The pixel path is employed to transmit finer-grained pixel features. Within the dual-path network framework, two essential modules, namely Dual Block and Merge Block, are designed. These leverage the Transformer architecture during the encoding and decoding stages. The Dual Block module facilitates information interaction between the semantic and pixel features by considering the interdependencies across both paths. Meanwhile, the Merge Block module enables parallel transfer of feature information by merging the dual path features, thereby facilitating the self-attention calculations for the overall feature representation. Our DTS is extensively evaluated on two public datasets and one private dataset. The DTS network demonstrates significant enhancements in quantitative evaluation results in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and naturalness image quality evaluator (NIQE). Furthermore, our qualitative analysis confirms that the DTS has significant improvements in despeckling performance, effectively suppressing speckle noise while preserving essential image structures.

The Effects of Signal to Noise Ratio, T60 , Wide-Dynamic Range Compression Speed, and Digital Noise Reduction in a Virtual Restaurant Setting

OBJECTIVES

Hearing aid processing in realistic listening environments is difficult to study effectively. Often the environment is unpredictable or unknown, such as in wearable aid trials with subjective report by the wearer. Some laboratory experiments create listening environments to exert tight experimental control, but those environments are often limited by physical space, a small number of sound sources, or room absorptive properties. Simulation techniques bridge this gap by providing greater experimental control over listening environments, effectively bringing aspects of the real-world into the laboratory. This project used simulation to study the effects of wide-dynamic range compression (WDRC) and digital noise reduction (DNR) on speech intelligibility in a reverberant environment with six spatialized competing talkers. The primary objective of this study was to determine the efficacy of WDRC and DNR in a complex listening environment using virtual auditory space techniques.

DESIGN

Participants of greatest interest were listeners with hearing impairment. A group of listeners with clinically normal hearing was included to assess the effects of the simulation absent the complex effects of hearing loss. Virtual auditory space techniques were used to simulate a small restaurant listening environment with two different reverberation times (0.8 and 1.8 sec) in a range of signal to noise ratios (SNRs) (-8.5 to 11.5 dB SNR). Six spatialized competing talkers were included to further enhance realism. A hearing aid simulation was used to examine the degree to which speech intelligibility was affected by slow and fast WDRC in conjunction with the presence or absence of DNR. The WDRC and DNR settings were chosen to be reasonable estimates of hearing aids currently available to consumers.

RESULTS

A WDRC × DNR × Hearing Status interaction was observed, such that DNR was beneficial for speech intelligibility when combined with fast WDRC speeds, but DNR was detrimental to speech intelligibility when WDRC speeds were slow. The pattern of the WDRC × DNR interaction was observed for both listener groups. Significant main effects of reverberation time and SNR were observed, indicating better performance with lower reverberation times and more positive SNR.

CONCLUSIONS

DNR reduced low-amplitude noise before WDRC-amplified the low-intensity portions of the signal, negating one potential downside of fast WDRC and leading to an improvement in speech intelligibility in this simulation. These data suggest that, in some real-world environments that include both reverberation and noise, older listeners with hearing impairment may find speech to be more intelligible if DNR is activated when the hearing aid has fast compression time constants. Additional research is needed to determine the appropriate DNR strength and to confirm results in wearable hearing aids and a wider range of listening environments.

Imaging of intracranial hemorrhage in photon counting computed tomography using virtual monoenergetic images

PURPOSE

To determine the optimal virtual monoenergetic image (VMI) for detecting and assessing intracranial hemorrhage in unenhanced photon counting CT of the head based on the evaluation of quantitative and qualitative image quality parameters.

METHODS

Sixty-three patients with acute intracranial hemorrhage and unenhanced CT of the head were retrospectively included. In these patients, 35 intraparenchymal, 39 intraventricular, 30 subarachnoidal, and 43 subdural hemorrhages were selected. VMIs were reconstructed using all available monoenergetic reconstruction levels (40-190 keV). Multiple regions of interest measurements were used for evaluation of the overall image quality, and signal, noise, signal-to-noise-ratio (SNR), and contrast-to-noise-ratio (CNR) of intracranial hemorrhage. Based on the results of the quantitative analysis, specific VMIs were rated by five radiologists on a 5-point Likert scale.

RESULTS

Signal, noise, SNR, and CNR differed significantly between different VMIs (p < 0.001). Maximum CNR for intracranial hemorrhage was reached in VMI with keV levels > 120 keV (intraparenchymal 143 keV, intraventricular 164 keV, subarachnoidal 124 keV, and subdural hemorrhage 133 keV). In reading, no relevant superiority in the detection of hemorrhage could be demonstrated using VMIs above 66 keV.

CONCLUSION

For the detection of hemorrhage in unenhanced CT of the head, the quantitative analysis of the present study on photon counting CT is generally consistent with the findings from dual-energy CT, suggesting keV levels just above 120 keV and higher depending on the location of the hemorrhage. However, on the basis of the qualitative analyses, no reliable statement can yet be made as to whether an additional VMI with higher keV is truly beneficial in everyday clinical practice.

Optimizing Microfluidic Impedance Cytometry by Bypass Electrode Layout Design

Microfluidic impedance cytometry (MIC) has emerged as a popular technique for single-cell analysis. Traditional MIC electrode designs consist of a pair of (or three) working electrodes, and their detection performance needs further improvements for microorganisms. In this study, we designed an 8-electrode MIC device in which the center pair was defined as the working electrode, and the connection status of bypass electrodes could be changed. This allowed us to compare the performance of layouts with no bypasses and those with floating or grounding electrodes by simulation and experiment. The results of detecting Φ 5 μm beads revealed that both the grounding and the floating electrode outperformed the no bypass electrode, and the grounding electrode demonstrated the best signal-to-noise ratio (SNR), coefficient of variation (CV), and detection sensitivity. Furthermore, the effects of different bypass grounding areas (numbers of grounding electrodes) were investigated. Finally, particles passing at high horizontal positions can be detected, and Φ 1 μm beads can be measured in a wide channel (150 μm) using a fully grounding electrode, with the sensitivity of bead volume detection reaching 0.00097%. This provides a general MIC electrode optimization technology for detecting smaller particles, even macromolecular proteins, viruses, and exosomes in the future.

Iterative image reconstruction algorithm analysis for optical CT radiochromic gel dosimetry

Background.Modern radiation therapy technologies aim to enhance radiation dose precision to the tumor and utilize hypofractionated treatment regimens. Verifying the dose distributions associated with these advanced radiation therapy treatments remains an active research area due to the complexity of delivery systems and the lack of suitable three-dimensional dosimetry tools. Gel dosimeters are a potential tool for measuring these complex dose distributions. A prototype tabletop solid-tank fan-beam optical CT scanner for readout of gel dosimeters was recently developed. This scanner does not have a straight raypath from source to detector, thus images cannot be reconstructed using filtered backprojection (FBP) and iterative techniques are required.Purpose.To compare a subset of the top performing algorithms in terms of image quality and quantitatively determine the optimal algorithm while accounting for refraction within the optical CT system. The following algorithms were compared: Landweber, superiorized Landweber with the fast gradient projection perturbation routine (S-LAND-FGP), the fast iterative shrinkage/thresholding algorithm with total variation penalty term (FISTA-TV), a monotone version of FISTA-TV (MFISTA-TV), superiorized conjugate gradient with the nonascending perturbation routine (S-CG-NA), superiorized conjugate gradient with the fast gradient projection perturbation routine (S-CG-FGP), superiorized conjugate gradient with with two iterations of CG performed on the current iterate and the nonascending perturbation routine (S-CG-2-NA).Methods.A ray tracing simulator was developed to track the path of light rays as they traverse the different mediums of the optical CT scanner. Two clinical phantoms and several synthetic phantoms were produced and used to evaluate the reconstruction techniques under known conditions. Reconstructed images were analyzed in terms of spatial resolution, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal non-uniformity (SNU), mean relative difference (MRD) and reconstruction time. We developed an image quality based method to find the optimal stopping iteration window for each algorithm. Imaging data from the prototype optical CT scanner was reconstructed and analysed to determine the optimal algorithm for this application.Results.The optimal algorithms found through the quantitative scoring metric were FISTA-TV and S-CG-2-NA. MFISTA-TV was found to behave almost identically to FISTA-TV however MFISTA-TV was unable to resolve some of the synthetic phantoms. S-CG-NA showed extreme fluctuations in the SNR and CNR values. S-CG-FGP had large fluctuations in the SNR and CNR values and the algorithm has less noise reduction than FISTA-TV and worse spatial resolution than S-CG-2-NA. S-LAND-FGP had many of the same characteristics as FISTA-TV; high noise reduction and stability from over iterating. However, S-LAND-FGP has worse SNR, CNR and SNU values as well as longer reconstruction time. S-CG-2-NA has superior spatial resolution to all algorithms while still maintaining good noise reduction and is uniquely stable from over iterating.Conclusions.Both optimal algorithms (FISTA-TV and S-CG-2-NA) are stable from over iterating and have excellent edge detection with ESF MTF 50% values of 1.266 mm-1and 0.992 mm-1. FISTA-TV had the greatest noise reduction with SNR, CNR and SNU values of 424, 434 and 0.91 × 10-4, respectively. However, low spatial resolution makes FISTA-TV only viable for large field dosimetry. S-CG-2-NA has better spatial resolution than FISTA-TV with PSF and LSF MTF 50% values of 1.581 mm-1and 0.738 mm-1, but less noise reduction. S-CG-2-NA still maintains good SNR, CNR, and SNU values of 168, 158 and 1.13 × 10-4, respectively. Thus, S-CG-2-NA is a well rounded reconstruction algorithm that would be the preferable choice for small field dosimetry.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals

Background

Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown.

Methods

This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy.

Results

The results indicate that the Modified Hodges detector - a simplified version of the threshold-based Hodges detector introduced in the current study - was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges.

Conclusions

Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

Three-row MRI receive array with remote circuitry to preserve radiation transparency

Objective.Up to this point, 1.5 T linac-compatible coil array layouts have been restricted to one or two rows of coils because of the desire to place radiation-opaque circuitry adjacent to the coils and outside the window through which the linac beam travels. Such layouts can limit parallel imaging performance. The purpose of this work was to design and build a three-row array in which remotely located circuits permitted a central row of coils while preserving the radiolucent window.Approach.The remote circuits consisted of a phase shifter to cancel the phase introduced by the coaxial link between the circuit and coil, followed by standard components for tuning, matching, detuning, and preamplifier decoupling. Tests were performed to compare prototype single-channel coils with remote or local circuits, which were followed by tests comparing two and three-row arrays .Main results.The single-channel coil with the remote circuit maintained 85% SNR at depths of 30 mm or more as compared to a coil with local circuit. The three-row array provided similar SNR as the two-row array, along with geometry factor advantages for parallel imaging acceleration in the head-foot direction.Significance.The remote circuit strategy could potentially support future MR-linac arrays by allowing greater flexibility in array layout compared to those confined by local circuits, which can be leveraged for parallel imaging acceleration.

A cardiac MRI motion artifact reduction method based on edge enhancement network

Cardiac magnetic resonance imaging (MRI) usually requires a long acquisition time. The movement of the patients during MRI acquisition will produce image artifacts. Previous studies have shown that clear MR image texture edges are of great significance for pathological diagnosis. In this paper, a motion artifact reduction method for cardiac MRI based on edge enhancement network is proposed. Firstly, the four-plane normal vector adaptive fractional differential mask is applied to extract the edge features of blurred images. The four-plane normal vector method can reduce the noise information in the edge feature maps. The adaptive fractional order is selected according to the normal mean gradient and the local Gaussian curvature entropy of the images. Secondly, the extracted edge feature maps and blurred images are input into the de-artifact network. In this network, the edge fusion feature extraction network and the edge fusion transformer network are specially designed. The former combines the edge feature maps with the fuzzy feature maps to extract the edge feature information. The latter combines the edge attention network and the fuzzy attention network, which can focus on the blurred image edges. Finally, extensive experiments show that the proposed method can obtain higher peak signal-to-noise ratio and structural similarity index measure compared to state-of-art methods. The de-artifact images have clear texture edges.

Solvent-Free and Cost-Efficient Fabrication of a High-Performance Nanocomposite Sensor for Recording of Electrophysiological Signals

Carbon nanotube (CNT)-based nanocomposites have found applications in making sensors for various types of physiological sensing. However, the sensors' fabrication process is usually complex, multistep, and requires longtime mixing and hazardous solvents that can be harmful to the environment. Here, we report a flexible dry silver (Ag)/CNT/polydimethylsiloxane (PDMS) nanocomposite-based sensor made by a solvent-free, low-temperature, time-effective, and simple approach for electrophysiological recording. By mechanical compression and thermal treatment of Ag/CNT, a connected conductive network of the fillers was formed, after which the PDMS was added as a polymer matrix. The CNTs make a continuous network for electrons transport, endowing the nanocomposite with high electrical conductivity, mechanical strength, and durability. This process is solvent-free and does not require a high temperature or complex mixing procedure. The sensor shows high flexibility and good conductivity. High-quality electroencephalography (EEG) and electrooculography (EOG) were performed using fabricated dry sensors. Our results show that the Ag/CNT/PDMS sensor has comparable skin-sensor interface impedance with commercial Ag/AgCl-coated dry electrodes, better performance for noninvasive electrophysiological signal recording, and a higher signal-to-noise ratio (SNR) even after 8 months of storage. The SNR of electrophysiological signal recording was measured to be 26.83 dB for our developed sensors versus 25.23 dB for commercial Ag/AgCl-coated dry electrodes. Our process of compress-heating the functional fillers provides a universal approach to fabricate various types of nanocomposites with different nanofillers and desired electrical and mechanical properties.

On the robustness of detective quantum efficiency within the limits of IEC 61267 RQA standard radiation qualities

IEC 61267 allows a certain leeway regarding the establishment of radiation qualities in order to enable the use of X-ray tubes having different anode angles and inherent filtrations. This allowance has a direct impact on the calculation of the detective quantum efficiency and may potentially complicate any comparison of different imaging detectors based on this quantity. This work investigates this effect by applying computational methods. To this end, an algorithm was implemented to calculate the variation of the squared signal-to-noise ratio per air kerma for RQA standard radiation qualities and to deduce corresponding uncertainties based on GUM Supplement 2. For RQA standard radiation qualities, the results show standard uncertainties for the squared signal-to-noise ratio per air kerma of between 0.05 and 2.1%. Comparing imaging detectors based on detective quantum efficiency is associated with substantial uncertainty for some radiation qualities. This is due to the different photon fluences with respect to energy that are allowed by IEC 61267 for identical standard radiation qualities.

Cross noise level PET denoising with continuous adversarial domain generalization

Objective.Performing positron emission tomography (PET) denoising within the image space proves effective in reducing the variance in PET images. In recent years, deep learning has demonstrated superior denoising performance, but models trained on a specific noise level typically fail to generalize well on different noise levels, due to inherent distribution shifts between inputs. The distribution shift usually results in bias in the denoised images. Our goal is to tackle such a problem using a domain generalization technique.Approach.We propose to utilize the domain generalization technique with a novel feature space continuous discriminator (CD) for adversarial training, using the fraction of events as a continuous domain label. The core idea is to enforce the extraction of noise-level invariant features. Thus minimizing the distribution divergence of latent feature representation for different continuous noise levels, and making the model general for arbitrary noise levels. We created three sets of 10%, 13%-22% (uniformly randomly selected), or 25% fractions of events from 9718F-MK6240 tau PET studies of 60 subjects. For each set, we generated 20 noise realizations. Training, validation, and testing were implemented using 1400, 120, and 420 pairs of 3D image volumes from the same or different sets. We used 3D UNet as the baseline and implemented CD to the continuous noise level training data of 13%-22% set.Main results.The proposed CD improves the denoising performance of our model trained in a 13%-22% fraction set for testing in both 10% and 25% fraction sets, measured by bias and standard deviation using full-count images as references. In addition, our CD method can improve the SSIM and PSNR consistently for Alzheimer-related regions and the whole brain.Significance.To our knowledge, this is the first attempt to alleviate the performance degradation in cross-noise level denoising from the perspective of domain generalization. Our study is also a pioneer work of continuous domain generalization to utilize continuously changing source domains.

IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction

Objective.Multi-contrast magnetic resonance imaging (MC MRI) can obtain more comprehensive anatomical information of the same scanning object but requires a longer acquisition time than single-contrast MRI. To accelerate MC MRI speed, recent studies only collect partial k-space data of one modality (target contrast) to reconstruct the remaining non-sampled measurements using a deep learning-based model with the assistance of another fully sampled modality (reference contrast). However, MC MRI reconstruction mainly performs the image domain reconstruction with conventional CNN-based structures by full supervision. It ignores the prior information from reference contrast images in other sparse domains and requires fully sampled target contrast data. In addition, because of the limited receptive field, conventional CNN-based networks are difficult to build a high-quality non-local dependency.Approach.In the paper, we propose an Image-Wavelet domain ConvNeXt-based network (IWNeXt) for self-supervised MC MRI reconstruction. Firstly, INeXt and WNeXt based on ConvNeXt reconstruct undersampled target contrast data in the image domain and refine the initial reconstructed result in the wavelet domain respectively. To generate more tissue details in the refinement stage, reference contrast wavelet sub-bands are used as additional supplementary information for wavelet domain reconstruction. Then we design a novel attention ConvNeXt block for feature extraction, which can capture the non-local information of the MC image. Finally, the cross-domain consistency loss is designed for self-supervised learning. Especially, the frequency domain consistency loss deduces the non-sampled data, while the image and wavelet domain consistency loss retain more high-frequency information in the final reconstruction.Main results.Numerous experiments are conducted on the HCP dataset and the M4Raw dataset with different sampling trajectories. Compared with DuDoRNet, our model improves by 1.651 dB in the peak signal-to-noise ratio.Significance.IWNeXt is a potential cross-domain method that can enhance the accuracy of MC MRI reconstruction and reduce reliance on fully sampled target contrast images.

Strategically Acquired Gradient Echo (STAGE) Imaging, part IV: Constrained Reconstruction of White Noise (CROWN) Processing as a Means to Improve Signal-to-Noise in STAGE Imaging at 3 Tesla

Increasing the signal-to-noise ratio (SNR) has always been of critical importance for magnetic resonance imaging. Although increasing field strength provides a linear increase in SNR, it is more and more costly as field strength increases. Therefore, there is a major effort today to use signal processing methods to improve SNR since it is more efficient and economical. There are a variety of methods to improve SNR such as averaging the data at the expense of imaging time, or collecting the data with a lower resolution, all of these methods, including imaging processing methods, usually come at the expense of loss of image detail or image blurring. Therefore, we developed a new mathematical approach called CROWN (Constrained Reconstruction of White Noise) to enhance SNR without loss of structural detail and without affecting scanning time. In this study, we introduced and tested the concept behind CROWN specifically for STAGE (strategically acquired gradient echo) imaging. The concept itself is presented first, followed by simulations to demonstrate its theoretical effectiveness. Then the SNR improvement on proton spin density (PSD) and R2⁎ maps was investigated using brain STAGE data acquired from 10 healthy controls (HCs) and 10 patients with Parkinson's disease (PD). For the PSD and R2* maps, the SNR and CNR between white matter and gray matter were improved by a factor of 1.87 ± 0.50 and 1.72 ± 0.88, respectively. The white matter hyperintensity lesions in PD patients were more clearly defined after CROWN processing. Using these improved maps, simulated images for any repeat time, echo time or flip angle can be created with improved SNR. The potential applications of this technology are to trade off the increased SNR for higher resolution images and/or faster imaging.

Predicted effects of image acquisition and analysis conditions on DTMRI tractography-based muscle architecture estimates

PURPOSE

To quantify the effects of the intrinsic signal pattern, image acquisition conditions, and data analysis conditions on diffusion-tensor MRI (DTMRI) tractography-based muscle architecture estimates using a sampling-reconstruction assessment framework.

METHODS

Numerical models of muscles were constructed with realistic architectural properties. DTMRI signals were computed at signal-to-noise ratio (SNR) of 24-96 and common voxel sizes. Fiber tracking was performed, and the results were compared with the known architectural properties.

RESULTS

SNR exerted the most significant impact on the outcome. The outcome variables approached asymptotes at SNR ≈ 54. Large in-plane voxel dimensions reduced the similarity between reconstructed fibers and the known architectural properties. Higher order polynomials helped reconstruct fibers with more complicated geometry but overfit noise for less complex geometries. The intrinsic fiber curvature also affected the robustness of polynomial smoothing to SNR. Other conditions, such as the fiber dimensionality, voxel aspect ratio, and slice thickness, did not affect the outcomes.

CONCLUSION

SNR ≥ 54 is recommended for accurate muscle architecture characterization using DTMRI. Averaged across all simulated conditions, the greatest percent errors under SNR = 54 were -5.6% and -4.0% for the pennation angle and fiber-tract length estimates, respectively. For fiber tracts with intermediate intrinsic curvature, the greatest percent error for the curvature estimate was 9.8% for SNR = 54. Smaller in-plane voxel size (≤1.5 mm) is preferred to minimize the estimation error in architectural properties. If necessary, slice thickness may be adjusted within typical ranges to achieve sufficient SNR when slices are aligned near the fiber direction. Third-order polynomial fitting is appropriate for smoothing fiber tracts.

Pancreatic cancer in photon-counting CT: Low keV virtual monoenergetic images improve tumor conspicuity

PURPOSE OF THE STUDY

The aim of the study was to identify differences in the tumor conspicuity of pancreatic adenocarcinomas in different monoenergetic or polyenergetic reconstructions and contrast phases in photon-counting CT (PCCT).

MATERIAL AND METHODS

34 patients were retrospectively enrolled in this study. Quantitative image analysis was performed with region of interest (ROI) measurements in different monoenergetic levels ranging from 40 up to 70 keV (5-point steps) and polyenergetic series. Tumor-parenchyma attenuation differences and contrast-to-noise-ratio (CNR) were calculated. A qualitative image analysis was accomplished by 4 radiologists using a 5-point Likert scale (1 = "not recognizable" up to 5 = "easy recognizable"). Differences between groups were evaluated for statistical significance using the Friedman test and in case of significant differences pair-wise post-hoc testing with Bonferroni correction was applied.

RESULTS

Tumor-parenchyma attenuation difference was significantly different between the different image reconstructions for both arterial- and portal-venous-phase-images (p < 0.001). Tumor-parenchyma attenuation difference was significantly higher on arterial-phase-images at mono40keV compared to polyenergetic images (p < 0.001) and mono55keV images or higher (p < 0.001). For portal-venous-phase-images tumor-parenchyma attenuation difference was significantly higher on mono40keV images compared to polyenergetic images (p < 0.001) and mono50keV images (p = 0.03) or higher (p < 0.001). The same trend was seen for CNR. Tumor conspicuity was rated best on mono40keV images with 4.3 ± 0.9 for arterial-phase-images and 4.3 ± 1.1 for portal-venous-phase-images. In contrast, overall image quality was rated best on polyenergetic-images with 4.8 ± 0.5 for arterial-phase-images and 4.7 ± 0.6 for portal-venous-phase-images.

CONCLUSION

Low keV virtual monoenergetic images significantly improve the tumor conspicuity of pancreatic adenocarcinomas in PCCT based on quantitative and qualitative results. On the other hand, readers prefer polyenergetic images for overall image quality.

Sensing and Controlling Strategy for Upper Extremity Prosthetics Based on Piezoelectric Micromachined Ultrasound Transducer

Surface electromyography (sEMG) is currently the primary method for user control of prosthetic manipulation. Its inherent limitations of low signal-to-noise ratio, limited specificity and susceptibility to noise, however, hinder successful implementation. Ultrasound provides a possible alternative, but current systems with medical probes are expense, bulky and non-wearable. This work proposes an innovative prosthetic control strategy based on a piezoelectric micromachined ultrasound transducer (PMUT) hardware system. Two PMUT-based probes were developed, comprising a 23×26 PMUT array and encapsulated in Ecoflex material. These compact and wearable probes represent a significant improvement over traditional ultrasound probes as they weigh only 1.8 grams and eliminate the need for ultrasound gel. A preliminary test of the probes was performed in non-disabled subjects performing 12 different hand gestures. The two probes were placed perpendicular to the flexor digitorum superficialis and brachioradialis muscles, respectively, to transmit/receive pulse-echo signals reflecting muscle activities. Hand gesture was correctly predicted 96% of the time with only these two probes. The adoption of the PMUT-based strategy greatly reduced the required number of channels, amount of processing circuit and subsequent analysis. The probes show promise for making prosthesis control more practical and economical.

Optimizing dual-energy CT technique for iodine-based contrast-to-noise ratio, a theoretical study

BACKGROUND

Dual-energy CT (DECT) systems provide valuable material-specific information by simultaneously acquiring two spectral measurements, resulting in superior image quality and contrast-to-noise ratio (CNR) while reducing radiation exposure and contrast agent usage. The selection of DECT scan parameters, including x-ray tube settings and fluence, is critical for the stability of the reconstruction process and hence the overall image quality.

PURPOSE

The goal of this study is to propose a systematic theoretical method for determining the optimal DECT parameters for minimal noise and maximum CNR in virtual monochromatic images (VMIs) for fixed subject size and total radiation dose.

METHODS

The noise propagation in the process of projection based material estimation from DECT measurements is analyzed. The main components of the study are the mean pixel variances for the sinogram and monochromatic image and the CNR, which were shown to depend on the Jacobian matrix of the sinograms-to-DECT measurements map. Analytic estimates for the mean sinogram and monochromatic image pixel variances and the CNR as functions of tube potentials, fluence, and VMI energy are derived, and then used in a virtual phantom experiment as an objective function for optimizing the tube settings and VMI energy to minimize the image noise and maximize the CNR.

RESULTS

It was shown that DECT measurements corresponding to kV settings that maximize the square of Jacobian determinant values over a domain of interest lead to improved stability of basis material reconstructions. Instances of non-uniqueness in DECT were addressed, focusing on scenarios where the Jacobian determinant becomes zero within the domain of interest despite significant spectral separation. The presence of non-uniqueness can lead to singular solutions during the inversion of sinograms-to-DECT measurements, underscoring the importance of considering uniqueness properties in parameter selection. Additionally, the optimal VMI energy and tube potentials for maximal CNR was determined. When the x-ray beam filter material was fixed at 2 mm of aluminum and the photon fluence for low and high kV scans were considered equal, the tube potential pair of 60/120 kV led to the maximal iodine CNR in the VMI at 53 keV.

CONCLUSIONS

Optimizing DECT scan parameters to maximize the CNR can be done in a systematic way. Also, choosing the parameters that maximize the Jacobian determinant over the set of expected line integrals leads to more stable reconstructions due to the reduced amplification of the measurement noise. Since the values of the Jacobian determinant depend strongly on the imaging task, careful consideration of all of the relevant factors is needed when implementing the proposed framework.

Piezoelectric Multi-Channel Bilayer Transducer for Sensing and Filtering Ossicular Vibration

This paper presents an acoustic transducer for fully implantable cochlear implants (FICIs), which can be implanted on the hearing chain to detect and filter the ambient sound in eight frequency bands between 250 and 6000 Hz. The transducer dimensions are conventional surgery compatible. The structure is formed with 3  × 3 × 0.36 mm active space for each layer and 5.2 mg total active mass excluding packaging. Characterization of the transducer is carried on an artificial membrane whose vibration characteristic is similar to the umbo vibration. On the artificial membrane, piezoelectric transducer generates up to 320.3 mVpp under 100 dB sound pressure level (SPL) excitation and covers the audible acoustic frequency. The measured signal-to-noise-ratio (SNR) of the channels is up to 84.2 dB. Sound quality of the transducer for fully implantable cochlear implant application is graded with an objective qualification method (PESQ) for the first time in the literature to the best of the knowledge, and scored 3.42/4.5.

Improved reconstruction for highly accelerated propeller diffusion 1.5 T clinical MRI

PURPOSE

Propeller fast-spin-echo diffusion magnetic resonance imaging (FSE-dMRI) is essential for the diagnosis of Cholesteatoma. However, at clinical 1.5 T MRI, its signal-to-noise ratio (SNR) remains relatively low. To gain sufficient SNR, signal averaging (number of excitations, NEX) is usually used with the cost of prolonged scan time. In this work, we leveraged the benefits of Locally Low Rank (LLR) constrained reconstruction to enhance the SNR. Furthermore, we enhanced both the speed and SNR by employing Convolutional Neural Networks (CNNs) for the accelerated PROPELLER FSE-dMRI on a 1.5 T clinical scanner.

METHODS

Residual U-Net (RU-Net) was found to be efficient for propeller FSE-dMRI data. It was trained to predict 2-NEX images obtained by Locally Low Rank (LLR) constrained reconstruction and used 1-NEX images obtained via simplified reconstruction as the inputs. The brain scans from healthy volunteers and patients with cholesteatoma were performed for model training and testing. The performance of trained networks was evaluated with normalized root-mean-square-error (NRMSE), structural similarity index measure (SSIM), and peak SNR (PSNR).

RESULTS

For 4 × under-sampled with 7 blades data, online reconstruction appears to provide suboptimal images-some small details are missing due to high noise interferences. Offline LLR enables suppression of noises and discovering some small structures. RU-Net demonstrated further improvement compared to LLR by increasing 18.87% of PSNR, 2.11% of SSIM, and reducing 53.84% of NRMSE. Moreover, RU-Net is about 1500 × faster than LLR (0.03 vs. 47.59 s/slice).

CONCLUSION

The LLR remarkably enhances the SNR compared to online reconstruction. Moreover, RU-Net improves propeller FSE-dMRI as reflected in PSNR, SSIM, and NRMSE. It requires only 1-NEX data, which allows a 2 × scan time reduction. In addition, its speed is approximately 1500 times faster than that of LLR-constrained reconstruction.

DMpDP: a Diagnostic Multiple-patient DermoFeature Profile store-and-forward teledermoscopy system

Telehealth demand is rapidly growing along with the necessity of providing wide-scale services covering multiple patients at the same time. In this work, the development of a store-and-forward (SAF) teledermoscopy system was considered. The dermoFeatures profile (DP) was proposed to decrease the size of the original dermoscopy image using its most significant features in the form of a newly generated diagonal alignment to generate a small-sized image DP, which is based on the extraction of a weighted intensity-difference frequency (WIDF) features along with morphological features (MOFs). These DPs were assembled to establish a Diagnostic Multiple-patient DermoFeature Profile (DMpDP). Different arrangements are proposed, namely the horizontally aligned, the diagonal-based, and the sequential-based DMpDPs to support the SAF systems. The DMpDPs are then embedded in a recorded patient-information signal (RPS) using a weight factor β to boost the transmitted patient-information signal. The effect of the different transform domains, β values, and number of DPs within the DMpDP were investigated in terms of the diagnostic classification accuracy at the receiver based on the extracted DPs, along with the recorded signal quality evaluation metrics of the recovered RPS. The sequential-based DMpDP achieved the highest classification accuracy, under - 5 dB additive white Gaussian noise, with a realized signal-to-noise ratio of 98.79% during the transmission of 248 DPs using β = 100, and spectral subtraction filtering.

Simultaneous time-of-flight MR angiography and quantitative susceptibility mapping with key time-of-flight features

A technique for combined time-of-flight (TOF) MR angiography (MRA) and quantitative susceptibility mapping (QSM) was developed with key features of standard three-dimensional (3D) TOF acquisitions, including multiple overlapping thin slab acquisition (MOTSA), ramped RF excitation, and venous saturation. The developed triple-echo 3D TOF-QSM sequence enabled TOF-MRA, susceptibility-weighted imaging (SWI), QSM, and R2* mapping. The effects of ramped RF, resolution, flip angle, venous saturation, and MOTSA were studied on QSM. Six volunteers were scanned at 3 T with the developed sequence, conventional TOF-MRA, and conventional SWI. Quantitative comparison of susceptibility values on QSM and normalized arterial and venous vessel-to-background contrasts on TOF and SWI were performed. The ramped RF excitation created an inherent phase variation in the raw phase. A generic correction factor was computed to remove the phase variation to obtain QSM without artifacts from the TOF-QSM sequence. No statistically significant difference was observed between the developed and standard QSM sequence for susceptibility values. However, maintaining standard TOF features led to compromises in signal-to-noise ratio for QSM and SWI, arising from the use of MOTSA rather than one large 3D slab, higher TOF spatial resolution, increased TOF background suppression due to larger flip angles, and reduced venous signal from venous saturation. In terms of vessel contrast, veins showed higher normalized contrast on SWI derived from TOF-QSM than the standard SWI sequence. While fast flowing arteries had reduced contrast compared with standard TOF-MRA, no statistical difference was observed for slow flowing arteries. Arterial contrast differences largely arise from the longer TR used in TOF-QSM over standard TOF-MRA to accommodate additional later echoes for SWI. In conclusion, although the sequence has a longer TR and slightly lower arterial contrast, provided an adequate correction is made for ramped RF excitation effects on phase, QSM may be performed from a multiecho sequence that includes all key TOF features, thus enabling simultaneous TOF-MRA, SWI, QSM, and R2* map computation.

MRI compatibility study of a prototype radiofrequency penetrable oval PET insert at 3 T

PURPOSE

To perform an MRI compatibility study of an RF field-penetrable oval-shaped PET insert that implements an MRI built-in body RF coil both as a transmitter and a receiver.

METHODS

Twelve electrically floating RF shielded PET detector modules were used to construct the prototype oval PET insert with a major axis of 440 mm, a minor axis of 350 mm, and an axial length of 225 mm. The electric floating of the PET detector modules was accomplished by isolating the cable shield from the detector shield using plastic tape. Studies were conducted on the transmit (B1) RF field, the image signal-to-noise ratio (SNR), and the RF pulse amplitude for a homogeneous cylindrical (diameter: 160 mm and length: 260 mm) phantom (NaCl + NiSO4 solution) in a 3 T clinical MRI system (Verio, Siemens, Erlangen, Germany).

RESULTS

The B1 maps for the oval insert were similar to the MRI-only field responses. Compared to the MRI-only values, SNR reductions of 51%, 45%, and 59% were seen, respectively, for the spin echo (SE), gradient echo (GE), and echo planar (EPI) images for the case of oval PET insert. Moreover, the required RF pulse amplitudes for the SE, GE, and EPI sequences were, respectively, 1.93, 1.85, and 1.36 times larger. However, a 30% reduction in the average RF reception sensitivity was observed for the oval insert.

CONCLUSIONS

The prototype floating PET insert was a safety concern for the clinical MRI system, and this compatibility study provided clearance for developing a large body size floating PET insert for the existing MRI system. Because of the RF shield of the insert, relatively large RF powers compared to the MRI-only case were required. Because of this and also due to low RF sensitivity of the body coil, the SNRs reduced largely.

Reflective optical imaging for scattering medium using chaotic laser

Significance

Optical imaging is a non-invasive imaging technology that utilizes near-infrared light, allows for the image reconstruction of optical properties like diffuse and absorption coefficients within the tissue. A recent trend is to use signal processing techniques or new light sources and expanding its application.

Aim

We aim to develop the reflective optical imaging using the chaotic correlation technology with chaotic laser and optimize the quality and spatial resolution of reflective optical imaging.

Approach

Scattering medium was measured using reflective configuration in different inhomogeneous regions to evaluate the performance of the imaging system. The accuracy of the recovered optical properties was investigated. The reconstruction errors of absorption coefficients and geometric centers were analyzed, and the feature metrics of the reconstructed images were evaluated.

Results

We showed how chaotic correlation technology can be utilized for information extraction and image reconstruction. This means that a higher signal-to-noise ratio and image reconstruction of inhomogeneous phantoms under different scenarios successfully were achieved.

Conclusions

This work highlights that the peak values of correlation of chaotic exhibit smaller reconstruction error and better reconstruction performance in optical imaging compared with reflective optical imaging with the continuous wave laser.

Accelerated multiple-quantum-filtered sodium magnetic resonance imaging using compressed sensing at 7 T

PURPOSE

Multiple-quantum-filtered (MQF) sodium magnetic resonance imaging (MRI), such as enhanced single-quantum and triple-quantum-filtered imaging of 23Na (eSISTINA), enables images to be weighted towards restricted sodium, a promising biomarker in clinical practice, but often suffers from clinically infeasible acquisition times and low image quality. This study aims to mitigate the above limitation by implementing a novel eSISTINA sequence at 7 T with the application of compressed sensing (CS) to accelerate eSISTINA acquisitions without a noticeable loss of information.

METHODS

A novel eSISTINA sequence with a 3D spiral-based sampling scheme was implemented at 7 T for the application of CS. Fully sampled datasets were obtained from one phantom and ten healthy subjects, and were then retrospectively undersampled by various undersampling factors. CS undersampled reconstructions were compared to fully sampled and undersampled nonuniform fast Fourier transform (NUFFT) reconstructions. Reconstruction performance was evaluated based on structural similarity (SSIM), signal-to-noise ratio (SNR), weightings towards total and compartmental sodium, and in vivo quantitative estimates.

RESULTS

CS-based phantom and in vivo images have less noise and better structural delineation while maintaining the weightings towards total, non-restricted (predominantly extracellular), and restricted (primarily intracellular) sodium. CS generally outperforms NUFFT with a higher SNR and a better SSIM, except for the SSIM in TQ brain images, which is likely due to substantial noise contamination. CS enables in vivo quantitative estimates with <15% errors at an undersampling factor of up to two.

CONCLUSIONS

Successful implementation of an eSISTINA sequence with an incoherent sampling scheme at 7 T was demonstrated. CS can accelerate eSISTINA by up to twofold at 7 T with reduced noise levels compared to NUFFT, while maintaining major structural information, reasonable weightings towards total and compartmental sodium, and relatively reliable in vivo quantification. The associated reduction in acquisition time has the potential to facilitate the clinical applicability of MQF sodium MRI.

A Direct Current-Sensing VCO-Based 2nd-Order Continuous-Time Sigma-Delta Modulator for Biosensor Readout Applications

A second-order voltage-controlled oscillator (VCO)-based continuous-time sigma-delta modulator (CTSDM) for current-sensing readout applications is proposed. Current signals from the sensor can directly be quantized by the proposed VCO-based CTSDM, which does not require any extra trans-impedance amplifiers. With the proportional-integral (PI) structure and a VCO phase integrator, the capability of second-order noise shaping is available to reduce the in-band quantization noise. The PI structure can be simply realized by a resistor in series with the integrating capacitor, which can reduce the architecture complexity and maintain the stability of the system. The current-steering digital-to-analog converter with tail and sink current sources is used on the feedback path for the subtraction of the current-type input signal. All the components of the circuit are scaling friendly and applicable to current-sensing readout applications in the Internet of Things (IoT). The proposed VCO-based CTSDM implemented in a 0.18-μm standard CMOS process has a measured signal-to-noise and distortion ratio (SNDR) of 74.6 dB at 10 kHz bandwidth and consumes 44.8 μw only under a supply voltage of 1.2 V, which can achieve a Figure-of-Merit (FoM) of 160.76 dB.

Elimination of Random Mixed Noise in ECG Using Convolutional Denoising Autoencoder With Transformer Encoder

Electrocardiogram (ECG) signals frequently encounter diverse types of noise, such as baseline wander (BW), electrode motion (EM) artifacts, muscle artifact (MA), and others. These noises often occur in combination during the actual data acquisition process, resulting in erroneous or perplexing interpretations for cardiologists. To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) in this study. The encoder of TCDAE is composed of three stacked gated convolutional layers and a Transformer encoder block with a point-wise multi-head self-attention module. To obtain minimal distortion in both time and frequency domains, we also propose a frequency weighted Huber loss function in training phase to better approximate the original signals. The TCDAE model is trained and tested on the QT Database (QTDB) and MIT-BIH Noise Stress Test Database (NSTDB), with the training data and testing data coming from different records. All the metrics perform the most robust in overall noise and separate noise intervals for RMN removal compared with the baseline methods. We also conduct generalization tests on the Icentia11k database where the TCDAE outperforms the state-of-the-art models, with a 55% reduction of the false positives in R peak detection after denoising. The TCDAE model approximates the short-term and long-term characteristics of ECG signals and has higher stability even under extreme RMN corruption. The memory consumption and inference speed of TCDAE are also feasible for its deployment in clinical applications.

English vowel recognition in multi-talker babbles mixed with different numbers of talkersa)

The present study examined English vowel recognition in multi-talker babbles (MTBs) in 20 normal-hearing, native-English-speaking adult listeners. Twelve vowels, embedded in the h-V-d structure, were presented in MTBs consisting of 1, 2, 4, 6, 8, 10, and 12 talkers (numbers of talkers [N]) and a speech-shaped noise at signal-to-noise ratios of -12, -6, and 0 dB. Results showed that vowel recognition performance was a non-monotonic function of N when signal-to-noise ratios were less favorable. The masking effects of MTBs on vowel recognition were most similar to consonant recognition but less so to word and sentence recognition reported in previous studies.

Spectral performance evaluation of a second-generation spectral detector CT

PURPOSE

The aim of this study was to characterize a second-generation wide-detector dual-layer spectral computed tomography (CT) system for material quantification accuracy, acquisition parameter and patient size dependencies, and tissue characterization capabilities.

METHODS

A phantom with multiple tissue-mimicking and material-specific inserts was scanned with a dual-layer spectral detector CT using different tube voltages, collimation widths, radiation dose levels, and size configurations. Accuracy of iodine density maps and virtual monoenergetic images (MonoE) were investigated. Additionally, differences between conventional and MonoE 70 keV images were calculated to evaluate acquisition parameter and patient size dependencies. To demonstrate material quantification and differentiation, liver-mimicking inserts with adipose and iron were analyzed with a two-base decomposition utilizing MonoE 50 and 150 keV, and root mean square error (RMSE) for adipose and iron content was reported.

RESULTS

Measured inserts exhibited quantitative accuracy across a wide range of MonoE levels. MonoE 70 keV images demonstrated reduced dependence compared to conventional images for phantom size (1 vs. 27 HU) and acquisition parameters, particularly tube voltage (4 vs. 37 HU). Iodine density quantification was successful with errors ranging from -0.58 to 0.44 mg/mL. Similarly, inserts with different amounts of adipose and iron were differentiated, and the small deviation in values within inserts corresponded to a RMSE of 3.49 ± 1.76% and 1.67 ± 0.84 mg/mL for adipose and iron content, respectively.

CONCLUSION

The second-generation dual-layer CT enables acquisition of quantitatively accurate spectral data without compromises from differences in patient size and acquisition parameters.

Impact of B 0 $$ {\mathrm{B}}_0 $$ field imperfections correction on BOLD sensitivity in 3D-SPARKLING fMRI data

PURPOSE

Static and dynamicB 0 $$ {\mathrm{B}}_0 $$ field imperfections are detrimental to functional MRI (fMRI) applications, especially at ultra-high magnetic fields (UHF). In this work, a field camera is used to assess the benefits of retrospectively correctingB 0 $$ {\mathrm{B}}_0 $$ field perturbations on Blood Oxygen Level Dependent (BOLD) sensitivity in non-Cartesian three-dimensional (3D)-SPARKLING fMRI acquisitions.

METHODS

fMRI data were acquired at 1 mm3 $$ {}^3 $$ and for a 2.4s-TR while concurrently monitoring in real-time field perturbations using a Skope Clip-on field camera in a novel experimental setting involving a shorter TR than the required minimal TR of the field probes. Measurements of the dynamic field deviations were used along with a staticΔ B 0 $$ \Delta {\mathrm{B}}_0 $$ map to retrospectively correct static and dynamic field imperfections, respectively. In order to evaluate the impact of such a correction on fMRI volumes, a comparative study was conducted on healthy volunteers.

RESULTS

Correction ofB 0 $$ {\mathrm{B}}_0 $$ deviations improved image quality and yielded between 20% and 30% increase in median temporal signal-to-noise ratio (tSNR).Using fMRI data collected during a retinotopic mapping experiment, we demonstrated a significant increase in sensitivity to the BOLD contrast and improved accuracy of the BOLD phase maps: 44% (resp., 159%) more activated voxels were retrieved when using a significance control level based on a p-value of 0.001 without correcting for multiple comparisons (resp., 0.05 with a false discovery rate correction).

CONCLUSION

3D-SPARKLING fMRI hugely benefits from static and dynamicB 0 $$ {\mathrm{B}}_0 $$ imperfections correction. However, the proposed experimental protocol is flexible enough to be deployed on a large spectrum of encoding schemes, including arbitrary non-Cartesian readouts.

Fast System Matrix Generation Based on Single Angle Calibration in Open-Sided Field Free Line Magnetic Particle Imaging

OBJECTIVE

Open-sided field-free line magnetic particle imaging (OS FFL MPI) is a novel medical imaging system configuration that has received significant attention in recent years. However, the measurement-based system matrix (SM) image reconstruction for OS FFL MPI typically requires multiple angle calibration (MAC), which is time-consuming in practice.

METHODS

To address this issue, we propose a fast 2D SM generation method that requires only a single angle calibration (SAC). The SAC method exploits the rotational invariance of the system function. Based on the measured single angle system function, the system function is rotated to generate system functions at other angles, and then the SM for image reconstruction is constructed. Then, we conducted various simulation experiments and built an OS FFL MPI scanner to evaluate the proposed SAC method.

RESULTS

The experiments demonstrating the effectiveness of SAC in reducing calibration workload, requiring fewer scanning numbers while maintaining a similar image reconstruction quality compared to MAC method. Furthermore, the SM generated by SAC produces consistent imaging results with the SM generated by MAC, regardless of the interpolation algorithms, the number of rotation angles, or the signal-to-noise ratios employed in phantom imaging experiments.

CONCLUSION

SAC has been experimentally verified to reduce acquisition time while maintaining accurate and robust reconstruction performance.

SIGNIFICANCE

The significance of SAC lies in its contribution to improving calibration efficiency in OS FFL MPI, potentially facilitating the implementation of MPI in a wider range of applications.

Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface

An essential priority of visual brain-computer interfaces (BCIs) is to enhance the information transfer rate (ITR) to achieve high-speed communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are achievable. In this study, we used information theory to study the characteristics and capacity of the visual-evoked channel, which leads us to investigate whether and how we can decode higher information rates in a visual BCI system. Using information theory, we estimate the upper and lower bounds of the information rate with the white noise (WN) stimulus. Consequently, we found out that the information rate is determined by the signal-to-noise ratio (SNR) in the frequency domain, which reflects the spectrum resources of the channel. Based on this discovery, we propose a broadband WN BCI by implementing stimuli on a broader frequency band than the steady-state visual evoked potentials (SSVEPs)-based BCI. Through validation, the broadband BCI outperforms the SSVEP BCI by an impressive 7 bps, setting a record of 50 bps. The integration of information theory and the decoding analysis presented in this study offers valuable insights applicable to general sensory-evoked BCIs, providing a potential direction of next-generation human-machine interaction systems.

SpiDe-Sr: blind super-resolution network for precise cell segmentation and clustering in spatial proteomics imaging

Spatial proteomics elucidates cellular biochemical changes with unprecedented topological level. Imaging mass cytometry (IMC) is a high-dimensional single-cell resolution platform for targeted spatial proteomics. However, the precision of subsequent clinical analysis is constrained by imaging noise and resolution. Here, we propose SpiDe-Sr, a super-resolution network embedded with a denoising module for IMC spatial resolution enhancement. SpiDe-Sr effectively resists noise and improves resolution by 4 times. We demonstrate SpiDe-Sr respectively with cells, mouse and human tissues, resulting 18.95%/27.27%/21.16% increase in peak signal-to-noise ratio and 15.95%/31.63%/15.52% increase in cell extraction accuracy. We further apply SpiDe-Sr to study the tumor microenvironment of a 20-patient clinical breast cancer cohort with 269,556 single cells, and discover the invasion of Gram-negative bacteria is positively correlated with carcinogenesis markers and negatively correlated with immunological markers. Additionally, SpiDe-Sr is also compatible with fluorescence microscopy imaging, suggesting SpiDe-Sr an alternative tool for microscopy image super-resolution.

PETformer network enables ultra-low-dose total-body PET imaging without structural prior

Objective.Positron emission tomography (PET) is essential for non-invasive imaging of metabolic processes in healthcare applications. However, the use of radiolabeled tracers exposes patients to ionizing radiation, raising concerns about carcinogenic potential, and warranting efforts to minimize doses without sacrificing diagnostic quality.Approach.In this work, we present a novel neural network architecture, PETformer, designed for denoising ultra-low-dose PET images without requiring structural priors such as computed tomography (CT) or magnetic resonance imaging. The architecture utilizes a U-net backbone, synergistically combining multi-headed transposed attention blocks with kernel-basis attention and channel attention mechanisms for both short- and long-range dependencies and enhanced feature extraction. PETformer is trained and validated on a dataset of 317 patients imaged on a total-body uEXPLORER PET/CT scanner.Main results.Quantitative evaluations using structural similarity index measure and liver signal-to-noise ratio showed PETformer's significant superiority over other established denoising algorithms across different dose-reduction factors.Significance.Its ability to identify and recover intrinsic anatomical details from background noise with dose reductions as low as 2% and its capacity in maintaining high target-to-background ratios while preserving the integrity of uptake values of small lesions enables PET-only fast and accurate disease diagnosis. Furthermore, PETformer exhibits computational efficiency with only 37 M trainable parameters, making it well-suited for commercial integration.

Self-supervised dual-domain balanced dropblock-network for low-dose CT denoising

Objective.Self-supervised learning methods have been successfully applied for low-dose computed tomography (LDCT) denoising, with the advantage of not requiring labeled data. Conventional self-supervised methods operate only in the image domain, ignoring valuable priors in the sinogram domain. Recently proposed dual-domain methods address this limitation but encounter issues with blurring artifacts in the reconstructed image due to the inhomogeneous distribution of noise levels in low-dose sinograms.Approach.To tackle this challenge, this paper proposes SDBDNet, an end-to-end dual-domain self-supervised method for LDCT denoising. With the network designed based on the properties of inhomogeneous noise in low-dose sinograms and the principle of moderate sinogram-domain denoising, SDBDNet achieves effective denoising in dual domains without introducing blurring artifacts. Specifically, we split the sinogram into two subsets based on the positions of detector cells to generate paired training data with high similarity and independent noise. These sub-sinograms are then restored to their original size using 1D interpolation and learning-based correction. To achieve adaptive and moderate smoothing in the sinogram domain, we integrate Dropblock, a type of convolution layer with regularization, into SDBDNet, and set a weighted average between the denoised sinograms and their noisy counterparts, leading to a well-balanced dual-domain approach.Main results.Numerical experiments show that our method outperforms popular non-learning and self-supervised learning methods, demonstrating its effectiveness and superior performance.Significance.While introducing a novel high-performance dual-domain self-supervised LDCT denoising method, this paper also emphasizes and verifies the importance of appropriate sinogram-domain denoising in dual-domain methods, which might inspire future work.