An adult and pediatric size-based contrast administration reduction phantom study for single and dual-energy CT through preservation of contrast-to-noise ratio

BACKGROUND

Global shortages of iodinated contrast media (ICM) during COVID-19 pandemic forced the imaging community to use ICM more strategically in CT exams.

PURPOSE

The purpose of this work is to provide a quantitative framework for preserving iodine CNR while reducing ICM dosage by either lowering kV in single-energy CT (SECT) or using lower energy virtual monochromatic images (VMI) from dual-energy CT (DECT) in a phantom study.

MATERIALS AND METHODS

In SECT study, phantoms with effective diameters of 9.7, 15.9, 21.1, and 28.5 cm were scanned on SECT scanners of two different manufacturers at a range of tube voltages. Statistical based iterative reconstruction and deep learning reconstruction were used. In DECT study, phantoms with effective diameters of 20, 29.5, 34.6, and 39.7 cm were scanned on DECT scanners from three different manufacturers. VMIs were created from 40 to 140 keV. ICM reduction by lowering kV levels for SECT or switching from SECT to DECT was calculated based on the linear relationship between iodine CNR and its concentration under different scanning conditions.

RESULTS

On SECT scanner A, while matching CNR at 120 kV, ICM reductions of 21%, 58%, and 72% were achieved at 100, 80, and 70 kV, respectively. On SECT scanner B, 27% and 80% ICM reduction was obtained at 80 and 100 kV. On the Fast-kV switch DECT, with CNR matched at 120 kV, ICM reductions were 35%, 30%, 23%, and 15% with VMIs at 40, 50, 60, and 68 keV, respectively. On the dual-source DECT, ICM reductions were 52%, 48%, 42%, 33%, and 22% with VMIs at 40, 50, 60, 70, and 80 keV. On the dual-layer DECT, ICM reductions were 74%, 62%, 45%, and 22% with VMIs at 40, 50, 60, and 70 keV.

CONCLUSIONS

Our work provided a quantitative baseline for other institutions to further optimize their scanning protocols to reduce the use of ICM.

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.

Prototype Description and Ex Vivo Evaluation of a System for Combined Endorectal Magnetic Resonance Imaging and In-Bore Biopsy of the Prostate

ABSTRACT

We describe early ex vivo proof-of-concept testing of a novel system composed of a disposable endorectal coil and converging multichannel needle guide with a reusable clamp stand, embedded electronics, and baseplate to allow for endorectal magnetic resonance (MR) imaging and in-bore MRI-targeted biopsy of the prostate as a single integrated procedure. Using prostate phantoms imaged with standard T 2 -weighted sequences in a Siemens 3T Prisma MR scanner, we measured the signal-to-noise ratio in successive 1-cm distances from the novel coil and from a commercially available inflatable balloon coil and measured the lateral and longitudinal deviation of the tip of a deployed MR compatible needle from the intended target point. Signal-to-noise ratio obtained with the novel system was significantly better than the inflatable balloon coil at each of five 1-cm intervals, with a mean improvement of 78% ( P < 0.05). In a representative sampling of 15 guidance channels, the mean lateral deviation for MR imaging-guided needle positioning was 1.7 mm and the mean longitudinal deviation was 2.0 mm. Our ex vivo results suggest that our novel system provides significantly improved signal-to-noise ratio when compared with an inflatable balloon coil and is capable of accurate MRI-guided needle deployment.

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.

Image Domain Multi-Material Decomposition Noise Suppression Through Basis Transformation and Selective Filtering

Spectral CT can provide material characterization ability to offer more precise material information for diagnosis purposes. However, the material decomposition process generally leads to amplification of noise which significantly limits the utility of the material basis images. To mitigate such problem, an image domain noise suppression method was proposed in this work. The method performs basis transformation of the material basis images based on a singular value decomposition. The noise variances of the original spectral CT images were incorporated in the matrix to be decomposed to ensure that the transformed basis images are statistically uncorrelated. Due to the difference in noise amplitudes in the transformed basis images, a selective filtering method was proposed with the low-noise transformed basis image as guidance. The method was evaluated using both numerical simulation and real clinical dual-energy CT data. Results demonstrated that compared with existing methods, the proposed method performs better in preserving the spatial resolution and the soft tissue contrast while suppressing the image noise. The proposed method is also computationally efficient and can realize real-time noise suppression for clinical spectral CT images.

Element Position Calibration for Matrix Array Transducers with Multiple Disjoint Piezoelectric Panels

Two-dimensional ultrasound transducers enable the acquisition of fully volumetric data that have been demonstrated to provide greater diagnostic information in the clinical setting and are a critical tool for emerging ultrasound methods, such as super-resolution and functional imaging. This technology, however, is not without its limitations. Due to increased fabrication complexity, some matrix probes with disjoint piezoelectric panels may require initial calibration. In this manuscript, two methods for calibrating the element positions of the Vermon 1024-channel 8 MHz matrix transducer are detailed. This calibration is a necessary step for acquiring high resolution B-mode images while minimizing transducer-based image degradation. This calibration is also necessary for eliminating vessel-doubling artifacts in super-resolution images and increasing the overall signal-to-noise ratio (SNR) of the image. Here, we show that the shape of the point spread function (PSF) can be significantly improved and PSF-doubling artifacts can be reduced by up to 10 dB via this simple calibration procedure.

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.

Label-free detection and profiling of individual solution-phase molecules

Most chemistry and biology occurs in solution, in which conformational dynamics and complexation underlie behaviour and function. Single-molecule techniques1 are uniquely suited to resolving molecular diversity and new label-free approaches are reshaping the power of single-molecule measurements. A label-free single-molecule method2-16 capable of revealing details of molecular conformation in solution17,18 would allow a new microscopic perspective of unprecedented detail. Here we use the enhanced light-molecule interactions in high-finesse fibre-based Fabry-Pérot microcavities19-21 to detect individual biomolecules as small as 1.2 kDa, a ten-amino-acid peptide, with signal-to-noise ratios (SNRs) >100, even as the molecules are unlabelled and freely diffusing in solution. Our method delivers 2D intensity and temporal profiles, enabling the distinction of subpopulations in mixed samples. Notably, we observe a linear relationship between passage time and molecular radius, unlocking the potential to gather crucial information about diffusion and solution-phase conformation. Furthermore, mixtures of biomolecule isomers of the same molecular weight and composition but different conformation can also be resolved. Detection is based on the creation of a new molecular velocity filter window and a dynamic thermal priming mechanism that make use of the interplay between optical and thermal dynamics22,23 and Pound-Drever-Hall (PDH) cavity locking24 to reveal molecular motion even while suppressing environmental noise. New in vitro ways of revealing molecular conformation, diversity and dynamics can find broad potential for applications in the life and chemical sciences.

Generalizable synthetic MRI with physics-informed convolutional networks

BACKGROUND

Magnetic resonance imaging (MRI) provides state-of-the-art image quality for neuroimaging, consisting of multiple separately acquired contrasts. Synthetic MRI aims to accelerate examinations by synthesizing any desirable contrast from a single acquisition.

PURPOSE

We developed a physics-informed deep learning-based method to synthesize multiple brain MRI contrasts from a single 5-min acquisition and investigate its ability to generalize to arbitrary contrasts.

METHODS

A dataset of 55 subjects acquired with a clinical MRI protocol and a 5-min transient-state sequence was used. The model, based on a generative adversarial network, maps data acquired from the five-minute scan to "effective" quantitative parameter maps (q*-maps), feeding the generated PD, T1, and T2 maps into a signal model to synthesize four clinical contrasts (proton density-weighted, T1-weighted, T2-weighted, and T2-weighted fluid-attenuated inversion recovery), from which losses are computed. The synthetic contrasts are compared to an end-to-end deep learning-based method proposed by literature. The generalizability of the proposed method is investigated for five volunteers by synthesizing three contrasts unseen during training and comparing these to ground truth acquisitions via qualitative assessment and contrast-to-noise ratio (CNR) assessment.

RESULTS

The physics-informed method matched the quality of the end-to-end method for the four standard contrasts, with structural similarity metrics above0.75 ± 0.08 $0.75\pm 0.08$ ( ± $\pm$ std), peak signal-to-noise ratios above22.4 ± 1.9 $22.4\pm 1.9$ , representing a portion of compact lesions comparable to standard MRI. Additionally, the physics-informed method enabled contrast adjustment, and similar signal contrast and comparable CNRs to the ground truth acquisitions for three sequences unseen during model training.

CONCLUSIONS

The study demonstrated the feasibility of physics-informed, deep learning-based synthetic MRI to generate high-quality contrasts and generalize to contrasts beyond the training data. This technology has the potential to accelerate neuroimaging protocols.

Transcranial volumetric imaging using a conformal ultrasound patch

Accurate and continuous monitoring of cerebral blood flow is valuable for clinical neurocritical care and fundamental neurovascular research. Transcranial Doppler (TCD) ultrasonography is a widely used non-invasive method for evaluating cerebral blood flow1, but the conventional rigid design severely limits the measurement accuracy of the complex three-dimensional (3D) vascular networks and the practicality for prolonged recording2. Here we report a conformal ultrasound patch for hands-free volumetric imaging and continuous monitoring of cerebral blood flow. The 2 MHz ultrasound waves reduce the attenuation and phase aberration caused by the skull, and the copper mesh shielding layer provides conformal contact to the skin while improving the signal-to-noise ratio by 5 dB. Ultrafast ultrasound imaging based on diverging waves can accurately render the circle of Willis in 3D and minimize human errors during examinations. Focused ultrasound waves allow the recording of blood flow spectra at selected locations continuously. The high accuracy of the conformal ultrasound patch was confirmed in comparison with a conventional TCD probe on 36 participants, showing a mean difference and standard deviation of difference as -1.51 ± 4.34 cm s-1, -0.84 ± 3.06 cm s-1 and -0.50 ± 2.55 cm s-1 for peak systolic velocity, mean flow velocity, and end diastolic velocity, respectively. The measurement success rate was 70.6%, compared with 75.3% for a conventional TCD probe. Furthermore, we demonstrate continuous blood flow spectra during different interventions and identify cascades of intracranial B waves during drowsiness within 4 h of recording.

Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI-guided radiation therapy

BACKGROUND

Cine magnetic resonance (MR) images have been used for real-time MR guided radiation therapy (MRgRT). However, the onboard MR systems with low-field strength face the problem of limited image quality.

PURPOSE

To improve the quality of cine MR images in MRgRT using prior image information provided by the patient planning and positioning MR images.

METHODS

This study employed MR images from 18 pancreatic cancer patients who received MR-guided stereotactic body radiation therapy. Planning 3D MR images were acquired during the patient simulation, and positioning 3D MR images and 2D sagittal cine MR images were acquired before and during the beam delivery, respectively. A deep learning-based framework consisting of two cycle generative adversarial networks (CycleGAN), Denoising CycleGAN and Enhancement CycleGAN, was developed to establish the mapping between the 3D and 2D MR images. The Denoising CycleGAN was trained to first denoise the cine images using the time domain cine image series, and the Enhancement CycleGAN was trained to enhance the spatial resolution and contrast by taking advantage of the prior image information from the planning and positioning images. The denoising performance was assessed by signal-to-noise ratio (SNR), structural similarity index measure, peak SNR, blind/reference-less image spatial quality evaluator (BRISQUE), natural image quality evaluator, and perception-based image quality evaluator scores. The quality enhancement performance was assessed by the BRISQUE and physician visual scores. In addition, the target contouring was evaluated on the original and processed images.

RESULTS

Significant differences were found for all evaluation metrics after Denoising CycleGAN processing. The BRISQUE and visual scores were also significantly improved after sequential Denoising and Enhancement CycleGAN processing. In target contouring evaluation, Dice similarity coefficient, centroid distance, Hausdorff distance, and average surface distance values were significantly improved on the enhanced images. The whole processing time was within 20 ms for a typical input image size of 512 × 512.

CONCLUSION

Taking advantage of the prior high-quality positioning and planning MR images, the deep learning-based framework enhanced the cine MR image quality significantly, leading to improved accuracy in automatic target contouring. With the merits of both high computational efficiency and considerable image quality enhancement, the proposed method may hold important clinical implication for real-time MRgRT.

Technical note: Characterization, validation, and spectral optimization of a dedicated breast CT system for contrast-enhanced imaging

BACKGROUND

The development of a new imaging modality, such as 4D dynamic contrast-enhanced dedicated breast CT (4D DCE-bCT), requires optimization of the acquisition technique, particularly within the 2D contrast-enhanced imaging modality. Given the extensive parameter space, cascade-systems analysis is commonly used for such optimization.

PURPOSE

To implement and validate a parallel-cascaded model for bCT, focusing on optimizing and characterizing system performance in the projection domain to enhance the quality of input data for image reconstruction.

METHODS

A parallel-cascaded system model of a state-of-the-art bCT system was developed and model predictions of the presampled modulation transfer function (MTF) and the normalized noise power spectrum (NNPS) were compared with empirical data collected in the projection domain. Validation was performed using the default settings of 49 kV with 1.5 mm aluminum filter and at 65 kV and 0.257 mm copper filter. A 10 mm aluminum plate was added to replicate the breast attenuation. Air kerma at the isocenter was measured at different tube current levels. Discrepancies between the measured projection domain metrics and model-predicted values were quantified using percentage error and coefficient of variation (CoV) for MTF and NNPS, respectively. The optimal filtration was for a 5 mm iodine disk detection task at 49, 55, 60, and 65 kV. The detectability index was calculated for the default aluminum filtration and for copper thicknesses ranging from 0.05 to 0.4 mm.

RESULTS

At 49 kV, MTF errors were +5.1% and -5.1% at 1 and 2 cycles/mm, respectively; NNPS CoV was 5.3% (min = 3.7%; max = 8.5%). At 65 kV, MTF errors were -0.8% and -3.2%; NNPS CoV was 13.1% (min = 11.4%; max = 16.9%). Air kerma output was linear, with 11.67 µGy/mA (R2 = 0.993) and 19.14 µGy/mA (R2 = 0.996) at 49 and 65 kV, respectively. For iodine detection, a 0.25 mm-thick copper filter at 65 kV was found optimal, outperforming the default technique by 90%.

CONCLUSION

The model accurately predicts bCT system performance, specifically in the projection domain, under varied imaging conditions, potentially contributing to the enhancement of 2D contrast-enhanced imaging in 4D DCE-bCT.

Flexible in-cavity MRI receiving coil for ultra-high-resolution imaging of the pituitary gland

OBJECTIVE

The objective of this study was the preclinical design and construction of a flexible intrasphenoid coil aiming for submillimeter resolution of the human pituitary gland.

METHODS

Sphenoid sinus measurements determined coil design constraints for use in > 95% of adult patients. Temperature safety parameters were tested. The 2-cm-diameter coil prototype was positioned in the sphenoid sinus of cadaveric human heads utilizing the transnasal endoscopic approach that is used clinically. Signal-to-noise ratio (SNR) was estimated for the transnasal coil prototype compared with a standard clinical head coil. One cadaveric pituitary gland was explanted and histologically examined for correlation to the imaging findings.

RESULTS

With the coil positioned directly atop the sella turcica at a 0° angle of the B0 static field, the craniocaudal distance (21.2 ± 0.8 mm) was the limiting constraint. Phantom experiments showed no detectable change in temperature at two sites over 15 minutes. The flexible coil was placed transnasally in cadaveric specimens using an endoscopic approach. The image quality was subjectively superior at higher spatial resolutions relative to that with the commercial 20-channel head coil. An average 17-fold increase in the SNR was achieved within the pituitary gland. Subtle findings visualized only with the transnasal coil had potential pathological correlation with immunohistochemical findings.

CONCLUSIONS

A transnasal radiofrequency coil feasibly provides a 17-fold boost in the SNR at 3 T. The ability to safely improve the quality of pituitary imaging may be helpful in the identification and subsequent resection of small functional pituitary lesions.

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.

A profile on the WISE cortical strip for intraoperative neurophysiological monitoring

INTRODUCTION

During intraoperative neurophysiological monitoring in neurosurgery, brain electrodes are placed to record electrocorticography or to inject current for direct cortical stimulation. A low impedance electrode may improve signal quality.

AREAS COVERED

We review here a brain electrode (WISE Cortical Strip, WCS®), where a thin polymer strip embeds platinum nanoparticles to create conductive electrode contacts. The low impedance contacts enable a high signal-to-noise ratio, allowing for better detection of small signals such as high-frequency oscillations (HFO). The softness of the WCS may hinder sliding the electrode under the dura or advancing it to deeper structures as the hippocampus but assures conformability with the cortex even in the resection cavity. We provide an extensive review on WCS including a market overview, an introduction to the device (mechanistics, cost aspects, performance standards, safety and contraindications) and an overview of the available pre- and post-approval data.

EXPERT OPINION

The WCS improves signal detection by lower impedance and better conformability to the cortex. The higher signal-to-noise ratio improves the detection of challenging signals. The softness of the electrode may be a disadvantage in some applications and an advantage in others.

Improving the detection of hypo-vascular liver metastases in multiphase contrast-enhanced CT with slice thickness less than 5 mm using DenseNet

INTRODUCTION

Thinner slices are more susceptible in detecting small lesions but suffer from higher statistical fluctuation. This work aimed to reduce image noise in multiphase contrast-enhanced CT reconstructed with slice thickness thinner than the clinical setting (i.e., 5 mm) using convolutional neural network (CNN) for enabling better detection of hypo-vascular liver metastasis.

METHODS

A DenseNet model was used to generate noise map for multiphase CT reconstructed with slice thickness of 2.5 mm and 1.25 mm. Image denoising was conducted by subtracting the CNN-generated noise map from CT images with reduced photon flux due to thinner slice thickness. The performance of DenseNet was evaluated on CT scans of electron density phantoms and patients with hypovascular liver metastases less than 1.5 cm in terms of Hounsfield Unit (HU) variation, statistical fluctuation, and contrast-to-noise ratio (CNR).

RESULTS

The phantom study demonstrated that the CNN-based denoising method was able to reduce statistical fluctuation in CT images reconstructed with slice thickness of 2.5 mm and 1.25 mm without causing significant edge blurring or variation in HU values. With regards to patient study, it was found that the denoised 2.5-mm and 1.25-mm slices had higher CNR than the conventional 5-mm slices for hypo-vascular liver metastases in all 4 phases of multiphase CT.

CONCLUSION

Our results demonstrated that the detection of hypo-vascular liver metastases in multiphase contrast-enhanced CT with slice thickness less than 5 mm could be improved by using the CNN-based denoising method.

IMPLICATIONS FOR PRACTICE

Reconstruction slice thickness has a strong influence on the image quality of CT imaging. A CNN-based denoising method was used in this work to reduce the image noise in multiphase contrast-enhanced CT reconstructed with slice thickness less than 5 mm.

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.

Image denoising and model-independent parameterization for IVIM MRI

Objective. To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging quality using a new image denoising technique and model-independent parameterization of the signal versusb-value curve.Approach. IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five of these patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the ill-posed mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the correlations between parameter changes in parotid glands with radiotherapy dose levels. The validity of blur kernel predictions was assessed by the testing the method's ability to recover artificial 'pseudokernels'. AUC parameters were compared with monoexponential, biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) around parotid glands, and relative importance via principal component analysis.Main results. Image denoising improved blind image quality metrics, smoothed the signal versusb-value curve, and strengthened correlations between IVIM parameters and dose levels. Image TV was reduced and parameter CNRs generally increased following denoising.AUCparameters were more correlated with dose and had higher relative importance than exponential model parameters.Significance. IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versusb-value curve with model-independent parameters like theAUCand preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility.

Improving resolution of panoramic radiographs: super-resolution concept

OBJECTIVES

Dental imaging plays a key role in the diagnosis and treatment of dental conditions, yet limitations regarding the quality and resolution of dental radiographs sometimes hinder precise analysis. Super-resolution with deep learning refers to a set of techniques used to enhance the resolution of images beyond their original size or quality using deep neural networks instead of traditional image interpolation methods which often result in blurred or pixelated images when attempting to increase resolution. Leveraging advancements in technology, this study aims to enhance the resolution of dental panoramic radiographs, thereby enabling more accurate diagnoses and treatment planning.

METHODS

About 1714 panoramic radiographs from 3 different open datasets are used for training (n = 1364) and testing (n = 350). The state of the art 4 different models is explored, namely Super-Resolution Convolutional Neural Network (SRCNN), Efficient Sub-Pixel Convolutional Neural Network, Super-Resolution Generative Adversarial Network, and Autoencoder. Performances in reconstructing high-resolution dental images from low-resolution inputs with different scales (s = 2, 4, 8) are evaluated by 2 well-accepted metrics Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR).

RESULTS

SSIM spans between 0.82 and 0.98 while PSNR are between 28.7 and 40.2 among all scales and models. SRCNN provides the best performance. Additionally, it is observed that performance decreased when images are scaled with higher values.

CONCLUSION

The findings highlight the potential of super-resolution concepts to significantly improve the quality and detail of dental panoramic radiographs, thereby contributing to enhanced interpretability.

Compound Acoustic Radiation Force Impulse Imaging of Bovine Eye by Using Phase-Inverted Ultrasound Transducer

In general, it is difficult to visualize internal ocular structure and detect a lesion such as a cataract or glaucoma using the current ultrasound brightness-mode (B-mode) imaging. This is because the internal structure of the eye is rich in moisture, resulting in a lack of contrast between tissues in the B-mode image, and the penetration depth is low due to the attenuation of the ultrasound wave. In this study, the entire internal ocular structure of a bovine eye was visualized in an ex vivo environment using the compound acoustic radiation force impulse (CARFI) imaging scheme based on the phase-inverted ultrasound transducer (PIUT). In the proposed method, the aperture of the PIUT is divided into four sections, and the PIUT is driven by the out-of-phase input signal capable of generating split-focusing at the same time. Subsequently, the compound imaging technique was employed to increase signal-to-noise ratio (SNR) and to reduce displacement error. The experimental results demonstrated that the proposed technique could provide an acoustic radiation force impulse (ARFI) image of the bovine eye with a broader depth-of-field (DOF) and about 80% increased SNR compared to the conventional ARFI image obtained using the in-phase input signal. Therefore, the proposed technique can be one of the useful techniques capable of providing the image of the entire ocular structure to diagnose various eye diseases.

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.

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review

Background

The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations.

Methods

We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines.

Results

Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations.

Conclusions

DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.