Spatial Resolution Enhancement Research Articles

Spatial resolution enhancement using deep learning improves chest disease diagnosis based on thick slice CT

CT is crucial for diagnosing chest diseases, with image quality affected by spatial resolution. Thick-slice CT remains prevalent in practice due to cost considerations, yet its coarse spatial resolution may hinder accurate diagnoses. Our multicenter study develops a deep learning synthetic model with Convolutional-Transformer hybrid encoder-decoder architecture for generating thin-slice CT from thick-slice CT on a single center (1576 participants) and access the synthetic CT on three cross-regional centers (1228 participants). The qualitative image quality of synthetic and real thin-slice CT is comparable (p = 0.16). Four radiologists’ accuracy in diagnosing community-acquired pneumonia using synthetic thin-slice CT surpasses thick-slice CT (p < 0.05), and matches real thin-slice CT (p > 0.99). For lung nodule detection, sensitivity with thin-slice CT outperforms thick-slice CT (p < 0.001) and comparable to real thin-slice CT (p > 0.05). These findings indicate the potential of our model to generate high-quality synthetic thin-slice CT as a practical alternative when real thin-slice CT is preferred but unavailable.

Super resolution reconstruction of fluorescence microscopy images by a convolutional network with physical priors.

Super-solution fluorescence microscopy, such as single-molecule localization microscopy (SMLM), is effective in observing subcellular structures and achieving excellent enhancement in spatial resolution in contrast to traditional fluorescence microscopy. Recently, deep learning has demonstrated excellent performance in SMLM in solving the trade-offs between spatiotemporal resolution, phototoxicity, and signal intensity. However, most of these researches rely on sufficient and high-quality datasets. Here, we propose a physical priors-based convolutional super-resolution network (PCSR), which incorporates a physical-based loss term and an initial optimization process based on the Wiener filter to create excellent super-resolution images directly using low-resolution images. The experimental results demonstrate that PCSR enables the achievement of a fast reconstruction time of 100 ms and a high spatial resolution of 10 nm by training on a limited dataset, allowing subcellular research with high spatiotemporal resolution, low cell phototoxic illumination, and high accessibility. In addition, the generalizability of PCSR to different live cell structures makes it a practical instrument for diverse cell research.

Resolution enhancement of SMOS brightness temperatures: Application to melt detection on the Antarctic and Greenland ice sheets

A large part of the surface of the Greenland Ice Sheet (GrIS) and the margins of Antarctica are melting every summer, affecting their surface mass balance. Wet/dry snow status has been detected for decades using the peaks of brightness temperature at 19 GHz, and more recently at L-band (1.4 GHz) using both the SMOS and SMAP missions. SMOS owns a longer time series than SMAP with data since 2010, but the 52.5°incidence bin in the Level 3 (L3) product from Centre Aval de Traitement des Données SMOS (CATDS) that was previously used to detect melt suffers from a coarse spatial resolution. For this reason, we developed a new SMOS enhanced resolution brightness temperature (TB) product building on the radiometer version of the Scatterometter Image Reconstruction (rSIR) algorithm. We also exploited the SMOS L1C observations near 40°incidence angle instead of 52.5°as the native spatial resolution of SMOS is better at low incidence. The new product is posted on a 12.5 km polar stereographic grid and covers all the GrIS and Antarctica for 2010–2024 with twice-daily morning and afternoon acquisitions. The effective spatial resolution was evaluated to ∼30 km, a 30% enhancement compared to the SMOS L3TB at 40°and almost a 50% enhancement compared to the SMOS L3TB at 52.5°. Then, we applied a melt detection algorithm to both the enhanced resolution product at 40°and the L3TB product at 52.5°which is used in the literature. The spatial resolution enhancement results not only in the detection of smaller melt regions but also in a widespread increase in the annual number of melt days. This increase is larger than 30 days per year in the GrIS percolation area and on multiple Antarctic ice shelves. This is primarily due to the mix of dry and wet snow regions near the ice shelves grounding line, resulting in lower brightness temperature peaks in the SMOS L3TB product due to a large power spread. These findings highlight the dependence of melt detection in particular, and geophysical applications in general, on the spatial resolution of passive microwave observations. This study provides a new open dataset suitable to monitor melt at the surface and at depth on the two main ice-sheets.

Spatial Resolution Enhancement Framework Using Convolutional Attention-Based Token Mixer

Spatial resolution enhancement in remote sensing data aims to augment the level of detail and accuracy in images captured by satellite sensors. We proposed a novel spatial resolution enhancement framework using the convolutional attention-based token mixer method. This approach leveraged spatial context and semantic information to improve the spatial resolution of images. This method used the multi-head convolutional attention block and sub-pixel convolution to extract spatial and spectral information and fused them using the same technique. The multi-head convolutional attention block can effectively utilize the local information of spatial and spectral dimensions. The method was tested on two kinds of data types, which were the visual-thermal dataset and the visual-hyperspectral dataset. Our method was also compared with the state-of-the-art methods, including traditional methods and deep learning methods. The experiment results showed that the method was effective and outperformed state-of-the-art methods in overall, spatial, and spectral accuracies.

Enhancing the performance of DAS using a dual-wavelength UWFBG array with alternating arrangement

We propose a novel distributed acoustic sensor (DAS) based on a dual-wavelength ultra-weak fiber Bragg grating (UWFBG) array. The UWFBGs in the array have two wavelengths that are arranged alternately. Two double-pulses with different wavelengths and staggered launching times are used for sensing in the UWFBG array. This approach successfully overcomes the trade-off between spatial resolution and sensing distance in the traditional UWFBG-based DAS and effectively doubles the maximum achievable sampling rate constrained by the sensing distance. The crosstalk of different wavelengths is avoided by time division multiplexing. Comprehensive analyses and simulations are conducted to validate the performance enhancements, compared to the traditional method. In our proof-of-concept experiments, employing a 2 km alternating arranged dual-wavelength UWFBG array, we achieve a spatial resolution enhancement from 10 m to 5 m, expand the dynamic strain measurement range from 34.2 rad to 68.2 rad, and increase the frequency response from 9 kHz to 19 kHz.

ImSpiRE: image feature-aided spatial resolution enhancement method.

The resolution of most spatially resolved transcriptomic technologies usually cannot attain the single-cell level, limiting their applications in biological discoveries. Here, we introduce ImSpiRE, an image feature-aided spatial resolution enhancement method for in situ capturing spatial transcriptome. Taking the information stored in histological images, ImSpiRE solves an optimal transport problem to redistribute the expression profiles of spots to construct new transcriptional profiles with enhanced resolution, together with extending the gene expression profiles into unmeasured regions. Applications to multiple datasets confirm that ImSpiRE can enhance spatial resolution to the subspot level while contributing to the discovery of tissue domains, signaling communication patterns, and spatiotemporal characterization.

Mapping and monitoring dense non-aqueous phase liquid source zone by fused surface and cross-borehole electrical resistivity tomography

Effective characterization of dense non-aqueous phase liquid (DNAPL) source zones is crucial for remediating polluted sites. DNAPL often reside as residuals or pools within high-permeability lenses and above impermeable layers due to soil heterogeneity, gravity, and capillary barriers. Given the high cost of drilling, electrical resistivity tomography (ERT) techniques—including surface ERT and cross-borehole ERT, are commonly used for DNAPL source zone mapping and monitoring. However, the low spatial resolution of ERT increases uncertainty in source zone investigations. This study proposes a method for improving DNAPL mapping and monitoring by fusing surface and cross-borehole ERT data. Sandbox experiments were conducted to simulate a heterogeneous DNAPL source zone, employing both ERT methods for static mapping and dynamic monitoring. Reflective light imaging (RLM) was used to visualize DNAPL migration and provide saturation data, allowing for the quantification of ERT's effectiveness in characterizing DNAPL distribution. The results indicate that individual ERT methods face significant challenges in DNAPL source zone mapping due to background interference. Surface ERT alone tends to underestimate the extent of deeper DNAPL source zones. However, fusing surface and cross-borehole ERT results in a complementary enhancement of vertical spatial resolution, thereby improving the characterization of DNAPL source zones. The fusion of static and time-lapse ERT data substantially enhances DNAPL source zone mapping and monitoring capabilities. By calculating the ratio of the ERT-monitored area to the actual area using resistivity change contours (5 %, 10 %, 15 %), it was found that fusing surface and cross-borehole ERT data improved monitoring resolution by 50.48 % compared to surface ERT alone and by 22.95 % compared to cross-borehole ERT. Principal component analysis (PCA) was effective in fusing time-lapse data, while the weighted average method (WAM) outperformed PCA for static resistivity data fusion.

Enhanced MALDI-2 Sensitivity with Reflecting Post-Ionization Laser for High-Resolution MS Imaging Combined with Real-Time Microscope Imaging.

Laser-induced matrix-assisted laser desorption/ionization post-ionization (MALDI-2) could improve the MALDI sensitivity of biological metabolites by over 1 order of magnitude. Herein, we demonstrate that MALDI-2 sensitivity can be further enhanced with reflecting post-ionization laser that multiplies the intersection times between laser and MALDI plume. This method, which we named MALDI-2+, typically brought over 2 times sensitivity improvement from conventional MALDI-2. Advancing in sensitivity thereby prompted us to pursue higher mass spectrometry imaging (MSI) spatial resolution. A dedicated T-shaped ion guide was designed to allow perpendicular incidence of ablation laser in reflection geometry MALDI. Although 8-10 μm pixel was used in MALDI imaging due to the limited precision of the motorized stage, the laser spot diameter could be down to 2.5 μm for potentially higher spatial resolution. In addition, this ion source enabled real-time and high-quality microscope imaging from backward of the sample plate. Beneficially, we were able to monitor the actual laser spot condition in real time as well as obtain high-resolution microscopic sample images that inherently register with MSI images. All of these benefits have been demonstrated by analyzing standard samples and imaging of cells. We believe that the enhancement in sensitivity, spatial resolution, and microscope capacity of our design could facilitate spatial omics studies.

Land cover classification in high-resolution remote sensing: using Swin Transformer deep learning with texture features

ABSTRACT The enhancement of spatial resolution has improved the spatial information conveyed by remote sensing images. Nevertheless, the impact of spectral-texture combined features on classification accuracy in deep learning remains understudied. This study develops five spectral texture features (Spe-GLCM, Spe-FRS, Spe-Gabor, Spe-CS-LMP, and Spe-FD) using GID15 and Vaihingen datasets. These features were compared with U-Net++, Deeplabv3+, and PSPNet models, incorporating fine-tuned model structures. The results indicate that integrating texture features enhances accuracy in both overall and sparsely sampled land cover classifications. This highlights the potential of deep learning combined with texture features to recognize land cover from high-resolution remote sensing imagery.

Oversampling for Enhanced Spatial Resolution of Zebrafish by Top-Hat IR-MALDESI-MSI.

Mass spectrometry imaging (MSI) has become a significant tool for measuring chemical species in biological tissues, where much of the impact of these platforms lies in their capability to report the spatial distribution of analytes for correlation to sample morphology. As a result, enhancement of spatial resolution has become a frontier of innovation in the field, and necessary developments are dependent on the ionization source. More particularly, laser-based imaging sources may require modifications to the optical train or alternative sampling techniques. These challenges are heightened for systems with infrared (IR) lasers, as their operating wavelength generates spot sizes that are inherently larger than their ultraviolet counterparts. Recently, the infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) source has shown the utility of a diffractive optical element (DOE) to produce square ablation patterns, termed top-hat IR-MALDESI. If the DOE optic is combined with oversampling methods, smaller ablation volumes can be sampled to render higher spatial resolution imaging experiments. Further, this approach enables reproducible spot sizes and ablation volumes for better comparison between scans. Herein, we investigate the utility of oversampling with top-hat IR-MALDESI to enhance the spatial resolution of measured lipids localized within the head of sectioned zebrafish tissue. Four different spatial resolutions were evaluated for data quality (e.g., mass measurement accuracy, spectral accuracy) and quantity of annotations. Other experimental parameters to consider for high spatial resolution imaging are also discussed. Ultimately, 20 μm spatial resolution was achieved in this work and supports feasibility for use in future IR-MALDESI studies.

Stimulated Raman-induced beam focusing.

Stimulated Raman scattering, employing a pump and a Stokes beam, exhibits itself through both the Raman loss observed in the pump beam and the Raman gain in the Stokes beam. This phenomenon finds application in spectroscopy for chemical analyses and microscopy for label-free bioimaging studies. Recent efforts have been made to implement super-resolution Raman microscopy using a doughnut-shaped pump, Stokes, or depletion beam. In this study, it is shown that the amplitude and phase of the pump or Stokes beam undergo significant modulation through the stimulated Raman process when they are configured as one of the higher-order Laguerre-Gauss modes, achieved using appropriate spiral phase plates or spatial light modulators. The resulting intensity distributions of the pump and Stokes beams are determined by a superposition of multiple Laguerre-Gauss modes that are coupled through nonlinear Raman gain and loss processes. Calculation results are used to elucidate the limitations associated with super-resolution coherent Raman imaging with a toroidal pump or Stokes beam. This stands in contrast with the stimulated emission depletion fluorescence microscopy technique, which has no fundamental limit in the spatial resolution enhancement.

Improving PRISMA hyperspectral spatial resolution and geolocation by using Sentinel-2: development and test of an operational procedure in urban and rural areas

Hyperspectral (HS) satellites like PRISMA (PRecursore IperSpettrale della Missione Applicativa) offer remarkable capabilities, yet they are constrained by a relatively coarse spatial resolution, curbing their efficacy in those applications that require pinpoint accuracy. Here we propose a fusion process, aimed at the enhancement of PRISMA HS spatial resolution by using the spatial and spectral information of Sentinel-2 multispectral (MS) data (HS-MS fusion process), validated against four airborne HS flights simultaneous to satellite overpasses on different land use distributions. Adopting the PRISMA panchromatic (PAN) image, the proposed solution was also compared with the results of a HS-PAN pansharpening process. A two-steps operational workflow is proposed, based on two state-of-the-art and open-source algorithms. The first step consisted of the geocoding of PRISMA L2 products using Senintel-2 as reference and was accomplished with the phase-based algorithm implemented in AROSICS (Automated and Robust Open-Source Image Co-registration Software). The geometric displacement in L2 data was found to be between 80 m and 250 m, irregularly spatially distributed throughout the same scene and among scenes, and it was corrected by means of thousands of regularly spatially distributed tie points. A second-order polynomial transformation function was integrated in the algorithm. The second step consisted of employing the HySure (HS Super resolution) fusion algorithm to perform both the HS-MS fusion and the HS-PAN pansharpening, returning a PRISMA HS improved dataset with a spatial resolution of 10 m and 5 m, respectively. Four different per-band accuracy metrics were used to evaluate the accuracy of both products against airborne data. Overall, HS-MS data achieved increased accuracy in all validation metrics, i.e. + 28 % (root mean square error, RMSE), +23 % (spectral angle mapper, SAM), +7% (peak signal-to-noise ratio, PSNR) and + 11 % (universal image quality index, UIQI), with respect of HS-PAN data. These outcomes showed that using the spectral information of Sentinel-2 both spectral and spatial patterns were reconstructed more consistently in three different urban and rural scenarios, avoiding the presence of blur and at-edge artefacts as opposed to HS-PAN pansharpening, therefore suggesting an optimal strategy for satellite HS data resolution enhancement.

All-dielectric super-lattice metasurfaces with fivefold spatial resolution enhancement for structured illumination microscopy.

Structured illumination microscopy (SIM) is a promising imaging technique for high-resolution imaging with a wide field of view. Although a periodic nanostructure is a versatile platform for engineering the spatial frequency of structured illumination patterns in SIM, challenges remain, including artifacts from Fourier space gaps. We designed an all-dielectric super-lattice metasurface (ADSLM) to generate structured illumination patterns with enhanced spatial frequency and broadened spatial frequency coverage with no intermediate frequency gaps. Our numerical simulations reveal that ADSLM-based image reconstruction is capable of producing high-contrast, artifact-free images, resulting in enhanced spatial resolution up to 5.7-fold for coherent SIM at 450 nm. Our results show that the ADSLM-SIM technique may facilitate high-resolution imaging using CMOS-compatible substrates, offering potential for compact miniaturized imaging applications.

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.

Spatial resolution enhancement in holographic imaging via angular spectrum expansion

Digital holography numerically restores three-dimensional image information using optically captured diffractive waves. The required bandwidth is larger than that of hologram pixel at a closer distance in the Fresnel diffraction regime, which results in the formation of aliased replica patterns in digital hologram. From the analysis of sampling phenomenon, the replica functions are revealed to be the components of higher angular spectra of hologram. A undersampled hologram consists of the moire patterns formed by the modulation of original function by complex exponential function. There is a one-to-one correspondence between the replicas in both real and Fourier spaces. The acquisition methods of high-resolution images over a wide field view are proposed on the basis of the expansion process of angular spectrum by using replicas. Only the captured hologram with low numerical aperature or low resolution restores a high-resolution image when using an optimization algorithm. Numerical simulations and optical experiments are performed to investigate the proposed scheme.

Spatial functional mapping of hypoxia inducible factor heterodimerisation and immune checkpoint regulators in clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is a highly malignant subtype of kidney cancer. Ninety percent of ccRCC have inactivating mutations of VHL that stabilise transcription factors, HIF1α and HIF2α, only stabilised in hypoxia. The varied response to HIF2 inhibition, in the preclinical and clinical settings, suggests that assessment of HIF2α activation state, not just expression levels is required as a biomarker of sensitivity to enable optimal clinical use.MethodsTwo-site amplified time-resolved Förster Resonance Energy Transfer (aiFRET), with FRET-Efficiency, Ef\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Ef$$\\end{document}, as its read out, provides functional proteomics quantification, a precise step forward from protein expression as a tool for patient stratification.To enhance the clinical accessibility of Ef\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Ef$$\\end{document}, we have devised a new computational approach, Functional Oncology map (FuncOmap).ResultsFuncOmap directly maps functional states of oncoproteins and allows functional states quantification at an enhanced spatial resolution. The innovative contributions in FuncOmap are the means to co-analyse and map expressional and functional state images and the enhancement of spatial resolution to facilitate clinical application. We show the spatial interactive states HIF2α and HIF1β in ccRCC patient samples.ConclusionFuncOmap can be used to quantify heterogeneity in patient response and improve accurate patient stratification, thus enhancing the power of precision.

Transformative Precision: Investigative Summary of PET/CT-Guided Radiation Therapy Simulation in Comprehensive Cancer Management

Positron Emission Tomography/Computed Tomography (PET/CT)-guided radiation therapy simulation has transformed cancer treatment, ushering in enhanced precision and individualization. This discussion delves into clinical indications, applications, procedures, and limitations, providing a comprehensive overview across cancer types. &#x0D; Clinical indications underscore PET/CT's role in accurate staging, target volume delineation, treatment response assessment, and post-treatment recurrence detection. Accurate staging is crucial for tailored treatment plans, while target volume delineation benefits from PET's identification of metabolic patterns. Ongoing treatment response assessment enables dynamic adjustments, and post-treatment, PET/CT aids in detecting recurrent disease. &#x0D; Applications highlight PET/CT's treatment planning optimization by combining anatomical and functional information. Fusion of PET&#x0D; and CT images customizes radiation plans, identifying active regions for targeted delivery while sparing healthy tissues. This fusion facilitates tailored strategies, minimizing radiation exposure and enabling dynamic adaptations. &#x0D; Procedural aspects detail imaging acquisition, image fusion, target delineation, treatment planning, and ongoing monitoring. Starting with radiotracer administration, typically fluorodeoxyglucose (FDG), PET/CT captures functional and anatomical data. Image fusion aids in target delineation and optimizing plans. Ongoing monitoring allows real-time adjustments. &#x0D; Specific clinical applications across cancers demonstrate PET/CT's versatility. In head and neck cancers, it ensures precise delineation while avoiding critical structures. In lung cancer, it improves tumor extent identification. Similar advantages apply to lymphomas, sarcomas, brain tumors, metastatic disease, and esophageal, gastrointestinal, breast, prostate, gynecological, and pediatric cancers. &#x0D; Limitations include spatial resolution challenges, false positives, cumulative radiation exposure, lesion size, histology, and standardization issues. Ongoing research targets spatial resolution enhancement, radiomics and AI integration, novel tracers, hybrid imaging, patient-specific dosimetry, clinical trials, multimodal workflows, cost-effectiveness, accessibility, and education. &#x0D; PET/CT-guided radiation therapy simulation is transformative. Ongoing advancements promise a more precise and individualized approach, enhancing patient outcomes in cancer management.

A Spatial Resolution Enhancement Method of Microwave Radiation Imager Data Based on Data Matching and Transformer

A Spatial Resolution Enhancement Method of Microwave Radiation Imager Data Based on Data Matching and Transformer

Spectrum Extension of a Real-Aperture Microwave Radiometer Using a Spectrum Extension Convolutional Neural Network for Spatial Resolution Enhancement

Enhancing the spatial resolution of real-aperture microwave radiometers is an essential research topic. The accuracy of the numerical values of brightness temperatures (BTs) observed using microwave radiometers directly affects the precision of the retrieval of marine environmental parameters. Hence, ensuring the accuracy of the enhanced brightness temperature values is of paramount importance when striving to enhance spatial resolution. A spectrum extension (SE) method is proposed in this paper, which restores the suppressed high-frequency components in the scene BT spectrum through frequency domain transformation and calculations, specifically, dividing the observed BT spectrum by the conjugate of the antenna pattern spectrum and applying a Taylor approximation to suppress error amplification, thereby extending the observed BT spectrum. By using a convolutional neural network to correct errors in the calculated spectrum and then reconstructing the BT through inverse fast Fourier transform (IFFT), the enhanced BTs are obtained. Since the extended BT spectrum contains more high-frequency components, namely, the spectrum is closer to that of the original scene BT, the reconstructed BT not only achieves an enhancement in spatial resolution, but also an improvement in the accuracy of BT values. Both the results from simulated data and satellite-measured data processing illustrate that the SE method is able to enhance the spatial resolution of real-aperture microwave radiometers and concurrently improve the accuracy of BT values.

Multipole engineering by displacement resonance: a new degree of freedom of Mie resonance

The canonical studies on Mie scattering unravel strong electric/magnetic optical responses in nanostructures, laying foundation for emerging meta-photonic applications. Conventionally, the morphology-sensitive resonances hinge on the normalized frequency, i.e. particle size over wavelength, but non-paraxial incidence symmetry is overlooked. Here, through confocal reflection microscopy with a tight focus scanning over silicon nanostructures, the scattering point spread functions unveil distinctive spatial patterns featuring that linear scattering efficiency is maximal when the focus is misaligned. The underlying physical mechanism is the excitation of higher-order multipolar modes, not accessible by plane wave irradiation, via displacement resonance, which showcases a significant reduction of nonlinear response threshold, sign flip in all-optical switching, and spatial resolution enhancement. Our result fundamentally extends the century-old light scattering theory, and suggests new dimensions to tailor Mie resonances.