Full Spatial Resolution Research Articles

Two different visual stimuli that cause axial eye shortening have no additive effect

Previous studies identified two visual stimuli that can shorten the human eye by thickening the choroid after short-term visual stimulation, potentially inhibiting myopia: (1) watching digitally filtered movies where the red plane has full spatial resolution while green and blue are low-pass filtered according to the human longitudinal chromatic aberration (LCA) function (the “red in focus” filter), and (2) reading text with inverted contrast. This study aimed to determine whether combining these two stimuli would have an additive effect on axial length. Twenty-two emmetropic subjects were recruited to read text (standard and inverted contrast) for 30 min from a large screen, 2 m away, either unfiltered or filtered with the “red in focus” filter. Axial length was measured before and after each reading episode using low-coherence interferometry (Lenstar LS 900, Haag Streit). Reading text with conventional contrast polarity (dark letters on a bright background) resulted in no significant axial length change. Adding the “red in focus” filter did not alter the outcome. Consistent with previous findings, reading inverted contrast text made emmetropic eyes shorter. Surprisingly, when the text was combined with the “red in focus” filter, eyes became longer rather than shorter. A possible explanation for this contradictory result is that, for the text stimulus, the “red in focus” filter removes spatial information in the blue channel needed by the retina to use LCA analysis to thicken the choroid.

Mapping “Brain Terrain” Regions on Mars Using Deep Learning

One of the main objectives of the Mars Exploration Program is to search for evidence of past or current life on the planet. To achieve this, Mars exploration has been focusing on regions that may have liquid or frozen water. A set of critical areas may have seen cycles of ice thawing in the relatively recent past in response to periodic changes in the obliquity of Mars. In this work, we use convolutional neural networks to detect surface regions containing “brain terrain,” a landform on Mars whose similarity in morphology and scale to sorted stone circles on Earth suggests that it may have formed as a consequence of freeze/thaw cycles. We use large images (∼100–1000 megapixels) from the Mars Reconnaissance Orbiter to search for these landforms at resolutions close to a few tens of centimeters per pixel (∼25–50 cm). Over 58,000 images (∼28 TB) were searched (∼5% of the Martian surface), and we found detections in 201 images. To expedite the processing, we leverage a classifier network (prior to segmentation) in the Fourier domain that can take advantage of JPEG compression by leveraging blocks of coefficients from a discrete cosine transform in lieu of decoding the entire image at the full spatial resolution. The hybrid pipeline approach maintains ∼93% accuracy while cutting down on ∼95% of the total processing time compared to running the segmentation network at the full resolution on every image.

Fast connectivity gradient approximation: maintaining spatially fine-grained connectivity gradients while reducing computational costs

Brain connectome analysis suffers from the high dimensionality of connectivity data, often forcing a reduced representation of the brain at a lower spatial resolution or parcellation. This is particularly true for graph-based representations, which are increasingly used to characterize connectivity gradients, capturing patterns of systematic spatial variation in the functional connectivity structure. However, maintaining a high spatial resolution is crucial for enabling fine-grained topographical analysis and preserving subtle individual differences that might otherwise be lost. Here we introduce a computationally efficient approach to establish spatially fine-grained connectivity gradients. At its core, it leverages a set of landmarks to approximate the underlying connectivity structure at the full spatial resolution without requiring a full-scale vertex-by-vertex connectivity matrix. We show that this approach reduces computational time and memory usage while preserving informative individual features and demonstrate its application in improving brain-behavior predictions. Overall, its efficiency can remove computational barriers and enable the widespread application of connectivity gradients to capture spatial signatures of the connectome. Importantly, maintaining a spatially fine-grained resolution facilitates to characterize the spatial transitions inherent in the core concept of gradients of brain organization.

Deep-Pathfinder: a boundary layer height detection algorithm based on image segmentation

Abstract. A novel atmospheric layer detection approach has been developed based on deep learning techniques for image segmentation. Our proof of concept estimated the layering in the atmosphere, distinguishing between pollution-rich layers closer to the surface and cleaner layers aloft. Knowledge of the spatio-temporal development of atmospheric layers, such as the mixing boundary layer height (MBLH), is important for the dispersion of air pollutants and greenhouse gases, as well as for assessing the performance of numerical weather prediction systems. Existing lidar-based layer detection algorithms typically do not use the full resolution of the available data, require manual feature engineering, often do not enforce temporal consistency of the layers, and lack the ability to be applied in near-real time. To address these limitations, our Deep-Pathfinder algorithm represents the MBLH profile as a mask and directly predicts it from an image with backscatter lidar observations. Deep-Pathfinder was applied to range-corrected signal data from Lufft CHM15k ceilometers at five locations of the operational ceilometer network in the Netherlands. Input samples of 224 × 224 px were extracted, each covering a 45 min observation period. A customised U-Net architecture was developed with a nighttime indicator and MobileNetV2 encoder for fast inference times. The model was pre-trained on 19.4×106 samples of unlabelled data and fine-tuned using 50 d of high-resolution annotations. Qualitative and quantitative results showed competitive performance compared to two reference methods: the Lufft and STRATfinder algorithms, applied to the same dataset. Deep-Pathfinder enhances temporal consistency and provides near-real-time estimates at full spatial and temporal resolution. These properties make our approach valuable for application in operational networks, as near-real-time and high-resolution MBLH detection better meets the requirements of users, such as in aviation, weather forecasting, and air quality monitoring.

This is SpArta: Rigorous Optimization of Regionally Resolved Energy Systems by Spatial Aggregation and Decomposition

Energy systems with high shares of renewable energy are characterized by local variability and grid limitations. The synthesis of such energy systems, therefore, requires models with high spatial resolution. However, high spatial resolution increases the computational effort. Here, we present the SpArta method for rigorous optimization of regionally resolved energy systems by Spatial Aggregation and decomposition. SpArta significantly reduces computational effort while providing a near-optimal solution at the full spatial resolution of sector-coupled energy systems.SpArta first reduces problem size by spatially aggregating the energy system using clustering. The aggregated problem is then relaxed and restricted to obtain a lower and an upper bound. The spatial resolution is iteratively increased until the difference between upper and lower bound satisfies a predefined optimality gap.Finally, each cluster of the aggregated problem is redesigned at full resolution. For this purpose, SpArta decomposes the original synthesis problem into subproblems for each cluster. Combining the redesigned cluster solutions yields an optimal feasible solution of the full-scale problem within a predefined optimality gap. SpArta thus optimizes large-scale energy systems rigorously with significant reductions in computational effort. We apply SpArta to a case study of the sector-coupled German energy system, reducing the computational time by a factor of 7.5, compared to the optimization of the same problem at full spatial resolution. As SpArta shows a linear increase in computational time with problem size, SpArta enables computing larger problems allowing to resolve energy system designs with improved accuracy.

Per-pixel unmixing of spectrally overlapping fluorophores using intra-exposure excitation modulation

Multilabel fluorescence imaging is essential for the visualization of complex systems, though a major challenge is the limited width of the useable spectral window. Here, we present a new method, exNEEMO, that enables per-pixel quantification of spectrally-overlapping fluorophores based on their light-induced dynamics, in a way that is compatible with a very broad range of timescales over which these dynamics may occur. Our approach makes use of intra-exposure modulation of the excitation light to distinguish the different emitters given their reference responses to this modulation. We use the approach to simultaneously image four green photochromic fluorescent proteins at the full spatial resolution of the imaging.

Illumination-Based Color Reconstruction for the Dynamic Vision Sensor.

This work demonstrates a novel, state-of-the-art method to reconstruct colored images via the dynamic vision sensor (DVS). The DVS is an image sensor that indicates only a binary change in brightness, with no information about the captured wavelength (color) or intensity level. However, the reconstruction of the scene's color could be essential for many tasks in computer vision and DVS. We present a novel method for reconstructing a full spatial resolution, colored image utilizing the DVS and an active colored light source. We analyze the DVS response and present two reconstruction algorithms: linear-based and convolutional-neural-network-based. Our two presented methods reconstruct the colored image with high quality, and they do not suffer from any spatial resolution degradation as other methods. In addition, we demonstrate the robustness of our algorithm to changes in environmental conditions, such as illumination and distance. Finally, compared with previous works, we show how we reach the state-of-the-art results. We share our code on GitHub.

Thin monolithic pixel sensors with fast operational amplifier output in a 65 nm imaging technology for ALICE ITS3

This work presents results on the Analog Pixel Test Structure (APTS), a 4 × 4 pixel matrix prototype equipped with an output buffer based on the fast individual operational amplifier (OPAMP) of analogue pixel signals to output pads for exploration of pixel timing performance. This work was part of the ALICE ITS3 upgrade and the CERN-EP R&D on monolithic sensors of the TPSCo 65-nm imaging technology. This upgrade will replace the inner layers of the ALICE Inner Tracking System at CERN with ultra-thin flexible wafer-scale monolithic silicon sensors. They will improve the material budget in this region, the tracking precision and the efficiency at low transverse momentum. This paper will show the gain of the signal chain in the APTS and its speed as a function of the configuration parameters, including calibration results with a 55Fe source. Two different pixel structures will be compared to demonstrate the possibility of enhancing the performance in terms of timing and charge collection efficiency by implant modifications in the epitaxial layer. Finally, test beam results planned at the Super Proton Synchrotron at CERN will provide full spatial and time resolution of the APTS OPAMP structures.

High-resolution multi-z confocal microscopy with a diffractive optical element.

There has been recent interest in the development of fluorescence microscopes that provide high-speed volumetric imaging for life-science applications. For example, multi-z confocal microscopy enables simultaneous optically-sectioned imaging at multiple depths over relatively large fields of view. However, to date, multi-z microscopy has been hampered by limited spatial resolution owing to its initial design. Here we present a variant of multi-z microscopy that recovers the full spatial resolution of a conventional confocal microscope while retaining the simplicity and ease of use of our initial design. By introducing a diffractive optical element in the illumination path of our microscope, we engineer the excitation beam into multiple tightly focused spots that are conjugated to axially distributed confocal pinholes. We discuss the performance of this multi-z microscope in terms of resolution and detectability and demonstrate its versatility by performing in-vivo imaging of beating cardiomyocytes in engineered heart tissues and neuronal activity in c. elegans and zebrafish brains.

Label-free high-resolution white light quantitative phase nanoscopy system.

We present a high-resolution white light quantitative phase nanoscopy (WLQPN) system that can be utilized to visualize nanoparticles and subcellular features of the biological specimens. The five-phase shifting technique, along with deconvolution, is adopted to obtain super-resolution in phase imaging. The phase shifting technique can provide full detector resolution, making it beneficial as compared to the well-known Fourier analysis method. The Fourier transform method requires minimum angle of , where is maximum achievable spatial frequency. It limits the highest achievable resolution to much below the actual diffraction limit of the system. Thus, to obtain a high-resolution phase map of the biological specimen, a two-step process is adopted. First, the phase map is recovered using the five-phase shifting algorithm, with full detector spatial resolution. Second, the complex field is obtained from the recovered phase map and further processed using the Richardson Lucy total variation deconvolution algorithm to obtain super-resolution phase images. The present technique was tested on 1951 USAF resolution chart, 200 nm polystyrene beads and Escherichia coli bacteria using a 50×, 0.55NA objective lens. The 200 nm polystyrene beads are visually resolvable and subcellular features of the E. coli bacteria are also observed, suggesting a significant improvement in the resolution.

Retrieval of Atmospheric Water Vapor Content in the Environment from AHI/H8 Using Both Physical and Random Forest Methods—A Case Study for Typhoon Maria (201808)

The advanced imagers onboard the new generation of geostationary satellites could provide multilayer atmospheric moisture information with unprecedented high spatial and temporal resolutions, while the physical retrieval algorithm (One-Dimensional Variational, 1DVAR) is performed for operational atmospheric water vapor products with reduced resolutions, which is due to the limited computational efficiency of the physical retrieval algorithm. In this study, a typical cost-efficient machine learning (Random Forecast, RF) algorithm is adopted and compared with the physical retrieval algorithm for retrieving the atmospheric moisture from the measurements of Advance Himawari Imager (AHI) onboard the Himawari-8 satellite during the typhoon Maria (201808). It is found that the accuracy of the RF-based algorithm has much high computational efficiency and provides moisture retrievals with accuracy 35–45% better than that of 1DVAR, which means the retrieval process can be conducted at full spatial resolution for potential operational application. Both the Global Forecast System (GFS) forecasts and the AHI measurements are necessary information for moisture retrievals; they provide added value for each other.

TransUNet++SAR: Change Detection with Deep Learning about Architectural Ensemble in SAR Images

In the application of change detection satellite remote sensing images, synthetic aperture radar (SAR) images have become a more important data source. This paper proposes a new end-to-end SAR image change network architecture—TransUNet++SAR—that combines Transformer with UNet++. First, the convolutional neural network (CNN) was used to obtain the feature maps of the single time SAR images layer by layer. Tokenized image patches were encoded to extract rich global context information. Using improved Transformer for effective modeling of global semantic relations can generate rich contextual feature representations. Then, we used the decoder to upsample the encoded features, connected the encoded multi-scale features with the high-level features by sequential connection to learn the local-global semantic features, recovered the full spatial resolution of the feature map, and achieved accurate localization. In the UNet++ structure, the bitemporal SAR images are composed of two single networks, which have shared weights to learn the features of the single temporal image layer by layer to avoid the influence of SAR image noise and pseudo-change on the deep learning process. The experiment results show that the experimental effect of TransUNet++SAR on the Beijing, Guangzhou, and Qingdao datasets were significantly better than other deep learning SAR image change detection algorithms. At the same time, compared with other Transformer related change detection algorithms, the description of the changed area edge was more accurate. In the dataset experiments, the model had higher indices than the other models, especially the Beijing building change datasets, where the IOU was 9.79% higher and F1-score was 4.38% higher.

Polarization contrast neutron imaging of magnetic crystallographic phases

Non-destructive characterization methods to observe phase transformations and thus, gain insights to transformation mechanisms in representative volumes are key for the development of advanced materials and manufacturing. Conventional methods are constrained to the surface and small sizes, thus, access to the bulk often implies tedious destructive approaches and hinders in-situ observations of phase evolution. In this work, we introduce a non-destructive technique that overcomes key limitations prevailing today for mapping the spatial distribution of magnetic phases in bulk materials. The use of polarized neutrons, being sensitive to sub-percent fractions of ferromagnetic phases and able to penetrate centimeter sized samples, enables micrometer-scale spatial and second-scale time resolutions. We demonstrate ex-situ and in-situ quantitative mapping of magnetic phases, in particular the evolution of martensite induced by uniaxial- and biaxial deformation in metastable 304 steel. The quantitative results obtained during in-situ deformation testing prove polarization contrast neutron imaging to be particularly effective in detecting small fractions of martensite. Especially during early stages of deformation where neutron diffraction, which in contrast does not provide full field spatial resolution, fails. The short exposure times and high sensitivity renders the method well suited for rapid 3D tomographic mapping and/or operando investigations of phase distributions.

Tunable two-element diffractive lenses with optimal bandwidth utilization.

Previous work has shown that a set of two diffractive optical elements arranged in series can form a diffractive lens with variable optical power that can be tuned by relative rotation of its two sub-elements about their common central axis. However, previous designs of these diffractive optical elements did not take advantage of the full spatial resolution required for the fabrication process because the corresponding sub-elements consisted of both, regions with very high phase gradients (requiring full resolution) and other extended regions with nearly vanishing phase gradients, where the available resolution is "wasted". Here, an advanced design is proposed that exploits the full spatial bandwidth of the production system. This is done by increasing the polar (angular) phase gradient of each sub-element such that it approaches the usually much larger radial phase gradient. A pair of these new sub-elements then composes a diffractive lens that has the same tuning range of its optical power than a standard tunable diffractive lens, but advantageously achieved within a much smaller relative rotation range. This has advantages in systems where high speed tuning of optical power is required, and in systems where the rotation angle is limited.

Tropospheric ozone retrieval by a combination of TROPOMI/S5P measurements with BASCOE assimilated data

Abstract. We present a new tropospheric ozone dataset based on TROPOspheric Monitoring Instrument (TROPOMI)/Sentinel-5 Precursor (S5P) total ozone measurements combined with stratospheric ozone data from the Belgian Assimilation System for Chemical ObsErvations (BASCOE) constrained by assimilating ozone observations from the Microwave Limb Sounder (MLS). The BASCOE stratospheric data are interpolated to the S5P observations and subtracted from the TROPOMI total ozone data. The difference is equal to the tropospheric ozone residual column from the surface up to the tropopause. The tropospheric ozone columns are retrieved at the full spatial resolution of the TROPOMI sensor (5.5×3.5 km2) with daily global coverage. Compared to the Ozone Mapping and Profiler Suite Modern-Era Retrospective analysis for Research and Applications 2 (OMPS-MERRA-2) data, a global mean positive bias of 3.3 DU is found for the analysed period April 2018 to June 2020. A small negative bias of about −0.91 DU is observed in the tropics relative to the operational TROPOMI tropical tropospheric data based on the convective cloud differential (CCD) algorithm throughout the same period. The new tropospheric ozone data (S5P-BASCOE) are compared to a set of globally distributed ozonesonde data integrated up to the tropopause level. We found 2254 comparisons with cloud-free TROPOMI observations within 25 km of the stations. In the global mean, S5P-BASCOE deviates by 2.6 DU from the integrated ozonesondes. Depending on the latitude the S5P-BASCOE deviate from the sondes and between −4.8 and 7.9 DU, indicating a good agreement. However, some exceptional larger positive deviations up to 12 DU are found, especially in the northern polar regions (north of 70∘). The monthly mean tropospheric column and time series for selected areas showed the expected spatial and temporal pattern, such as the wave one structure in the tropics or the seasonal cycle, including a summer maximum, in the mid-latitudes.

Snapshot compressive imaging based digital image correlation: temporally super-resolved full-resolution deformation measurement.

The limited throughput of a digital image correlation (DIC) system hampers measuring deformations at both high spatial resolution and high temporal resolution. To address this dilemma, in this paper we propose to integrate snapshot compressive imaging (SCI)-a recently proposed computational imaging approach-into DIC for high-speed, high-resolution deformation measurement. Specifically, an SCI-DIC system is established to encode a sequence of fast changing speckle patterns into a snapshot and a high-accuracy speckle decompress SCI (Sp-DeSCI) algorithm is proposed for computational reconstruction of the speckle sequence. To adapt SCI reconstruction to the unique characteristics of speckle patterns, we propose three techniques under SCI reconstruction framework to secure high-precision reconstruction, including the normalized sum squared difference criterion, speckle-adaptive patch search strategy, and adaptive group aggregation. For efficacy validation of the proposed Sp-DeSCI, we conducted extensive simulated experiments and a four-point bending SCI-DIC experiment on real data. Both simulation and real experiments verify that the Sp-DeSCI successfully removes the deviations of reconstructed speckles in DeSCI and provides the highest displacement accuracy among existing algorithms. The SCI-DIC system together with the Sp-DeSCI algorithm can offer temporally super-resolved deformation measurement at full spatial resolution, and can potentially replace conventional high-speed DIC in real measurements.

Deep Learning Augmented Data Assimilation: Reconstructing Missing Information with Convolutional Autoencoders

Abstract Remote sensing data play a critical role in improving numerical weather prediction (NWP). However, the physical principles of radiation dictate that data voids frequently exist in physical space (e.g., subcloud area for satellite infrared radiance or no-precipitation region for radar reflectivity). Such data gaps impair the accuracy of initial conditions derived from data assimilation (DA), which has a negative impact on NWP. We use the barotropic vorticity equation to demonstrate the potential of deep learning augmented data assimilation (DDA), which involves reconstructing spatially complete pseudo-observation fields from incomplete observations and using them for DA. By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep learning approximation of the “reconstruction operator,” which maps spatially incomplete observations to a model state with full spatial coverage and resolution. The CAE was applied to an incomplete streamfunction observation (∼30% missing) from a high-resolution benchmark simulation and demonstrated satisfactory reconstruction performance, even when only very sparse (1/16 of T63 grid density) observations were used as input. When only spatially incomplete observations are used, the analysis fields obtained from ensemble square root filter (EnSRF) assimilation exhibit significant error. However, in DDA, when EnSRF takes in the combined data from the incomplete observations and CAE reconstruction, analysis error reduces significantly. Such gains are more pronounced with sparse observation and small ensemble size because the DDA analysis is much less sensitive to observation density and ensemble size than the conventional DA analysis, which is based solely on incomplete observations. Significance Statement Data assimilation plays a critical role in improving the skills of modern numerical weather prediction by establishing accurate initial conditions. However, unobservable regions are common in observation data, particularly those derived from remote sensing. The nonlinear relationship between data from observable regions and the physical state of unobservable regions may impede DA efficiency. As a result, we propose that deep learning be used to improve data assimilation in such cases by reconstructing a spatially complete first guess of the physical state with deep learning and then applying data assimilation to the reconstructed field. Such deep learning augmentation is found effective in improving the accuracy of data assimilation, especially for sparse observation and small ensemble size.

Learning the Link between Albedo and Reflectance: Machine Learning-Based Prediction of Hyperspectral Bands from CTX Images

The instruments of the Mars Reconnaissance Orbiter (MRO) provide a large quantity and variety of imagining data for investigations of the Martian surface. Among others, the hyper-spectral Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) captures visible to infrared reflectance across several hundred spectral bands. However, Mars is only partially covered with targeted CRISM at full spectral and spatial resolution. In fact, less than one percent of the Martian surface is imaged in this way. In contrast, the Context Camera (CTX) onboard the MRO delivers images with a higher spatial resolution and the image data cover almost the entire Martian surface. In this work, we examine to what extent machine learning systems can learn the relation between morphology, albedo and spectral composition. To this end, a dataset of 67 CRISM-CTX image pairs is created and different deep neural networks are trained for the pixel-wise prediction of CRISM bands solely based on the albedo information of a CTX image. The trained models enable us to estimate spectral bands across large areas without existing CRISM data and to predict the spectral composition of any CTX image. The predictions are qualitatively similar to the ground-truth spectra and are also able to recover finer grained details, such as dunes or small craters.

On the use of polychromatic cameras for high spatial resolution spectral dose measurements

Objective. Despite the demonstrated benefits of hyperspectral formalism for stem effect corrections in the context of fiber dose measurements, this approach has not been yet translated into volumetric measurements where cameras are typically used for their distinguishing spatial resolution. This work investigates demosaicing algorithms for polychromatic cameras based spectral imaging. Approach. The scintillation and Cherenkov signals produced in a radioluminescent phantom are imaged by a polychromatic camera and isolated using the spectral formalism. To do so, five demosaicing algorithms are investigated from calibration to measurements: a clustering method and four interpolation algorithms. The resulting accuracy of scintillation and Cherenkov images is evaluated with measurements of the differences (mean ± standard deviation) between the obtained and expected signals from profiles drawn across a scintillation spot. Signal-to-noise ratio and signal-to-background ratio are further measured and compared in the resulting scintillation images. Finally, the resulting differences on the scintillation signal from a 0.2 × 0.2 cm2 region-of-interest (ROI) were reported. Main results. Clustering, OpenCV, bilinear, Malvar and Menon demosaicing algorithms respectively yielded differences of 3 ± 5%, 1 ± 3%, 1 ± 3%, 1 ± 2% and 2 ± 4% in the resulting scintillation images. For the Cherenkov images, all algorithms provided differences below 1%. All methods enabled measurements over the detectability (SBR > 2) and sensitivity (SNR > 5) thresholds with the bilinear algorithm providing the best SNR value. Clustering, OpenCV, bilinear, Malvar and Menon demosaicing algorithms respectively provided differences on the ROI analysis of 7 ± 5%, 3 ± 2%, 3 ± 2%, 4 ± 2%, 7 ± 3%. Significance. Radioluminescent signals can accurately be isolated using a single polychromatic camera. Moreover, demosaicing using a bilinear kernel provided the best results and enabled Cherenkov signal subtraction while preserving the full spatial resolution of the camera.

Simultaneous multislice steady-state free precession myocardial perfusion with full left ventricular coverage and high resolution at 1.5 T.

PurposeTo implement and evaluate a simultaneous multi‐slice balanced SSFP (SMS‐bSSFP) perfusion sequence and compressed sensing reconstruction for cardiac MR perfusion imaging with full left ventricular (LV) coverage (nine slices/heartbeat) and high spatial resolution (1.4 × 1.4 mm2) at 1.5T.MethodsA preliminary study was performed to evaluate the performance of blipped controlled aliasing in parallel imaging (CAIPI) and RF‐CAIPI with gradient‐controlled local Larmor adjustment (GC‐LOLA) in the presence of fat. A nine‐slice SMS‐bSSFP sequence using RF‐CAIPI with GC‐LOLA with high spatial resolution (1.4 × 1.4 mm2) and a conventional three‐slice sequence with conventional spatial resolution (1.9 × 1.9 mm2) were then acquired in 10 patients under rest conditions. Qualitative assessment was performed to assess image quality and perceived signal‐to‐noise ratio (SNR) on a 4‐point scale (0: poor image quality/low SNR; 3: excellent image quality/high SNR), and the number of myocardial segments with diagnostic image quality was recorded. Quantitative measurements of myocardial sharpness and upslope index were performed.ResultsFat signal leakage was significantly higher for blipped CAIPI than for RF‐CAIPI with GC‐LOLA (7.9% vs. 1.2%, p = 0.010). All 10 SMS‐bSSFP perfusion datasets resulted in 16/16 diagnostic myocardial segments. There were no significant differences between the SMS and conventional acquisitions in terms of image quality (2.6 ± 0.6 vs. 2.7 ± 0.2, p = 0.8) or perceived SNR (2.8 ± 0.3 vs. 2.7 ± 0.3, p = 0.3). Inter‐reader variability was good for both image quality (ICC = 0.84) and perceived SNR (ICC = 0.70). Myocardial sharpness was improved using the SMS sequence compared to the conventional sequence (0.37 ± 0.08 vs 0.32 ± 0.05, p < 0.001). There was no significant difference between measurements of upslope index for the SMS and conventional sequences (0.11 ± 0.04 vs. 0.11 ± 0.03, p = 0.84).ConclusionSMS‐bSSFP with multiband factor 3 and compressed sensing reconstruction enables cardiac MR perfusion imaging with three‐fold increased spatial coverage and improved myocardial sharpness compared to a conventional sequence, without compromising perceived SNR, image quality, upslope index or number of diagnostic segments.