A comprehensive multi-domain dataset for mitotic figure detection

The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species

Mitosis domain generalization in histopathology images -- The MIDOG challenge

The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitotic figures by pathologists is known to be subject to a strong inter-rater bias, which limits the prognostic value. State-of-the-art deep learning methods can support the expert in this assessment but are known to strongly deteriorate when applied in a different clinical environment than was used for training. One decisive component in the underlying domain shift has been identified as the variability caused by using different whole slide scanners. The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As a test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were given. The best approaches performed on an expert level, with the winning algorithm yielding an F_1 score of 0.748 (CI95: 0.704-0.781). In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance.Comment: 19 pages, 9 figures, summary paper of the 2021 MICCAI MIDOG challeng

Fast higher-order MR image reconstruction using singular-vector separation

Medical resonance imaging (MRI) conventionally relies on spatially linear gradient fields for image encoding. However, in practice various sources of nonlinear fields can perturb the encoding process and give rise to artifacts unless they are suitably addressed at the reconstruction level. Accounting for field perturbations that are neither linear in space nor constant over time, i.e., dynamic higher-order fields, is particularly challenging. It was previously shown to be feasible with conjugate-gradient iteration. However, so far this approach has been relatively slow due to the need to carry out explicit matrix-vector multiplications in each cycle. In this work, it is proposed to accelerate higher-order reconstruction by expanding the encoding matrix such that fast Fourier transform can be employed for more efficient matrix-vector computation. The underlying principle is to represent the perturbing terms as sums of separable functions of space and time. Compact representations with this property are found by singular-vector analysis of the perturbing matrix. Guidelines for balancing the accuracy and speed of the resulting algorithm are derived by error propagation analysis. The proposed technique is demonstrated for the case of higher-order field perturbations due to eddy currents caused by diffusion weighting. In this example, image reconstruction was accelerated by two orders of magnitude

Thermal variation in gradient response: measurement and modeling

Purpose Many aspects and imperfections of gradient dynamics in MRI have been successfully captured by linear time-invariant (LTI) models. Changes in gradient behavior due to heating, however, violate time invariance. The goal of this work is to study such changes at the level of transfer functions and model them by thermal extension of the LTI framework. Methods To study the impact of gradient heating on transfer functions, a clinical MR system was heated using a range of high-amplitude DC and AC waveforms, each followed by measuring transfer functions in rapid succession while the system cooled down. Simultaneously, gradient temperature was monitored with an array of temperature sensors positioned according to initial infrared recordings of the gradient tube. The relation between temperatures and transfer functions is cast into local and global linear models. The models are analysed in terms of self-consistency, conditioning, and prediction performance. Results Pronounced thermal effects are observed in the time resolved transfer functions, largely attributable to in-coil eddy currents and mechanical resonances. Thermal modeling is found to capture these effects well. The keys to good model performance are well-placed temperature sensors and suitable training data. Conclusion Heating changes gradient response, violating time invariance. The utility of LTI modeling can nevertheless be recovered by a linear thermal extension, relying on temperature sensing and adequate one-time training.ISSN:0740-3194ISSN:1522-259

Chronic cervical spinal cord injury: DTI correlates with clinical and electrophysiological measures

Diffusion tensor imaging (DTI) is rarely applied in spinal cord injury (SCI). The aim of this study was to correlate diffusion properties after SCI with electrophysiological and neurological measures. Nineteen traumatic cervical SCI subjects and 28 age-matched healthy subjects participated in this study. DTI data of the spinal cord were acquired with a Philips Achieva 3 T MR scanner using an outer volume suppressed, reduced field of view (FOV) acquisition with oblique slice excitation and a single-shot EPI readout. Neurological and electrophysiological measures, American Spinal Injury Association (ASIA) impairment scale scores, and motor (MEP) and somatosensory evoked potentials (SSEP) were assessed in SCI subjects. Fractional anisotropy (FA) values were decreased in the SCI subjects compared to the healthy subjects. In upper cervical segments, the decrease in FA was significant for the evaluation of the entire cross-sectional area of the spinal cord, and for corticospinal and sensory tracts. A decreasing trend was also found at the thoracic level for the corticospinal tracts. The decrease of DTI values correlated with the clinical completeness of SCI, and with SSEP amplitudes. The reduced DTI values seen in the SCI subjects are likely due to demyelination and axonal degeneration of spinal tracts, which are related to clinical and electrophysiological measures. A reduction in DTI values in regions remote from the injury site suggests their involvement with wallerian axonal degeneration. DTI can be used for the quantitative evaluation of the extent of spinal cord damage, and eventually to monitor the effects of future regeneration-inducing treatments

Gradient system characterization by impulse response measurements with a dynamic field camera

This work demonstrates a fast, sensitive method of characterizing the dynamic performance of MR gradient systems. The accuracy of gradient time-courses is often compromised by field imperfections of various causes, including eddy currents and mechanical oscillations. Characterizing these perturbations is instrumental for corrections by pre-emphasis or post hoc signal processing. Herein, a gradient chain is treated as a linear time-invariant system, whose impulse response function is determined by measuring field responses to known gradient inputs. Triangular inputs are used to probe the system and response measurements are performed with a dynamic field camera consisting of NMR probes. In experiments on a whole-body MR system, it is shown that the proposed method yields impulse response functions of high temporal and spectral resolution. Besides basic properties such as bandwidth and delay, it also captures subtle features such as mechanically induced field oscillations. For validation, measured response functions were used to predict gradient field evolutions, which was achieved with an error below 0.2%. The field camera used records responses of various spatial orders simultaneously, rendering the method suitable also for studying cross-responses and dynamic shim systems. It thus holds promise for a range of applications, including pre-emphasis optimization, quality assurance, and image reconstruction

Filling the dead-time gap in zero echo time MRI: Principles compared

PURPOSE: MRI of tissues with short coherence lifetimes T2 or T2* can be performed efficiently using zero echo time (ZTE) techniques such as algebraic ZTE, pointwise encoding time reduction with radial acquisition (PETRA), and water- and fat-suppressed proton projection MRI (WASPI). They share the principal challenge of recovering data in central k-space missed due to an initial radiofrequency dead time. The purpose of this study was to compare the three techniques directly, with a particular focus on their behavior in the presence of ultra-short-lived spins. METHODS: The most direct comparison was enabled by aligning acquisition and reconstruction strategies of the three techniques. Image quality and short- T2* performance were investigated using point spread functions, 3D simulations, and imaging of phantom and bone samples with short (<1 ms) and ultra-short (<100 μs) T2*. RESULTS: Algebraic ZTE offers favorable properties but is limited to k-space gaps up to approximately three Nyquist dwells. At larger gaps, PETRA enables robust imaging with little compromise in image quality, whereas WASPI may be prone to artifacts from ultra-short T2* species. CONCLUSION: For small k-space gaps (<4 dwells) and T2* much larger than the dead time, all techniques enable artifact-free short- T2* MRI. However, if these requirements are not fulfilled careful consideration is needed and PETRA will generally achieve better image quality. Magn Reson Med 79:2036-2045, 2018. © 2017 International Society for Magnetic Resonance in Medicine

On the signal-to-noise ratio benefit of spiral acquisition in diffusion MRI

Purpose Spiral readouts combine several favorable properties that promise superior net sensitivity for diffusion imaging. The purpose of this study is to verify the signal‐to‐noise ratio (SNR) benefit of spiral acquisition in comparison with current echo‐planar imaging (EPI) schemes. Methods Diffusion‐weighted in vivo brain data from three subjects were acquired with a single‐shot spiral sequence and several variants of single‐shot EPI, including full‐Fourier and partial‐Fourier readouts as well as different diffusion‐encoding schemes. Image reconstruction was based on an expanded signal model including field dynamics obtained by concurrent field monitoring. The effective resolution of each sequence was matched to that of full‐Fourier EPI with 1 mm nominal resolution. SNR maps were generated by determining the noise statistics of the raw data and analyzing the propagation of equivalent synthetic noise through image reconstruction. Using the same approach, maps of noise amplification due to parallel imaging (g‐factor) were calculated for different acceleration factors. Results Relative to full‐Fourier EPI at b = 0 s/mm2, spiral acquisition yielded SNR gains of 42‐88% and 40‐89% in white and gray matter, respectively, depending on the diffusion‐encoding scheme. Relative to partial‐Fourier EPI, the gains were 36‐44% and 34‐42%. Spiral g‐factor maps exhibited less spatial variation and lower maxima than their EPI counterparts. Conclusion Spiral readouts achieve significant SNR gains in the order of 40‐80% over EPI in diffusion imaging at 3T. Combining systematic effects of shorter echo time, readout efficiency, and favorable g‐factor behavior, similar benefits are expected across clinical and neurosciences uses of diffusion imaging.ISSN:0740-3194ISSN:1522-259

Feasibility of spiral fMRI based on an LTI gradient model

Spiral imaging is very well suited for functional MRI, however its use has been limited by the fact that artifacts caused by gradient imperfections and B0 inhomogeneity are more difficult to correct compared to EPI. Effective correction requires accurate knowledge of the traversed k-space trajectory. With the goal of making spiral fMRI more accessible, we have evaluated image reconstruction using trajectories predicted by the gradient impulse response function (GIRF), which can be determined in a one-time calibration step. GIRF-predicted reconstruction was tested for high-resolution (0.8 mm) fMRI at 7T. Image quality and functional results of the reconstructions using GIRF-prediction were compared to reconstructions using the nominal trajectory and concurrent field monitoring. The reconstructions using nominal spiral trajectories contain substantial artifacts and the activation maps contain misplaced activation. Image artifacts are substantially reduced when using the GIRF-predicted reconstruction, and the activation maps for the GIRF-predicted and monitored reconstructions largely overlap. The GIRF reconstruction provides a large increase in the spatial specificity of the activation compared to the nominal reconstruction. The GIRF-reconstruction generates image quality and fMRI results similar to using a concurrently monitored trajectory. The presented approach does not prolong or complicate the fMRI acquisition. Using GIRF-predicted trajectories has the potential to enable high-quality spiral fMRI in situations where concurrent trajectory monitoring is not available.ISSN:1053-8119ISSN:1095-957

A comprehensive approach for correcting voxel-wise b-value errors in diffusion MRI

Purpose In diffusion MRI, the actual b‐value played out on the scanner may deviate from the nominal value due to magnetic field imperfections. A simple image‐based correction method for this problem is presented. Methods The apparent diffusion constant (ADC) of a water phantom was measured voxel‐wise along 64 diffusion directions at b = 1000 s/mm2. The true diffusion constant of water was estimated, considering the phantom temperature. A voxel‐wise correction factor, providing an effective b‐value including any magnetic field deviations, was determined for each diffusion direction by relating the measured ADC to the true diffusion constant. To test the method, the measured b‐value map was used to calculate the corrected voxel‐wise ADC for additionally acquired diffusion data sets on the same water phantom and data sets acquired on a small water phantom at three different positions. Diffusion tensor was estimated by applying the measured b‐value map to phantom and in vivo data sets. Results The b‐value‐corrected ADC maps of the phantom showed the expected spatial uniformity as well as a marked improvement in consistency across diffusion directions. The b‐value correction for the brain data resulted in a 5.8% and 5.5% decrease in mean diffusivity and angular differences of the primary diffusion direction of 2.71° and 0.73° inside gray and white matter, respectively. Conclusion The actual b‐value deviates significantly from its nominal setting, leading to a spatially variable error in the common diffusion outcome measures. The suggested method measures and corrects these artifacts.ISSN:0740-3194ISSN:1522-259