Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data.

PURPOSE: Although immune checkpoint inhibitors (ICIs) have improved outcomes in certain patients with cancer, they can also cause life-threatening immunotoxicities. Predicting immunotoxicity risks alongside response could provide a personalized risk-benefit profile, inform therapeutic decision making, and improve clinical trial cohort selection. We aimed to build a machine learning (ML) framework using routine electronic health record (EHR) data to predict hepatitis, colitis, pneumonitis, and 1-year overall survival. METHODS: Real-world EHR data of more than 2,200 patients treated with ICI through December 31, 2018, were used to develop predictive models. Using a prediction time point of ICI initiation, a 1-year prediction time window was applied to create binary labels for the four outcomes for each patient. Feature engineering involved aggregating laboratory measurements over appropriate time windows (60-365 days). Patients were randomly partitioned into training (80%) and test (20%) sets. Random forest classifiers were developed using a rigorous model development framework. RESULTS: The patient cohort had a median age of 63 years and was 61.8% male. Patients predominantly had melanoma (37.8%), lung cancer (27.3%), or genitourinary cancer (16.4%). They were treated with PD-1 (60.4%), PD-L1 (9.0%), and CTLA-4 (19.7%) ICIs. Our models demonstrate reasonably strong performance, with AUCs of 0.739, 0.729, 0.755, and 0.752 for the pneumonitis, hepatitis, colitis, and 1-year overall survival models, respectively. Each model relies on an outcome-specific feature set, though some features are shared among models. CONCLUSION: To our knowledge, this is the first ML solution that assesses individual ICI risk-benefit profiles based predominantly on routine structured EHR data. As such, use of our ML solution will not require additional data collection or documentation in the clinic.

Frontal and anterior temporal hypometabolism post chemoradiation in head and neck cancer: A real-world PET study.

BACKGROUND AND PURPOSE: Adverse neurological effects after cancer therapy are common, but biomarkers to diagnose, monitor, or risk stratify patients are still not validated or used clinically. An accessible imaging method, such as fluorodeoxyglucose positron emission tomography (FDG PET) of the brain, could meet this gap and serve as a biomarker for functional brain changes. We utilized FDG PET to evaluate which brain regions are most susceptible to altered glucose metabolism after chemoradiation in patients with head and neck cancer (HNCa). METHODS: Real-world FDG PET images were acquired as standard of care before and after chemoradiation for HNCa in 68 patients. Linear mixed-effects voxelwise models assessed changes after chemoradiation in cerebral glucose metabolism quantified with standardized uptake value ratio (SUVR), covarying for follow-up time and patient demographics. RESULTS: Voxelwise analysis revealed two large clusters of decreased glucose metabolism in the medial frontal and polar temporal cortices following chemoradiation, with decreases of approximately 5% SUVR after therapy. CONCLUSIONS: These findings provide evidence that standard chemoradiation for HNCa can lead to decreased neuronal glucose metabolism, contributing to literature emphasizing the vulnerability of the frontal and anterior temporal lobes, especially in HNCa, where these areas may be particularly vulnerable to indirect radiation-induced injury. FDG PET shows promise as a sensitive biomarker for assessing these changes.

Polyunsaturated fatty acids cause physiological and behavioral changes in Vibrio alginolyticus and Vibrio fischeri.

Vibrio alginolyticus and Vibrio (Aliivibrio) fischeri are Gram-negative bacteria found globally in marine environments. During the past decade, studies have shown that certain Gram-negative bacteria, including Vibrio species (cholerae, parahaemolyticus, and vulnificus) are capable of using exogenous polyunsaturated fatty acids (PUFAs) to modify the phospholipids of their membrane. Moreover, exposure to exogenous PUFAs has been shown to affect certain phenotypes that are important factors of virulence. The purpose of this study was to investigate whether V. alginolyticus and V. fischeri are capable of responding to exogenous PUFAs by remodeling their membrane phospholipids and/or altering behaviors associated with virulence. Thin-layer chromatography (TLC) analyses and ultra-performance liquid chromatography-electrospray ionization mass spectrometry (UPLC/ESI-MS) confirmed incorporation of all PUFAs into membrane phosphatidylglycerol and phosphatidylethanolamine. Several growth phenotypes were identified when individual fatty acids were supplied in minimal media and as sole carbon sources. Interestingly, several PUFAs acids inhibited growth of V. fischeri. Significant alterations to membrane permeability were observed depending on fatty acid supplemented. Strikingly, arachidonic acid (20:4) reduced membrane permeability by approximately 35% in both V. alginolyticus and V. fischeri. Biofilm assays indicated that fatty acid influence was dependent on media composition and temperature. All fatty acids caused decreased swimming motility in V. alginolyticus, while only linoleic acid (18:2) significantly increased swimming motility in V. fischeri. In summary, exogenous fatty acids cause a variety of changes in V. alginolyticus and V. fischeri, thus adding these bacteria to a growing list of Gram-negatives that exhibit versatility in fatty acid utilization and highlighting the potential for environmental PUFAs to influence phenotypes associated with planktonic, beneficial, and pathogenic associations.

A Symptom-Based Rule for Diagnosis of COVID-19.

SARS-CoV-19 PCR testing has a turn-around time that makes it impractical for real-time decision-making, and current point-of-care tests have limited sensitivity, with frequent false negatives. The study objective was to develop a clinical prediction rule to use with a point-of-care test to diagnose COVID-19 in symptomatic outpatients. A standardized clinical questionnaire was administered prior to SARS-CoV-2 PCR testing. Data was extracted by a physician blinded to the result status. Individual symptoms were combined into 326 unique clinical phenotypes. Multivariable logistic regression was used to identify independent predictors of COVID-19, from which a weighted clinical prediction rule was developed, to yield stratified likelihood ratios for varying scores. A retrospective cohort of 120 SARS-CoV-2-positive cases and 120 SARS-CoV-2-negative matched controls among symptomatic outpatients in a Connecticut HMO was used for rule development. A temporally distinct cohort of 40 cases was identified for validation of the rule. Clinical phenotypes independently associated with COVID-19 by multivariable logistic regression include loss of taste or smell (olfactory phenotype, 2 points) and fever and cough (febrile respiratory phenotype, 1 point). Wheeze or chest tightness (reactive airways phenotype, - 1 point) predicted non-COVID-19 respiratory viral infection. The AUC of the model was 0.736 (0.674-0.798). Application of a weighted C19 rule yielded likelihood ratios for COVID-19 diagnosis for varying scores ranging from LR 15.0 for 3 points to LR 0.1 for - 1 point. Using a Bayesian diagnostic approach, combining community prevalence with the evidence-based C19 rule to adjust pretest probability, clinicians can apply a point of care test with limited sensitivity across a range of clinical scenarios to differentiate COVID-19 infection from influenza and respiratory viral infection.

Slice-selective extended phase graphs in gradient-crushed, transient-state free precession sequences: An application to MR fingerprinting.

PURPOSE: Slice-selective, gradient-crushed, transient-state sequences such as those used in MR fingerprinting (MRF) relaxometry are sensitive to slice profile effects. Whereas balanced steady-state free precession MRF profile effects have been studied, less attention has been given to gradient-crushed MRF forms. Extensions of the extended phase graph (EPG) formalism, called slice-selective EPG (ssEPG), are proposed that model slice profile effects. THEORY AND METHODS: The hard-pulse approximation to slice-selective excitation in the spatial domain is reformulated in k-space. Excitation is modeled by standard EPG shift and transition operators. This ssEPG modeling is validated against Bloch simulations and phantom slice profile measurements. ssEPG relaxometry accuracy and variability are compared with other EPG methods in phantoms and human leg in vivo. The role of ∆B0 interactions with slice profile and gradient crushers is investigated. RESULTS: Simulations and slice profile measurements show that ssEPG can model highly dynamic slice profile effects of gradient-crushed sequences. The MRF ssEPG T2 estimates over 0 < T2 < 100 ms improve accuracy by > 10 ms at some values relative to other modeling approaches. Small deviations in B0 can produce substantial bias in T2 estimations from a range of MRF sequence types, and these effects can be modeled and understood by ssEPG. CONCLUSION: Transient-state, gradient-crushed sequences such as those used in MRF are sensitive to slice profile effects, and these effects depend on RF pulse choice, gradient crusher strength, and ∆B0 . It was found ssEPG was the most accurate EPG-based means to model these effects.

Dynamic MR image reconstruction based on total generalized variation and low-rank decomposition.

PURPOSE: Propose a novel decomposition-based model employing the total generalized variation (TGV) and the nuclear norm, which can be used in compressed sensing-based dynamic MR reconstructions. THEORY AND METHODS: We employ the nuclear norm to represent the time-coherent background and the spatiotemporal TGV functional for the sparse dynamic component above. We first design an algorithm using the classical first-order primal-dual method for solving the proposed model and then give the norm estimation for the convergence condition. The proposed model is compared with the state-of-the-art methods on different data sets under different sampling schemes and acceleration factors. RESULTS: The proposed model achieves higher SERs and SSIMs than kt-SLR, kt-RPCA, L+S, and ICTGV on cardiac perfusion and breast DCE-MRI data sets under both the pseudoradial and the Cartesian sampling schemes. In addition, the proposed model better suppresses the spatial artifacts and preserves the edges. CONCLUSIONS: The proposed model outperforms the state-of-the-art methods and generates high-quality reconstructions under different sampling schemes and different acceleration factors.

snapMRF: GPU-accelerated magnetic resonance fingerprinting dictionary generation and matching using extended phase graphs.

PURPOSE: Magnetic resonance fingerprinting (MRF) is a state-of-the-art quantitative MRI technique with a computationally demanding reconstruction process, the accuracy of which depends on the accuracy of the signal model employed. Having a fast, validated, open-source MRF reconstruction would improve the dependability and accuracy of clinical applications of MRF. METHODS: We parallelized both dictionary generation and signal matching on the GPU by splitting the simulation and matching of dictionary atoms across threads. Signal generation was modeled using both Bloch equation simulation and the extended phase graph (EPG) formalism. Unit tests were implemented to ensure correctness. The new package, snapMRF, was tested with a calibration phantom and an in vivo brain. RESULTS: Compared with other online open-source packages, dictionary generation was accelerated by 10-1000× and signal matching by 10-100×. On a calibration phantom, T1 and T2 values were measured with relative errors that were nearly identical to those from existing packages when using the same sequence and dictionary configuration, but errors were much lower when using variable sequences that snapMRF supports but that competitors do not. CONCLUSION: Our open-source package snapMRF was significantly faster and retrieved accurate parameters, possibly enabling real-time parameter map generation for small dictionaries. Further refinements to the acquisition scheme and dictionary setup could improve quantitative accuracy.

Trajectory optimized NUFFT: Faster non-Cartesian MRI reconstruction through prior knowledge and parallel architectures.

PURPOSE: The non-uniform fast Fourier transform (NUFFT) involves interpolation of non-uniformly sampled Fourier data onto a Cartesian grid, an interpolation that is slowed by complex, non-local data access patterns. A faster NUFFT would increase the clinical relevance of the plethora of advanced non-Cartesian acquisition methods. METHODS: Here we customize the NUFFT procedure for a radial trajectory and GPU architecture to eliminate the bottlenecks encountered when allowing for arbitrary trajectories and hardware. We call the result TRON, for TRajectory Optimized NUFFT. We benchmark the speed and accuracy TRON on a Shepp-Logan phantom and on whole-body continuous golden-angle radial MRI. RESULTS: TRON was 6-30× faster than the closest competitor, depending on test data set, and was the most accurate code tested. CONCLUSIONS: Specialization of the NUFFT algorithm for a particular trajectory yielded significant speed gains. TRON can be easily extended to other trajectories, such as spiral and PROPELLER. TRON can be downloaded at http://github.com/davidssmith/TRON.

Williams Syndrome and Music: A Systematic Integrative Review.

Background: Researchers and clinicians have often cited a strong relationship between individuals with Williams syndrome (WS) and music. This review systematically identified, analyzed, and synthesized research findings related to WS and music. Methods: Thirty-one articles were identified that examined this relationship and were divided into seven areas. This process covered a diverse array of methodologies, with aims to: (1) report current findings; (2) assess methodological quality; and (3) discuss the potential implications and considerations for the clinical use of music with this population. Results: Results indicate that individuals with WS demonstrate a high degree of variability in skill and engagement in music, presenting with musical skills that are more in line with their cognitive abilities than chronological age (CA). Musical strengths for this population appear to be based more in musicality and expressivity than formal musical skills, which are expressed through a heightened interest in music, a greater propensity toward musical activities, and a heightened emotional responsiveness to music. Individuals with WS seem to conserve the overall structure of musical phrases better than they can discriminate or reproduce them exactly. The affinity for music often found in this population may be rooted in atypical auditory processing, autonomic irregularities, and differential neurobiology. Conclusions: More studies are needed to explore how this affinity for music can be harnessed in clinical and educational interventions.

Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast.

PURPOSE: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled k-space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast. METHODS: We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second-order total generalized variation (TGV α2), and nuclear norm (NN). We measured the signal-to-error ratio (SER) of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs) of the derived pharmacokinetic parameters Ktrans (volume transfer constant) and ve (extravascular-extracellular volume fraction) across a population of random sampling schemes. RESULTS: NN produced the lowest image error (SER: 29.1), while TV/TGV α2 produced the most accurate Ktrans (CCC: 0.974/0.974) and ve (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate Ktrans (CCC: 0.842) and ve (CCC: 0.799). CONCLUSION: TV/TGV α2 should be used as temporal constraints for CS DCE-MRI of the breast.

Can local adaptation explain varying patterns of herbivory tolerance in a recently introduced woody plant in North America?

Patterns of woody-plant mortality have been linked to global-scale environmental changes, such as extreme drought, heat stress, more frequent and intense fires, and episodic outbreaks of insects and pathogens. Although many studies have focussed on survival and mortality in response to specific physiological stresses, little attention has been paid to the role of genetic heritability of traits and local adaptation in influencing patterns of plant mortality, especially in non-native species. Tamarix spp. is a dominant, non-native riparian tree in western North America that is experiencing dieback in some areas of its range due to episodic herbivory by the recently introduced northern tamarisk leaf beetle (Diorhabda carinulata). We propose that genotype × environment interactions largely underpin current and future patterns of Tamarix mortality. We anticipate that (i) despite its recent introduction, and the potential for significant gene flow, Tamarix in western North America is generally adapted to local environmental conditions across its current range in part due to hybridization of two species; (ii) local adaptation to specific climate, soil and resource availability will yield predictable responses to episodic herbivory; and (iii) the ability to cope with a combination of episodic herbivory and increased aridity associated with climate change will be largely based on functional tradeoffs in resource allocation. This review focusses on the potential heritability of plant carbon allocation patterns in Tamarix, focussing on the relative contribution of acquired carbon to non-structural carbohydrate (NSC) pools versus other sinks as the basis for surviving episodic disturbance. Where high aridity and/or poor edaphic position lead to chronic stress, NSC pools may fall below a minimum threshold because of an imbalance between the supply of carbon and its demand by various sinks. Identifying patterns of local adaptation of traits related to resource allocation will improve forecasting of Tamarix population susceptibility to episodic herbivory.

Dynamic Imaging of the Eye, Optic Nerve, and Extraocular Muscles With Golden Angle Radial MRI.

Purpose: The eye and its accessory structures, the optic nerve and the extraocular muscles, form a complex dynamic system. In vivo magnetic resonance imaging (MRI) of this system in motion can have substantial benefits in understanding oculomotor functioning in health and disease, but has been restricted to date to imaging of static gazes only. The purpose of this work was to develop a technique to image the eye and its accessory visual structures in motion. Methods: Dynamic imaging of the eye was developed on a 3-Tesla MRI scanner, based on a golden angle radial sequence that allows freely selectable frame-rate and temporal-span image reconstructions from the same acquired data set. Retrospective image reconstructions at a chosen frame rate of 57 ms per image yielded high-quality in vivo movies of various eye motion tasks performed in the scanner. Motion analysis was performed for a left-right version task where motion paths, lengths, and strains/globe angle of the medial and lateral extraocular muscles and the optic nerves were estimated. Results: Offline image reconstructions resulted in dynamic images of bilateral visual structures of healthy adults in only ∼15-s imaging time. Qualitative and quantitative analyses of the motion enabled estimation of trajectories, lengths, and strains on the optic nerves and extraocular muscles at very high frame rates of ∼18 frames/s. Conclusions: This work presents an MRI technique that enables high-frame-rate dynamic imaging of the eyes and orbital structures. The presented sequence has the potential to be used in furthering the understanding of oculomotor mechanics in vivo, both in health and disease.

A novel anthropomorphic flow phantom for the quantitative evaluation of prostate DCE-MRI acquisition techniques.

A novel anthropomorphic flow phantom device has been developed, which can be used for quantitatively assessing the ability of magnetic resonance imaging (MRI) scanners to accurately measure signal/concentration time-intensity curves (CTCs) associated with dynamic contrast-enhanced (DCE) MRI. Modelling of the complex pharmacokinetics of contrast agents as they perfuse through the tumour capillary network has shown great promise for cancer diagnosis and therapy monitoring. However, clinical adoption has been hindered by methodological problems, resulting in a lack of consensus regarding the most appropriate acquisition and modelling methodology to use and a consequent wide discrepancy in published data. A heretofore overlooked source of such discrepancy may arise from measurement errors of tumour CTCs deriving from the imaging pulse sequence itself, while the effects on the fidelity of CTC measurement of using rapidly-accelerated sequences such as parallel imaging and compressed sensing remain unknown. The present work aimed to investigate these features by developing a test device in which 'ground truth' CTCs were generated and presented to the MRI scanner for measurement, thereby allowing for an assessment of the DCE-MRI protocol to accurately measure this curve shape. The device comprised a four-pump flow system wherein CTCs derived from prior patient prostate data were produced in measurement chambers placed within the imaged volume. The ground truth was determined as the mean of repeat measurements using an MRI-independent, custom-built optical imaging system. In DCE-MRI experiments, significant discrepancies between the ground truth and measured CTCs were found for both tumorous and healthy tissue-mimicking curve shapes. Pharmacokinetic modelling revealed errors in measured K trans, v e and k ep values of up to 42%, 31%, and 50% respectively, following a simple variation of the parallel imaging factor and number of signal averages in the acquisition protocol. The device allows for the quantitative assessment and standardisation of DCE-MRI protocols (both existing and emerging).

Whole-body continuously moving table fat-water MRI with dynamic B0 shimming at 3 Tesla.

PURPOSE: The purpose of this work was to develop a rapid and robust whole-body fat-water MRI (FWMRI) method using a continuously moving table (CMT) with dynamic field corrections at 3 Tesla. METHODS: CMT FWMRI was developed at 3 Tesla with a multiecho golden angle (GA) radial trajectory and dynamic B0 field shimming. Whole-body imaging was performed with 4 echoes and superior-inferior coverage of 1.8 meters without shims in 90 s. 716 axial images were reconstructed with GA profile binning followed by B0 field map generation using fast three-point seeded region growing fat-water separation and slice-specific 0(th) and 1(st) order shim calculation. Slice-specific shims were applied dynamically in a repeated CMT FWMRI scan in the same session. The resulting images were evaluated for field homogeneity improvements and quality of fat-water separation with a whole-image energy optimized algorithm. RESULTS: GA sampling allowed high quality whole-body FWMRI from multiecho CMT data. Dynamic B0 shimming greatly improved field homogeneity in the body and produced high quality water and fat only images as well as fat signal fraction and R2 * relaxivity maps. CONCLUSION: A rapid and robust technique for whole-body fat-water quantification has been developed with CMT MRI with dynamic B0 field correction. Magn Reson Med 76:183-190, 2016. © 2015 Wiley Periodicals, Inc.

DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis.

We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of noise, with an estimated 10-20× speedup in run time compared to existing tools. To explain the observed trends in the fitting errors, we present an argument about the conditioning of the Jacobian in the limit of small and large parameter values. We also demonstrate its use on an in vivo data set to measure performance on a realistic application. For a 192 × 192 breast image, we achieved run times of <1 s. Finally, we analyze run times scaling with problem size and find that the run time per voxel scales as O(N (1.9)), where N is the number of time points in the tissue concentration curve. DCEMRI.jl was much faster than any other analysis package tested and produced comparable accuracy, even in the presence of noise.

Continuously moving table MRI with golden angle radial sampling.

PURPOSE: Continuously moving table (CMT) MRI is a high throughput technique that has multiple applications in whole-body imaging. In this work, CMT MRI based on golden angle (GA, 111.246° azimuthal step) radial sampling is developed at 3 Tesla (T), with the goal of increased flexibility in image reconstruction using arbitrary profile groupings. THEORY AND METHODS: CMT MRI with GA and linear angle (LA) schemes were developed for whole-body imaging at 3T with a table speed of 20 mm/s. Imaging was performed in phantoms and a human volunteer with extended z fields of view of up to 1.8 meters. Four separate LA and a single GA scan were performed to enable slice reconstructions at four different thicknesses. RESULTS: GA CMT MRI produced high image quality in phantoms and humans and allowed complete flexibility in reconstruction of slices with arbitrary slice thickness and position from a single data set. LA CMT MRI was constrained by predetermined parameters, required multiple scans and suffered from stair step artifacts that were not present in GA images. CONCLUSION: GA sampling provides a robust flexible approach to CMT whole-body MRI with the ability to reconstruct slices at arbitrary positions and thicknesses from a single scan.

Socio-sexuality and episodic memory function in women: further evidence of an adaptive "mating mode".

The functionalist memory perspective predicts that information of adaptive value may trigger specific processing modes. It was recently demonstrated that women's memory is sensitive to cues of male sexual dimorphism (i.e., masculinity) that convey information of adaptive value for mate choice because they signal health and genetic quality, as well as personality traits important in relationship contexts. Here, we show that individual differences in women's mating strategies predict the effect of facial masculinity cues upon memory, strengthening the case for functional design within memory. Using the revised socio-sexual orientation inventory, Experiment 1 demonstrates that women pursuing a short-term, uncommitted mating strategy have enhanced source memory for men with exaggerated versus reduced masculine facial features, an effect that reverses in women who favor long-term committed relationships. The reversal in the direction of the effect indicates that it does not reflect the sex typicality of male faces per se. The same pattern occurred within women's source memory for women's faces, implying that the memory bias does not reflect the perceived attractiveness of faces per se. In Experiment 2, we reran the experiment using men's faces to establish the reliability of the core finding and replicated Experiment 1's results. Masculinity cues may therefore trigger a specific mode within women's episodic memory. We discuss why this mode may be triggered by female faces and its possible role in mate choice. In so doing, we draw upon the encoding specificity principle and the idea that episodic memory limits the scope of stereotypical inferences about male behavior.

Potential of compressed sensing in quantitative MR imaging of cancer.

Classic signal processing theory dictates that, in order to faithfully reconstruct a band-limited signal (e.g., an image), the sampling rate must be at least twice the maximum frequency contained within the signal, i.e., the Nyquist frequency. Recent developments in applied mathematics, however, have shown that it is often possible to reconstruct signals sampled below the Nyquist rate. This new method of compressed sensing (CS) requires that the signal have a concise and extremely dense representation in some mathematical basis. Magnetic resonance imaging (MRI) is particularly well suited for CS approaches, owing to the flexibility of data collection in the spatial frequency (Fourier) domain available in most MRI protocols. With custom CS acquisition and reconstruction strategies, one can quickly obtain a small subset of the full data and then iteratively reconstruct images that are consistent with the acquired data and sparse by some measure. Successful use of CS results in a substantial decrease in the time required to collect an individual image. This extra time can then be harnessed to increase spatial resolution, temporal resolution, signal-to-noise, or any combination of the three. In this article, we first review the salient features of CS theory and then discuss the specific barriers confronting CS before it can be readily incorporated into clinical quantitative MRI studies of cancer. We finally illustrate applications of the technique by describing examples of CS in dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI.