Loss of spontaneous vasomotion precedes impaired cerebrovascular reactivity and microbleeds in a mouse model of cerebral amyloid angiopathy.

Background: Cerebral amyloid angiopathy (CAA) is a cerebral small vessel disease in which amyloid-ß accumulates in vessel walls. CAA is a leading cause of symptomatic lobar intracerebral hemorrhage and an important contributor to age-related cognitive decline. Recent work has suggested that vascular dysfunction may precede symptomatic stages of CAA, and that spontaneous slow oscillations in arteriolar diameter (termed vasomotion), important for amyloid-ß clearance, may be impaired in CAA. Methods: To systematically study the progression of vascular dysfunction in CAA, we used the APP23 mouse model of amyloidosis, which is known to develop spontaneous cerebral microbleeds mimicking human CAA. Using in vivo 2-photon microscopy, we longitudinally imaged unanesthetized APP23 transgenic mice and wildtype littermates from 7 to 14 months of age, tracking amyloid-ß accumulation and vasomotion in individual pial arterioles over time. MRI was used in separate groups of 12-, 18-, and 24-month-old APP23 transgenic mice and wildtype littermates to detect microbleeds and to assess cerebral blood flow and cerebrovascular reactivity with pseudo-continuous arterial spin labeling. Results: We observed a significant decline in vasomotion with age in APP23 mice, while vasomotion remained unchanged in wildtype mice with age. This decline corresponded in timing to initial vascular amyloid-ß deposition (∼8-10 months of age), although was more strongly correlated with age than with vascular amyloid-ß burden in individual arterioles. Declines in vasomotion preceded the development of MRI-visible microbleeds and the loss of smooth muscle actin in arterioles, both of which were observed in APP23 mice by 18 months of age. Additionally, evoked cerebrovascular reactivity was intact in APP23 mice at 12 months of age, but significantly lower in APP23 mice by 24 months of age. Conclusions: Our findings suggest that a decline in spontaneous vasomotion is an early, potentially pre-symptomatic, manifestation of CAA and vascular dysfunction, and a possible future treatment target.

Computational peptide discovery with a genetic programming approach.

The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POET Regex , where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitivity of peptides in magnetic resonance imaging with chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET models and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. By combining the power of genetic programming with the flexibility of regular expressions, new peptide targets were identified that improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.

Dynamic and rapid deep synthesis of chemical exchange saturation transfer and semisolid magnetization transfer MRI signals.

Model-driven analysis of biophysical phenomena is gaining increased attention and utility for medical imaging applications. In magnetic resonance imaging (MRI), the availability of well-established models for describing the relations between the nuclear magnetization, tissue properties, and the externally applied magnetic fields has enabled the prediction of image contrast and served as a powerful tool for designing the imaging protocols that are now routinely used in the clinic. Recently, various advanced imaging techniques have relied on these models for image reconstruction, quantitative tissue parameter extraction, and automatic optimization of acquisition protocols. In molecular MRI, however, the increased complexity of the imaging scenario, where the signals from various chemical compounds and multiple proton pools must be accounted for, results in exceedingly long model simulation times, severely hindering the progress of this approach and its dissemination for various clinical applications. Here, we show that a deep-learning-based system can capture the nonlinear relations embedded in the molecular MRI Bloch-McConnell model, enabling a rapid and accurate generation of biologically realistic synthetic data. The applicability of this simulated data for in-silico, in-vitro, and in-vivo imaging applications is then demonstrated for chemical exchange saturation transfer (CEST) and semisolid macromolecule magnetization transfer (MT) analysis and quantification. The proposed approach yielded 63-99% acceleration in data synthesis time while retaining excellent agreement with the ground truth (Pearson's r > 0.99, p < 0.0001, normalized root mean square error < 3%).

Computational Peptide Discovery with a Genetic Programming Approach.

Background: The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search space. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and facilitating the discovery of new peptides. Results: This study presents the development and use of a variant of the initial POET algorithm, called POETRegex, which is based on genetic programming, where individuals are represented by a list of regular expressions. The program was trained on a small curated dataset and employed to predict new peptides that can improve the problem of sensitivity in detecting peptides through magnetic resonance imaging using chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET variant and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. Conclusions: By combining the power of genetic programming with the flexibility of regular expressions, new potential peptide targets were identified to improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.

Development of a synthetic biosensor for chemical exchange MRI utilizing in silico optimized peptides.

Chemical exchange saturation transfer (CEST) MRI has been identified as a novel alternative to classical diagnostic imaging. Over the last several decades, many studies have been conducted to determine possible CEST agents, such as endogenously expressed compounds or proteins, that can be utilized to produce contrast with minimally invasive procedures and reduced or non-existent levels of toxicity. In recent years there has been an increased interest in the generation of genetically engineered CEST contrast agents, typically based on existing proteins with CEST contrast or modified to produce CEST contrast. We have developed an in silico method for the evolution of peptide sequences to optimize CEST contrast and showed that these peptides could be combined to create de novo biosensors for CEST MRI. A single protein, superCESTide, was designed to be 198 amino acids. SuperCESTide was expressed in E. coli and purified with size exclusion chromatography. The magnetic transfer ratio asymmetry generated by superCESTide was comparable to levels seen in previous CEST reporters, such as protamine sulfate (salmon protamine) and human protamine. These data show that novel peptides with sequences optimized in silico for CEST contrast that utilize a more comprehensive range of amino acids can still produce contrast when assembled into protein units expressed in complex living environments.

Development of a Synthetic Biosensor for Chemical Exchange MRI Utilizing In Silico Optimized Peptides.

Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) has been identified as a novel alternative to classical diagnostic imaging. Over the last several decades, many studies have been conducted to determine possible CEST agents, such as endogenously expressed compounds or proteins, that can be utilized to produce contrast with minimally invasive procedures and reduced or non-existent levels of toxicity. In recent years there has been an increased interest in the generation of genetically engineered CEST contrast agents, typically based on existing proteins with CEST contrast or modified to produce CEST contrast. We have developed an in-silico method for the evolution of peptide sequences to optimize CEST contrast and showed that these peptides could be combined to create de novo biosensors for CEST MRI. A single protein, superCESTide 2.0, was designed to be 198 amino acids. SuperCESTide 2.0 was expressed in E. coli and purified with size-exclusion chromatography. The magnetic transfer ratio asymmetry (MTR asym ) generated by superCESTide 2.0 was comparable to levels seen in previous CEST reporters, such as protamine sulfate (salmon protamine, SP), Poly-L-Lysine (PLL), and human protamine (hPRM1). This data shows that novel peptides with sequences optimized in silico for CEST contrast that utilizes a more comprehensive range of amino acids can still produce contrast when assembled into protein units expressed in complex living environments.

Mapping the Unseen: In Vivo CEST-MRI of Creatine Reveals Improved Cardiac Energetics in Subjects with Obesity Following Bariatric Surgery.

BACKGROUND: Obesity is associated with derangement of cardiac metabolism and the development of subclinical cardiovascular disease. This prospective study examined the impact of bariatric surgery on cardiac function and metabolism. METHODS: Subjects with obesity underwent cardiac magnetic resonance imaging (CMR) at Massachusetts General Hospital before and after bariatric surgery between 2019 and 2021. The imaging protocol included Cine for global cardiac function assessment and creatine chemical exchange saturation transfer (CEST) CMR for myocardial creatine mapping. RESULTS: Thirteen subjects were enrolled, and 6 subjects [mean BMI 40.5 ± 2.6] had completed the second CMR (i.e. post-surgery), with a median follow-up of 10 months. The median age was 46.5 years, 67% were female, and 16.67% had diabetes. Bariatric surgery led to significant weight loss, with achieved mean BMI of 31.0 ± 2.0. Additionally, bariatric surgery resulted in significant reduction in left ventricular (LV) mass, LV mass index, and epicardial adipose tissue (EAT) volume. This was accompanied by slight improvement in LV ejection fraction compared to baseline. Following bariatric surgery, there was a significant increase in creatine CEST contrast. Subjects with obesity had significantly lower CEST contrast compared to subjects with normal BMI (n = 10), but this contrast was normalized after the surgery, and statistically similar to non-obese cohort, indicating an improvement in myocardial energetics. CONCLUSIONS: CEST-CMR has the ability to identify and characterize myocardial metabolism in vivo non-invasively. These results demonstrate that in addition to reducing BMI, bariatric surgery may favorably affect cardiac function and metabolism.

A Genetic Programming Approach to Engineering MRI Reporter Genes.

Here we develop a mechanism of protein optimization using a computational approach known as "genetic programming". We developed an algorithm called Protein Optimization Engineering Tool (POET). Starting from a small library of literature values, the use of this tool allowed us to develop proteins that produce four times more MRI contrast than what was previously state-of-the-art. Interestingly, many of the peptides produced using POET were dramatically different with respect to their sequence and chemical environment than existing CEST producing peptides, and challenge prior understandings of how those peptides function. While existing algorithms for protein engineering rely on divergent evolution, POET relies on convergent evolution and consequently allows discovery of peptides with completely different sequences that perform the same function with as good or even better efficiency. Thus, this novel approach can be expanded beyond developing imaging agents and can be used widely in protein engineering.

Oncolytic HSV1 targets different growth phases of breast cancer leptomeningeal metastases.

Leptomeningeal metastasis is a fatal complication of breast cancer which results when cancer cells seed in the meninges. Currently there is no cure, limiting survival to less than four months. Treatment options are palliative. We studied a replication conditional Herpes simplex virus 1 (HSV1) in this regard and present the therapeutic efficacy of oncolytic HSV1 on different stages of breast cancer leptomeningeal metastases growth, namely the lag, intermediate, and exponential phases. These phases characterized in a murine model represent the early, intermediate, and late stages of leptomeningeal disease in patients. In this model, virus was introduced into the ventricular system by stereotactic surgery, the same path cancer cells were introduced to create leptomeningeal metastases. Tumor growth was measured with Gd-MRI and virus replication was assessed by FHBG-PET and Fluc bioluminescence. Imaging results were correlated with H&E and HSV-TK immunohistochemical staining. A remarkable growth inhibition was observed when the lag phase was targeted which was associated with multiple virus replication cycles. The onset of debilitating symptoms was delayed, and survival was lengthened by nearly 2 weeks. A growth inhibition similar to the lag phase was observed when the intermediate phase was targeted, associated with robust virus replication. The regression of existing tumor led to a reversal of neurological symptoms, extending survival by nearly one week. A modest response was observed when the lag phase was targeted lengthening survival by 3 days. Oncolytic HSV1 presents a novel treatment option for breast cancer leptomeningeal metastases with potential for targeting different disease stages where virus replication and tumor response can be monitored with molecular imaging techniques that are in the clinic.

Accelerated and quantitative three-dimensional molecular MRI using a generative adversarial network.

PURPOSE: To substantially shorten the acquisition time required for quantitative three-dimensional (3D) chemical exchange saturation transfer (CEST) and semisolid magnetization transfer (MT) imaging and allow for rapid chemical exchange parameter map reconstruction. METHODS: Three-dimensional CEST and MT magnetic resonance fingerprinting (MRF) datasets of L-arginine phantoms, whole-brains, and calf muscles from healthy volunteers, cancer patients, and cardiac patients were acquired using 3T clinical scanners at three different sites, using three different scanner models and coils. A saturation transfer-oriented generative adversarial network (GAN-ST) supervised framework was then designed and trained to learn the mapping from a reduced input data space to the quantitative exchange parameter space, while preserving perceptual and quantitative content. RESULTS: The GAN-ST 3D acquisition time was 42-52 s, 70% shorter than CEST-MRF. The quantitative reconstruction of the entire brain took 0.8 s. An excellent agreement was observed between the ground truth and GAN-based L-arginine concentration and pH values (Pearson's r > 0.95, ICC > 0.88, NRMSE < 3%). GAN-ST images from a brain-tumor subject yielded a semi-solid volume fraction and exchange rate NRMSE of 3 . 8 ± 1 . 3 % $$ 3.8\pm 1.3\% $$ and 4 . 6 ± 1 . 3 % $$ 4.6\pm 1.3\% $$ , respectively, and SSIM of 96 . 3 ± 1 . 6 % $$ 96.3\pm 1.6\% $$ and 95 . 0 ± 2 . 4 % $$ 95.0\pm 2.4\% $$ , respectively. The mapping of the calf-muscle exchange parameters in a cardiac patient, yielded NRMSE < 7% and SSIM > 94% for the semi-solid exchange parameters. In regions with large susceptibility artifacts, GAN-ST has demonstrated improved performance and reduced noise compared to MRF. CONCLUSION: GAN-ST can substantially reduce the acquisition time for quantitative semi-solid MT/CEST mapping, while retaining performance even when facing pathologies and scanner models that were not available during training.

CEST MR fingerprinting (CEST-MRF) for brain tumor quantification using EPI readout and deep learning reconstruction.

PURPOSE: To develop a clinical CEST MR fingerprinting (CEST-MRF) method for brain tumor quantification using EPI acquisition and deep learning reconstruction. METHODS: A CEST-MRF pulse sequence originally designed for animal imaging was modified to conform to hardware limits on clinical scanners while keeping scan time under 2 min. Quantitative MRF reconstruction was performed using a deep reconstruction network (DRONE) to yield the water relaxation and chemical exchange parameters. The feasibility of the six parameter DRONE reconstruction was tested in simulations using a digital brain phantom. A healthy subject was scanned with the CEST-MRF sequence, conventional MRF and CEST sequences for comparison. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter and gray matter. The clinical utility of CEST-MRF was demonstrated on four patients with brain metastases in comparison to standard clinical imaging sequences. Tumors were segmented into edema, solid core, and necrotic core regions and the CEST-MRF values compared to the contra-lateral side. RESULTS: DRONE reconstruction of the digital phantom yielded a normalized RMS error of ≤7% for all parameters. The CEST-MRF parameters were in good agreement with those from conventional MRF and CEST sequences and previous studies. The mean concordance correlation coefficient for all six parameters was 0.98 ± 0.01 in white matter and 0.98 ± 0.02 in gray matter. The CEST-MRF values in nearly all tumor regions were significantly different (P = 0.05) from each other and the contra-lateral side. CONCLUSION: Combination of EPI readout and deep learning reconstruction enabled fast, accurate and reproducible CEST-MRF in brain tumors.

MR fingerprinting for semisolid magnetization transfer and chemical exchange saturation transfer quantification.

Chemical exchange saturation transfer (CEST) MRI has positioned itself as a promising contrast mechanism, capable of providing molecular information at sufficient resolution and amplified sensitivity. However, it has not yet become a routinely employed clinical technique, due to a variety of confounding factors affecting its contrast-weighted image interpretation and the inherently long scan time. CEST MR fingerprinting (MRF) is a novel approach for addressing these challenges, allowing simultaneous quantitation of several proton exchange parameters using rapid acquisition schemes. Recently, a number of deep-learning algorithms have been developed to further boost the performance and speed of CEST and semi-solid macromolecule magnetization transfer (MT) MRF. This review article describes the fundamental theory behind semisolid MT/CEST-MRF and its main applications. It then details supervised and unsupervised learning approaches for MRF image reconstruction and describes artificial intelligence (AI)-based pipelines for protocol optimization. Finally, practical considerations are discussed, and future perspectives are given, accompanied by basic demonstration code and data.

Total ice content and lipid saturation determine adipose tissue cryolipolysis by injection of ice-slurry.

OBJECTIVES: Cryolipolysis uses tissue cooling to solidify lipids, preferentially damaging lipid-rich cells. Topical cooling is popular for the reduction of local subcutaneous fat. Injection of biocompatible ice-slurry is a recently introduced alternative. We developed and verified a quantitative model that simulates the heat exchange and phase changes involved, offering insights into ice-slurry injection for treating subcutaneous fat. METHODS: Finite element method was used to model the spatial and temporal progression of heat transfer between adipose tissue and injected ice-slurry, estimating dose-response relationships between properties of the slurry and size of tissue affected by cryolipolysis. Phase changes of both slurry and adipose tissue lipids were considered. An in vivo swine model was used to validate the numerical solutions. Oils with different lipid compositions were exposed to ice-slurry in vitro to evaluate the effects of lipid freezing temperature. Microscopy and nuclear magnetic resonance (NMR) were performed to detect lipid phase changes. RESULTS: A ball of granular ice was deposited at the injection site in subcutaneous fat. Total injected ice content determines both the effective cooling region of tissue, and the duration of tissue cooling. Water's high latent heat of fusion enables tissue cooling long after slurry injection. Slurry temperature affects the rate of tissue cooling. In swine, when 30 ml slurry injection at -3.5°C was compared to 15 ml slurry injection at -4.8°C (both with the same total ice content), the latter led to almost twice faster tissue cooling. NMR showed a large decrease in diffusion upon lipid crystallization; saturated lipids with higher freezing temperatures were more susceptible to solidification after ice-slurry injection. CONCLUSIONS: Total injected ice content determines both the volume of tissue treated by cryolipolysis and the cooling duration after slurry injection, while slurry temperature affects the cooling rate. Lipid saturation, which varies with diet and anatomic location, also has an important influence.

Magnetic resonance imaging of cardiac metabolism in heart failure: how far have we come?

As one of the highest energy consumer organs in the body, the heart requires tremendous amount of adenosine triphosphate (ATP) to maintain its continuous mechanical work. Fatty acids, glucose, and ketone bodies are the primary fuel source of the heart to generate ATP with perturbations in ATP generation possibly leading to contractile dysfunction. Cardiac metabolic imaging with magnetic resonance imaging (MRI) plays a crucial role in understanding the dynamic metabolic changes occurring in the failing heart, where the cardiac metabolism is deranged. Also, targeting and quantifying metabolic changes in vivo noninvasively is a promising approach to facilitate diagnosis, determine prognosis, and evaluate therapeutic response. Here, we summarize novel MRI techniques used for detailed investigation of cardiac metabolism in heart failure including magnetic resonance spectroscopy (MRS), hyperpolarized MRS, and chemical exchange saturation transfer based on evidence from preclinical and clinical studies and to discuss the potential clinical application in heart failure.

An end-to-end AI-based framework for automated discovery of rapid CEST/MT MRI acquisition protocols and molecular parameter quantification (AutoCEST).

PURPOSE: To develop an automated machine-learning-based method for the discovery of rapid and quantitative chemical exchange saturation transfer (CEST) MR fingerprinting acquisition and reconstruction protocols. METHODS: An MR physics-governed AI system was trained to generate optimized acquisition schedules and the corresponding quantitative reconstruction neural network. The system (termed AutoCEST) is composed of a CEST saturation block, a spin dynamics module, and a deep reconstruction network, all differentiable and jointly connected. The method was validated using a variety of chemical exchange phantoms and in vivo mouse brains at 9.4T. RESULTS: The acquisition times for AutoCEST optimized schedules ranged from 35 to 71 s, with a quantitative image reconstruction time of only 29 ms. The resulting exchangeable proton concentration maps for the phantoms were in good agreement with the known solute concentrations for AutoCEST sequences (mean absolute error = 2.42 mM; Pearson's r=0.992 , p<0.0001 ), but not for an unoptimized sequence (mean absolute error = 65.19 mM; Pearson's r=-0.161 , p=0.522 ). Similarly, improved exchange rate agreement was observed between AutoCEST and quantification of exchange using saturation power (QUESP) methods (mean absolute error: 35.8 Hz, Pearson's r=0.971 , p<0.0001 ) compared to an unoptimized schedule and QUESP (mean absolute error = 58.2 Hz; Pearson's r=0.959 , p<0.0001 ). The AutoCEST in vivo mouse brain semi-solid proton volume fractions were lower in the cortex (12.77% ± 0.75%) compared to the white matter (19.80% ± 0.50%), as expected. CONCLUSION: AutoCEST can automatically generate optimized CEST/MT acquisition protocols that can be rapidly reconstructed into quantitative exchange parameter maps.

Quantitative imaging of apoptosis following oncolytic virotherapy by magnetic resonance fingerprinting aided by deep learning.

Non-invasive imaging methods for detecting intratumoural viral spread and host responses to oncolytic virotherapy are either slow, lack specificity or require the use of radioactive or metal-based contrast agents. Here we show that in mice with glioblastoma multiforme, the early apoptotic responses to oncolytic virotherapy (characterized by decreased cytosolic pH and reduced protein synthesis) can be rapidly detected via chemical-exchange-saturation-transfer magnetic resonance fingerprinting (CEST-MRF) aided by deep learning. By leveraging a deep neural network trained with simulated magnetic resonance fingerprints, CEST-MRF can generate quantitative maps of intratumoural pH and of protein and lipid concentrations by selectively labelling the exchangeable amide protons of endogenous proteins and the exchangeable macromolecule protons of lipids, without requiring exogenous contrast agents. We also show that in a healthy volunteer, CEST-MRF yielded molecular parameters that are in good agreement with values from the literature. Deep-learning-aided CEST-MRF may also be amenable to the characterization of host responses to other cancer therapies and to the detection of cardiac and neurological pathologies.

Tracking the Migration of Injectable Microdevices in the Rodent Brain Using a 9.4T Magnetic Resonance Imaging Scanner.

Wirelessly powered microdevices are being miniaturized to improve safety, longevity, and spatial resolution in a wide range of biomedical applications. Some wireless microdevices have reached a point where they can be injected whole into the central nervous system. However, the state-of-the-art floating microdevices have not yet been tested in chronic brain applications, and there is a growing concern that the implants might migrate through neural tissue over time. Using a 9.4T MRI scanner, we attempt to address the migration question by tracking ultra-small devices injected in different areas of the brain (cortico-subcortical) of rats over 5 months. We demonstrate that injectable microdevices smaller than 0.01 mm3 remain anchored in the brain at the targeted injection site over this time period. Based on CD68 (microglia) and GFAP (astrocytes) immunoreactivity to the microdevice, we hypothesize that glial scar formation is preventing the migration of chronically implanted microdevices in the brain over time.

Pulseq-CEST: Towards multi-site multi-vendor compatibility and reproducibility of CEST experiments using an open-source sequence standard.

PURPOSE: As the field of CEST grows, various novel preparation periods using different parameters are being introduced. At the same time, large, multisite clinical studies require clearly defined protocols, especially across different vendors. Here, we propose a CEST definition standard using the open Pulseq format for a shareable, simple, and exact definition of CEST protocols. METHODS: We present the benefits of such a standard in three ways: (1) an open database on GitHub, where fully defined, human-readable CEST protocols can be shared; (2) an open-source Bloch-McConnell simulation to test and optimize CEST preparation periods in silico; and (3) a hybrid MR sequence that plays out the CEST preparation period and can be combined with any existing readout module. RESULTS: The exact definition of the CEST preparation period, in combination with the flexible simulation, leads to a good match between simulations and measurements. The standard allowed finding consensus on three amide proton transfer-weighted protocols that could be compared in healthy subjects and a tumor patient. In addition, we could show coherent multisite results for a sophisticated CEST method, highlighting the benefits regarding protocol sharing and reproducibility. CONCLUSION: With Pulseq-CEST, we provide a straightforward approach to standardize, share, simulate, and measure different CEST preparation schemes, which are inherently completely defined.

Redesigned reporter gene for improved proton exchange-based molecular MRI contrast.

Reporter gene imaging allows for non-invasive monitoring of molecular processes in living cells, providing insights on the mechanisms underlying pathology and therapy. A lysine-rich protein (LRP) chemical exchange saturation transfer (CEST) MRI reporter gene has previously been developed and used to image tumor cells, cardiac viral gene transfer, and oncolytic virotherapy. However, the highly repetitive nature of the LRP reporter gene sequence leads to DNA recombination events and the expression of a range of truncated LRP protein fragments, thereby greatly limiting the CEST sensitivity. Here we report the use of a redesigned LRP reporter (rdLRP), aimed to provide excellent stability and CEST sensitivity. The rdLRP contains no DNA repeats or GC rich regions and 30% less positively charged amino-acids. RT-PCR of cell lysates transfected with rdLRP demonstrated a stable reporter gene with a single distinct band corresponding to full-length DNA. A distinct increase in CEST-MRI contrast was obtained in cell lysates of rdLRP transfected cells and in in vivo LRP expressing mouse brain tumors ([Formula: see text], n = 10).