Deep Variational Network Toward Blind Image Restoration.

Blind image restoration (IR) is a common yet challenging problem in computer vision. Classical model-based methods and recent deep learning (DL)-based methods represent two different methodologies for this problem, each with its own merits and drawbacks. In this paper, we propose a novel blind image restoration method, aiming to integrate both the advantages of them. Specifically, we construct a general Bayesian generative model for the blind IR, which explicitly depicts the degradation process. In this proposed model, a pixel-wise non-i.i.d. Gaussian distribution is employed to fit the image noise. It is with more flexibility than the simple i.i.d. Gaussian or Laplacian distributions as adopted in most of conventional methods, so as to handle more complicated noise types contained in the image degradation. To solve the model, we design a variational inference algorithm where all the expected posteriori distributions are parameterized as deep neural networks to increase their model capability. Notably, such an inference algorithm induces a unified framework to jointly deal with the tasks of degradation estimation and image restoration. Further, the degradation information estimated in the former task is utilized to guide the latter IR process. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts. The source code is available at https://github.com/zsyOAOA/VIRNet.

Radiograph-comparable image synthesis for spine alignment analysis using deep learning with prospective clinical validation.

Background: Adolescent idiopathic scoliosis (AIS) is the most common type of spinal disorder affecting children. Clinical screening and diagnosis require physical and radiographic examinations, which are either subjective or increase radiation exposure. We therefore developed and validated a radiation-free portable system and device utilising light-based depth sensing and deep learning technologies to analyse AIS by landmark detection and image synthesis. Methods: Consecutive patients with AIS attending two local scoliosis clinics in Hong Kong between October 9, 2019, and May 21, 2022, were recruited. Patients were excluded if they had psychological and/or systematic neural disorders that could influence the compliance of the study and/or the mobility of the patients. For each participant, a Red Green Blue-Depth (RGBD) image of the nude back was collected using our in-house radiation-free device. Manually labelled landmarks and alignment parameters by our spine surgeons were considered as the ground truth (GT). Images from training and internal validation cohorts (n = 1936) were used to develop the deep learning models. The model was then prospectively validated on another cohort (n = 302) which was collected in Hong Kong and had the same demographic properties as the training cohort. We evaluated the prediction accuracy of the model on nude back landmark detection as well as the performance on radiograph-comparable image (RCI) synthesis. The obtained RCIs contain sufficient anatomical information that can quantify disease severities and curve types. Findings: Our model had a consistently high accuracy in predicting the nude back anatomical landmarks with a less than 4-pixel error regarding the mean Euclidian and Manhattan distance. The synthesized RCI for AIS severity classification achieved a sensitivity and negative predictive value of over 0.909 and 0.933, and the performance for curve type classification was 0.974 and 0.908, with spine specialists' manual assessment results on real radiographs as GT. The estimated Cobb angle from synthesized RCIs had a strong correlation with the GT angles (R2 = 0.984, p < 0.001). Interpretation: The radiation-free medical device powered by depth sensing and deep learning techniques can provide instantaneous and harmless spine alignment analysis which has the potential for integration into routine screening for adolescents. Funding: Innovation and Technology Fund (MRP/038/20X), Health Services Research Fund (HMRF) 08192266.

Deep Face Video Inpainting via UV Mapping.

This paper addresses the problem of face video inpainting. Existing video inpainting methods target primarily at natural scenes with repetitive patterns. They do not make use of any prior knowledge of the face to help retrieve correspondences for the corrupted face. They therefore only achieve sub-optimal results, particularly for faces under large pose and expression variations where face components appear very differently across frames. In this paper, we propose a two-stage deep learning method for face video inpainting. We employ 3DMM as our 3D face prior to transform a face between the image space and the UV (texture) space. In Stage I, we perform face inpainting in the UV space. This helps to largely remove the influence of face poses and expressions and makes the learning task much easier with well aligned face features. We introduce a frame-wise attention module to fully exploit correspondences in neighboring frames to assist the inpainting task. In Stage II, we transform the inpainted face regions back to the image space and perform face video refinement that inpaints any background regions not covered in Stage I and also refines the inpainted face regions. Extensive experiments have been carried out which show our method can significantly outperform methods based merely on 2D information, especially for faces under large pose and expression variations. Project page: https://ywq.github.io/FVIP.

Spine-GFlow: A hybrid learning framework for robust multi-tissue segmentation in lumbar MRI without manual annotation.

Most learning-based magnetic resonance image (MRI) segmentation methods rely on the manual annotation to provide supervision, which is extremely tedious, especially when multiple anatomical structures are required. In this work, we aim to develop a hybrid framework named Spine-GFlow that combines the image features learned by a CNN model and anatomical priors for multi-tissue segmentation in a sagittal lumbar MRI. Our framework does not require any manual annotation and is robust against image feature variation caused by different image settings and/or underlying pathology. Our contributions include: 1) a rule-based method that automatically generates the weak annotation (initial seed area), 2) a novel proposal generation method that integrates the multi-scale image features and anatomical prior, 3) a comprehensive loss for CNN training that optimizes the pixel classification and feature distribution simultaneously. Our Spine-GFlow has been validated on 2 independent datasets: HKDDC (containing images obtained from 3 different machines) and IVDM3Seg. The segmentation results of vertebral bodies (VB), intervertebral discs (IVD), and spinal canal (SC) are evaluated quantitatively using intersection over union (IoU) and the Dice coefficient. Results show that our method, without requiring manual annotation, has achieved a segmentation performance comparable to a model trained with full supervision (mean Dice 0.914 vs 0.916).

An artificial intelligence powered platform for auto-analyses of spine alignment irrespective of image quality with prospective validation.

BACKGROUND: Assessment of spine alignment is crucial in the management of scoliosis, but current auto-analysis of spine alignment suffers from low accuracy. We aim to develop and validate a hybrid model named SpineHRNet+, which integrates artificial intelligence (AI) and rule-based methods to improve auto-alignment reliability and interpretability. METHODS: From December 2019 to November 2020, 1,542 consecutive patients with scoliosis attending two local scoliosis clinics (The Duchess of Kent Children's Hospital at Sandy Bay in Hong Kong; Queen Mary Hospital in Pok Fu Lam on Hong Kong Island) were recruited. The biplanar radiographs of each patient were collected with our medical machine EOS™. The collected radiographs were recaptured using smartphones or screenshots, with deidentified images securely stored. Manually labelled landmarks and alignment parameters by a spine surgeon were considered as ground truth (GT). The data were split 8:2 to train and internally test SpineHRNet+, respectively. This was followed by a prospective validation on another 337 patients. Quantitative analyses of landmark predictions were conducted, and reliabilities of auto-alignment were assessed using linear regression and Bland-Altman plots. Deformity severity and sagittal abnormality classifications were evaluated by confusion matrices. FINDINGS: SpineHRNet+ achieved accurate landmark detection with mean Euclidean distance errors of 2·78 and 5·52 pixels on posteroanterior and lateral radiographs, respectively. The mean angle errors between predictions and GT were 3·18° and 6·32° coronally and sagittally. All predicted alignments were strongly correlated with GT (p < 0·001, R2 > 0·97), with minimal overall difference visualised via Bland-Altman plots. For curve detections, 95·7% sensitivity and 88·1% specificity was achieved, and for severity classification, 88·6-90·8% sensitivity was obtained. For sagittal abnormalities, greater than 85·2-88·9% specificity and sensitivity were achieved. INTERPRETATION: The auto-analysis provided by SpineHRNet+ was reliable and continuous and it might offer the potential to assist clinical work and facilitate large-scale clinical studies. FUNDING: RGC Research Impact Fund (R5017-18F), Innovation and Technology Fund (ITS/404/18), and the AOSpine East Asia Fund (AOSEA(R)2019-06).

Deep Photometric Stereo for Non-Lambertian Surfaces.

This paper addresses the problem of photometric stereo, in both calibrated and uncalibrated scenarios, for non-Lambertian surfaces based on deep learning. We first introduce a fully convolutional deep network for calibrated photometric stereo, which we call PS-FCN. Unlike traditional approaches that adopt simplified reflectance models to make the problem tractable, our method directly learns the mapping from reflectance observations to surface normal, and is able to handle surfaces with general and unknown isotropic reflectance. At test time, PS-FCN takes an arbitrary number of images and their associated light directions as input and predicts a surface normal map of the scene in a fast feed-forward pass. To deal with the uncalibrated scenario where light directions are unknown, we introduce a new convolutional network, named LCNet, to estimate light directions from input images. The estimated light directions and the input images are then fed to PS-FCN to determine the surface normals. Our method does not require a pre-defined set of light directions and can handle multiple images in an order-agnostic manner. Thorough evaluation of our approach on both synthetic and real datasets shows that it outperforms state-of-the-art methods in both calibrated and uncalibrated scenarios.

Volatile anaesthesia and peri-operative outcomes related to cancer: a feasibility and pilot study for a large randomised control trial.

Published data suggest that the type of general anaesthesia used during surgical resection for cancer may impact on patient long-term outcome. However, robust prospective clinical evidence is essential to guide a change in clinical practice. We explored the feasibility of conducting a randomised controlled trial to investigate the impact of total intravenous anaesthesia with propofol vs. inhalational volatile anaesthesia on postoperative outcomes of patients undergoing major cancer surgery. We undertook a randomised, double-blind feasibility and pilot study of propofol total intravenous anaesthesia or volatile-based maintenance anaesthesia during cancer resection surgery at three tertiary hospitals in Australia and the USA. Patients were randomly allocated to receive propofol total intravenous anaesthesia or volatile-based maintenance anaesthesia. Primary outcomes for this study were successful recruitment to the study and successful delivery of the assigned anaesthetic treatment as per randomisation arm. Of the 217 eligible patients approached, 146 were recruited, a recruitment rate of 67.3% (95%CI 60.6-73.5%). One hundred and forty-five patients adhered to the randomised treatment arm, 99.3% (95%CI 96.2-100%). Intra-operative patient characteristics and postoperative complications were comparable between the two intervention groups. This feasibility and pilot study supports the viability of the protocol for a large, randomised controlled trial to investigate the effect of anaesthesia technique on postoperative cancer outcomes. The volatile anaesthesia and peri-operative outcomes related to cancer (VAPOR-C) study that is planned to follow this feasibility study is an international, multicentre trial with the aim of providing evidence-based guidelines for the anaesthetic management of patients undergoing major cancer surgery.

Fixed Viewpoint Mirror Surface Reconstruction Under an Uncalibrated Camera.

This paper addresses the problem of mirror surface reconstruction, and proposes a solution based on observing the reflections of a moving reference plane on the mirror surface. Unlike previous approaches which require tedious calibration, our method can recover the camera intrinsics, the poses of the reference plane, as well as the mirror surface from the observed reflections of the reference plane under at least three unknown distinct poses. We first show that the 3D poses of the reference plane can be estimated from the reflection correspondences established between the images and the reference plane. We then form a bunch of 3D lines from the reflection correspondences, and derive an analytical solution to recover the line projection matrix. We transform the line projection matrix to its equivalent camera projection matrix, and propose a cross-ratio based formulation to optimize the camera projection matrix by minimizing reprojection errors. The mirror surface is then reconstructed based on the optimized cross-ratio constraint. Experimental results on both synthetic and real data are presented, which demonstrate the feasibility and accuracy of our method.

Learning Spatial Attention for Face Super-Resolution.

General image super-resolution techniques have difficulties in recovering detailed face structures when applying to low resolution face images. Recent deep learning based methods tailored for face images have achieved improved performance by jointly trained with additional task such as face parsing and landmark prediction. However, multi-task learning requires extra manually labeled data. Besides, most of the existing works can only generate relatively low resolution face images (e.g., 128×128 ), and their applications are therefore limited. In this paper, we introduce a novel SPatial Attention Residual Network (SPARNet) built on our newly proposed Face Attention Units (FAUs) for face super-resolution. Specifically, we introduce a spatial attention mechanism to the vanilla residual blocks. This enables the convolutional layers to adaptively bootstrap features related to the key face structures and pay less attention to those less feature-rich regions. This makes the training more effective and efficient as the key face structures only account for a very small portion of the face image. Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (e.g., 16×16 ). Quantitative comparisons on various kinds of metrics (including PSNR, SSIM, identity similarity, and landmark detection) demonstrate the superiority of our method over current state-of-the-arts. We further extend SPARNet with multi-scale discriminators, named as SPARNetHD, to produce high resolution results (i.e., 512×512 ). We show that SPARNetHD trained with synthetic data can not only produce high quality and high resolution outputs for synthetically degraded face images, but also show good generalization ability to real world low quality face images. Codes are available at https://github.com/chaofengc/Face-SPARNet.

Impact of a risk-stratified thromboprophylaxis protocol on the incidence of postoperative venous thromboembolism and bleeding.

Efforts to reduce postoperative venous thromboembolism are challenging due to heterogeneity in thromboprophylaxis practice. As a result, a 'one-size-fits-all' approach that accounts for surgery-specific risk, but fails to account for patient-level variation, is often adopted by healthcare networks. Updated clinical practice guidelines have advocated an individualised risk-stratified approach that balances the risk:benefit ratio associated with thromboprophylaxis; however, there are limited data confirming effectiveness of these recommendations on the incidence of postoperative venous thromboembolism and bleeding. We developed the surgical-thrombo-embolism-prevention protocol, a novel risk-stratified algorithm that classified patients into low-, intermediate-, and high-risk profiles according to surgical procedure and patient baseline medical risk. Expert-endorsed risk-specific thromboprophylaxis strategies were then applied. A staged quality improvement program was developed to implement the protocol. We postulated that compliance with the protocol would reduce postoperative venous thromboembolism rates without increasing the incidence of postoperative bleeding. Between June 2013 and March 2018, we evaluated the efficacy, safety and sustainability of this risk-stratified approach in 24,953 surgical admissions at a dedicated cancer centre. By final implementation, program compliance was 91%. Postoperative venous thromboembolism rates reduced from 3.1 per 1000 surgical admissions to 0.6 per 1000 surgical admissions (relative risk reduction 79%; p < 0.005). Postoperative bleeding rates also declined from 10.0 per 1000 surgical admissions to 6.3 per 1000 surgical admissions (relative risk reduction 37%; p = 0.02). Sustained improvement was evident more than 3 years after implementation. Implementation of the surgical-thrombo-embolism-prevention protocol significantly reduced the incidence of postoperative venous thromboembolism supporting its validation at other institutions.

An MDS-specific frailty index based on cumulative deficits adds independent prognostic information to clinical prognostic scoring.

The frailty index (FI) is based on the principle that the more deficits an individual has, the greater their risk of adverse outcomes. It is expressed as a ratio of the number of deficits present to the total number of deficits considered. We developed an MDS-specific FI using a prospective MDS registry and assessed its ability to add prognostic power to conventional prognostic scores in MDS. The 42 deficits included in this FI included measurements of physical performance, comorbidities, laboratory values, instrumental activities of daily living, quality of life and performance status. Of 644 patients, 440 were eligible for FI calculation. The median FI score was 0.25 (range 0.05-0.67), correlated with age and IPSS/IPSS-R risk scores and discriminated overall survival. With a follow-up of 20 months, survival was 27 months (95% CI 24-30.4). By multivariate analysis, age >70, FI, transfusion dependence, and IPSS were significant covariates associated with OS. The incremental discrimination improvement of the frailty index was 37%. We derived a prognostic score with five risk groups and distinct survivals ranging from 7.4 months to not yet reached. If externally validated, the MDS-FI could be used as a tool to refine the risk stratification of current clinical prognostication models.

Strategies for success: a multi-institutional study on robot-assisted partial nephrectomy for complex renal lesions.

OBJECTIVE: To describe our technique, illustrated with images and videos, of robot-assisted partial nephrectomy (RAPN) for challenging renal tumours. PATIENTS AND METHODS: A study of 249 patients who underwent RAPN in multiple institutions was performed. Patients were identified using prospective RAPN databases. Complex renal lesion were defined as those with a RENAL nephrometry score ≥10. Data were analysed and differences among groups examined. RESULTS: A total of 31 (12.4%) RAPNs were performed for complex renal tumours. The median (interquartile range [IQR]) patient age was 57 (50.5-70.5) years and 21 patients (67.7%) were men. The median (IQR) American Society of Anesthesiologists score was 2 (2-3). The median (IQR) operating time was 200 (50-265) min, warm ischaemia time was 23 (18.5-29) min, and estimated blood loss was 200 (50-265) mL. There were no intra-operative complications. Two patients (6.4%) had postoperative complications. One patient (3.2%) had a positive surgical margin. The median (IQR) length of stay was 3.5 (3-5) days and the median (IQR) follow-up was 12.5 (7-24) months. There were no recurrences. RAPN resulted in statistically significant changes in renal function 3 months after RAPN compared with preoperative renal function (P < 0.001). CONCLUSION: Our results showed that RAPN was a safe approach for selected patients with complex renal tumours and may facilitate tumour resection and renorrhaphy for challenging cases, offering a minimally invasive surgical option for patients who may otherwise require open surgery.

Aspergillus galactomannan detection in exhaled breath condensate compared to bronchoalveolar lavage fluid for the diagnosis of invasive aspergillosis in immunocompromised patients.

OBJECTIVES: Exhaled breath condensate (EBC) is a noninvasive means of sampling the airways that has shown significant promise in the diagnosis of many disorders. There have been no reports of its usefulness in the detection of galactomannan (GM), a component of the cell wall of Aspergillus. The suitability of EBC for the detection of GM for the diagnosis of invasive aspergillosis (IA) using the Platelia Aspergillus enzyme-linked immunosorbent assay was investigated. METHODS: Prospective, cross-sectional study of lung transplant recipient and haemotologic malignancy patients at a university centre. EBC samples were compared to concomitant bronchoalveolar lavage (BAL) samples among lung transplant recipients and healthy controls. EBC was collected over 10 minutes using a refrigerated condenser according to the European Respiratory Society/American Thoracic Society recommendations, with the BAL performed immediately thereafter. RESULTS: A total of 476 EBC specimens with 444 matched BAL specimens collected from lung transplant recipients (n = 197) or haemotologic malignancy patients (n = 133) were examined. Both diluted and untreated EBC optical density (OD) values (0.0830, interquartile range (IQR) 0.0680-0.1040; and 0.1130, IQR 0.0940-0.1383), respectively, from all patients regardless of clinical syndrome were significantly higher than OD values in healthy control EBCs (0.0508, IQR 0.0597-0.0652; p < 0.0001). However, the OD index values did not correlate with the diagnosis of IA (44 samples were associated with IA). Furthermore, no significant correlation was found between EBC GM and the matched BAL specimen. CONCLUSIONS: GM is detectable in EBC; however, no correlation between OD index values and IA was noted in lung transplant recipients.

Changes in psychosocial health during a 7-week paediatric weight management program.

Youth with obesity are at increased risk of psychosocial symptoms; however, little is known regarding the impact of paediatric weight management (PWM) on psychosocial health. The aim of the study was to investigate changes in psychosocial health among children who completed a 7-week PWM program. Participants aged 5 to 16 years with a BMI ≥85th percentile completed a 7-week, family-centred PWM program focused on health behaviour education, exercise and mentored goal setting. The Paediatric Symptom Checklist (PSC) was assessed via parent report to evaluate psychosocial symptoms before and after the program, and subscales were calculated for internalizing (PSC-IS), externalizing (PSC-ES) and attention symptoms (PSC-AS). At baseline, positive screen rates for psychosocial symptoms among the 317 patients included 16.1% for PSC, 14.1% for PSC-ES, 18.6% for PSC-IS and 12.3% for PSC-AS. Among program completers, total PSC scores improved in those with normal (p = 0.010) and elevated p < .001 psychosocial symptoms at baseline. Youth with positive screens for elevated PSC subscales improved their subscale scores, on average, and the majority reduced scores to below elevated levels for PSC (54.2%), PSC-ES (64.7%), PSC-IS (78.3%) and PSC-AS (64.7%). Improvements in PSC remained significant after adjusting for BMI changes during treatment, but BMI differed across PSC-change groups, including BMI increases among participants with PSC deterioration (0.33 ± 0.64 kg m-2 ) (P = 0.035) and BMI decreases among patients with no reliable PSC change (-0.26 ± 1.04 kg m-2 ) (P = 0.038) or reliable PSC improvement (-0.22 ± 0.74 kg m-2 ) (P = 0.025). Youth with positive screens for psychosocial symptoms can improve emotional and behavioural functioning during short-term PWM. Future research is needed to elucidate mechanisms and long-term outcome durability.

Efficient Message Passing Methods With Fully Connected Models for Early Vision.

Fully connected Markov random fields and conditional random fields have recently been shown to be advantageous in many early vision tasks being formulated as multi-labeling problems, such as stereo matching and image segmentation. The maximum posterior marginal (MPM) inference method in solving fully connected models uses a hybrid framework of mean-field (MF) method and a filtering like approach, and yields excellent results. In this paper, we extend this framework in several aspects. First, we provide an alternative inference method employing fractional belief propagation based method instead of MF. Second, we reformulate the MPM problem into a maximum a posterior (MAP) problem and provide efficient algorithms for solving this. Third, we extend the fully connected model into a multi-resolution approach. Finally, we propose an integral image based approach which makes it possible for efficiently integrating the local linear regression technique into this framework. Comparisons are carried out among different algorithms and different formulations to find the best combination. We demonstrate that the use of our multi-resolution approach with MAP formulation substantially outperforms the ordinary MF-based inference scheme.

A phase 1b/2b multicenter study of oral panobinostat plus azacitidine in adults with MDS, CMML or AML with ⩽30% blasts.

Treatment with azacitidine (AZA), a demethylating agent, prolonged overall survival (OS) vs conventional care in patients with higher-risk myelodysplastic syndromes (MDS). As median survival with monotherapy is <2 years, novel agents are needed to improve outcomes. This phase 1b/2b trial (n=113) was designed to determine the maximum tolerated dose (MTD) or recommended phase 2 dose (RP2D) of panobinostat (PAN)+AZA (phase 1b) and evaluate the early efficacy and safety of PAN+AZA vs AZA monotherapy (phase 2b) in patients with higher-risk MDS, chronic myelomonocytic leukemia or oligoblastic acute myeloid leukemia with <30% blasts. The MTD was not reached; the RP2D was PAN 30 mg plus AZA 75 mg/m2. More patients receiving PAN+AZA achieved a composite complete response ([CR)+morphologic CR with incomplete blood count+bone marrow CR (27.5% (95% CI, 14.6-43.9%)) vs AZA (14.3% (5.4-28.5%)). However, no significant difference was observed in the 1-year OS rate (PAN+AZA, 60% (50-80%); AZA, 70% (50-80%)) or time to progression (PAN+AZA, 70% (40-90%); AZA, 70% (40-80%)). More grade 3/4 adverse events (97.4 vs 81.0%) and on-treatment deaths (13.2 vs 4.8%) occurred with PAN+AZA. Further dose or schedule optimization may improve the risk/benefit profile of this regimen.

Development and validation of liquid chromatography tandem mass spectrometry method quantitative determination of polymyxin B1, polymyxin B2, polymyxin B3 and isoleucine-polymyxin B1 in human plasma and its application in clinical studies.

Polymyxin B (PB) is an antibiotic consisting of a cyclic heptapeptide and a tripeptide side chain used in treatment of infections caused by Gram-negative bacteria. Commercial formulations of PB contain multiple structurally related components with major constituents of PB1, PB2, PB3 and ile-PB1. To understand the pharmacokinetics of these major components, we have developed and validated a LC-MS/MS method to quantify PB1, PB2, PB3 and ile-PB1 in human plasma. PB was extracted from plasma by protein precipitation using trichloroacetic acid followed by chromatographic separation on Zorbax Bonus-RP column (100mm×2.1mm, 1.8µm) using stepwise gradient elution of water containing 0.1% of formic acid and 0.1% of trichloroacetic acid (mobile phase A) and 90% acetonitrile with 0.1% formic acid (mobile phase B). Despite of structural similarities, these PBs were completely resolved in the analytical run time of 6.5min. Detection and quantification of PBs were performed by selected reaction monitoring (SRM) under positive ionization mode in the mass spectrometer. Separation of PB1 and ile-PB1, as well as PB2 and PB3, before quantification is crucial because they are structural isomers detected based the same SRM. Excellent linearity was achieved (r2>0.99) in the calibration curves of PB. The developed method was accurate (95.3-111.7%) and precise (CV<5.1%). Recovery of PB from the plasma extraction was between 53 and 76% and reproducible (CV<4.5%). Matrix effect was not observed by post-column infusion of PB in the mass spectrometer. This methodology has been successfully applied to clinical study of patients dosed with intravenous infusions of PB.

Determination of an optimally sensitive and specific chemical exchange saturation transfer MRI quantification metric in relevant biological phantoms.

The purpose of this study was to develop realistic phantom models of the intracellular environment of metastatic breast tumour and naïve brain, and using these models determine an analysis metric for quantification of CEST MRI data that is sensitive to only labile proton exchange rate and concentration. The ability of the optimal metric to quantify pH differences in the phantoms was also evaluated. Novel phantom models were produced, by adding perchloric acid extracts of either metastatic mouse breast carcinoma cells or healthy mouse brain to bovine serum albumin. The phantom model was validated using 1 H NMR spectroscopy, then utilized to determine the sensitivity of CEST MRI to changes in pH, labile proton concentration, T1 time and T2 time; six different CEST MRI analysis metrics (MTRasym , APT*, MTRRex , AREX and CESTR* with and without T1 /T2 compensation) were compared. The new phantom models were highly representative of the in vivo intracellular environment of both tumour and brain tissue. Of the analysis methods compared, CESTR* with T1 and T2 time compensation was optimally specific to changes in the CEST effect (i.e. minimal contamination from T1 or T2 variation). In phantoms with identical protein concentrations, pH differences between phantoms could be quantified with a mean accuracy of 0.6 pH units. We propose that CESTR* with T1 and T2 time compensation is the optimal analysis method for these phantoms. Analysis of CEST MRI data with T1 /T2 time compensated CESTR* is reproducible between phantoms, and its application in vivo may resolve the intracellular alkalosis associated with breast cancer brain metastases without the need for exogenous contrast agents.