Clinical Imaging System Research Articles

Development of a mechanical characterisation device for intracranial aneurysms: Calibration on polymeric phantom arteries

Intracranial aneurysm is a major health issue related to biomechanical arterial wall degradation. Currently, no method allows predicting rupture risk based on in vivo quantitative mechanical data. This work is part of a large-scale project aimed at providing clinicians with a non-invasive patient-specific decision support tool, based on the in vivo mechanical characterisation of the aneurysm wall. Thus, the primary objective of the project was to develop a deformation device prototype (DDP) of the artery wall and to calibrate it on polymeric phantom arteries. The deformations induced on the phantom arteries were quantified experimentally using a Digital Image Correlation (DIC) system. The results indicated that the DIC system was able to measure the small displacements generated by the DDP. We also observed that the flow mimicking the blood flow did not significantly disturb the measurements of the artery wall displacement caused by the DDP. Finally, a limit displacement value generated by the DDP was evaluated. This value corresponds to the lowest displacement value detectable by the clinical imaging system that will be tested on animals in the future (Spectral Photon Counting CT).

Compact Linear Flow Phantom Model for Retinal Blood-Flow Evaluation.

Impaired retinal blood flow is associated with ocular diseases such as glaucoma, macular degeneration, and diabetic retinopathy. Among several ocular imaging techniques developed to measure retinal blood flow both invasively and non-invasively, adaptive optics (AO)-enabled scanning laser ophthalmoscopy (AO-SLO) resolves individual red blood cells and provides a high resolution with which to measure flow across retinal microvasculature. However, cross-validation of flow measures remains a challenge owing to instrument and patient-specific variability in each imaging technique. Hence, there is a critical need for a well-controlled clinical flow phantom for standardization and to establish blood-flow measures as clinical biomarkers for early diagnosis. Here, we present the design and validation of a simple, compact, portable, linear flow phantom based on a direct current motor and a conveyor-belt system that provides linear velocity tuning within the retinal microvasculature range (0.5-7 mm/s). The model was evaluated using a sensitive AO-SLO line-scan technique, which showed a <6% standard deviation from the true velocity. Further, a clinical SLO instrument showed a linear correlation with the phantom's true velocity (r2 > 0.997). This model has great potential to calibrate, evaluate, and improve the accuracy of existing clinical imaging systems for retinal blood flow and aid in the diagnosis of ocular diseases with abnormal blood flow.

Super‐resolution ultrasound localization microscopy for the non‐invasive imaging of human testicular microcirculation and its differential diagnosis role in male infertility

AbstractTesticular microcirculation is closely related to spermatogenic function and seminiferous tubular function. The diagnosis and monitoring of testicular diseases can be associated with testicular microcirculation; however, there are currently no effective non‐invasive methods for super‐resolution imaging of testicular microcirculation. In this study, we introduced state‐of‐the‐art graph‐based tracking with the Kalman motion model algorithm to non‐invasively image human testicular microcirculation for the first time with a regular frame‐rate clinical ultrasound imaging system (37 Hz). Two distinct testicular vessels with an 81 µm separation were resolved in the testicular vasculature, surpassing all other imaging modalities. In a retrospective study, we performed contrast‐enhanced ultrasound examinations(CEUS) and ultrasound localization microscopy (ULM) processing on the included 76 infertile patients and 15 healthy controls from August 2021 to May 2023 and obtained super‐resolution images of testicular microcirculation with sub‐diffraction resolution. Through the results of one‐way analysis of variance tests and receiver operating characteristic analyses, we found that the ULM‐based parameters hold promise as clinical guidance for differentiating between non‐obstructive and obstructive male infertility. The mean vessel diameter achieved an area under the curve (AUC) of 0.920 (95% confidence interval [CI]: 0.847–0.994, p &lt; .001) with a cut‐off value of 170.9 µm in oligoasthenospermia, and an AUC of 0.952 (95% CI: 0.875–1.000, p &lt; .001) with a cut‐off value of 169.9 µm in azoospermia patients, respectively, addressing a significant clinical challenge.

Bedside Clinical Hand-held Ultrasound in an Internal Medicine Department: The "Bed Med-Us" Experience of Codogno and its Clinical Utility in the Management of Diagnosis and Therapy in 1007 Patients.

Purpose Handheld ultrasound (HH-US) answers simple clinical questions in emergencies. We performed conventional US with HH-US at the patient's bedside (BED) during a medical visit (MED) (BED MED-US). The purpose of this prospective study is to estimate BED MED-US reliability, its clinical impact in helping the clinician to formulate correct diagnoses, and its ability to save time and money. Materials and Methods 1007 patients (519 M; age:76.42) were assessed (from March 2021 to November 2022) in one or more districts. Final diagnosis was determined with clinical and reference tests (chest RX/CT, abdominal CT, endoscopy, etc.). Sensitivity, specificity, LR+ and LR-, and corresponding AUROC were evaluated. HH-US diagnoses were classified as: confirmation (HH-US revealed the sonographic signs that confirmed the clinical diagnosis) (CO), exclusion (HH-US excluded the presence of the ultrasound signs of other pathologies, in the clinical differential diagnosis) (EX), etiological (HH-US reaches diagnosis in clinically doubtful cases) (ET), or clinically relevant incidental (HH-US diagnoses that change the patient's process completely) (INC). Results HH-US reliability: true-pos: 752; true-neg: 242; false-pos: 7; false-neg: 6 (sens: 99.1%, spec: 97.6%, LR+: 98.5; LR-: 00.15, AUROC: 0.997); clinical impact: CO-diagnosis: 21%; EX: 25%; ET: 47%; INC: 7%; saved time and money: approximately 35,572 minutes of work and 9324 euros. Conclusion BED MED-US is a reliable clinical imaging system, with an important clinical impact both in diagnosis (etiological in 47%, incidental in 7%) and in the management of personnel resources.

NIMG-86. ESTIMATING BRAIN TUMOR RELATED EDEMA FROM NON-INVASIVE IMAGING IN A CLINICALLY RELEVANT TIMELINE

Abstract Image-based mathematical modeling is emerging as an advantageous tool to integrate into the care routine for patients with brain tumors. These tools can be used for a variety of tasks including predicting tumor growth, indicating spatial genomic alterations, and guiding radiation therapy. Often, the methods to produce these maps are developed in silos, using cohorts of retrospective patients and switching to a per patient map calculation can be an afterthought. Automatic pipelines are needed to enable creating mathematical maps in a clinically-relevant workflow. To this end, we developed a pipeline to create an image-based mathematical map of edema abundance based on T2-weighted (T2W) magnetic resonance imaging that includes preprocessing steps, abnormality and tissue type segmentation, brain extraction, map generation, and conversion between image file formats. The python-based pipeline was designed to take in T1-weighted (T1W), T1W with gadolinium contrast (T1Gd), T2W, and fluid attenuated inversion recovery (FLAIR) DICOM images for a single patient as input and convert them to NIfTIs for downstream steps. The pipeline preprocessed images by correcting bias field fluctuations using the N4 algorithm and normalizing intensities. The T1W, T1Gd, and FLAIR are then registered to the T2W. T1Gd and FLAIR are used in a deep learning algorithm to delineate the brain and abnormality including enhancing tumor, necrosis, and peripheral edema. Normal tissue segmentations are performed using the statistical parametric mapping (SPM) segmentation routine. The segmentations and preprocessed images are all utilized in the edema mathematical model. The resulting edema map is then reformatted as a DICOM and output for further use by clinicians and clinical imaging systems. Our future goals include integrating our pipeline in a clinical workstation and sending final maps to our hospital’s picture archiving and communication system (PACS) to allow for deployment of these models for patient care in a clinically efficient timeline.

Standardization of dual-energy x-ray visceral adipose tissue measures for comparison across clinical imaging systems.

Excess visceral adipose tissue (VAT) is a major risk factor for metabolic syndrome (MetS) and clinical guidelines have been proposed to define VAT levels associated with increased risk. The aim was to standardize VAT measures between two dual-energy x-ray absorptiometry (DXA) manufacturers who provide different VAT estimates to support standardization of measures across imaging modalities. Scans from 114 individuals (ages 18-81 years) on GE HealthCare (GEHC) and Hologic DXA systems were compared via Deming regression to standardize VAT between the two systems, validated in a separate sample (n = 15), with κ statistics to assess agreement of VAT measurements for classifying patients into risk categories. The GEHC and Hologic VAT measures were highly correlated and validated in the separate data set (r2 = 0.97). VAT area measures substantially agreed for metabolic risk classification (weighted κ = 0.76) with no significant differences in the population mean values. VAT measures can be estimated from GEHC and Hologic scans that classify individuals in a substantially similar way into metabolic risk categories, and systematic bias between the measures can be removed using simple regression equations. These findings allow for DXA VAT measures to be used in complement to other imaging modalities, regardless of whether scans used GEHC or Hologic systems.

Challenges in Acquiring Clinical Simultaneous SPECT-MRI on a PET-MRI Scanner

The INSERT is the world’s first clinical SPECTMRI brain imaging system based on scintillation detectors with a SiPM readout. Here we demonstrate its use within a clinical MRI environment for the first time. Using a standard transmit-receive head coil, and with an appropriate selection of a custom MRI sequence (GRE), we overcome mutual interference. The INSERT and its bulky 50 kg tungsten collimator introduce magnetic field inhomogeneity. Due to the specific MRI-compatible collimator design, inhomogeneity is compensated by shimming, leading to simultaneous acquisition. We process the SPECT data acquired alongside the MRI sequence to evaluate the SPECT system performance and the impact of the MRI. Finally, we present a set of simultaneous SPECT-MRI acquisitions, demonstrating multimodal imaging capabilities, albeit with a limited MRI sequence.

A stopping criterion for iterative proton CT image reconstruction based on correlated noise properties.

Whereas filtered back projection algorithms for voxel-based CT image reconstruction have noise properties defined by the filter, iterative algorithms must stop at some point in their convergence and do not necessarily produce consistent noise properties for images with different degrees of heterogeneity. A least-squares iterative algorithm for proton CT (pCT) image reconstruction converges toward a unique solution for relative stopping power (RSP) that optimally fits the protons. We present a stopping criterion that delivers solutions with the property that correlations of RSP noise between voxels are relatively low. This provides a method to produce pCT images with consistent noise properties useful for proton therapy treatment planning, which relies on summing RSP along lines of voxels. Consistent noise properties will also be useful for future studies of image quality using metrics such as contrast to noise ratio, and to compare RSP noise and dose of pCT with other modalities such as dual-energyCT. With simulated and real images with varying heterogeneity from a prototype clinical proton imaging system, we calculate average RSP correlations between voxel pairs in uniform regions-of-interest versus distance between voxels. We define a parameter r, the remaining distance to the unique solution relative to estimated RSP noise, and our stopping criterion is based on r falling below a chosenvalue. We find large correlations between voxels for larger values of r, and anticorrelations for smaller values. For r in the range of 0.5-1, voxels are relatively uncorrelated, and compared to smaller values of r have lower noise with only slight loss of spatialresolution. Iterative algorithms not using a specific metric or rationale for stopping iterations may produce images with an unknown and arbitrary level of convergence or smoothing. We resolve this issue by stopping iterations of a least-squares iterative algorithm when r reaches the range of 0.5-1. This defines a pCT image reconstruction method with consistent statistical properties optimal for clinical use, including for treatment planning with pCTimages.

Nerve-Sparing Gynecologic Surgery Enabled by A Near-Infrared Nerve-Specific Fluorophore Using Existing Clinical Fluorescence Imaging Systems.

Patients undergoing gynecological procedures suffer from lasting side effects due to intraoperative nerve damage. Small, delicate nerves with complex and nonuniform branching patterns in the female pelvic neuroanatomy make nerve-sparing efforts during standard gynecological procedures such as hysterectomy, cystectomy, and colorectal cancer resection difficult, and thus many patients are left with incontinence and sexual dysfunction. Herein, a near-infrared (NIR) fluorescent nerve-specific contrast agent, LGW08-35, that is spectrally compatible with clinical fluorescence guided surgery (FGS) systems is formulated and characterized for rapid implementation for nerve-sparing gynecologic surgeries. The toxicology, pharmacokinetics (PK), and pharmacodynamics (PD) of micelle formulated LGW08-35 are examined, enabling the determination of the optimal imaging doses and time points, blood and tissue uptake parameters, and maximum tolerated dose (MTD). Application of the formulated fluorophore to imaging of female rat and swine pelvic neuroanatomy validates the continued clinical translation and use for real-time identification of important nerves such as the femoral, sciatic, lumbar, iliac, and hypogastric nerves. Further development of LGW08-35 for clinical use will unlock a valuable tool for surgeons in direct visualization of important nerves and contribute to the ongoing characterization of the female pelvic neuroanatomy to eliminate the debilitating side effects of nerve damage during gynecological procedures.

Mapping estimates of vascular permeability with a clinical indocyanine green fluorescence imaging system in experimental pancreatic adenocarcinoma tumors.

Pancreatic cancer tumors are known to be avascular, but their neovascular capillaries are still chaotic leaky vessels. Capillary permeability could have significant value for therapy assessment, and its quantification might be possible with macroscopic imaging of indocyanine green (ICG) kinetics in tissue. The capacity of using standard fluorescence surgical systems for ICG kinetic imaging as a probe for capillary leakage was evaluated using a clinical surgical fluorescence imaging system, as interpreted through vascular permeability modeling. Xenograft pancreatic adenocarcinoma models were imaged in mice during bolus injection of ICG to capture the kinetics of uptake. Image analysis included ratiometric data, normalization, and match to theoretical modeling. Kinetic data were converted into the extraction fraction of the capillary leakage. Pancreatic tumors were usually less fluorescent than the surrounding healthy tissues, but still the rate of tumor perfusion could be assessed to quantify capillary extraction. Model simulations showed that flow kinetics stabilized after about 1min beyond the initial bolus injection and that the relative extraction fraction model estimates matched the experimental data of normalized uptake within the tissue. The kinetics in the time period of 1 to 2min post-injection provided optimal differential data between AsPC1 and BxPC3 tumors, although high individual variation exists between tumors. ICG kinetic imaging during the initial leakage phase was diagnostic for quantitative vascular permeability within pancreatic tumors. Methods for autogain correction and normalized model-based interpretation allowed for quantification of extraction fraction and difference identification between tumor types in early timepoints.

Panoramic volumetric clinical handheld photoacoustic and ultrasound imaging

Photoacoustic (PA) imaging has gained much attention, providing structural and functional information in combination with clinical ultrasound (US) imaging systems. 2D PA and US imaging is easily implemented, but its heavy dependence on operator skills makes 3D imaging preferable. In this study, we propose a panoramic volumetric clinical PA and US imaging system equipping a handheld imaging scanner weighing 600 g and measuring 70 × 62 × 110 mm3. Multiple PA/US scans were performed to cover a large field-of-view (FOV), and the acquired PA/US volumes were mosaic-stitched after manually correcting the positions and rotations in a total of 6 degrees of freedom. PA and US maximum amplitude projection images were visualized online, while spectral unmixed data was quantified offline. The performance of the system was tested via tissue-mimicking phantom experiments. The system’s potential was confirmed in vivo by panoramically imaging vascular networks in human arms and necks, with FOVs of 331 × 38 and 129 × 120 mm2, respectively. Further, we quantified hemoglobin oxygen saturation levels in the radial artery, brachial artery, carotid artery, and jugular vein. We hope that this system can be applied for various clinical fields such as cardiovascular imaging, dermatology, vascular surgery, internal medicine, and oncology.

Association between serum uric acid concentration and radiographic axial spondylarthritis: a cross-sectional study of 202 patients.

Association between serum uric acid concentration and radiographic axial spondylarthritis: a cross-sectional study of 202 patients.

Accurate gingival segmentation from 3D images with artificial intelligence: an animal pilot study

BackgroundGingival phenotype plays an important role in dental diagnosis and treatment planning. Traditionally, determining the gingival phenotype is done by manual probing of the gingival soft tissues, an invasive and time-consuming procedure. This study aims to evaluate the feasibility and accuracy of an alternatively novel, non-invasive technology based on the precise 3-dimension (3D) soft tissue reconstruction from intraoral scanning and cone beam computed tomography (CBCT) to predict the gingival biotype.MethodsAs a proof-of-concept, Yorkshire pig mandibles were scanned, and the CBCT data were fed into a deep-learning model to reconstruct the teeth and surrounding bone structure in 3D. By overlaying the CBCT scan with the intraoral scans, an accurate superposition was created and used for virtual measurements of the soft tissue thickness. Meanwhile, gingival thicknesses were also measured by a periodontal probe and digital caliper on the buccal and lingual sides at 3 mm apical to the gingival margin of the posterior teeth and compared with the virtual assessment at the same location. The data obtained from virtual and clinical measurements were compared by Wilcoxon matched-pairs signed-rank analysis, while their correlation was determined by Pearson’s r value. The Mann–Whitney U test was used for intergroup comparisons of the amount of difference.ResultsAmong 108 investigated locations, the clinical and virtual measurements are strongly positively correlated (r = 0.9656, P < 0.0001), and only clinically insignificant differences (0.066 ± 0.223 mm) were observed between the two assessments. There is no difference in the agreement between the virtual and clinical measurements on sexually matured samples (0.087 ± 0.240 mm) and pre-pubertal samples (0.033 ± 0.195 mm). Noticeably, there is a greater agreement between the virtual and clinical measurements at the buccal sites (0.019 ± 0.233 mm) than at the lingual sites (0.116 ± 0.215 mm).ConclusionIn summary, the artificial intelligence-based virtual measurement proposed in this work provides an innovative technique potentially for accurately measuring soft tissue thickness using clinical routine 3D imaging systems, which will aid clinicians in generating a more comprehensive diagnosis with less invasive procedures and, in turn, optimize the treatment plans with more predictable outcomes.

GVirtualXray (gVXR): Simulating X-ray radiographs and CT volumes of anthropomorphic phantoms

gVirtualXray (gVXR) is an open-source framework that relies on the Beer–Lambert law to simulate X-ray images in realtime on a graphics processor unit (GPU) using triangular meshes. We produced four Jupyter Notebooks to compare images simulated with gVXR and ground truth image of an anthropomorphic phantom: (i) an X-ray projection generated using a Monte Carlo simulation code, (ii) real digitally reconstructed radiographs (DRRs), (iii) computed tomography (CT) slices, and (iv) a real radiograph acquired with a clinical X-ray imaging system. Image registration was deployed in two Notebooks to align the simulated image on the corresponding ground truth image. We demonstrated that accurate images can be generated in milliseconds with gVirtualXray when scattering can be ignored.

Performance of two clinical fluorescence imaging systems with different targeted and non-targeted near-infrared fluorophores: a cadaveric explorative study.

Near-infrared (NIR) fluorescence-guided surgery is increasingly utilized in humans and pets. As clinical imaging systems are optimized for Indocyanine green (ICG) detection, the usage of targeted dyes necessitates the validation of these systems for each dye. We investigated the impact of skin pigmentation and tissue overlay on the sensitivity of two NIR cameras (IC-FlowTM, VisionsenseTM VS3 Iridum) for the detection of non-targeted (ICG, IRDye800) and targeted (AngiostampTM, FAP-Cyan) NIR fluorophores in an ex vivo big animal model. We quantitatively measured the limit of detection (LOD) and signal-to-background ratio (SBR) and implemented a semi-quantitative visual score to account for subjective interpretation of images by the surgeon. VisionsenseTM VS3 Iridum outperformed IC-FlowTM in terms of LOD and SBR for the detection of all dyes except FAP-Cyan. Median SBR was negatively affected by skin pigmentation and tissue overlay with both camera systems. Level of agreement between quantitative and semi-quantitative visual score and interobserver agreement were better with VisionsenseTM VS3 Iridum. The overlay of different tissue types and skin pigmentation may negatively affect the ability of the two tested camera systems to identify nanomolar concentrations of targeted-fluorescent dyes and should be considered when planning surgical applications.

Benefits of using removable filters in dual-layer flat panel detectors

Objective. Existing dual-layer flat panel detectors (DL-FPDs) use a thin scintillator layer to preferentially detect low-energy x-rays, followed by a permanent Cu filter to absorb residual low-energy x-rays, and finally, a thicker scintillator layer to preferentially detect high-energy x-rays. The image outputs of the two scintillator layers can be jointly processed for dual-energy (DE) planar and cone-beam CT imaging. In clinical practice, a given FPD is often used for not only DE imaging but also routine single-energy (SE) imaging. With the permanent Cu layer, the total x-ray absorption is unsatisfactory for SE imaging since more than 30% of x-rays can be lost in the Cu layer. The purpose of this work was to demonstrate the benefits of using a removable filter material in DL-FPDs for SE and DE imaging applications. Approach. The proposed detector contains a removable filter between the two scintillator layers. The filter can be either a chamber filled with a liquid high-Z eff material or a removable solid filter. When DE imaging is not clinically indicated, the DL-FPD can switch to a high-efficiency SE imaging mode by retracting the filter from the inter-scintillator space. For commonly available filter materials (iodine, gadolinium, and Cu), their optimal area densities were theoretically calculated for both water-bone decomposition and water-iodine decomposition DE imaging tasks. Preliminary experimental studies were also performed to compare the SE performance of the proposed DL-FPD with the existing DL-FPD with the permanent Cu filter and study the stability of the liquid filter on a rotating gantry. Main results. The optimal filter material was found to be an iodine solution (approximately 375 mg cm−2). With this liquid filter in place, the proposed DL-FPD has equivalent or better DE imaging performance compared with the existing DL-FPD with the Cu filter. When the filter is removed from the inter-scintillator space for SE imaging, the total x-ray absorption efficiency of the proposed DL-FPD ranges from 73% (100 kVp) to 54% (140 kVp), compared with 51% (100 kVp) to 41% (140 kVp) for the existing DL-FPD with a permanent 1 mm Cu filter. Significance. The removable filter provides a boost to the total x-ray absorption efficiency of DL-FPDs for SE imaging without compromising DE imaging. This can facilitate the adoption of DL-FPDs in clinical x-ray imaging systems that usually perform more SE imaging procedures than DE imaging series.

Simulation of X-ray projections on GPU: Benchmarking gVirtualXray with clinically realistic phantoms

Background and objectives: This study provides a quantitative comparison of images created using gVirtualXray (gVXR) to both Monte Carlo (MC) and real images of clinically realistic phantoms. gVirtualXray is an open-source framework that relies on the Beer-Lambert law to simulate X-ray images in realtime on a graphics processor unit (GPU) using triangular meshes.Methods: Images are generated with gVirtualXray and compared with a corresponding ground truth image of an anthropomorphic phantom: (i) an X-ray projection generated using a Monte Carlo simulation code, (ii) real digitally reconstructed radiographs (DRRs), (iii) computed tomography (CT) slices, and (iv) a real radiograph acquired with a clinical X-ray imaging system. When real images are involved, the simulations are used in an image registration framework so that the two images are aligned.Results: The mean absolute percentage error (MAPE) between the images simulated with gVirtualXray and MC is 3.12%, the zero-mean normalised cross-correlation (ZNCC) is 99.96% and the structural similarity index (SSIM) is 0.99. The run-time is 10 days for MC and 23 ms with gVirtualXray. Images simulated using surface models segmented from a CT scan of the Lungman chest phantom were similar to (i) DRRs computed from the CT volume and (ii) an actual digital radiograph. CT slices reconstructed from images simulated with gVirtualXray were comparable to the corresponding slices of the original CT volume.Conclusions: When scattering can be ignored, accurate images that would take days using MC can be generated in milliseconds with gVirtualXray. This speed of execution enables the use of repetitive simulations with varying parameters, e.g. to generate training data for a deep-learning algorithm, and to minimise the objective function of an optimisation problem in image registration. The use of surface models enables the combination of X-ray simulation with real-time soft-tissue deformation and character animation, which can be deployed in virtual reality applications.

Sub-pT oscillatory magnetometric system using magnetoresistive sensor array for a low-field magnetic particle imaging

Implementing low ac excitation fields toward clinical magnetic particle imaging (MPI) system is critical to avoid magnetostimulation effects. However, low-field MPI scenario demands high sensitivity to probe the change in monotone magnetization response of magnetic nanoparticles. Here, we use an array of ultrasensitive MR sensors to detect sub-pT magnetic signal and obtain its spatial distribution. While each sensor is operated at 5 V, signal processing circuit rises its sensitivity to 20 mV pT−1 at 10 kHz with 0.25 pT noise level. In evaluating sensor performance, we initially measured a 10-kHz magnetic field from a 40-turns coil with 1 mm in diameter. MR sensor recognizes magnetic signal of mini coil to be linear with the coil input current. We then recorded the signals simultaneously from a 6 × 6 sensor channels to map the fields at 0.2 kHz. While placing mini coil 50 mm apart from the array, we obtained high-contrast field image showing coil position and its change in field polarity. For practical nanoparticle detection, we later used a 3 × 3 array configuration for the 10 kHz oscillatory magnetometry while compensating the MR sensor magnetically from the ac excitation field. Despite a 20-pT noise floor associated with the field compensation circuit, we were able to map stray fields of a 37-mgFe iron oxide nanoparticle sample distanced 100 mm from the array, under 12.4 µT/μ0 field amplitude. This result highlights usability of MR sensor array for low-field MPI system.

Publicly available framework for simulating and experimentally validating clinical PET systems.

Monte Carlo (MC) simulations are a powerful tool to model medical imaging systems. However, before simulations can be considered the ground truth, they have to be validated with experiments. To provide a pipeline that models a clinical positron emission tomography (PET)/CT system using MC simulations after extensively validating the results against experimental measurements. A clinical four-ring PET imaging system was modeled using Geant4 application for tomographic emission (v. 9.0). To validate the simulations, PET images were acquired of a cylindrical phantom, point source, and image quality phantom with the modeled system and the simulations of the experimental procedures. For the purpose of validating the quantification capabilities and image quality provided by the simulation pipeline, the simulations were compared against the measurements in terms of their count rates and sensitivity as well as their image uniformity, resolution, recovery coefficients (RCs), coefficients of variation, contrast, and background variability. When compared to the measured data, the number of true detections in the MC simulations was within 5%. The scatter fraction was found to be 30.0%±2.2% and 28.8%±1.7% in the measured and simulated scans, respectively. Analyzing the measured and simulated sinograms, the sensitivities were found to be 8.2 and 7.8cps/kBq, respectively. The fraction of random coincidences were 19% in the measured data and 25% in the simulation. When calculating the image uniformity within the axial slices, the measured image exhibited a uniformity of 0.015±0.005, whereas the simulated image had a uniformity of 0.029±0.011. In the axial direction, the uniformity was measured to be 0.024±0.006 and 0.040±0.015 for the measured and simulated data, respectively. Comparing the image resolution, an average percentage difference of 2.9% was found between the measurements and simulations. The RCs calculated in both the measured and simulated images were found to be within the EARL ranges, except for that of the simulation of the smallest sphere. The coefficients of variation for the measured and simulated images were found to be 12% and 13%, respectively. Lastly, the background variability was consistent between the measurements and simulations, whereas the average percentage difference in the sphere contrasts was found to be 8.8%. The clinical PET/CT system was modeled and validated to provide a simulation pipeline for the community. The pipeline and the validation procedures have been made available (https://github.com/teaghan/PET_MonteCarlo).

A clinical Raman spectroscopy imaging system and safety requirements for in situ intraoperative tissue characterization.

Raman spectroscopy imaging is a technique that can be adapted for intraoperative tissue characterization to be used for surgical guidance. Here we present a macroscopic line scanning Raman imaging system that has been modified to ensure suitability for intraoperative use. The imaging system has a field of view of 1 × 1 cm2 and acquires Raman fingerprint images of 40 × 42 pixels, typically in less than 5 minutes. The system is mounted on a mobile cart, it is equiped with a passive support arm and possesses a removable and sterilizable probe muzzle. The results of a proof of concept study are presented in porcine adipose and muscle tissue. Supervised machine learning models (support vector machines and random forests) were trained and they were tested on a holdout dataset consisting of 7 Raman images (10 080 spectra) acquired in different animal tissues. This led to a detection accuracy >96% and prediction confidence maps providing a quantitative detection assessment for tissue border visualization. Further testing was accomplished on a dataset acquired with the imaging probe's contact muzzle and tailored classification models showed robust classifications capabilities with specificity, sensitivity and accuracy all surpassing 95% with a support vector machine classifier. Finally, laser safety, biosafety and sterilization of the system was assest. The safety assessment showed that the system's laser can be operated safetly according to the American National Standards Institute's standard for maximum permissible exposures for eyes and skin. It was further shown that during tissue interrogation, the temperature-history in cumulative equivalent minutes at 43 °C (CEM43 °C) never exceeded a safe threshold of 5 min.