Phantom Data Research Articles

Validation of 4D Flow based relative pressure maps in aortic flows.

While the clinical gold standard for pressure difference measurements is invasive catheterization, 4D Flow MRI is a promising tool for enabling a non-invasive quantification, by linking highly spatially resolved velocity measurements with pressure differences via the incompressible Navier-Stokes equations. In this work we provide a validation and comparison with phantom and clinical patient data of pressure difference maps estimators. We compare the classical Pressure Poisson Estimator (PPE) and the new Stokes Estimator (STE) against catheter pressure measurements under a variety of stenosis severities and flow intensities. Specifically, we use several 4D Flow data sets of realistic aortic phantoms with different anatomic and hemodynamic severities and two patients with aortic coarctation. The phantom data sets are enriched by subsampling to lower resolutions, modification of the segmentation and addition of synthetic noise, in order to study the sensitivity of the pressure difference estimators to these factors. Overall, the STE method yields more accurate results than the PPE method compared to catheterization data. The superiority of the STE becomes more evident at increasing Reynolds numbers with a better capacity of capturing pressure gradients in strongly convective flow regimes. The results indicate an improved robustness of the STE method with respect to variation in lumen segmentation. However, with heuristic removal of the wall-voxels, the PPE can reach a comparable accuracy for lower Reynolds' numbers.

A methodology and clinical dataset with ground-truth to evaluate registration accuracy quantitatively in computer-assisted Laparoscopic Liver Resection

ABSTRACT Augmented Reality (AR) can assist Laparoscopic Liver Resection (LLR) by registering a preoperative 3D model with laparoscopic images. Evaluating the accuracy of the registration methods requires measuring Target Registration Error (TRE). Previous work evaluates TRE on simulated, phantom and animal data but not on clinical data. Our contribution is a methodology for groundtruth acquisition using Laparoscopic Ultrasound (LUS) in clinical LLR, two evaluation criteria, a four-patient dataset and an evaluation of two existing registration methods. The LUS is coregistered with the laparoscope and its observations are made transferable to 3D laparoscope coordinates. The average position error in our LUS registered images is estimated to be about 1 mm, which is far better than the measured errors for the state-of-the-art registration methods, making our dataset relevant for their evaluation. We ran a preliminary evaluation of two registration methods. The dataset is available publicly at http://igt.ip.uca.fr/ab/code_and_datasets/datasets/llr_reg_evaluation_by_lus.

NIMG-10. OPTIMIZING IMAGE QUALITY FOR MAGNETIC RESONANCE SIMULATION IN SPINE STEREOTACTIC BODY RADIATION THERAPY

Abstract BACKGROUND Currently, the standard MRI sequence for SC imaging in SBRT has been axial 2D T2-weighted Turbo Spin Echo (TSE). Even though 3D T2-weighted sequences such as SPACE (Sampling Perfection with Application optimized Contrasts using different flip angle Evolution) image a whole volume simultaneously and thus offer better reconstruction, they have not been clinically implemented due to their long acquisition times. However, the application of Compressed Sensing (CS) methods on SPACE sequences, achieving clinically acceptable time. METHODS A 3D T2 CS SPACE was obtained and evaluated against the standard 2D TSE for spine SBRT based on a MagPhan RT quality assurance phantom and patients data, analysis was done using the phantom manufacturer software ImageOwl that calculates image distortions by comparing the known position of phantom features to their detected position in the image. RESULTS Results of phantom comparison between 3D T2 and 2D T2 indicate that although the 3D sequence had lower signal-to-noise ratio (SNR) than the 2D sequence, it presented less geometric distortions caused by gradient non-linearities, particularly in the anterior-posterior (A/P) and head-feet (H/F) directions. Distortions caused by chemical shift are in theory smaller for the 3D T2 CS SPACE, amounting to 0.85mm compared to 1.62mm with 2D T2. Between 2D versus 3D MRI defined SC data among 4 patients, average deviation of the centroid point cord contours was 0.08cm. The volume of the cord showed 1cc larger 3D volumes compared to 2D T2. Finally, the mean voxel count overlap coefficient and DICE coefficient was 0.92 and 0.87 respectively. CONCLUSIONS Since 3D MRI is under consideration to replace 2D MRI, it is important to compare SC contours from 3D to 2D MRI and assess their impact on treatment plans. Positive results would pave the path for larger subject cohort evaluation.

SVD-based filtering to detect intraplaque hemorrhage using single wavelength photoacoustic imaging.

.Significance: Intraplaque hemorrhage (IPH) is an important indicator of plaque vulnerability. Early detection could aid the prevention of stroke.Aim: We aim to detect IPH with single wavelength PA imaging in vivo and to improve image quality.Approach: We developed a singular value decomposition (SVD)-based filter to detect the nonstationary and stationary components in ultrasound data. A PA mask was created to detect stationary (IPH) sources. The method was tested ex vivo using phantoms and in vivo in patients.Results: The flow and IPH channels were successfully separated in the phantom data. We can also detect the PA signals from IPH and reject signals from the carotid lumen in vivo. Generalized contrast-to-noise ratio improved in both ex vivo and in vivo in US imaging.Conclusions: SVD-based filtering can successfully detect IPH using a single laser wavelength, opening up opportunities for more economical and cost-effective laser sources.

A two-layer cylinder phantom developed for film-based isocenter verification of radiotherapy machine.

A two-layer cylinder (TLC) phantom was developed for simplifying film-based isocenter verification of linear accelerators in radiotherapy. The phantom mainly consists of two parts: (1) two nested solid cylinders between which a radiochromic film can be inserted and irradiated; (2) a tungsten ball supported by a thin rod and located at the phantom center for alignment with the mechanical isocenter. In practice, the phantom was first positioned by the room laser to align the tungsten ball to the mechanical isocenter of the linear accelerator. Then, a radiochromic film was precisely inserted into the gap between the two cylinders of the phantom and irradiated by beams with preset gantry and couch angles. Later the irradiated film was scanned and processed by an in-house developed analysis software. Finally, the offset of the radiation isocenter from the mechanical isocenter was determined by the built-in three-dimensional (3D) reconstruction algorithms. The accuracy of this method was evaluated by positioning the phantom with a known couch shift, then checking the residual error after couch shift correction. The reliability of this method was evaluated by comparing the calculated offset with the corresponding result determined by the traditional star-shot method. For the accuracy test, the residual errors were -0.14 ± 0.03mm, 0.05 ± 0.06mm, and 0.05 ± 0.06mm in the lateral, longitudinal, and vertical axes, respectively. For the reliability test, the differences between the calculated offset and the result determined by the star-shot method were -0.10mm, 0.12mm, and 0.12mm in the lateral, longitudinal, and vertical axes, respectively. The proposed method is able to reconstruct beams in 3D with one film, which is more time-saving and accurate. Additionally, with this design, the phantom positioning, film loading, beam delivery, and data analyzing are simpler. This phantom and analysis software provides an efficient and effective way to perform film-based isocenter verification of linear accelerators in radiotherapy.

Anisotropic structure property based image reconstruction method for limited-angle computed tomography.

Limited-angle computed tomography (CT) may appear in restricted CT scans. Since the available projection data is incomplete, the images reconstructed by filtered back-projection (FBP) or algebraic reconstruction technique (ART) often encounter shading artifacts. However, using the anisotropy property of the shading artifacts that coincide with the characteristic of limited-angle CT images can reduce the shading artifacts. Considering this concept, we combine the anisotropy property of the shading artifacts with the anisotropic structure property of an image to develop a new algorithm for image reconstruction. Specifically, we propose an image reconstruction method based on adaptive weighted anisotropic total variation (AwATV). This method, termed as AwATV method for short, is designed to preserve image structures and then remove the shading artifacts. It characterizes both of above properties. The anisotropy property of the shading artifacts accounts for reducing artifacts, and the anisotropic structure property of an image accounts for preserving structures. In order to evaluate the performance of AwATV, we use the simulation projection data of FORBILD head phantom and real CT data for image reconstruction. Experimental results show that AwATV can always reconstruct images with higher SSIM and PSNR, and smaller RMSE, which means that AwATV enables to reconstruct images with higher quality in term of artifact reduction and structure preservation.

In vivo stiffness of multiple sclerosis lesions is similar to that of normal-appearing white matter

In 1868, French neurologist Jean-Martin Charcot coined the term multiple sclerosis (MS) after his observation that numerous white matter (WM) glial scars felt like sclerotic tissue. Nowadays, magnetic resonance elastography (MRE) can generate images with contrast of stiffness (CS) in soft in vivo tissues and may therefore be sensitive to MS lesions, provided that sclerosis is indeed a mechanical signature of this disease.We analyzed CS in a total of 147 lesions in patients with relapsing-remitting MS, compared with control regions in contralateral brain regions, and phantom data as well as performed numerical simulations to determine the delineation limits of multifrequency MRE (20 - 40 Hz) in MS.MRE analysis of simulated waves revealed a delineation limit of approximately 10% CS for detecting 9-mm lesions (mean size in our patient population). Due to inversion bias, this limit is reached when true CS is -11% for soft and 35% for stiff lesions. In vivo MRE identified 35 stiffer lesions and 17 softer lesions compared with surrounding WM (mean stiffness: 934±82 Pa). However, a similar pattern was found in the contralateral brain, suggesting that the range of stiffness changes in WM lesions due to MS is within the normal range of WM variability and normal heterogeneity-related CS.Consequently, Charcot's original intuition that MS is a focal sclerotic disease can neither be dismissed nor confirmed by in vivo MRE. However, the observation that MS lesions do not markedly differ in stiffness from surrounding brain tissue suggests that marked tissue sclerosis is not a mechanical signature of MS. Statement of significanceMultiple sclerosis (MS) was named by J.M. Charcot after the sclerotic changes in brain tissue he found in post-mortem autopsies. Since then, nothing has been revealed about the actual stiffening of MS lesions in vivo. Studying the viscoelastic properties of plaques in their natural environment is a major challenge that can only be overcome by MR elastography (MRE). Therefore, we used multifrequency MRE to answer the question whether MS lesions in patients with a relapsing-remitting disease course are mechanically different than surrounding tissue. Our findings suggest that the range of stiffness changes in white matter lesions due to MS is within the normal range of white matter variability and in vivo tissue sclerosis might not be a mechanical signature of MS.

A convolutional neural network for estimating cone-beam CT intensity deviations from virtual CT projections

Extending cone-beam CT (CBCT) use toward dose accumulation and adaptive radiotherapy (ART) necessitates more accurate HU reproduction since cone-beam geometries are heavily degraded by photon scatter. This study proposes a novel method which aims to demonstrate how deep learning based on phantom data can be used effectively for CBCT intensity correction in patient images. Four anthropomorphic phantoms were scanned on a CBCT and conventional fan-beam CT system. Intensity correction is performed by estimating the cone-beam intensity deviations from prior information contained in the CT. Residual projections were extracted by subtraction of raw cone-beam projections from virtual CT projections. An improved version of U-net is utilized to train on a total of 2001 projection pairs. Once trained, the network could estimate intensity deviations from input patient head and neck raw projections. The results from our novel method showed that corrected CBCT images improved the (contrast-to-noise ratio) with respect to uncorrected reconstructions by a factor of 2.08. The mean absolute error and structural similarity index improved from 318 HU to 74 HU and 0.750 to 0.812 respectively. Visual assessment based on line-profile measurements and difference image analysis indicate the proposed method reduced noise and the presence of beam-hardening artefacts compared to uncorrected and manufacturer reconstructions. Projection domain intensity correction for cone-beam acquisitions of patients was shown to be feasible using a convolutional neural network trained on phantom data. The method shows promise for further improvements which may eventually facilitate dose monitoring and ART in the clinical radiotherapy workflow.

Imaging Radiotherapy-Induced Cherenkov Emission in Color

<h3>Purpose/Objective(s)</h3> In the last decade, Cherenkov imaging has been demonstrated as a useful clinical technique both for real-time surface dose visualization during patient treatment and for various high-resolution QA measurements. However there remains much work to be done in the development of Cherenkov-based in vivo dosimetry. In this work, the concept of in vivo color Cherenkov imaging is introduced. It is hypothesized that Cherenkov images with sensitivity to the spectral quality of Cherenkov emission induced inside tissue can provide increased information about tissue composition as opposed to monochromatic, red-weighted images used in prior work. <h3>Materials/Methods</h3> A custom Color Cherenkov camera was created from three time-gated intensified CMOS cameras, each with varying spectral sensitivity of the photocathode, and sharing one imaging lens. A dichroic mirror assembly allowed for incoming Cherenkov light signals to be redirected according to wavelength to the appropriate camera resulting in three raw image stacks for each acquisition. These images were then reconstructed into three-channel color images via color calibration, summed, and background subtracted. In vivo color Cherenkov images of three right-sided breast patients were acquired for one treatment fraction each as part of an IRB-approved clinical trial. These images were interpreted alongside images of liquid tissue-simulating phantoms that matched tissue optical properties. Variations in oxygenation and blood volume were mimicked in this way, and the data was used to interpret the clinical images. Impacts on Cherenkov emission spectra from tissue under these various conditions were also estimated. <h3>Results</h3> The presence of oxygenated vs deoxygenated blood in tissue has a visible impact on the color of Cherenkov emission, leading to a redder hue due to decreased attenuation of red wavelengths by oxyhemoglobin, similar to erythema or blush of tissue. This was verified in the tissue phantoms. Variations in blood concentration produce a strong blue-red trend in color space (R² = 0.96), as verified by detailed analysis. Color Cherenkov images of right breast radiotherapy treatments displayed pink regions across the breast coincident with the treated area for all three patients, and the variations in color were interpreted relative to the phantom data. <h3>Conclusion</h3> This first investigation of color Cherenkov imaging uncovered the natural relationship between tissue composition and the spectral quality of emitted Cherenkov light. The correlation between blood content and color will be translated in future work to in vivo sensing of variations in fat and fibroglandular content in irradiated tissues. These findings will allow for the correction of Cherenkov images to produce accurate in vivo surface dose maps during patient treatments.

Accelerating compressed sensing reconstruction of subsampled radial k-space data using geometrically-derived density compensation

Objective. To accelerate compressed sensing (CS) reconstruction of subsampled radial k-space data using a geometrically-derived density compensation function (gDCF) without significant loss in image quality. Approach. We developed a theoretical framework to calculate a gDCF based on Nyquist distance along the radial and circumferential directions of a discrete polar coordinate system. Our gDCF was compared against standard DCF (e.g. ramp filter) and another commonly used DCF (modified Shepp–Logan (SL) filter). The resulting image quality produced by each DCF was quantified using normalized root-mean-square-error (NRMSE), blur metric (1 = blurriest; 0 = sharpest), and structural similarity index (SSIM; 1 = perfect match; 0 = no match) compared with the reference. For filtered backprojection (FBP) of phantom data obtained at the Nyquist sampling rate, Cartesian k-space sampling was used as the reference. For CS reconstruction of subsampled cardiac magnetic resonance imaging datasets (real-time cardiac cine data with 11 projections per frame, n = 20 patients; cardiac perfusion data with 30 projections per frame, n = 19 patients), CS reconstruction without DCF was used as the reference. Main results. The NRMSE, SSIM, and blur metrics of the phantom data were good for all DCFs, confirming that our gDCF produces uniform densities at the upper limit (Nyquist). For CS reconstruction of subsampled real-time cine and cardiac perfusion datasets, the image quality metrics (SSIM, NRMSE) were significantly (p < 0.05) higher for our gDCF than other DCFs, and the reconstruction time was significantly (p < 0.05) faster for our gDCF (reference) than no DCF (11.9%–52.9% slower), standard DCF (23.9%–57.6% slower), and modified SL filter (13.5%–34.8% slower). Significance. The proposed gDCF accelerates CS reconstruction of subsampled radial k-space data without significant loss in image quality compared with no DCF as the reference.

Optimization of spectrally selective 180° radiofrequency pulse timings in J-difference editing (MEGA) of lactate.

J-Difference editing (MEGA) provides an effective spectroscopic means of selectively measuring low-concentration metabolites having weakly coupled spins. The fractional inphase and antiphase coherences are determined by the radiofrequency (RF) pulses and inter-RF pulse intervals of the sequence. We examined the timings of the spectrally selective editing 180° pulses (E180) in MEGA-PRESS to maximize the edited signal amplitude in lactate at 3T. The time evolution of the lactate spin coherences was analytically and numerically calculated for non-volume localized and single-voxel localized MEGA sequences. Single-voxel localized MEGA-PRESS simulations and phantom experiments were conducted for echo time (TE) 60-160 ms and for all possible integer-millisecond timings of the E180 pulses. Optimized E180 timings of 144, 103, and 109 ms TEs, tailored with simulation and phantom data, were tested in brain tumor patients in vivo. Lactate signals, broadened to singlet linewidths (~6 Hz), were compared between simulation, phantom, and in vivo data. Theoretical and experimental data indicated consistently that the MEGA-edited signal amplitude and width are sensitive to the E180 timings. In volume-localized MEGA, the lactate peak amplitudes in E180-on and difference spectra were maximized at specific E180 timings for individual TEs, largely due to the chemical-shift displacement effects. The E180 timings for maximum lactate peak amplitude were different from those of maximum inphase coherence in in vivo linewidth situations. In in vivo MEGA editing, the E180 pulse timings can be effectively used for manipulating the inphase and antiphase coherences and increasing the edited signal amplitude, following TE optimization.

Application of a Flexible PET Scanner Combined with 3T MRI Using Non-local Means Reconstruction: Qualitative and Quantitative Comparison with Whole-Body PET/CT.

Flexible positron emission tomography (fxPET) employing a non-local means reconstruction algorithm was designed to fit existing magnetic resonance imaging (MRI) systems. We aimed to compare the qualitative and quantitative performance of fxPET among fxPET with MR-based attenuation correction (MRAC), fxPET with CT-based attenuation correction (CTAC) using CT as a part of WB PET/CT, and whole-body (WB) PET/CT. Sixteen patients with suspected head and neck cancer underwent 2-deoxy-2-[18F]fluoro-D-glucose WB PET/CT scans, followed by fxPET and 3T MRI scans. Phantom data were compared among the three datasets. For registration accuracy, we measured the distance between the center of the tumor determined by fxPET and that in MRI. We compared image quality, detection rates, and quantitative values including maximal standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and tumor-to-muscle ratio (TMR) among the three datasets. The phantom data in fxPET, except the percent contrast recoveries of 17-mm and 22-mm hot spheres, were inferior to those in WB PET/CT. The mean registration accuracy was 4.4mm between fxPET using MRAC and MRI. The image quality was comparable between two fxPET datasets, but significantly inferior to WB PET/CT (p < 0.0001). In contrast, detection rates were comparable among the three datasets. SUVmax was significantly higher, and MTV and TLG were significantly lower in the two fxPET datasets compared with the WB PET/CT dataset (p < 0.005). There were no significant differences in SUVmax, MTV, and TLG between the two fxPET datasets or in TMR among the three datasets. All quantitative values had significantly positive correlations. Compared with WB PET/CT, the phantom data and image quality were inferior in fxPET. However, the results of the detection rates and quantitative values suggested the clinical feasibility of fxPET.

The IAEA remote and automated quality control methodology for radiography and mammography.

Radiography remains the most widely used imaging modality throughout the world. Additionally, while it has been demonstrated that a quality control (QC) program, especially in mammography, improves image quality, weekly technologist QC testing might be lacking even where there is clinical qualified medical physicist (CQMP) support. Therefore, the International Atomic Energy Agency (IAEA) developed simple QC phantoms that can easily be used on a regular basis (daily/weekly) for radiography and mammography. These are simple in design and use materials that are easily accessible in most parts of the world. A software application is also developed that automatically analyzes images and Digital Imaging and Communications in Medicine (DICOM) header information. It exports data to a comma‐separated values (CSV) file that is read by a Microsoft Excel® spreadsheet for documentation and graphical analysis. The phantom and the software were tested in four institutions (in Costa Rica and the United States of America) both on computed radiography and direct digital mammography and radiography systems. Data were collected over a 3‐year period. No corrective actions were taken on the data, but service was performed on two of the units. Results demonstrated noise that could be attributed to suboptimal placement of the phantom and incorrect data being put into the DICOM header. Preliminary evaluation of the IAEA methodology has demonstrated that it can provide meaningful QC data that are sensitive to changes in the imaging systems. Care must be taken at implementation to properly train personnel and ensure that the image data, including the DICOM header, are being correctly transmitted. The methodology gives the opportunity for a single CQMP to provide QC services even to remote sites where travel is prohibitive, and it is feasible and easy to implement.

Impact of reconstruction parameters on lesion detection and localization in joint ictal/inter-ictal SPECT reconstruction.

Previously, a joint ictal/inter-ictal SPECT reconstruction was proposed to reconstruct a differential image representing the change of brain SPECT image from an inter-ictal to an ictal study. The so-called joint method yielded better performance for epileptic foci localization than the conventional subtraction method. In this study, we evaluated the performance of different reconstruction settings of the joint reconstruction of ictal/inter-ictal SPECT data, which creates a differential image showing the difference between ictal and inter-ictal images, in lesion detection and localization in epilepsy imaging. Differential images reconstructed from phantom data using the joint and the subtraction methods were compared based on lesion detection performance (channelized Hotelling observer signal-to-noise ratio (SNRCHO) averaged across four lesion-to-background contrast levels) at the optimal iteration. The joint-initial method which was the joint method that was initialized by the subtraction method at optimal iteration was also used to reconstruct differential images. These three methods with respective optimal iteration and the subtraction method with four iterations were applied to epileptic patient datasets. A human observer lesion localization study was performed based on localization receiver operating characteristic (LROC) analysis. From the phantom study, at their respective optimal iteration, the joint method yielded an improvement in lesion detection performance over the subtraction method of 26%, which increased to 145% when using the joint-initial method. From the patient study, the joint-initial method yielded the highest area under the LROC curve as compared with those of the joint and the subtraction methods with optimal iteration and with 4 iterations (0.44 vs 0.41, 0.39 and 0.36, respectively). In lesion detection and localization, the joint method at optimal iteration outperformed the subtraction method at optimal iteration and at iteration typically used in clinical practice. Furthermore, initialization by the subtraction method improved the performance of the joint method.

Feasibility of magnetic resonance-only high-dose-rate surface brachytherapy for clinical application.

In this article, we investigate the feasibility of magnetic resonance (MR)-only imaging for high-dose-rate (HDR) surface brachytherapy (SABT). We examined whether a standard CT-based planning can be replaced with an MR-only planning. For this purpose, the MRI digitization and plan quality check processes were compared against the standard CT-based processes. A prospective clinical implementation of the MR-only planning was evaluated on a clinical data set. A pointwise encoding time reduction with radial acquisition (PETRA) sequence was optimized for visualization of Freiburg flap (FF) on MR images. MR and conventional CT images were acquired with a FF applicator (Elekta, Stockholm, Sweden) placed on the following phantoms: (1) flat styrofoam (FST), FF locked-in placed with supporting structure; (2) cast-made facemask, and (3) porcine leg (PL). Catheters were digitized and activated with 10mm step size on Oncentra Brachy 4.5.3 Treatment Planning System. The CT-only and MR-only treatment plans were generated by optimizing the dose to the target defined as volume at 3mm skin depth. To compare the plans, the MRI-to-CT alignment was performed via rigid registration. Positional displacements of dwell positions between CT and MR plans were compared on the FST phantom and the relative percent dose difference in 2210 different points from CT or MR-only plans was compared. For all three phantoms, the comparabilities between CT and MR-only plans were assessed by calculating dice similarity coefficient (DSC) for volumes enclosing 150%, 125%, 100%, 95%, 90%, 80%, and 65% isodose lines (V150 -V65 ). The MR images of FF placed on the forearm of a healthy subject were acquired with this optimized PETRA sequence and used for treatment planning. The relative percent dose was calculated on 140 representative points placed at 3mm skin depth to evaluate the dose to the skin. Using the optimized PETRA sequence, MRTP digitization accuracy was<1mm in each dimension and on three-dimensional (3D) displacement for the FST phantom. In each phantom and clinical data set, it was possible to generate MR-only treatment plans with the 3mm skin depth prescription. In the FST phantom, the mean relative dose at the points was not significantly different(<0.1% difference) for CT or MR-based plans. The assessment of similarities in dose profiles between CT and MR-only plans' provided DSC values greater than 0.96, 0.92, and 0.73 for all volumes enclosing up to 100%, 125%, and 150% isodose lines, respectively. The feasibility of generating a HDR treatment plan with FF using MR-only has been evaluated in phantoms with varying geometry and for a clinical data set. The optimization of a standard MRI sequence-PETRA-implemented in this study showed that FF-based catheters can be digitized and a plan can be generated using only MRI. The resulting MR-only plans were comparable to the conventional CT-based plans, suggesting that MRI alone can generate clinically acceptable plans for FF in phantoms and on a clinical data set. Reliable MR-only treatment planning could improve treatment prescription through more accurate characterization of soft tissue targets.

Evaluation of attenuation correction in PET/MRI with synthetic lesion insertion.

Purpose: One major challenge facing simultaneous positron emission tomography (PET)/ magnetic resonance imaging (MRI) is PET attenuation correction (AC) measurement and evaluation of its accuracy. There is a crucial need for the evaluation of current and emergent PET AC methodologies in terms of absolute quantitative accuracy in the reconstructed PET images. Approach: To address this need, we developed and evaluated a lesion insertion tool for PET/MRI that will facilitate this evaluation process. This tool was developed for the Biograph mMR and evaluated using phantom and patient data. Contrast recovery coefficients (CRC) from the NEMA IEC phantom of synthesized lesions were compared to measurements. In addition, SUV biases of lesions inserted in human brain and pelvis images were assessed from PET images reconstructed with MRI-based AC (MRAC) and CT-based AC (CTAC). Results: For cross-comparison PET/MRI scanners AC evaluation, we demonstrated that the developed lesion insertion tool can be harmonized with the GE-SIGNA lesion insertion tool. About CRC curves difference between simulation and measurement was achieved. An average of 1.6% between harmonized simulated CRC curves obtained with mMR and SIGNA lesion insertion tools was achieved. A range of to 12% MRAC to CTAC SUV bias was respectively achieved in the vicinity and inside bone tissues in patient images in two anatomical regions, the brain, and pelvis. Conclusions: A lesion insertion tool was developed for the Biograph mMR PET/MRI scanner and harmonized with the SIGNA PET/MRI lesion insertion tool. These tools will allow for an accurate evaluation of different PET/MRI AC approaches and permit exploration of subtle attenuation correction differences across systems.

Multilayer residual sparsifying transform (MARS) model for low-dose CT image reconstruction.

Signal models based on sparse representations have received considerable attention in recent years. On the other hand, deep models consisting of a cascade of functional layers, commonly known as deep neural networks, have been highly successful for the task of object classification and have been recently introduced to image reconstruction. In this work, we develop a new image reconstruction approach based on a novel multilayer model learned in an unsupervised manner by combining both sparse representations and deep models. The proposed framework extends the classical sparsifying transform model for images to a Multilayer residual sparsifying transform (MARS) model, wherein the transform domain data are jointly sparsified over layers. We investigate the application of MARS models learned from limited regular-dose images for low-dose CT reconstruction using penalized weighted least squares (PWLS) optimization. We propose new formulations for multilayer transform learning and image reconstruction. We derive an efficient block coordinate descent algorithm to learn the transforms across layers, in an unsupervised manner from limited regular-dose images. The learned model is then incorporated into the low-dose image reconstruction phase. Low-dose CT experimental results with both the XCAT phantom and Mayo Clinic data show that the MARS model outperforms conventional methods such as filtered back-projection and PWLS methods based on the edge-preserving (EP) regularizer in terms of two numerical metrics (RMSE and SSIM) and noise suppression. Compared with the single-layer learned transform (ST) model, the MARS model performs better in maintaining some subtle details. This work presents a novel data-driven regularization framework for CT image reconstruction that exploits learned multilayer or cascaded residual sparsifying transforms. The image model is learned in an unsupervised manner from limited images. Our experimental results demonstrate the promising performance of the proposed multilayer scheme over single-layer learned sparsifying transforms. Learned MARS models also offer better image quality than typical nonadaptive PWLS methods.

Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data

PurposeThe purpose of this study was to compare the effect of two deep learning image reconstruction (DLR) algorithms in chest computed tomography (CT) with different clinical indications. Material and methodsAcquisitions on image quality and anthropomorphic phantoms were performed at six dose levels (CTDIvol: 10/7.5/5/2.5/1/0.5mGy) on two CT scanners equipped with two different DLR algorithms (TrueFidelityTM and AiCE). Raw data were reconstructed using the filtered back-projection (FBP) and the lowest/intermediate/highest DLR levels (L-DLR/M-DLR/H-DLR) of each algorithm. Noise power spectrum, task-based transfer function (TTF) and detectability index (d’) were computed: d’ modelled detection of a soft tissue mediastinal nodule, ground-glass opacity, or high-contrast pulmonary lesion. Subjective image quality of anthropomorphic phantom images was analyzed by two radiologists. ResultsFor the L-DLR/M-DLR levels, the noise magnitude was lower with TrueFidelityTM than with AiCE from 2.5 to 10 mGy. For H-DLR, noise magnitude was lower with AiCE . For L-DLR and M-DLR, the average NPS spatial frequency (fav) values were greater for AiCE except for 0.5 mGy. For H-DLR levels, fav was greater for TrueFidelityTM than for AiCE. TTF50% values were greater with AiCE for the air insert, and lower than TrueFidelityTM for the polyethylene insert. From 2.5 to10 mGy, d’ was greater for AiCE than for TrueFidelityTM for H-DLR for all lesions, but similar for L-DLR and M-DLR. Image quality was rated clinically appropriate for all levels of both algorithms, for dose from 2.5 to 10 mGy, except for L-DLR of AiCE. ConclusionDLR algorithms reduce the image-noise and improve lesion detectability. Their operations and properties impacted both noise-texture and spatial resolution.

Comparison of 90Y SIRT predicted and delivered absorbed doses using a PSF conversion method.

The aims of this study were to develop and apply a method to correct for the differences in partial volume effects of pre-therapy Technetium-99m (99mTc)-MAA SPECT and post-therapy Yttrium-90 (90Y) bremsstrahlung SPECT imaging in selective internal radiation therapy, and to use this method to improve quantitative comparison of predicted and delivered 90Y absorbed doses. The spatial resolution of 99mTc SPECT data was converted to that of 90Y SPECT data using a function calculated from 99mTc and 90Y point spread functions. This resolution conversion method (RCM) was first applied to 99mTc and 90Y SPECT phantom data to validate the method, and then to clinical data to assess the power of 99mTc SPECT imaging to predict the therapeutic absorbed dose. The maximum difference between absorbed doses to phantom spheres was 178%. This was reduced to 27% after the RCM was applied. The clinical data demonstrated differences within 38% for mean absorbed doses delivered to the normal liver, which were reduced to 20% after application of the RCM. Analysis of clinical data showed that therapeutic absorbed doses delivered to tumours greater than 100cm3 were predicted to within 52%, although there were differences of up to 210% for smaller tumours, even after the RCM was applied. The RCM was successfully verified using phantom data. Analysis of the clinical data established that the 99mTc pre-therapy imaging was predictive of the 90Y absorbed dose to the normal liver to within 20%, but had poor predictability for tumours smaller than 100cm3.

Nuclear Fragmentation Imaging for Carbon-Ion Radiation Therapy Monitoring: an In Silico Study.

PurposeThis article presents an in vivo imaging technique based on nuclear fragmentation of carbon ions in irradiated tissues for potential real-time monitoring of carbon-ion radiation therapy (CIRT) treatment delivery and quality assurance purposes in clinical settings.Materials and MethodsA proof-of-concept imaging and monitoring system (IMS) was devised to implement the technique. Monte Carlo simulations were performed for a prospective pencil-beam scanning CIRT nozzle. The development IMS benchmark considered a 5×5-cm2 pixelated charged-particle detector stack positioned downstream from a target phantom and list-mode data acquisition. The abundance and production origins, that is, vertices, of the detected fragments were studied. Fragment trajectories were approximated by straight lines and a beam back-projection algorithm was built to reconstruct the vertices. The spatial distribution of the vertices was then used to determine plan relevant markers.ResultsThe IMS technique was applied for a simulated CIRT case, a primary brain tumor. Four treatment plan monitoring markers were conclusively recovered: a depth dose distribution correlated profile, ion beam range, treatment target boundaries, and the beam spot position. Promising millimeter-scale (3-mm, ≤10% uncertainty) beam range and submillimeter (≤0.6-mm precision for shifts <3 cm) beam spot position verification accuracies were obtained for typical therapeutic energies between 150 and 290 MeV/u.ConclusionsThis work demonstrated a viable online monitoring technique for CIRT treatment delivery. The method's strong advantage is that it requires few signal inputs (position and timing), which can be simultaneously acquired with readily available technology. Future investigations will probe the technique's applicability to motion-sensitive organ sites and patient tissue heterogeneities. In-beam measurements with candidate detector-acquisition systems are ultimately essential to validate the IMS benchmark performance and subsequent deployment in the clinic.