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Purpose: This study aimed to investigate a dose rate optimization framework based on the spot-scanning patterns to improve ultrahigh-dose-rate coverage of critical organs at risk (OARs) for proton pencil beam scanning (PBS) FLASH radiation therapy (ultrahigh dose-rate (often referred to as >40 Gy per second) delivery) and present implementation of a genetic algorithm (GA) method for spot sequence optimization to achieve PBS FLASH dose rate optimization under relatively low nozzle beam currents., Methods and Materials: First, a multifield FLASH plan was developed to meet all the dosimetric goals and optimal FLASH dose rate coverage by considering the deliverable minimum monitor unit constraint. Then, a GA method was implemented into the in-house treatment platform to maximize the dose rate by exploring the best spot delivery sequence. A phantom study was performed to evaluate the effectiveness of the dose rate optimization. Then, 10 consecutive plans for patients with lung cancer previously treated using PBS intensity-modulated proton therapy were optimized using 45 GyRBE in 3 fractions for both transmission and Bragg peak FLASH radiation therapy for further validation. The spot delivery sequence of each treatment field was optimized using this GA. The ultrahigh-dose-rate-volume histogram and dose rate coverage V 40GyRBE/s were investigated to assess the efficacy of dose rate optimization quantitatively., Results: Using a relatively low monitor unit/spot of 150, corresponding to a nozzle beam current of 65 nA, the FLASH dose rate ratio V 40GyRBE/s of the OAR contour of the core was increased from 0% to ∼60% in the phantom study. In the patients with lung cancer, the ultrahigh-dose-rate coverage V 40GyRBE/s was improved from 15.2%, 15.5%, 17.6%, and 16.0% before the delivery sequence optimization to 31.8%, 43.5%, 47.6%, and 30.5% after delivery sequence optimization in the lungs-GTV (gross tumor volume), spinal cord, esophagus, and heart (for all, P < .001). When the beam current increased to 130 nA, V 40GyRBE/s was improved from 45.1%, 47.1%, 51.2%, and 51.4% to 65.3%, 83.5%, 88.1%, and 69.4% (P < .05). The averaged V 40GyRBE/s for the target and OARs increased from 12.9% to 41.6% and 46.3% to 77.5% for 65 and 130 nA, respectively, showing significant improvements based on a clinical proton system. After optimizing the dose rate for the Bragg peak FLASH technique with a beam current of 340 nA, the V 40GyRBE/s values for the lung GTV, spinal cord, esophagus, and heart were increased by 8.9%, 15.8%, 22%, and 20.8%, respectively., Conclusions: An optimal plan quality can be maintained as the spot delivery sequence optimization is a separate independent process after the plan optimization. Both the phantom and patient results demonstrated that novel spot delivery sequence optimization can effectively improve the ultrahigh-dose-rate coverage for critical OARs, which can potentially be applied in clinical practice for better OARs-sparing efficacy., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Purpose: The presence of metal implants can produce artifacts and distort Hounsfield units (HU) in patient computed tomography (CT) images. The purpose of this work was to characterize a novel metal artifact reduction (MAR) algorithm for reconstruction of CBCT images obtained by the HyperSight imaging system., Methods: Three tissue-equivalent phantoms were fitted with materials commonly used in medical applications. The first consisted of a variety of metal samples centered within a solid water block, the second was an Advanced Electron Density phantom with metal rods, and the third consisted of hip prostheses positioned within a water tank. CBCT images of all phantoms were acquired and reconstructed using the MAR and iCBCT Acuros algorithms on the HyperSight system. The signal-to-noise ratio (SNR), artifact index (AI), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean-square error (MSE) were computed to assess the image quality in comparison to artifact-free reference images. The mean HU at various VOI positions around the cavity was calculated to evaluate the artifact dependence on distance and angle from the center of the cavity. The artifact volume of the phantom (excluding the cavity) was estimated by summing the volume of all voxels with HU values outside the 5th and 95th percentiles of the phantom CBCT with no artifact., Results: The SNR, AI, SSIM, PSNR, and MSE metrics demonstrated significantly higher similarity to baseline when using MAR compared to iCBCT Acuros for all high-density materials, except for aluminum. Mean HU returned to expected solid water background at a shorter distance from metal sample in the MAR images, and the standard deviation remained lower for the MAR images at all distances from the insert. The artifact volume decreased using the novel MAR algorithm for all metal samples excluding aluminum (p < 0.001) and all hip prostheses (p < 0.05)., Conclusion: Varian's HyperSight MAR reconstruction algorithm shows a reduction in metal artifact metrics, motivating the use of MAR reconstruction for patients with metal implants., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: Kilovoltage cone beam computed tomography (kVCBCT)-guided adaptive radiation therapy (ART) uses daily deformed CT (dCT), which is generated automatically through deformable registration methods. These registration methods may perform poorly in reproducing volumes of the target organ, rectum, and bladder during treatment. We analyzed the registration errors between the daily kVCBCTs and corresponding dCTs for these organs using the default optical flow algorithm and two registration procedures. We validated the effectiveness of these registration methods in replicating the geometry for dose calculation on kVCBCT for ART., Methods: We evaluated three deformable image registration (DIR) methods to assess their registration accuracy and dose calculation effeciency in mapping target and critical organs. The DIR methods include (1) default intensity-based deformable registration, (2) hybrid deformable registration, and (3) a two-step deformable registration process. Each technique was applied to a computerized imaging reference system (CIRS) phantom (Model 062 M) and to five patients who received volumetric modulated arc therapy to the prostate. Registration accuracy was assessed using the 95% Hausdorff distance (HD 95 ) and Dice similarity coefficient (DSC), and each method was compared with the intensity-based registration method. The improvement in the dCT image quality of the CIRS phantom and five patients was assessed by comparing dCT with kVCBCT. Image quality quantitative metrics for the phantom included the signal-to-noise ratio (SNR), uniformity, and contrast-to-noise ratio (CNR), whereas those for the patients included the mean absolute error (MAE), mean error, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). To determine dose metric differences, we used a dose-volume histogram (DVH) and 3.0%/0.3 mm gamma analysis to compare planning computed tomography (pCT) and kVCBCT recalculations with restimulated CT images used as a reference., Results: The dCT images generated by the hybrid (dCT H ) and two-step (dCT C ) registration methods resulted in significant improvements compared to kVCBCT in the phantom model. Specifically, the SNR improved by 107% and 107.2%, the uniformity improved by 90% and 75%, and the CNR improved by 212.2% and 225.6 for dCT H and dCT C methods, respectively. For the patient images, the MAEs improved by 98% and 94%, the PSNRs improved by 16.3% and 22.9%, and the SSIMs improved by 1% and 1% in the dCT H and dCT C methods, respectively. For the geometric evaluation, only the two-step registration method improved registration accuracy. The dCT H method yielded an average HD 95 of 12 mm and average DSC of 0.73, whereas dCT C yielded an average HD 95 of 2.9 mm and average DSC of 0.902. The DVH showed that the dCT C -based dose calculations differed by <2% from the expected results for treatment targets and volumes of organs at risk. Additionally, gamma indices for dCT C -based treatment plans were >95% at all points, whereas they were <95% for kVCBCT-based treatment plans., Conclusion: The two-step registration method outperforms the intensity-based and hybrid registration methods. While the hybrid and two-step-based methods improved the image quality of kVCBCT in a linear accelerator, only the two-step method improved the registration accuracy of the corresponding structures among the pCT and kVCBCT datasets. A two-step registration process is recommended for applying kVCBCT to ART, which achieves better registration accuracy for local and global image structures. This method appears to be beneficial for radiotherapy dose calculation in patients with pelvic cancer., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: To evaluate the feasibility of an open-source, semi-automated, and reproducible vertex placement tool to improve the efficiency of lattice radiotherapy (LRT) planning. We used polymer gel dosimetry with a Cone Beam CT (CBCT) readout to commission this LRT technique., Material and Methods: We generated a volumetric modulated arc therapy (VMAT)-based LRT plan on a 2 L NIPAM polymer gel dosimeter using our Eclipse Acuros version 15.6 AcurosXB beam model, and also recalculated the plan with a pre-clinical Acuros v18.0 dose calculation algorithm with the enhanced leaf modelling (ELM). With the assistance of the MAAS-SFRThelper software, a lattice vertex diameter of 1.5 cm and center-to-center spacing of 3 cm were used to place the spheres in a hexagonal, closed packed structure. The verification plan included four gantry arcs with 15°, 345°, 75°, 105° collimator angles. The spheres were prescribed 20 Gy to 50% of their combined volume. The 6 MV Flattening Filter Free beam energy was used to deliver the verification plan. The dosimetric accuracy of the LRT delivery was evaluated with 1D dose profiles, 2D isodose maps, and a 3D global gamma analysis., Results: Qualitative comparisons between the 1D dose profiles of the Eclipse plan and measured gel showed good consistency at the prescription dose mark. The average diameter measured 13.3 ± 0.2 mm (gel for v15.6), 12.6 mm (v15.6 plan), 13.1 ± 0.2 mm (gel for v18.0), and 12.3 mm (v18.0 plan). 3D gamma analysis showed that all gamma pass percent were > 95% except at 1% and 2% at the 1 mm distance to agreement criteria., Conclusion: This study presents a novel application of gel dosimetry in verifying the dosimetric accuracy of LRT, achieving excellent 3D gamma results. The treatment planning was facilitated by publicly available software that automatically placed the vertices for consistency and efficiency., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: For reliable DCE MRI parameter estimation, k-space undersampling is essential to meet resolution, coverage, and signal-to-noise requirements. Pseudo-spiral (PS) sampling achieves this by sampling k-space on a Cartesian grid following a spiral trajectory. The goal was to optimize PS k-space sampling patterns for abdomin al DCE MRI., Methods: The optimal PS k-space sampling pattern was determined using an anthropomorphic digital phantom. Contrast agent inflow was simulated in the liver, spleen, pancreas, and pancreatic ductal adenocarcinoma (PDAC). A total of 704 variable sampling and reconstruction approaches were created using three algorithms using different parametrizations to control sampling density, halfscan and compressed sensing regularization. The sampling patterns were evaluated based on image quality scores and the accuracy and precision of the DCE pharmacokinetic parameters. The best and worst strategies were assessed in vivo in five healthy volunteers without contrast agent administration. The best strategy was tested in a DCE scan of a PDAC patient., Results: The best PS reconstruction was found to be PS-diffuse based, with quadratic distribution of readouts on a spiral, without random shuffling, halfscan factor of 0.8, and total variation regularization of 0.05 in the spatial and temporal domains. The best scoring strategy showed sharper images with less prominent artifacts in healthy volunteers compared to the worst strategy. Our suggested DCE sampling strategy also showed high quality DCE images in the PDAC patient., Conclusion: Using an anthropomorphic digital phantom, we identified an optimal PS sampling strategy for abdominal DCE MRI, and demonstrated feasibility in a PDAC patient., (© 2024 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Filter exchange imaging (FEXI) is a double diffusion-encoding (DDE) sequence that is specifically sensitive to exchange between sites with different apparent diffusivities. FEXI uses a diffusion-encoding filtering block followed by a detection block at varying mixing times to map the exchange rate. Long mixing times enhance the sensitivity to exchange, but they pose challenges for imaging applications that require a stimulated echo sequence with crusher gradients. Thin imaging slices require strong crushers, which can introduce significant diffusion weighting and bias exchange rate estimates. Here, we treat the crushers as an additional encoding block and consider FEXI as a triple diffusion-encoding sequence. This allows the bias to be corrected in the case of multi-Gaussian diffusion, but not easily in the presence of restricted diffusion. Our approach addresses challenges in the presence of restricted diffusion and relies on the ability to independently gauge sensitivities to exchange and restricted diffusion for arbitrary gradient waveforms. It follows two principles: (i) the effects of crushers are included in the forward model using signal cumulant expansion; and (ii) timing parameters of diffusion gradients in filter and detection blocks are adjusted to maintain the same level of restriction encoding regardless of the mixing time. This results in the tuned exchange imaging (TEXI) protocol. The accuracy of exchange mapping with TEXI was assessed through Monte Carlo simulations in spheres of identical sizes and gamma-distributed sizes, and in parallel hexagonally packed cylinders. The simulations demonstrate that TEXI provides consistent exchange rates regardless of slice thickness and restriction size, even with strong crushers. However, the accuracy depends on b-values, mixing times, and restriction geometry. The constraints and limitations of TEXI are discussed, including suggestions for protocol adaptations. Further studies are needed to optimize the precision of TEXI and assess the approach experimentally in realistic, heterogeneous substrates., (© 2024 The Author(s). NMR in Biomedicine published by John Wiley & Sons Ltd.)

Purpose: To reduce the ringing artifacts of the motion-resolved images in free-breathing dynamic pulmonary MRI., Methods: A golden-step based interleaving (GSI) technique was proposed to reduce ringing artifacts induced by diaphragm drifting. The pulmonary MRI data were acquired using a superior-inferior navigated 3D radial UTE sequence in an interleaved manner during free breathing. Successive interleaves were acquired in an incoherent fashion along the polar direction. Four-dimensional images were reconstructed from the motion-resolved k-space data obtained by retrospectively binning. The reconstruction algorithms included standard nonuniform fast Fourier transform (NUFFT), Voronoi-density-compensated NUFFT, extra-dimensional UTE, and motion-state weighted motion-compensation reconstruction. The proposed interleaving technique was compared with a conventional sequential interleaving (SeqI) technique on a phantom and eight subjects., Results: The quantified ringing artifacts level in the motion-resolved image is positively correlated with the quantified nonuniformity level of the corresponding k-space. The nonuniformity levels of the end-expiratory and end-inspiratory k-space binned from GSI data (0.34 ± 0.07, 0.33 ± 0.05) are significantly lower with statistical significance (p < 0.05) than that binned from SeqI data (0.44 ± 0.11, 0.42 ± 0.12). Ringing artifacts are substantially reduced in the dynamic images of eight subjects acquired using the proposed technique in comparison with that acquired using the conventional SeqI technique., Conclusion: Ringing artifacts in the motion-resolved images induced by diaphragm drifting can be reduced using the proposed GSI technique for free-breathing dynamic pulmonary MRI. This technique has the potential to reduce ringing artifacts in free-breathing liver and kidney MRI based on full-echo interleaved 3D radial acquisition., (© 2024 International Society for Magnetic Resonance in Medicine.)

Purpose: To introduce an alternative idea for fat suppression that is suited both for low-field applications where conventional fat-suppression approaches become ineffective due to narrow spectral separation and for applications with strong B0 homogeneities., Methods: Separation of fat and water is achieved by sweeping the frequency of RF saturation pulses during continuous radial acquisition and calculating frequency-resolved images using regularized iterative reconstruction. Voxel-wise signal-response curves are extracted that reflect tissue's response to RF saturation at different frequencies and allow the classification into fat or water. This information is then utilized to generate water-only composite images. The principle is demonstrated in free-breathing abdominal and neck examinations using stack-of-stars 3D balanced SSFP (bSSFP) and gradient-recalled echo (GRE) sequences at 0.55 and 3T. Moreover, a potential extension toward quantitative fat/water separation is described., Results: Experiments with a proton density fat fraction (PDFF) phantom validated the reliability of fat/water separation using signal-response curves. As demonstrated for abdominal imaging at 0.55T, the approach resulted in more uniform fat suppression without loss of water signal and in improved CSF-to-fat signal ratio. Moreover, the approach provided consistent fat suppression in 3T neck exams where conventional spectrally-selective fat saturation failed due to strong local B0 inhomogeneities. The feasibility of simultaneous fat/water quantification has been demonstrated in a PDFF phantom., Conclusion: The proposed principle achieves reliable fat suppression in low-field applications and adapts to high-field applications with strong B0 inhomogeneity. Moreover, the principle potentially provides a basis for developing an alternative approach for PDFF quantification., (© 2024 International Society for Magnetic Resonance in Medicine.)

Objective: To demonstrate the potential of low-dose ultra-high-resolution CT (UHRCT) images to generate high-quality radiographic images on extremity phantoms and to estimate the radiation dose required for this., Materials and Methods: A hand and knee phantom containing real human bones was imaged on an UHRCT scanner at full-dose, half-dose, and quarter-dose levels using a high-resolution extremity protocol. The raw data was reconstructed using both filtered back projection (FBP) and an iterative reconstruction algorithm (AIDR3D). Using custom designed software, each CT volume data set was converted to attenuation coefficients, and then a synthesized radiograph (synDX) was generated by forward projecting the volume data sets from a point source onto a 2D synthetic detector. The signal-to-noise ratio (SNR) was measured in the synDXs across all dose levels and the root-mean-squared error (RMSE) was computed with the FD synDXs as the reference., Results: The proposed workflow generates high-quality synDXs at any arbitrary angle. For FBP, the SNR largely tracked with the radiation dose levels for both the knee and hand phantoms. For the knee phantom, iterative reconstruction provided a 6.1% higher SNR when compared to FBP. The RMSE was overall higher for the lowest dose levels and monotonically decreased with increasing dose. No substantial differences were observed qualitatively in the visualization of skeletal detail of the phantoms., Conclusion: The fine detail provided by UHRCT acquisitions of extremities facilitates the ability to generate quality radiographs, potentially eliminating the need for additional scanning on a conventional digital radiography system., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Purpose: To mitigate the B 0 /B 1 + sensitivity of velocity-selective inversion (VSI) pulse trains for velocity-selective arterial spin labeling (VSASL) by implementing adiabatic refocusing. This approach aims to achieve artifact-free VSI-based perfusion imaging through single-pair label-control subtractions, reducing the need for the currently required four-pair dynamic phase-cycling (DPC) technique when using a velocity-insensitive control., Methods: We introduce a Fourier-transform VSI (FT-VSI) train that incorporates sinc-modulated hard excitation pulses with MLEV-8-modulated adiabatic hyperbolic secant refocusing pairs. We compare performance between this train and the standard composite refocusing train, including with and without DPC, for dual-module VSI VSASL. We evaluate (1) simulated velocity-selective profiles and subtraction fidelity across a broad B 0 /B 1 + range, (2) subtraction fidelity in phantoms, and (3) image quality, artifact presence, and gray-matter perfusion heterogeneity (as measured by the spatial coefficient of variation) in healthy human subjects., Results: Adiabatic refocusing significantly improves FT-VSI robustness to B 0 /B 1 + inhomogeneity for a single label-control subtraction. Subtraction fidelity is dramatically improved in both simulation and phantoms compared with composite refocusing without DPC, and is similar compared with DPC methods. In humans, marked artifacts seen with the non-DPC composite refocusing approach are eliminated, corroborated by significantly reduced gray-matter heterogeneity (via lower spatial coefficient of variation values)., Conclusion: A novel VSASL labeling train using adiabatic refocusing pulses for VSI was found to reduce artifacts related to B 0 /B 1 + inhomogeneity, thereby providing an alternative to DPC and its associated limitations, which include increased vulnerability to physiological noise and motion, reduced functional MRI applicability, and suboptimal data censoring., (© 2024 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Computed tomography (CT) has been used worldwide as a non-invasive test to assist in diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential health risks such as cancer. The desire for lower radiation doses has driven researchers to improve reconstruction quality. Although previous studies on low-dose computed tomography (LDCT) denoising have demonstrated the effectiveness of learning-based methods, most were developed on the simulated data. However, the real-world scenario differs significantly from the simulation domain, especially when using the multi-slice spiral scanner geometry. This paper proposes a two-stage method for the commercially available multi-slice spiral CT scanners that better exploits the complete reconstruction pipeline for LDCT denoising across different domains. Our approach makes good use of the high redundancy of multi-slice projections and the volumetric reconstructions while leveraging the over-smoothing issue in conventional cascaded frameworks caused by aggressive denoising. The dedicated design also provides a more explicit interpretation of the data flow. Extensive experiments on various datasets showed that the proposed method could remove up to 70% of noise without compromised spatial resolution, while subjective evaluations by two experienced radiologists further supported its superior performance against state-of-the-art methods in clinical practice. Code is available at https://github.com/YCL92/TMD-LDCT.

The reconstruction of dynamic magnetic resonance images from incomplete k-space data has sparked significant research interest due to its potential to reduce scan time. However, traditional iterative optimization algorithms fail to faithfully reconstruct images at higher acceleration factors and incur long reconstruction time. Furthermore, end-to-end deep learning-based reconstruction algorithms suffer from large model parameters and lack robustness in the reconstruction results. Recently, unrolled deep learning models, have shown immense potential in algorithm stability and applicability flexibility. In this paper, we propose an unrolled deep learning network based on a second-order Half-Quadratic Splitting(HQS) algorithm, where the forward propagation process of this framework strictly follows the computational flow of the HQS algorithm. In particular, we propose a degradation-sense module by associating random sampling patterns with intermediate variables to guide the iterative process. We introduce the Information Fusion Transformer(IFT) to extract both local and non-local prior information from image sequences, thereby removing aliasing artifacts resulting from random undersampling. Finally, we impose low-rank constraints within the HQS algorithm to further enhance the reconstruction results. The experiments demonstrate that each component module of our proposed model contributes to the improvement of the reconstruction task. Our proposed method achieves comparably satisfying performance to the state-of-the-art methods and it exhibits excellent generalization capabilities across different sampling masks. At the low acceleration factor, there is a 0.7% enhancement in the PSNR. Furthermore, when the acceleration factor reached 8 and 12, the PSNR achieves an improvement of 3.4% and 5.8% respectively., (Copyright © 2024 Elsevier Inc. All rights reserved.)

In the field of medical imaging, microwave tomography (MWT) is based on the scattering and absorption characteristics of different tissues to microwaves and can reconstruct the electromagnetic property distribution of biological tissues non-invasively and without ionizing radiation. However, due to the inherently nonlinear and ill-posed characteristics of MWT calculations, actual imaging is prone to overfitting or artifacts. To address this, this paper proposes a two-step iterative imaging approach for rapid medical microwave tomography. This method establishes corresponding objective functions for microwave imaging across multiple frequencies and conducts iterative calculations on images at varying resolutions. This effectively enhances image clarity and accuracy while alleviating the issue of prolonged computational time associated with imaging complex structures at high resolution due to insufficient prior information during iterative processes. In the electromagnetic simulation section, we simulated a three-layer brain model and conducted imaging experiments. The results demonstrate that the algorithm significantly enhances imaging resolution, accurately pinpointing cerebral hemorrhages at different locations using an eight-antenna array and successfully reconstructs tomography images with a hemorrhage area radius of 1 cm. Lastly, experiments were conducted using a medical microwave tomography platform and four simplified human brain models, achieving millimeter-level accuracy in MWT.

Objective. With advancements in high-dose rate brachytherapy, the clinical translation of intensity modulated brachytherapy (IMBT) innovations necessitates utilization of model-based dose calculation algorithms (MBDCA) for accurate and rapid dose calculations. This study uniquely benchmarks a commercial MBDCA, BrachyVision ACUROS TM (BVA), against Monte Carlo (MC) simulations, evaluating dose distributions for a novel IMBT applicator, termed as the six-groove Direction Modulated Brachytherapy (DMBT) tandem, expanding beyond previous focus on partially shielded vaginal cylinder applicators, through a novel methodology. Approach. The DMBT tandem applicator, made of a tungsten alloy with six evenly spaced grooves, was simulated using the GEANT4 MC code. Subsequently, two main scenarios were created using the BVA and reproduced by the MC simulations: ' Source at the Center of the Water Phantom (SACWP) ' and ' Source at the Middle of the Applicator (SAMA) ' for three cubical virtual water phantoms (20 cm) 3 , (30 cm) 3 , and (40 cm) 3 . A track length estimator was utilized for dose calculation and 2D/3D scoring were performed. The difference in isodose surfaces/lines (i.e. coverage) at each voxel, ΔD Isodose Levels/Lines , was thus calculated for relevant normalization points ( r ref ). Results. The coverage was comparable, based on 2D scoring, between the BVA and MC isodose surfaces/lines for the region of clinical relevance, (i.e. within 5 cm radius from the source) with ΔD Isodose Lines ( r ref : 1 cm from the source) falling within 2% for the two scenarios for all phantom sizes. For the phantom (20 cm) 3 , ΔD Isodose Levels (3D scoring) recorded the range [-3.0% +6.5%] ([-7.4% +7.3%]) for 95% of the voxels of the same scoring volume for the SACWP (SAMA) scenario. Significance. The results indicated that the BVA could render comparable coverage as compared to the MC simulations in the region of clinical relevance for different phantom sizes. ΔD Isodose Lines may offer an advantageous metric for evaluation of MBDCAs in clinical setting., (Creative Commons Attribution license.)

Objective: To propose a dual-domain CBCT reconstruction framework (DualSFR-Net) based on generative projection interpolation to reduce artifacts in sparse-view cone beam computed tomography (CBCT) reconstruction., Methods: The proposed method DualSFR-Net consists of a generative projection interpolation module, a domain transformation module, and an image restoration module. The generative projection interpolation module includes a sparse projection interpolation network (SPINet) based on a generative adversarial network and a full-view projection restoration network (FPRNet). SPINet performs projection interpolation to synthesize full-view projection data from the sparse-view projection data, while FPRNet further restores the synthesized full-view projection data. The domain transformation module introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes. The image restoration module includes an image restoration network FIRNet that fine-tunes the domain-transformed images to eliminate residual artifacts and noise., Results: Validation experiments conducted on a dental CT dataset demonstrated that DualSFR-Net was capable to reconstruct high-quality CBCT images under sparse-view sampling protocols. Quantitatively, compared to the current best methods, the DualSFR-Net method improved the PSNR by 0.6615 and 0.7658 and increased the SSIM by 0.0053 and 0.0134 under 2-fold and 4-fold sparse protocols, respectively., Conclusion: The proposed generative projection interpolation-based dual-domain CBCT sparse-view reconstruction method can effectively reduce stripe artifacts to improve image quality and enables efficient joint training for dual-domain imaging networks in sparse-view CBCT reconstruction.

Background: Dual-energy subtraction (DES) imaging is critical in contrast-enhanced mammography (CEM), as the recombination of low-energy (LE) and high-energy (HE) images produces contrast enhancement while reducing anatomical noise. The study's purpose was to compare the performance of the DES algorithm among three different CEM systems using a commercial phantom., Methods: A CIRS Model 022 phantom, designed for CEM, was acquired using all available automatic exposure modes (AECs) with three CEM systems from three different manufacturers (CEM1, CEM2, and CEM3). Three studies were acquired for each system/AEC mode to measure both radiation dose and image quality metrics, including estimation of measurement error. The mean glandular dose (MGD) calculated over the three acquisitions was used as the dosimetry index, while contrast-to-noise ratio (CNR) was obtained from LE and HE images and DES images and used as an image quality metric., Results: On average, the CNR of LE images of CEM1 was 2.3 times higher than that of CEM2 and 2.7 times higher than that of CEM3. For HE images, the CNR of CEM1 was 2.7 and 3.5 times higher than that of CEM2 and CEM3, respectively. The CNR remained predominantly higher for CEM1 even when measured from DES images, followed by CEM2 and then CEM3. CEM1 delivered the lowest MGD (2.34 ± 0.03 mGy), followed by CEM3 (2.53 ± 0.02 mGy) in default AEC mode, and CEM2 (3.50 ± 0.05 mGy). The doses of CEM2 and CEM3 increased by 49.6% and 8.0% compared with CEM1, respectively., Conclusion: One system outperformed others in DES algorithms, providing higher CNR at lower doses., Relevance Statement: This phantom study highlighted the variability in performance among the DES algorithms used by different CEM systems, showing that these differences can be translated in terms of variations in contrast enhancement and radiation dose., Key Points: DES images, obtained by recombining LE and HE images, have a major role in CEM. Differences in radiation dose among CEM systems were between 8.0% and 49.6%. One DES algorithm achieved superior technical performance, providing higher CNR values at a lower radiation dose., (© 2024. The Author(s).)

We aimed to implement a fully automatic computed tomography (CT) image-detection programming algorithm as a pectus excavatum (PE) diagnostic tool, facilitating comprehensive chest wall deformity evaluation. We developed our algorithm using MATLAB, leveraging the Hounsfield unit threshold and region growing methods. The MATLAB graphical user interface enables the direct use of our program. We validated the model using CT images of anthropomorphic phantoms and one normal individual. The measurement values obtained by our algorithm demonstrated very small differences compared to the known anthropomorphic phantom model data and manual measurement. For algorithm testing, 17,214 chest CT scans obtained from 57 PE patients were processed by the algorithm and independently reviewed by a radiologist and a thoracic surgeon. The measurements of transverse, anteroposterior, and sternum-to-vertebral distance of the thoracic cavity, along with the calculated data of four indices, exhibited high positive correlations (0.94-0.99). The asymmetry index and maximum anteroposterior hemithorax distance exhibited moderate correlation (0.40-0.83). Our automatic PE diagnostic tool demonstrated high accuracy; four chest wall deformity indices were obtained simultaneously without any initial manual marking, correlating well with manual measurements., (© 2024. The Author(s).)

Diffusion model has emerged as a potential tool to tackle the challenge of sparse-view CT reconstruction, displaying superior performance compared to conventional methods. Nevertheless, these prevailing diffusion models predominantly focus on the sinogram or image domains, which can lead to instability during model training, potentially culminating in convergence towards local minimal solutions. The wavelet transform serves to disentangle image contents and features into distinct frequency-component bands at varying scales, adeptly capturing diverse directional structures. Employing the wavelet transform as a guiding sparsity prior significantly enhances the robustness of diffusion models. In this study, we present an innovative approach named the Stage-by-stage Wavelet Optimization Refinement Diffusion (SWORD) model for sparse-view CT reconstruction. Specifically, we establish a unified mathematical model integrating low-frequency and high-frequency generative models, achieving the solution with an optimization procedure. Furthermore, we perform the low-frequency and high-frequency generative models on wavelet's decomposed components rather than the original sinogram, ensuring the stability of model training. Our method is rooted in established optimization theory, comprising three distinct stages, including low-frequency generation, high-frequency refinement and domain transform. The experimental results demonstrated that the proposed method outperformed existing state-of-the-art methods both quantitatively and qualitatively.

Purpose: To achieve high-resolution fetal brain anatomical imaging without introducing image artifacts by reducing the FOV, and to demonstrate improved image quality compared to conventional full-FOV fetal brain imaging., Methods: Reduced FOV was achieved by applying outer volume suppression (OVS) pulses immediately prior to standard single-shot fast spin echo (SSFSE) imaging. In the OVS preparation, a saturation RF pulse followed by a gradient spoiler was repeated three times with optimized flip-angle weightings and a variable spoiler scheme to enhance signal suppression. Simulations and phantom and in-vivo experiments were performed to evaluate OVS performance. In-vivo high-resolution SSFSE images acquired using the proposed approach were compared with conventional and high-resolution SSFSE images with a full FOV, using image quality scores assessed by neuroradiologists and calculated image metrics., Results: Excellent signal suppression in the saturation bands was confirmed in phantom and in-vivo experiments. High-resolution SSFSE images with a reduced FOV acquired using OVS demonstrated the improved depiction of brain structures without significant motion and blurring artifacts. The proposed method showed the highest image quality scores in the criteria of sharpness, contrast, and artifact and was selected as the best method based on overall image quality. The calculated image sharpness and tissue contrast ratio were also the highest with the proposed method., Conclusion: High-resolution fetal brain anatomical images acquired using a reduced FOV with OVS demonstrated improved image quality both qualitatively and quantitatively, suggesting the potential for enhanced diagnostic accuracy in detecting fetal brain abnormalities in utero., (© 2024 International Society for Magnetic Resonance in Medicine.)

Purpose: This study aimed to facilitate research progress in MR elastography (MRE) by providing a versatile and convenient application for MRE reconstruction, namely the MRE research tool (MRE-rTool). It can be used for a series of MRE image analyses, including phase unwrapping, arbitrary bandpass and directional filtering, noise assessment of the wave propagation image (motion SNR), and reconstruction of the elastogram in both 2D and 3D MRE acquisitions. To reinforce the versatility of MRE-rTool, the conventional method of motion SNR was modified into a new method that reflects the effects of image filtering., Methods: MRE tests of the phantom and liver were performed using different estimation algorithms for stiffness value (algebraic inversion of the differential equation [AIDE], local frequency estimation [LFE] in MRE-rTool, and multimodel direct inversion [MMDI] in clinical reconstruction) and acquiring dimensions (2D and 3D acquisitions). This study also tested the accuracy of masking low SNR regions using modified and conventional motion SNR under various mechanical vibration powers., Results: The stiffness values estimated using AIDE/LFE in MRE-rTool were comparable to that of MMDI (phantom, 3.71 ± 0.74, 3.60 ± 0.32, and 3.60 ± 0.54 kPa in AIDE, LFE, and MMDI; liver, 2.26 ± 0.31, 2.74 ± 0.16, and 2.21 ± 0.26 kPa in AIDE, LFE, and MMDI). The stiffness value in 3D acquisition was independent of the direction of the motion-encoding gradient and was more accurate than that of 2D acquisition. The masking of low SNR regions using the modified motion SNR worked better than that in the conventional motion SNR for each vibration power, especially when using a directional filter., Conclusion: The performance of MRE-rTool on test data reached the level required in clinical MRE studies. MRE-rTool has the potential to facilitate MRE research, contribute to the future development of MRE, and has been freely released online.

Purpose: To develop a non-coplanar cone-beam computed tomography (CBCT) image reconstruction method using projections within a limited angle range for non-coplanar radiotherapy., Methods: A generative adversarial network (GAN) was utilized to reconstruct non-coplanar CBCT images. Data from 40 patients with brain tumors and two head phantoms were used in this study. In the training stage, the generator of the GAN used coplanar CBCT and non-coplanar projections as the input, and an encoder with a dual-branch structure was utilized to extract features from the coplanar CBCT and non-coplanar projections separately. Non-coplanar CBCT images were then reconstructed using a decoder by combining the extracted features. To improve the reconstruction accuracy of the image details, the generator was adversarially trained using a patch-based convolutional neural network as the discriminator. A newly designed joint loss was used to improve the global structure consistency rather than the conventional GAN loss. The proposed model was evaluated using data from eight patients and two phantoms at four couch angles (±45°, ±90°) that are most commonly used for brain non-coplanar radiotherapy in our department. The reconstructed accuracy was evaluated by calculating the root mean square error (RMSE) and an overall registration error ε, computed by integrating the rigid transformation parameters., Results: In both patient data and phantom data studies, the qualitative and quantitative metrics results indicated that ± 45° couch angle models performed better than ±90° couch angle models and had statistical differences. In the patient data study, the mean RMSE and ε values of couch angle at 45°, -45°, 90°, and -90° were 58.5 HU and 0.42 mm, 56.8 HU and 0.41 mm, 73.6 HU and 0.48 mm, and 65.3 HU and 0.46 mm, respectively. In the phantom data study, the mean RMSE and ε values of couch angle at 45°, -45°, 90°, and -90° were 91.2 HU and 0.46 mm, 95.0 HU and 0.45 mm, 114.6 HU and 0.58 mm, and 102.9 HU and 0.52 mm, respectively., Conclusions: The results show that the reconstructed non-coplanar CBCT images can potentially enable intra-treatment three-dimensional position verification for non-coplanar radiotherapy., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: To develop multiphoton excitation techniques for simultaneous multislice (SMS) imaging and evaluate their performance and specific absorption rate (SAR) benefit. To improve multiphoton SMS reconstruction quality with a novel CAIPIRINHA (controlled aliasing in parallel imaging results in higher acceleration) design., Theory and Methods: When a conventional single-slice RF field is applied together with an oscillating gradient field, the two can combine to generate multiphoton excitation at multiple discrete spatial locations. Because the conventional RF is reused at multiple spatial locations, multiphoton excitation offers reduced SAR for SMS applications. CAIPIRINHA shifts are often used to improve parallel-imaging acceleration. Interestingly, CAIPIRINHA-type shifts can be obtained for multiphoton SMS by updating the oscillating gradient phase at every phase encode. In this work, both a gradient-echo and a spin-echo sequence with multiphoton CAIPIRINHA-SMS excitation pulses are implemented for in vivo human imaging at 3 T., Results: For three slices, multiphoton SMS provides a 51% reduction in SAR compared with conventional superposition SMS, whereas for five slices, SAR is reduced by 66%. Multiphoton SMS outperforms PINS (power independent of number of slices) and MultiPINS in terms of SAR reduction especially when the pulse duration is short, slices are thin, and/or the slice spacing is large. A custom CAIPIRINHA phase-encoding design for multiphoton SMS significantly improves reconstruction quality., Conclusion: Multiphoton SMS excitation can be obtained by combining conventional single-slice RF pulses with an oscillating gradient and offers significant SAR benefits compared with conventional superposition SMS. A novel CAIPIRINHA design allows higher multiband factors for multiphoton SMS imaging., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Objective: This paper proposes an ultrasound imaging algorithm based on sub-beamformer and multi-apodization with cross-correlation (SUB-MAX), aiming to achieve high resolution close to the minimum variance (MV) beamforming with low complexity and to enhance image contrast while maintaining background quality., Methods: The output of two (N/2)-element DAS beamformers with asymmetric phase centers is subtracted, resulting in a large drop in the main-lobe amplitude, while the sidelobe maintains a relatively high amplitude level. Inspired by this characteristic, the coefficients with opposite trends compared with the subtracted output are obtained and fused with the normalized cross-correlation (NCC) weighting matrix acquired by using multi-pair received apodization, the proposed SUB-MAX obtains a new weighting matrix to weight the output of the DAS beamformer., Results: For ats&#95;wire point targets, the average full-width at half-maximum (FWHM) of SUB-MAX compared with DAS, DMAS, CF, and MAX decreases by 52.7%, 43.5%, 33.3%, and 52.7%, respectively. For geabr&#95;0 cysts, the average contrast ratio (CR) of SUB-MAX compared with DAS, MV, DMAS, and CF increases by 57.7%, 86.8%, 2.5%, and 14.4%, respectively. Experiments on rat&#95;tumor dataset also indicate that SUB-MAX has a superior comprehensive imaging performance., Conclusion: The experimental results indicate that the superior comprehensive imaging performance of the proposed SUB-MAX is expected to be suitable for real-time imaging systems due to its non-reliance on covariance matrix inversion., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2024 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Breast cancer has a high incidence and mortality rate among women, early diagnosis is essential as it gives insight regarding the most appropriate therapeutic strategy for each case. Among all imaging diagnostic methods, digital breast tomosynthesis (DBT) is effective for early breast cancer detection. In DBT images, high-density object artifacts are generated when imaging objects with high X-ray absorptivity, which include metal artifacts, ripple artifacts, and deformation artifacts. In this study, we analyze the causes of these artifacts and propose a set of high-density object reconstruction methods based on iterative algorithms. Our method includes a reprojection-based high-density object projection data segmentation algorithm and an iterative reconstruction algorithm based on volume expansion. The experiments on simulation data and the human breast data with artificial surgical needles prove the effectiveness of our method. By using our algorithm, the problem of distorting the shape, size, and position of high-density objects during DBT reconstruction is raised, the influence of these artifacts is reduced, and the quality of the DBT reconstructed image is improved. We hope that our algorithm might contribute to promoting the usage of DBT., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

The score-based generative model (SGM) has received significant attention in the field of medical imaging, particularly in the context of limited-angle computed tomography (LACT). Traditional SGM approaches achieved robust reconstruction performance by incorporating a substantial number of sampling steps during the inference phase. However, these established SGM-based methods require large computational cost to reconstruct one case. The main challenge lies in achieving high-quality images with rapid sampling while preserving sharp edges and small features. In this study, we propose an innovative rapid-sampling strategy for SGM, which we have aptly named the time-reversion fast-sampling (TIFA) score-based model for LACT reconstruction. The entire sampling procedure adheres steadfastly to the principles of robust optimization theory and is firmly grounded in a comprehensive mathematical model. TIFA's rapid-sampling mechanism comprises several essential components, including jump sampling, time-reversion with re-sampling, and compressed sampling. In the initial jump sampling stage, multiple sampling steps are bypassed to expedite the attainment of preliminary results. Subsequently, during the time-reversion process, the initial results undergo controlled corruption by introducing small-scale noise. The re-sampling process then diligently refines the initially corrupted results. Finally, compressed sampling fine-tunes the refinement outcomes by imposing regularization term. Quantitative and qualitative assessments conducted on numerical simulations, real physical phantom, and clinical cardiac datasets, unequivocally demonstrate that TIFA method (using 200 steps) outperforms other state-of-the-art methods (using 2000 steps) from available [0°, 90°] and [0°, 60°]. Furthermore, experimental results underscore that our TIFA method continues to reconstruct high-quality images even with 10 steps. Our code at https://github.com/tianzhijiaoziA/TIFADiffusion.

Background: The advantages of proton therapy can be further enhanced with online magnetic resonance imaging (MRI) guidance. One of the challenges in the realization of MRI-guided proton therapy (MRPT) is accurately calculating the radiation dose in the presence of magnetic fields., Purpose: This study aims to develop an efficient and accurate proton dose calculation algorithm adapted to the presence of magnetic fields., Methods: An analytical-numerical radiation dose calculation algorithm, Proton and Ion Dose Engine (PRIDE), was developed. The algorithm combines the pencil beam algorithm (PBA) with a novel iterative voxel-based ray-tracing algorithm. The new ray-tracing method uses fewer assumptions and ensures broader applicability for proton beam trajectory prediction in magnetic fields, and has been compared to Wolf's method and Schellhammer's method. The accuracy of PRIDE algorithm was validated on three phantoms and two practical plans (one single-field water plan and one prostate tumor plan) in different magnetic field strengths up to 3.0 T. The validation was performed by comparing the results against the Monte Carlo (MC) simulations, using the global gamma index criteria of 2%/2 mm and 3%/3 mm with a 10% threshold., Results: PRIDE showed good agreement with MC in homogeneous and slab heterogeneous phantom, achieving gamma passing rates (%GPs) above 99% for 2%/2 mm criteria when magnetic field strength is not greater than 1.5 T. Although the agreement decreased for scenarios involving high proton energy (240 MeV) and strong magnetic field (3.0 T), the 2%/2 mm %GPs still remained above 98%. In lateral heterogeneous phantom, the accuracy of PRIDE decreased due to the PBA's limitation. For the two practical plans in different magnetic fields, %GPs exceeded 98% and 99% for 2%/2 mm and 3%/3 mm criteria, respectively., Conclusions: PRIDE can perform efficient and accurate proton dose calculation in magnetic fields up to 3.0 T, and is expected to work as a useful tool for proton dose calculation in MRPT., (© 2024 American Association of Physicists in Medicine.)

Background: Magnetic Resonance Imaging (MRI) is a highly demanded medical imaging system due to high resolution, large volumetric coverage, and ability to capture the dynamic and functional information of body organs e.g. cardiac MRI is employed to assess cardiac structure and evaluate blood flow dynamics through the cardiac valves. Long scan time is the main drawback of MRI, which makes it difficult for the patients to remain still during the scanning process., Objective: By collecting fewer measurements, MRI scan time can be shortened, but this undersampling causes aliasing artifacts in the reconstructed images. Advanced image reconstruction algorithms have been used in literature to overcome these undersampling artifacts. These algorithms are computationally expensive and require a long time for reconstruction which makes them infeasible for real-time clinical applications e.g. cardiac MRI. However, exploiting the inherent parallelism in these algorithms can help to reduce their computation time., Methods: Low-rank plus sparse (L+S) matrix decomposition model is a technique used in literature to reconstruct the highly undersampled dynamic MRI (dMRI) data at the expense of long reconstruction time. In this paper, Compressed Singular Value Decomposition (cSVD) model is used in L+S decomposition model (instead of conventional SVD) to reduce the reconstruction time. The results provide improved quality of the reconstructed images. Furthermore, it has been observed that cSVD and other parts of the L+S model possess highly parallel operations; therefore, a customized GPU based parallel architecture of the modified L+S model has been presented to further reduce the reconstruction time., Results: Four cardiac MRI datasets (three different cardiac perfusion acquired from different patients and one cardiac cine data), each with different acceleration factors of 2, 6 and 8 are used for experiments in this paper. Experimental results demonstrate that using the proposed parallel architecture for the reconstruction of cardiac perfusion data provides a speed-up factor up to 19.15× (with memory latency) and 70.55× (without memory latency) in comparison to the conventional CPU reconstruction with no compromise on image quality., Conclusion: The proposed method is well-suited for real-time clinical applications, offering a substantial reduction in reconstruction time., (© 2023. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

Stroke is one of the leading causes of death and disability. To address this challenge, microwave imaging has been proposed as a portable medical imaging modality. However, accurate stroke classification using microwave signals is still an open challenge. In addition, identified features of microwave signals used for stroke classification need to be linked back to the original data. This work attempts to address these issues by proposing a wavelet convolutional neural network (CNN), which combines multiresolution analysis and CNN to learn distinctive patterns in the scalogram for accurate classification. A game theoretic approach is used to explain the model and indicate distinctive features for discriminating stroke types. The proposed algorithm is tested in simulation and experiments. Different types of noise and manufacturing tolerances are modeled using data collected from healthy human trials and added to the simulation data to bridge the gap between the simulation and real-life data. The achieved classification accuracy using the proposed method ranges from 81.7% for 3D simulations to 95.7% for lab experiments using simple head phantoms. Obtained explanations using the method indicate the relevance of wavelet coefficients on frequencies 0.95-1.45 GHz and the time slot of 1.3 to 1.7 ns for distinguishing ischemic from hemorrhagic strokes.

Purpose: In this work, the use of joint Total Generalized Variation (TGV) regularization to improve Multipool-Lorentzian fitting of chemical exchange saturation transfer (CEST) Spectra in terms of stability and parameter signal-to-noise ratio (SNR) was investigated., Theory and Methods: The joint TGV term was integrated into the nonlinear parameter fitting problem. To increase convergence and weight the gradients, preconditioning using a voxel-wise singular value decomposition was applied to the problem, which was then solved using the iteratively regularized Gauss-Newton method combined with a Primal-Dual splitting algorithm. The TGV method was evaluated on simulated numerical phantoms, 3T phantom data and 7T in vivo data with respect to systematic errors and robustness. Three reference methods were also implemented: The standard nonlinear fitting, a method using a nonlocal-means filter for denoising and the pyramid scheme, which uses downsampled images to acquire accurate start values., Results: The proposed regularized fitting method showed significantly improved robustness (compared to the reference methods). In testing, over a range of SNR values the TGV fit outperformed the other methods and showed accurate results even for large amounts of added noise. Parameter values found were closer or comparable to the ground truth. For in vivo datasets, the added regularization increased the parameter map SNR and prevented instabilities., Conclusion: The proposed fitting method using TGV regularization leads to improved results over a range of different data-sets and noise levels. Furthermore, it can be applied to all Z-spectrum data, with different amounts of pools, where the improved SNR and stability can increase diagnostic confidence., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

This investigation aimed to optimize gradient positioning for radiochromic film calibration to facilitate a uniform distribution of calibration points. The study investigated the influence of various parameters on gradient dose profiles generated by a physical wedge, assessing their impact on the field's dose dynamic range, a scalar quantity representing the span of absorbed doses. Numerical parameterization of the physical wedge profile was used to visualize and quantify the impact of field size, depth, and energy on the dynamic range of dose gradients. This concept enabled the optimization of the gradient positioning and estimation of the necessary number of exposures for the desired calibration dose range. An optimization algorithm based on histogram bin height minimization was developed and presented. The maximum dynamic range was achieved with a 20 × $\times$ 20 cm 2 $\textrm {cm}^{2}$ field size at 5 cm depth. Optimization of wedge gradient positioning yielded the most uniform dose distribution with 7 exposures for the [1,10] Gy range and 8 exposures for the [1,20] Gy range. Film calibration using gradients centered at 1.6, 3, 3.5, and 7 Gy central axis (CAX), obtained through optimized gradient positioning, was showcased. The presented work demonstrates the potential for an improved film calibration process, with efficient material utilization and enhanced dosimetric accuracy for clinical applications. While the method was described for the use of a physical wedge, the methodology can be easily extended to the use of a more convenient dynamic wedge., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: To develop a practical method to enable 3D T 1 mapping of brain metabolites., Theory and Methods: Due to the high dimensionality of the imaging problem underlying metabolite T 1 mapping, measurement of metabolite T 1 values has been currently limited to a single voxel or slice. This work achieved 3D metabolite T 1 mapping by leveraging a recent ultrafast MRSI technique called SPICE (spectroscopic imaging by exploiting spatiospectral correlation). The Ernst-angle FID MRSI data acquisition used in SPICE was extended to variable flip angles, with variable-density sparse sampling for efficient encoding of metabolite T 1 information. In data processing, a novel generalized series model was used to remove water and subcutaneous lipid signals; a low-rank tensor model with prelearned subspaces was used to reconstruct the variable-flip-angle metabolite signals jointly from the noisy data., Results: The proposed method was evaluated using both phantom and healthy subject data. Phantom experimental results demonstrated that high-quality 3D metabolite T 1 maps could be obtained and used for correction of T 1 saturation effects. In vivo experimental results showed metabolite T 1 maps with a large spatial coverage of 240 × 240 × 72 mm 3 and good reproducibility coefficients (< 11%) in a 14.5-min scan. The metabolite T 1 times obtained ranged from 0.99 to 1.44 s in gray matter and from 1.00 to 1.35 s in white matter., Conclusion: We successfully demonstrated the feasibility of 3D metabolite T 1 mapping within a clinically acceptable scan time. The proposed method may prove useful for both T 1 mapping of brain metabolites and correcting the T 1 -weighting effects in quantitative metabolic imaging., (© 2024 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: To develop a new sequence to simultaneously acquire Cartesian sodium ( 23 Na) MRI and accelerated Cartesian single (SQ) and triple quantum (TQ) sodium MRI of in vivo human brain at 7 T by leveraging two dedicated low-rank reconstruction frameworks., Theory and Methods: The Double Half-Echo technique enables short echo time Cartesian 23 Na MRI and acquires two k-space halves, reconstructed by a low-rank coupling constraint. Additionally, three-dimensional (3D) 23 Na Multi-Quantum Coherences (MQC) MRI requires multi-echo sampling paired with phase-cycling, exhibiting a redundant multidimensional space. Simultaneous Autocalibrating and k-Space Estimation (SAKE) were used to reconstruct highly undersampled 23 Na MQC MRI. Reconstruction performance was assessed against five-dimensional (5D) CS, evaluating structural similarity index (SSIM), root mean squared error (RMSE), signal-to-noise ratio (SNR), and quantification of tissue sodium concentration and TQ/SQ ratio in silico, in vitro, and in vivo., Results: The proposed sequence enabled the simultaneous acquisition of fully sampled 23 Na MRI while leveraging prospective undersampling for 23 Na MQC MRI. SAKE improved TQ image reconstruction regarding SSIM by 6% and reduced RMSE by 35% compared to 5D CS in vivo. Thanks to prospective undersampling, the spatial resolution of 23 Na MQC MRI was enhanced from 8 × 8 × 15 $$ 8\times 8\times 15 $$ mm 3 to 8 × 8 × 8 $$ 8\times 8\times 8 $$ mm 3 while reducing acquisition time from 2 × 31 $$ 2\times 31 $$ min to 2 × 23 $$ 2\times 23 $$ min., Conclusion: The proposed sequence, coupled with low-rank reconstructions, provides an efficient framework for comprehensive whole-brain sodium MRI, combining TSC, T2*, and TQ/SQ ratio estimations. Additionally, low-rank matrix completion enables the reconstruction of highly undersampled 23 Na MQC MRI, allowing for accelerated acquisition or enhanced spatial resolution., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Background: When applied to thermoacoustic imaging (TAI), the delay-and-sum (DAS) algorithm produces strong sidelobes due to its disadvantages of uniform aperture weighting. As a result, the quality of TAI images recovered by DAS is often severely degraded by strong non-coherent clutter, which restricts the development and application of TAI., Purpose: To address this issue, we propose an adaptive complementary neighboring sub-aperture (NSA) beamforming algorithm for TAI., Methods: In NSA, we introduce a coordinate system transformation when calculating the normalized cross-correlation (NCC) matrix. This approach enables the computation of the NCC coefficient within the specified kernel without complex coordinate calculations. We first conducted the numerical simulation experiment to validate NSA using a tree branch phantom. In addition, we also conducted phantom (five sauce tubes), ex vivo (ablation needle in ex vivo porcine liver), and in vivo (human arm) TAI experiments using our TAI system with a center frequency of 3 GHz., Results: In the numerical simulation experiment, the structural similarity index (SSIM) value for NSA is increased from 0.37828 for DAS to 0.75492. In the point target phantom TAI experiment, the generalized contrast-to-noise ratio (gCNR) value for NSA is increased from 0.936 for DAS to 0.962. The experimental results show that NSA can recover clearer thermoacoustic images compared to DAS. In the ex vivo TAI experiment, the full width at half maxima (FWHM) of an ablation needle (diameter = 1.5 mm) for coherence factor (CF) weighted DAS and NSA are 0.9 and 1.3 mm, respectively. Furthermore, in the in vivo TAI experiment, CF reduces the signals within the arm compared to NSA. Therefore, compared with CF, NSA can maintain the integrity of target information in TAI while effectively suppressing non-coherent background clutter., Conclusions: NSA can effectively reduce non-coherent background noise while ensuring the completeness of the target information. So, NSA offers the potential to provide high-quality thermoacoustic images and further advance their clinical application., (© 2024 American Association of Physicists in Medicine.)

Score-based generative model (SGM) has risen to prominence in sparse-view CT reconstruction due to its impressive generation capability. The consistency of data is crucial in guiding the reconstruction process in SGM-based reconstruction methods. However, the existing data consistency policy exhibits certain limitations. Firstly, it employs partial data from the reconstructed image of the iteration process for image updates, which leads to secondary artifacts with compromising image quality. Moreover, the updates to the SGM and data consistency are considered as distinct stages, disregarding their interdependent relationship. Additionally, the reference image used to compute gradients in the reconstruction process is derived from the intermediate result rather than ground truth. Motivated by the fact that a typical SGM yields distinct outcomes with different random noise inputs, we propose a Multi-channel Optimization Generative Model (MOGM) for stable ultra-sparse-view CT reconstruction by integrating a novel data consistency term into the stochastic differential equation model. Notably, the unique aspect of this data consistency component is its exclusive reliance on original data for effectively confining generation outcomes. Furthermore, we pioneer an inference strategy that traces back from the current iteration result to ground truth, enhancing reconstruction stability through foundational theoretical support. We also establish a multi-channel optimization reconstruction framework, where conventional iterative techniques are employed to seek the reconstruction solution. Quantitative and qualitative assessments on 23 views datasets from numerical simulation, clinical cardiac and sheep's lung underscore the superiority of MOGM over alternative methods. Reconstructing from just 10 and 7 views, our method consistently demonstrates exceptional performance.

Lowering radiation dose per view and utilizing sparse views per scan are two common CT scan modes, albeit often leading to distorted images characterized by noise and streak artifacts. Blind image quality assessment (BIQA) strives to evaluate perceptual quality in alignment with what radiologists perceive, which plays an important role in advancing low-dose CT reconstruction techniques. An intriguing direction involves developing BIQA methods that mimic the operational characteristic of the human visual system (HVS). The internal generative mechanism (IGM) theory reveals that the HVS actively deduces primary content to enhance comprehension. In this study, we introduce an innovative BIQA metric that emulates the active inference process of IGM. Initially, an active inference module, implemented as a denoising diffusion probabilistic model (DDPM), is constructed to anticipate the primary content. Then, the dissimilarity map is derived by assessing the interrelation between the distorted image and its primary content. Subsequently, the distorted image and dissimilarity map are combined into a multi-channel image, which is inputted into a transformer-based image quality evaluator. By leveraging the DDPM-derived primary content, our approach achieves competitive performance on a low-dose CT dataset.

Objective: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone., Materials and Methods: Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed., Results: AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone ( P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone ( P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness., Conclusion: Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2024 The Korean Society of Radiology.)

Background: In recent years, genetic algorithms have been applied in the field of nuclear technology design, producing superior optimization results compared to traditional methods. They can be employed in the design and optimization of beam shaping assemblies (BSA) BSA to obtain the desired neutron beams. But it should be noted that the direct combination of Monte Carlo methods with genetic algorithms requires a significant amount of computational resources and time., Purpose: Design and optimize BSA more efficiently to achieve neutron beams that meet specified recommendations., Methods: We propose an approach of NSGA II with crucial variables which are identified by multivariate statistical techniques. This approach significantly reduces the problem sizes, thus reducing the time required for optimization. We illustrate this methodology using the example of BSA design for AB-BNCT., Results: The computational efficiency has tripled with crucial variables. By using NSGA II, we obtained optimized models conforming to both the new and old version IAEA BNCT guidelines through a single optimization process and subjected them to phantom analysis. The results demonstrate that models obtained through this method can meet the IAEA recommendations with deep advantage depth (AD) and high absorbed ratio (AR)., Conclusion: The genetic algorithm with crucial variables displays tremendous potential in addressing BSA optimization challenges., (© 2024 American Association of Physicists in Medicine.)

Background: Novel on-board CBCT allows for improved image quality and Hounsfield unit accuracy. When coupled with online adaptive tools, this may have potential to allow for simulation and treatment to be completed in a single on-table session., Purpose: To study the feasibility of a high-efficiency radiotherapy treatment workflow without the use of a separate session for simulation imaging. The dosimetric accuracy, overall efficiency, and technical feasibility were used to evaluate the clinical potential of CT simulation-free adaptive radiotherapy., Methods: Varian's Ethos adaptive radiotherapy treatment platform was upgraded with a novel CBCT system, HyperSight which reports image quality and Hounsfield unit accuracy specifications comparable to standard fan-beam CT. Using in-house developed MATLAB software, CBCT images were imported into the system and used for planning. Two test cases were completed on anthropomorphic phantoms equipped with small volume ion chambers (cross-calibrated to an ADCL traceable dose standard) to evaluate the feasibility and accuracy of the workflows. A simulated palliative spine treatment was planned with 8 Gy in one fraction, and an intact prostate treatment was planned with 60 Gy in 20 fractions. The CBCTs were acquired using HyperSight with default thorax and pelvis imaging protocols and reconstructed using an iterative algorithm with scatter removal, iCBCT Acuros. CBCTs were used for contouring and planning, and treatment was delivered via an online adaptive workflow. In addition, an external dosimetry audit was completed using only on-board CBCT imaging in an end-to-end head and neck phantom irradiation., Results: An extended-field CBCT acquisition can be acquired in 12 s, in addition to the time for longitudinal table shifts, and reconstructed in approximately 1 min. The superior-inferior extent for the CBCT planning images was 38.2 cm, which captured the full extent of relevant anatomy. The contouring and treatment planning for the spine and prostate were completed in 30 and 18 min, respectively. The dosimetric agreement between ion chamber measurements and the treatment plan was within a range of -1.4 to 1.6%, and a mean and standard deviation of 0.41 ± 1.16%. All metrics used in the external audit met the passing criteria, and the dosimetric comparison between fan-beam and CBCT techniques had a gamma passing rate of 99.0% with a criteria of 2%/2 mm., Conclusion: Using an in-house workflow, CT simulation-free radiation therapy was shown to be feasible with acceptable workflow efficiency and dosimetric accuracy. This approach may be particularly applicable for urgent palliative treatments. With the availability of software to enable this workflow, and the continued advancement of on-treatment adaptation, single-visit radiation therapy may replace current practice for some clinical indications., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Electron beam dosimetry is sensitive to the surface contour of the patient. Over 10% difference between Treatment Planning System (TPS) and independent monitor-unit (IMU) calculations have been reported in the literature. Similar results were observed in our clinic between Radformation ClearCalc IMU and Eclipse TPS electron Monte Carlo (eMC) algorithm (v.16.1). This paper presents data measured under 3D printed spherical and cylindrical phantoms to validate the eMC algorithm in the presence of curved geometries. Measurements were performed with multiple detectors and compared to calculations made in Eclipse for the 6, 9 and 12 MeV electron energies. This data is used to create curvature correction factors (CCFs), defined as the ratio of the detector reading with the curved-surface phantom to a flat phantom at the same depth. The mean difference between the TPS calculated and measured CCFs using the NACP, Diode E, microSilicon, and microDiamond detectors were 1.3, 0.9, 0.7 and 0.7% respectively, with maximum differences of 4.5, 2.3, 1.9, and 1.8% respectively. Applying CCFs to previous failing patient IMU calculations improved agreement to the TPS. CCFs were implemented in our clinic for patient-specific IMU calculations with the assistance of a ESAPI script., (© 2024. Crown.)

Background: Inserting noise into existing patient projection data to simulate lower-radiation-dose exams has been frequently used in traditional energy-integrating-detector (EID)-CT to optimize radiation dose in clinical protocols and to generate paired images for training deep-learning-based reconstruction and noise reduction methods. Recent introduction of photon counting detector CT (PCD-CT) also requires such a method to accomplish these tasks. However, clinical PCD-CT scanners often restrict the users access to the raw count data, exporting only the preprocessed, log-normalized sinogram. Therefore, it remains a challenge to employ projection domain noise insertion algorithms on PCD-CT., Purpose: To develop and validate a projection domain noise insertion algorithm for PCD-CT that does not require access to the raw count data., Materials and Methods: A projection-domain noise model developed originally for EID-CT was adapted for PCD-CT. This model requires, as input, a map of the incident number of photons at each detector pixel when no object is in the beam. To obtain the map of incident number of photons, air scans were acquired on a PCD-CT scanner, then the noise equivalent photon number (NEPN) was calculated from the variance in the log normalized projection data of each scan. Additional air scans were acquired at various mA settings to investigate the impact of pulse pileup on the linearity of NEPN measurement. To validate the noise insertion algorithm, Noise Power Spectra (NPS) were generated from a 30 cm water tank scan and used to compare the noise texture and noise level of measured and simulated half dose and quarter dose images. An anthropomorphic thorax phantom was scanned with automatic exposure control, and noise levels at different slice locations were compared between simulated and measured half dose and quarter dose images. Spectral correlation between energy thresholds T1 and T2, and energy bins, B1 and B2, was compared between simulated and measured data across a wide range of tube current. Additionally, noise insertion was performed on a clinical patient case for qualitative assessment., Results: The NPS generated from simulated low dose water tank images showed similar shape and amplitude to that generated from the measured low dose images, differing by a maximum of 5.0% for half dose (HD) T1 images, 6.3% for HD T2 images, 4.1% for quarter dose (QD) T1 images, and 6.1% for QD T2 images. Noise versus slice measurements of the lung phantom showed comparable results between measured and simulated low dose images, with root mean square percent errors of 5.9%, 5.4%, 5.0%, and 4.6% for QD T1, HD T1, QD T2, and HD T2, respectively. NEPN measurements in air were linear up until 112 mA, after which pulse pileup effects significantly distort the air scan NEPN profile. Spectral correlation between T1 and T2 in simulation agreed well with that in the measured data in typical dose ranges., Conclusions: A projection-domain noise insertion algorithm was developed and validated for PCD-CT to synthesize low-dose images from existing scans. It can be used for optimizing scanning protocols and generating paired images for training deep-learning-based methods., (© 2024 American Association of Physicists in Medicine.)

Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of coronary artery diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads to increased image noise. Limited-view (LV) SPECT, such as the latest GE MyoSPECT ES system, enables accelerated scanning and reduces hardware expenses but degrades reconstruction accuracy. Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps ( μ -maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments. Although various methods have been developed to solely focus on LD denoising, LV reconstruction, or CT-free AC in SPECT, the solution for simultaneously addressing these tasks remains challenging and under-explored. Furthermore, it is essential to explore the potential of fusing cross-domain and cross-modality information across these interrelated tasks to further enhance the accuracy of each task. Thus, we propose a Dual-Domain Coarse-to-Fine Progressive Network (DuDoCFNet), a multi-task learning method for simultaneous LD denoising, LV reconstruction, and CT-free μ -map generation of cardiac SPECT. Paired dual-domain networks in DuDoCFNet are cascaded using a multi-layer fusion mechanism for cross-domain and cross-modality feature fusion. Two-stage progressive learning strategies are applied in both projection and image domains to achieve coarse-to-fine estimations of SPECT projections and CT-derived μ -maps. Our experiments demonstrate DuDoCFNet's superior accuracy in estimating projections, generating μ -maps, and AC reconstructions compared to existing single- or multi-task learning methods, under various iterations and LD levels. The source code of this work is available at https://github.com/XiongchaoChen/DuDoCFNet-MultiTask.

Intelligent systems in interventional healthcare depend on the reliable perception of the environment. In this context, photoacoustic tomography (PAT) has emerged as a non-invasive, functional imaging modality with great clinical potential. Current research focuses on converting the high-dimensional, not human-interpretable spectral data into the underlying functional information, specifically the blood oxygenation. One of the largely unexplored issues stalling clinical advances is the fact that the quantification problem is ambiguous, i.e. that radically different tissue parameter configurations could lead to almost identical photoacoustic spectra. In the present work, we tackle this problem with conditional Invertible Neural Networks (cINNs). Going beyond traditional point estimates, our network is used to compute an approximation of the conditional posterior density of tissue parameters given the photoacoustic spectrum. To this end, an automatic mode detection algorithm extracts the plausible solution from the sample-based posterior. According to a comprehensive validation study based on both synthetic and real images, our approach is well-suited for exploring ambiguity in quantitative PAT.

Purpose: The performance of modern image reconstruction methods is commonly judged using quantitative error metrics like root mean squared-error and the structural similarity index, which are calculated by comparing reconstructed images against fully sampled reference data. In practice, the reference data will contain noise and is not a true gold standard. In this work, we demonstrate that the "hidden noise" present in reference data can substantially confound standard approaches for ranking different image reconstruction results., Methods: Using both experimental and simulated k-space data and several different image reconstruction techniques, we examined whether there was correlation between performance metrics obtained with typical noisy reference data versus those obtained with higher-quality reference data., Results: For conventional performance metrics, the reconstructions that matched best with the higher-quality reference data were substantially different from the reconstructions that matched best with typical noisy reference data. This leads to suboptimal reconstruction results if the performance with respect to noisy reference data is used to select which reconstruction methods/parameters to employ. These issues were reduced when employing alternative error metrics that better account for noise., Conclusion: Reference data containing hidden noise can substantially mislead the ranking of image reconstruction methods when using conventional error metrics, but this issue can be mitigated with alternative error metrics., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: Sodium triple quantum (TQ) signal has been shown to be a valuable biomarker for cell viability. Despite its clinical potential, application of Sodium TQ signal is hindered by complex pulse sequences with long scan times. This study proposes a method to approximate the TQ signal using a single excitation pulse without phase cycling., Methods: The proposed method is based on a single excitation pulse and a comparison of the free induction decay (FID) with the integral of the FID combined with a shifting reconstruction window. The TQ signal is calculated from this FID only. As a proof of concept, the method was also combined with a multi-echo UTE imaging sequence on a 9.4 T preclinical MRI scanner for the possibility of fast TQ MRI., Results: The extracted Sodium TQ signals of single-pulse and spin echo FIDs were in close agreement with theory and TQ measurement by traditional three-pulse sequence (TQ time proportional phase increment [TQTPPI)]. For 2%, 4%, and 6% agar samples, the absolute deviations of the maximum TQ signals between SE and theoretical (time proportional phase increment TQTPPI) TQ signals were less than 1.2% (2.4%), and relative deviations were less than 4.6% (6.8%). The impact of multi-compartment systems and noise on the accuracy of the TQ signal was small for simulated data. The systematic error was <3.4% for a single quantum (SQ) SNR of 5 and at maximum <2.5% for a multi-compartment system. The method also showed the potential of fast in vivo SQ and TQ imaging., Conclusion: Simultaneous SQ and TQ MRI using only a single-pulse sequence and SQ time efficiency has been demonstrated. This may leverage the full potential of the Sodium TQ signal in clinical applications., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: To develop acceleration strategies for 3D multi-slab diffusion weighted imaging (3D ms-DWI) for enabling applications that require simultaneously high spatial (1 mm isotropic) and angular (> 30 directions) resolutions., Methods: 3D ms-DWI offers high SNR-efficiency, with the ability to achieve high isotropic spatial resolution, yet suffers from long scan-times for studies requiring high angular resolutions. We develop 6D k-q space acceleration strategies to reduce the scan-time. Specifically, we develop non-uniform 3D k y -k z under-sampling employing a shot-selective 2D CAIPI sampling approach. To achieve inter-shot phase-compensation, 2D navigators were employed that utilize the same CAIPI trajectory. An iterative model-based 3D multi-shot reconstruction was designed by incorporating phase into the forward encoding process. Additionally, the shot-selective non-uniform k y -k z CAIPI acceleration was randomized along the q-dimension. The 3D model-based multi-shot reconstruction is then extended to a joint reconstruction that simultaneously reconstructs all the q-space points, with the help of a spatial total variation and deep-learned q-space regularization., Results: The proposed reconstruction is shown to achieve adequate phase-compensation in both 2D CAIPI accelerated and additional k y -k z under-sampled cases. Using retrospective under-sampling experiments, we show that k-q accelerations close a factor of 12 can be achieved with a reconstruction error < 3% for both single and multi-shell data. This translates to a scan-time reduction by 3-fold for experiments with simultaneously high spatial and angular resolutions., Conclusion: The proposed method facilitates the utilization of 3D ms-DWI for simultaneously high k-q resolution applications with close to 3× reduced scan-time., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Rationale and Objectives: To evaluate and compare the effectiveness of contrast media subtraction and kidney stone detection between a virtual non-iodine reconstruction algorithm (VNI; PureCalcium) and a virtual non-contrast (VNC) algorithm in excretory phase photon-counting detector computed tomography (PCD-CT), using a 3D printed kidney phantom under various tube voltages and radiation doses., Materials and Methods: A 3D-printed kidney phantom, holding Calcium Oxalate (CaOx) and uric acid stones within contrast-enhanced calyces, was created. The calyx density mirrored the average density observed in 200 excretory phase patients (916 HU at 110 kV). Imaging was conducted on a clinical dual-source PCD-CT at 120 kV and 140 kV, with radiation doses set at 5, 10, and 15 mGy. VNI and VNC algorithms were applied. Two blinded readers evaluated the image quality, along with the degree of contrast media and kidney stone subtraction, using visual scales. Krippendorff's alpha was calculated to determine inter-reader agreement, and the Chi-squared test was employed for comparing ordinal data., Results: Reader 2 rated overall image quality higher for VNI than VNC (4.90 vs. 4.00; P < .05), while Reader 1 found no significant difference (4.96 vs. 5.00; P > .05). Substantial agreement was observed between readers for contrast media subtraction in both VNC and VNI (Krippendorff's alpha range: 0.628-0.748). Incomplete contrast media subtraction occurred more frequently with VNI for both readers (Reader 1: 29% vs. 15%; P < .05; Reader 2: 24% vs. 20%; P > .05). Uric acid and smaller stones (<5 mm) were more likely to be subtracted than CaOx and larger stones in both VNC and VNI. Overall, a higher rate of stone subtraction was noted with VNI compared to VNC (Reader 1: 22% vs. 16%; Reader 2: 25% vs. 10%; P < .05). Neither radiation dose nor tube voltage significantly influenced stone subtraction (P > .05)., Conclusion: VNC demonstrated greater accuracy than VNI for contrast media subtraction and kidney stone visibility. Radiation dose and tube voltage had no significant impact. Nonetheless, both algorithms still exhibited frequent incomplete contrast media subtraction and partial kidney stone subtraction., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Andre Euler reports a relationship with SIEMENS that includes: speaking and lecture fees. Hatem Alkadhi reports a relationship with SIEMENS that includes: speaking and lecture fees. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: SRS MapCHECK (SMC) is a commercially available patient-specific quality assurance (PSQA) tool for stereotactic radiosurgery (SRS) applications. This study investigates the effects of degree of modulation, location off-axis, and low dose threshold (LDT) selection on gamma pass rates (GPRs) between SMC and treatment planning system, Analytical Anisotropic Algorithm (AAA), or Vancouver Island Monte Carlo (VMC++ algorithm) system calculated dose distributions., Methods: Volumetric-modulated arc therapy (VMAT) plans with modulation factors (MFs) ranging from 2.7 to 10.2 MU/cGy were delivered to SMC at isocenter and 6 cm off-axis. SMC measured dose distributions were compared against AAA and VMC++ via gamma analysis (3%/1 mm) with LDT of 10% to 80% using SNC Patient software., Results: Comparing on-axis SMC dose against AAA and VMC++ with LDT of 10%, all AAA-calculated plans met the acceptance criteria of GPR ≥ 90%, and only one VMC++ calculated plan was marginally outside the acceptance criteria with pass rate of 89.1%. Using LDT of 80% revealed decreasing GPR with increasing MF. For AAA, GPRs reduced from 100% at MF of 2.7 MU/cGy to 57% at MF of 10.2 MU/cGy, and for VMC++ calculated plans, the GPRs reduced from 89% to 60% in the same MF range. Comparison of SMC dose off-axis against AAA and VMC++ showed more pronounced reduction of GPR with increasing MF. For LDT of 10%, AAA GPRs reduced from 100% to 83% in the MF range of 2.7 to 9.8 MU/cGy, and VMC++ GPR reduced from 100% to 91% in the same range. With 80% LDT, GPRs dropped from 100% to 42% for both algorithms., Conclusions: MF, dose calculation algorithm, and LDT selections are vital in VMAT-based SRT PSQA. LDT of 80% enhances sensitivity of gamma analysis for detecting dose differences compared to 10% LDT. To achieve better agreement between calculated and SMC dose, it is recommended to limit the MF to 4.6 MU/cGy on-axis and 3.6 MU/cGy off-axis., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: MRI using 3D stack-of-spirals (SoS) readout on a high-performance gradient system is subject to strong second-order, spatially varying concomitant fields, which can lead to signal dropout and blurring artifacts that become more significant at locations farther from the gradient isocenter. A method for compensating for second-order concomitant fields in 3D axial SoS image reconstruction is described., Methods: We retrospectively correct for second-order concomitant field-induced phase error in the 3D SoS data by slice-dependent k-space phase compensation based on the nominal spiral readout trajectories. The effectiveness of the method was demonstrated in phantom and healthy volunteer scans in which 3D pseudo-continuous arterial spin labeling imaging was performed with SoS fast spin-echo readout at 3 T., Results: Substantial reduction in blurring was observed with the proposed method. In phantom scans, blurring was reduced by about 53% at 98 mm from the gradient isocenter. In the in vivo 3D pseudo-continuous arterial spin labeling scans, differences of up to 10% were observed at 78 mm from the isocenter, especially around the white-matter and gray-matter interfaces, between the corrected and uncorrected proton density images, perfusion-weighted images, and cerebral blood flow maps., Conclusions: The described retrospective correction method provides a means to correct erroneous phase accruals due to second-order concomitant fields in 3D axial stack-of-spirals imaging., (© 2024 International Society for Magnetic Resonance in Medicine.)

Purpose: Develop a true real-time implementation of MR signature matching (MRSIGMA) for free-breathing 3D MRI with sub-200 ms latency on the Elekta Unity 1.5T MR-Linac., Methods: MRSIGMA was implemented on an external computer with a network connection to the MR-Linac. Stack-of-stars with partial k z sampling was used to accelerate data acquisition and ReconSocket was employed for simultaneous data transmission. Movienet network computed the 4D MRI motion dictionary and correlation analysis was used for signature matching. A programmable 4D MRI phantom was utilized to evaluate MRSIGMA with respect to a ground-truth translational motion reference. In vivo validation was performed on patients with pancreatic cancer, where 15 patients were employed to train Movienet and 7 patients to test the real-time implementation of MRSIGMA. Dice coefficients between real-time MRSIGMA and a retrospectively computed 4D reference were used to evaluate motion tracking performance., Results: Motion dictionary was computed in under 5 s. Signature acquisition and matching presented 173 ms latency on the phantom and 193 ms on patients. MRSIGMA presented a mean error of 1.3-1.6 mm for all phantom experiments, which was below the 2 mm acquisition resolution along the motion direction. The Dice coefficient over time between MRSIGMA and reference contours was 0.88 ± 0.02 (GTV), 0.87 ± 0.02(duodenum-stomach), and 0.78 ± 0.02(small bowel), demonstrating high motion tracking performance for both tumor and organs at risk., Conclusion: The real-time implementation of MRSIGMA enabled true real-time free-breathing 3D MRI with sub-200 ms imaging latency on a clinical MR-Linac system, which can be used for treatment monitoring, adaptive radiotherapy and dose accumulation mapping in tumors affected by respiratory motion., (© 2024 International Society for Magnetic Resonance in Medicine.)

Purpose: To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation., Methods: SPICER consists of two modules to simultaneously reconstructs accurate MR images and estimates high-quality coil sensitivity maps (CSMs). The first module, CSM estimation module, uses a convolutional neural network (CNN) to estimate CSMs from the raw measurements. The second module, DMBA-based MRI reconstruction module, forms reconstructed images from the input measurements and the estimated CSMs using both the physical measurement model and learned CNN prior. With the benefit of our self-supervised learning strategy, SPICER can be efficiently trained without any fully sampled reference data., Results: We validate SPICER on both open-access datasets and experimentally collected data, showing that it can achieve state-of-the-art performance in highly accelerated data acquisition settings (up to 10 × $$ 10\times $$ ). Our results also highlight the importance of different modules of SPICER-including the DMBA, the CSM estimation, and the SPICER training loss-on the final performance of the method. Moreover, SPICER can estimate better CSMs than pre-estimation methods especially when the ACS data is limited., Conclusion: Despite being trained on noisy undersampled data, SPICER can reconstruct high-quality images and CSMs in highly undersampled settings, which outperforms other self-supervised learning methods and matches the performance of the well-known E2E-VarNet trained on fully sampled ground-truth data., (© 2024 International Society for Magnetic Resonance in Medicine.)