Physics in medicine and biology最新文献

Objective.Large irradiated volumes are a major contributor to severe side-effects in patients with head and neck cancer undergoing curatively intended radiotherapy. We propose a data-driven approach for defining the elective clinical target volume (CTV-E) on a patient-specific basis, with the potential to reduce irradiated volumes compared to standard guidelines.Approach.We introduce a bilateral Bayesian network (BN), trained on a large cohort, to estimate the patient-specific risk of undetected nodal involvement for both ipsilateral and contralateral lymph node levels (LNLs) I, II, III, and IV, using clinical features, such as patterns of nodal involvement, T-stage, tumor location. By applying risk thresholds, we generated individualized, risk-dependent CTV-E's for representative patient scenarios and compared the resulting treatment volumes and residual risk to those recommended by standard clinical guidelines.Main results.We computed the risks for a set of representative patient scenarios including (1) N0 (T1 and T2 tumor stage), (2) N+ in ipsilateral LNL II (T1 and T2 tumor stage), (3) N+ in ipsilateral LNL II and III (T1 and T2 tumor stage), and (4) N+ of both ipsilateral and contralateral LNL II (T3 and T4 tumor stage). Depending on the chosen risk threshold, the bilateral BN allowed for reductions in irradiated volumes relative to standard clinical protocols. For every patient scenario considered, the CTV-E's defined by the applied risk thresholds were associated with a low estimated probability of undetected nodal involvement in any excluded LNL.Significance.We present a data-driven framework for personalized CTV-E definition, encouraging the discussion of more patient-specific elective nodal target volumes, with potential for de-escalation of irradiated elective volumes.

Objective To investigate dynamic shuttle-mode xenon (Xe)-enhanced dual-energy CT (Xe-DECT) imaging for a regional assessment of ventilation in in vivo rabbit lungs. Approach Four mechanically ventilated rabbits were scanned during the washout of a 70% xenon in 30% oxygen gas mixture using dynamic shuttle-mode DECT at baseline and during methacholine (MCh)-induced bronchoconstriction (post-MCh). Material decomposition was applied to generate xenon and tissue density images (mg/mL). A tissue-based correction was used to isolate the xenon concentration (CXe) in the gas phase of the xenon density images. The resultant CXe images were used to investigate regional ventilation defects (VDs) by comparing the VD fraction (VDF, expressed as percentage) between baseline and post-MCh conditions. Additionally, regional ventilation efficiency within the VDs and surrounding (non-VD) areas was quantified as specific ventilation (sV ̇ in min-1). Ventilation was also qualitatively assessed by evaluating ventilation distributions during washout. Main results MCh-induced bronchoconstriction resulted in an increase in VDF. The average VDF at baseline was 13.8% ± 8.5%, compared to an average post-MCh VDF of 29.6% ± 7.7%, p = 0.026. The VDs at baseline did not reveal a reduced ventilation efficiency (〖sV ̇〗_VD: 8.4 ± 2.7 min-1), compared to non-VD areas ( 〖sV ̇〗_(non-VD): 7.0 ± 3.1 min-1), p = 0.306. In contrast, MCh-induced VDs were found to have a reduced ventilation efficiency (〖sV ̇〗_VD: 4.9 ± 2.3 min-1), compared to non-VD areas (〖sV ̇〗_(non-VD): 6.4 ± 2.3 min-1), p = 0.004. Significance Dynamic shuttle-mode Xe-DECT during washout enabled regional evaluation of ventilation in healthy and pathological in vivo rabbit lungs. As traditional lung function tests offer only global assessments of respiratory impairment, there is a growing interest in pulmonary functional imaging to enable quantitative evaluation of regional lung function. .

Objective: Elective nodal irradiation (ENI) is common clinical practice for many cancer sites including head-and-neck squamous cell carcinoma (HNSCC). ENI is performed to increase regional tumor control (TCP) but contributes to normal tissue complication probability (NTCP). We aim to improve the tradeoff between NTCP and regional TCP.

Approach: Based on a previously developed model of lymphatic tumor progression for HNSCC, we estimate the probability of occult lymph node metastases in clinically negative lymph node levels (LNLs). We present a TCP model that predicts the regional TCP in the LNL irradiated with an arbitrary dose distribution. The TCP model is used for treatment plan optimization together with NTCP models.

Main results: We illustrate the approach for typical HNSCC patients, considering the tradeoff between 1) xerostomia and ENI of contralateral LNL II, 2) dysphagia and ENI of LNL III, and 3) hypothyroidism and ENI of LNL IV. We show that NTCP may be lowered along with only minor reductions in regional TCP by compromising coverage of the LNL near relevant organs at risk.

Significance: We present a method that allows for trade-off between tumour control and risk of normal tissue complications in treatment plan optimization and demonstrate its application in a clinically relevant context.

Objective: Despite major advances in dual-energy CT, obtaining accurate attenuation values for quantitative applications remains a technical challenge. To address this topic, we introduce a novel projection data domain material decomposition method that is an extension of an approach we recently proposed for beam hardening correction in single energy CT. Approach. The proposed method employs object-specific scatter correction and an analytical energy response model. We compare its performance to image-based material decomposition on accuracy of attenuation values using the ACR-CT accreditation phantom, scanned with consecutive low and high energy axial scans in centered and off-centered positions. Accuracy is assessed across the five inserts, and the images are analyzed for beam hardening artifacts and noise. Additionally, we assess the usefulness of object-specific scatter correction, and we assess performance over conventional data domain material decomposition and for anthropomorphic abdomen phantom imaging. Main results. In the ACR phantom, the proposed method yielded a significant improvement in accuracy of the attenuation values, particularly at low energies (< 70keV), and an important reduction in beam hardening artifacts. While similarly high accuracy was achieved for water, quantitative error within the non-water inserts was lower and more uniform across the 30-140keV range, especially in the more challenging off-centered positioning of the phantom. Noise showed expected parabolic behavior, but with minimum at lower keV, which may be clinically advantageous. Object-specific scatter correction was shown to prevent major artifacts. Advantages over conventional data-domain decomposition clearly appeared when only a standard phantom is available to calibrate the latter. Lastly, the proposed method was shown to perform well, without any changes, in the more complex scenario of abdominal phantom imaging. Significance. This work demonstrates that data-based material decomposition using an analytical energy response model with object-specific scatter correction offers a promising pathway to improve accuracy of CT attenuation values.

Objective: Geometric distortions in diffusion-weighted imaging (DWI) compromise accurate tumor delineation and spatial localization, limiting its utility in radiation therapy planning and response monitoring. These distortions can be corrected through multimodal registration between distorted DWI and undistorted anatomical images, while conventional mutual information-based optimization often fails due to local minima and produces non-smooth, physically implausible deformations. Approach: This study proposes a landmark matching B-spline implicit neural representation (LMBS-INR) framework for DWI distortion correction. The method integrates anatomical correspondences from a foundation landmark matching model with B-spline parameterized deformation fields to overcome local minima inherent in mutual information optimization. The framework employs Fourier-encoded multi-layer perceptrons to model B-spline deformation fields while ensuring physically plausible transformations, enabling robust multimodal registration between distorted DWI and anatomical references. Main Results: Evaluation on brain and abdominal datasets demonstrated superior performance compared to established methods. The proposed approach achieved average Dice coefficients of 0.919 ± 0.038 (brain) and 0.926 ± 0.032 (abdomen), significantly outperforming all baseline methods. On simulated data, our method achieved an average PSNR of 25.912 ± 3.148 dB, NCC of 0.911 ± 0.137, and SSIM of 0.888 ± 0.107, the best among all methods. Significance: By combining the regularization properties of B-spline parameterization with the cross-modal matching capabilities of foundation models, our method achieves more accurate correction of geometric distortions in DWI, with the potential to enhance the precision of intra/post-radiotherapy assessment.

Objective: Rapid and accurate mapping of brain tissue pH is crucial for early diagnosis and management of ischemic stroke. Amide proton transfer (APT) imaging has been used for this purpose but suffers from hypointense contrast and low signal intensity in lesions. Guanidine chemical exchange saturation transfer (CEST) imaging provides hyperintense contrast and higher signal intensity in lesions at appropriate saturation power, making it a promising complementary approach. However, quantifying the guanidine CEST effect remains challenging due to its proximity to water resonance and the influence of multiple confounding effects. This study presents a machine learning (ML) framework to improve the accuracy and robustness of guanidine CEST quantification with reduced scan time.

Approach: The model was trained on partially synthetic data, where measured line-shape information from experiments were incorporated into a simulation framework along with other CEST pools whose solute fraction (fs), exchange rate (ksw), and relaxation parameters were systematically varied. Gradient-based feature selection was used to identify the most informative frequency offsets to reduce the number of acquisition points.

Main results: The proposed model achieved significantly higher accuracy than polynomial fitting, multi-pool Lorentzian fitting, and ML models trained solely on synthetic or in vivo data. Gradient-based feature selection identified the most informative frequency offsets, reducing acquisition points from 69 to 19, a 72% reduction in CEST scan time without loss of accuracy. In vivo, conventional fitting methods produced unclear lesion contrast, whereas our model predicted clear hyperintense lesion maps. The strong negative correlation between guanidine and APT effects supports its physiological relevance to tissue acidosis.

Significance: The use of partially synthetic training data combines realistic spectral features with known ground-truth values, overcoming limitations of purely synthetic or limited in vivo datasets. Leveraging this data with ML, enables robust quantification of guanidine CEST effects, showing potential for rapid pH-sensitive imaging.

Objective. Low-intensity focused ultrasound has emerged as a versatile tool for various applications including noninvasive neuromodulation and blood-brain barrier (BBB) opening. To achieve precise individual targeting, phase aberration correction (PAC) is essential to compensate for the heterogeneities introduced by the skull. Traditional methods for PAC are restricted to single point-based targets, resulting in elongated, cigar-shaped focal beams that often fail to align with the geometry of the intended target. Additionally, these approaches demand lengthy simulation times, making the simultaneous sonication of multiple targets within a reasonable timeframe infeasible.Approach. This work introduces rapid optimization-based sonication of volumetric brain targets. By leveraging a pair of linear phased array transducers aligned orthogonally above the skull, the approach is capable of optimizing phase and amplitude parameters within seconds to focus acoustic pressure at multiple targets inside target volumes while limiting potential off-target activation.Main results. Three brain areas were targeted under different orthogonal transducer alignments, enforcing the desired intracranial peak pressure at a minimum of three target points in each region. Further results demonstrate the sensitivity of transducer displacements, particularly with translational and rotational misalignments. A ray tracing correction scheme was employed, restoring the peak pressure at the intended target region while keeping the increase in off-target pressure below 20%.Significance. Overall, these advancements hold promise for enhancing targeting in focused ultrasound-guided BBB opening and neuromodulatory applications, expanding the utility of ultrasound in clinical and experimental settings.

Objective.For rodent brain PET imaging, spatial resolution is the most important factor for identifying small brain structures. Previously, we developed a submillimeter resolution PET scanner with 1 mm crystal pitch using 3-layer depth-of-interaction (DOI) detectors. However, the spatial resolution was over 0.5 mm due to a relatively large crystal pitch and an unoptimized crystal layer design. Here we use Geant4 Application Tomographic Emission (GATE) Monte Carlo simulations to design and optimize a sub-0.5 mm resolution PET scanner with 3-layer DOI detectors.Methods.The proposed PET scanner has 2 rings, each of which has 16 DOI detectors, resulting in a 23.4 mm axial coverage. Each DOI detector has 3-layer lutetium yttrium orthosilicate crystal arrays with a 0.8 mm crystal pitch. We employed GATE Monte Carlo simulations to optimize three crystal layer designs, A (4 + 4 + 7 mm), B (3 + 4 + 4 mm), and C (3 + 3 + 5 mm). Spatial resolution and imaging performance were evaluated with a point source and resolution phantom using analytical and iterative algorithms.Main results.Among the three designs, design C provided the most uniform spatial resolution up to the radial offset of 15 mm. The 0.45 mm diameter rod structures were resolved clearly with design C using the iterative algorithm. The GATE simulation results agreed with the experimental data in terms of radial resolution except at the radial offset of 15 mm.Significance.We optimized the crystal layer design of the mouse brain PET scanner with GATE simulations, thereby achieving sub-0.5 mm resolution in the resolution phantom study.

Objective: Online-adaptive, Magnetic-Resonance-(MR)-guided radiotherapy on a hybrid MR-linear accelerators enables stereotactic body radiotherapy (SBRT) of abdominal/pelvic tumors with large interfractional motion. However, overlaps between planning target volume (PTV) and dose-limiting organs at risk (OARs) often force compromises in PTV-coverage. Overlap-guided adaptive fractionation (AF) leverages daily variations in PTV/OAR overlap to improve PTV-coverage by administering variable fraction doses based on measured overlap volume. This study aims to assess the potential benefits of overlap-guided AF. Approach: We analyzed 58 patients with abdominal/pelvic tumors having received 5-fraction MR-guided SBRT (>6Gy/fraction), in whom PTV-overlap with at least one dose-limiting OAR (bowel, duodenum, stomach) occurred in ≥ 1 fraction. Dose-limiting OARs were constrained to 1cc ≤ 6Gy per fraction, rendering overlapping PTV volumes underdosed. AF aims to reduce this underdosage by delivering higher doses to the PTV on days with less overlap volume, lower doses on days with more. PTV-coverage-gain compared to uniform fractionation was quantified by the area above the PTV dose-volume-histogram-curve and expressed in ccGy (1ccGy = 1cc receiving 1Gy more). The optimal dose for each fraction was determined through dynamic programming by formulating AF as a Markov decision process. Main results: PTV/OAR overlap volume variation (standard deviation) varied substantially between patients (0.02 - 5.76cc). Algorithm-based calculations showed that 55 of 58 patients benefited in PTV-coverage from AF. Mean cohort benefit was 2.93ccGy (range -4.44 (disadvantage) to 22.42ccGy). Higher PTV/OAR overlap variation correlated with larger AF benefit. Significance: Overlap-guided AF for abdominal/pelvic SBRT is a promising strategy to improve PTV-coverage without compromising OAR sparing. Since the benefit of AF depends on PTV/OAR overlap variation-which is low in many patients-the mean cohort advantage is modest. However, well-selected patients with marked PTV/OAR overlap variation derive a relevant dosimetric benefit. Prospective studies are needed to evaluate AF feasibility and quantify clinical benefits.