Biomedical Physics & Engineering Express最新文献

Magnetic resonance guided focused ultrasound (MRgFUS) thalamotomy is an established treatment for tremor. MRgFUS utilises ultrasound to non-invasively thermally ablate or "lesion" tremorgenic tissue. The success of treatment is contingent on accurate lesioning as assessed by tremor improvement and minimisation of adverse effects. However, coordinate planning and post-procedure lesion visualisation are difficult as the key targets, cannot be seen on standard clinical imaging. Thus, a computational tool is needed to aid target visualisation. A 3D atlas-based model was created using the Schaltenbrand-Wahren atlas. Key nuclei were manually delineated, interpolated and smoothed in 3D Slicer to create the model. Evaluation of targeting approaches across a seven-year period and patient-specific analyses of tremor treatments were performed. The anatomical position of MRgFUS lesions in the model were compared against varying clinical outcomes. The model provides an anatomical visualisation of how the change in targeting approach led to improved tremor suppression and a reduction in adverse effects for patients. This study demonstrates the successful development of a 3D atlas-based computational model of the brain target nuclei in MRgFUS thalamotomy and its clinical utility for tremor treatment analysis.

Objective: In the revised version of the TRS-398 Code of Practice (CoP), Monte Carlo (MC) results were added to existing experimental data to derive the recommended beam quality correction factors (kQ) for ionisation chambers in proton beams. While part of these results were obtained from versions v10.3 and v10.4 of the Geant4 simulation tool, this paper demonstrates that the use of a more recent version, such as v11.2, can affect the value of the kQ factors.

Approach: The chamber-specific proton contributions (fQ) of the kQ factors were derived for four ionisation chambers using two different versions of the code, namely Geant4-v.10.3 and Geant4-v11.2. A comparison of the total absorbed dose values is performed, as well as the comparison of the dose contribution for primary and secondary particles.

Main results: Larger absorbed dose values per incident particle were derived with Geant4-v11.2 compared to Geant4-v10.3 especially for dose-to-air at high proton beam energies between 150 MeV and 250 MeV, leading to deviations in the kQ values up to 1%. These deviations are mainly due to a change in the physics of secondary helium ions for which the significant deviations between the Geant4 versions is the most stringent within the entrance window or the shell of the ionisation chambers.

Significance: Although significant deviations in the MC calculated fQ values were observed between the two Geant4 versions, the dominant uncertainty of the Wair values currently allows to achieve the agreement at the kQ level. As these values also agree with the current data presented in the TRS-398 CoP, it is not possible at the moment to discriminate between Geant4-v10.3 and Geant4-v11.2, which are therefore both suitable for kQ calculation.

Objective: This study investigates the denoising of low-cost magnetocardiography (MCG) signals recorded under strong noise conditions.

Approach: We propose LG-BiTCN (Least-Squares Generative Adversarial Network with Gated Bidirectional Temporal Convolutional Network), which combines long-range temporal feature extraction with adversarial training for signal denoising. Using clean MCG signals from the Kiel Cardio Database, we design a composite noise model consisting of baseline drift, 1/f (pink) noise, and white Gaussian noise.

Main results: In all composite noise conditions, LG-BiTCN achieves the best denoising performance. At -20 dB input signal-to-noise ratio (SNR) with baseline drift + 1/f noise + white noise, LG-BiTCN improves SNR by 24.21 dB, outperforming traditional algorithms by more than 8.84 dB. Additionally, LG-BiTCN demonstrates superior waveform fidelity, as reflected by higher SSIM and lower MAE_{QRS} compared to baseline methods. We find that at very low SNR, larger receptive field designs are more beneficial for improving denoising performance, while at higher SNR, smaller receptive fields better preserve signal details.

Significance: These results demonstrate that LG-BiTCN can effectively enhance MCG signal denoising under high-noise conditions, providing valuable insights for methods in unshielded MCG denoising tasks.

In this study, we propose a motor imagery(MI) method based on Somatosensory Attentional Orientation(SAO) to enhance the performance of MI based brain-computer interfaces (BCI). In this BCI system, participants perform unilateral hand MI tasks while maintaining attention to the corresponding hand, as if the wrist skin is actually receiving tactile stimulation(TS). A total of 44 participants were recruited and randomly divided into the experimental group(SAO and MI joint group, SMI group) and control group(MI group). The MI group performed right hand MI tasks, and two sessions were conducted, the content of the two experiments was identical. Each session was divided into two stages: the first stage including 1 run was the right hand MI mental task with TS on the right wrist, and the second stage including 6 runs was the right hand MI mental task without TS . For SAO group, first session was the same with the MI group. However, the second stage for SAO group was the right hand MI mental task with SAO. Compared with the first session, the performance in the first session was comparable between the MI group and SMI group, indicating similar MI abilities in both set of participants. For SAO group, A 6.5% performance enhancement was observed in the second session relative to the first session(p < 0.05). However, no significant improvement was observed in the MI group(p > 0.05), indicating no evidence of learning effect. EEG topographic mapping demonstrated robust bilateral hemispheric engagement when right hand MI mental task was performed for MI group. While in the SAO mental task, EEG exhibited clear hemispheric lateralization. This paradigm combining attention mechanisms with MI restructures the bilateral control modality inherent in conventional MI paradigms. As SAO paradigm engages endogenous cognitive processes, this approach augments corticomotor excitability during MI task, thereby improving BCI control performance.

Objective. To classify digital mammograms based on radiological findings using morphology and texture descriptors with artificial neural networks (ANN) for breast cancer detection.Approach.The mammography dataset from High Specialty Regional Hospital of Oaxaca (HRAEO) (median patient age (mpa), 48 years [interquartile range (IQR), 41-54 years]) with radiological findings was retrospectively analyzed. All patients underwent breast biopsy and were not previously treated. External testing was performed using mammograms from the National Cancer Institute (INCAN) (mpa: 47 years [IQR, 37-62 years]). The morphology was analyzed using a circularity descriptor (к), and the texture was analyzed using the mean height/width ratio of the extrema descriptor (ρ). These results were compared with cancer/benign histopathology, which was binarily classified using ANNs. The F1-score, Cohen's kappa (K), and area under the ROC curve (AUC) were employed as evaluation metrics, and the Wilcoxon rank-sum test was used for statistical analysis (h = 0, with p > 0.05, was considered as not statistically significant).Main results.216 raw mammograms from HRAEO and 33 mammograms from INCAN (95 + 16 breast cancer and 121 + 17 benign findings) were included. The best internal testing results were obtained with a one-hidden-layer ANN with 100 neurons, achieving a F1-score of 0.95, K of 0.91, and an AUC of 0.953 (95% confidence interval [CI]: 0.917, 0.977) (h = 0, p > 0.99). However, the external testing results were significantly lower: 0.38 F1-score, 0.02 K, and 0.509 AUC (95% CI: 0.344, 0.664) (h = 0, p = 0.14) due to not exactly meeting the inclusion criteria and possible demographic and spectrum bias, or domain-adaptation issues.Significance. The proposed morphology (к) and texture (ρ) descriptors show promise for detecting breast cancer in raw mammograms, with radiological findings, in a local context. However, their poor external performance highlights the need for substantial further work before this approach can be deemed suitable for broader diagnostic applications.

Purpose. Accurate detection and segmentation of brain metastases (BM) from MRI are critical for the appropriate management of cancer patients. This study investigates strategies to enhance the robustness of artificial intelligence (AI)-based BM detection and segmentation models.Method. A DeepMedic-based network with a loss function, tunable with a sensitivity/specificity tradeoff weighting factorα- was trained on T1 post-contrast MRI datasets from two institutions (514 patients, 4520 lesions). Robustness was evaluated on an external dataset from a third institution dataset (91 patients, 397 lesions), featuring ground truth annotations from two physicians. We investigated the impact of loss function weighting factor,αand training dataset combinations. Detection performance (sensitivity, precision, F1 score) and segmentation accuracy (Dice similarity, and 95% Hausdorff distance (HD95)) were evaluated using one physician's contours as the reference standard. The optimal AI model was then directly compared to the performance of the second physician.Results. Varyingαdemonstrated a trade-off between sensitivity (higherα) and precision (lowerα), withα= 0.5 yielding the best F1 score (0.80 versus 0.78 forα= 0.95 and 0.72 forα= 0.99) on the external dataset. The optimally trained model achieved detection performance comparable to the physician (F1: AI = 0.83, Physician = 0.83), but slightly underperformed in segmentation (Dice: 0.81 versus AI = 0.69; HD95: 3.0 mm versus AI = 4.94 mm, p < 0.05).Conclusion. The derived optimal model achieves detection and segmentation performance comparable to an expert physician in a parallel comparison.

Objective. This review investigates the use of cone-beam computed tomography (CBCT) in conjunction with radiomics for external beam radiation therapy (EBRT) in cancer treatment. CBCT, which provides high-resolution, volumetric images, offers a promising tool for precision treatment delivery. By integrating radiomics and quantitative features extracted from CBCT, this review explores potential advancements in tumor characterization, treatment planning, and monitoring treatment responses in personalized cancer therapy.Approach. We conducted this systematic review using the PRISMA (preferred reporting items for systematic reviews and meta-analyses) framework. This study focused on CBCT-only radiomics applications, examining publications in PubMed, Embase, and Scopus databases. The inclusion criteria were strictly peer-reviewed journal articles, resulting in 29 studies being selected for analysis. These studies were divided into two main categories: (1) method development for treatment outcome prediction; (2) verification, validation, and uncertainty quantification (VVUQ) for CBCT-based radiomics.Main Results. The literature encompasses a range of investigations into CBCT-based radiomics for EBRT, covering different cancer types such as head-and-neck squamous cell carcinoma, non-small cell lung cancer, esophageal squamous cell cancer, hepatocellular carcinoma, prostate cancer, and rectal cancer. These studies used radiomics to predict outcomes including tumor response, local failure, tissue toxicity, and patient survival. VVUQ studies addressed the robustness and reproducibility of radiomic features. Furthermore, the emerging field of 4D-CBCT radiomics shows potential in improving image quality.Significance. CBCT-based radiomics presents a promising advancement in personalized radiotherapy, allowing for enhanced cancer prognosis and treatment adaptation. However, challenges of imaging quality and acquisition need to be addressed to ensure consistency and reliability. Future research should focus on standardizing imaging protocols and incorporating multi-institutional collaborations to further validate the clinical applicability of CBCT-based radiomics. Integration of this technology can potentially induce a paradigm shift in personalized cancer radiotherapy. New technologies promise to make CBCT even more valuable in the future.

Parkinson's disease (PD) is the second most common progressive neurodegenerative disorder, characterized by both motor and non-motor symptoms. In this study, we conducted a meta-analysis of gene expression profiles from four GEO datasets (comprising 59 PD patients and 41 participants control) to identify consistently differentially expressed messenger ribonucleic acids (DEmRNAs). We identified 5,495 down-regulated and 9,850 up-regulated DEmRNAs, of which 64 and 25, respectively, were common across all datasets. Functional enrichment analysis revealed that down-regulated DEmRNAs were primarily enriched in pathways related to neurotransmitter transport, dopamine biosynthesis, and dopaminergic synapse function, while up-regulated DEmRNAs were linked to cell cycle regulation and PI3K-Akt signaling. Notably, dysregulation of key genes, including SNCA (encodingα-synuclein), SLC6A3, TUBB, TUBB3, TUBB4B, and NDUFA9, were associated with PD as well as other neurodegenerative disorders, such as Alzheimer's, Huntington's, and Prion diseases. These DEmRNAs and pathways may offer potential biomarkers and therapeutic targets for PD and related neurological disorders.

Lumbar disc herniation (LDH) is one of the most common degenerative diseases of the spine. Magnetic resonance image is the most effective way to detect LDH. The variety of shapes and blurred boundaries of diseased discs, along with the unclear classification basis of existing methods and their poor ability to differentiate between lesion types, make computer-aided diagnosis (CAD) of LDH challenging. We propose an enhanced classification of LDH through region-of-interest guidance and geometric shape features (RGGS-Net) to address these challenges. RGCG-Net establishes the connection between the segmentation of diseased lumbar disc and the classification of lesion types in LDH. A region-of-interest guided module, combined with region-of-interest supervision, is proposed to refine the features from the encoder. Weighted skip connections are used to balance the ratio between the original feature and the refined feature. Hierarchical supervision is used to reduce the training difficulty of the deep decoder and improve the final segmentation performance. Finally, the precise classification of LDH is achieved based on the geometrical features of its different types. Numerous experiments have demonstrated the effectiveness of the RGGS-Net. The classification accuracy of the RGGS-Net in the LDH classification task is 0.965. The Dice of the RGGS-Net reaches 0.957 in vertebrae and disc segmentation task.

Respiratory phase mismatch between single-photon emission computed tomography (SPECT) and computed tomography (CT) acquisition phases presents a challenge in lung perfusion scintigraphy using SPECT/CT. This study simulated lung volume and SPECT counts changes under free-breathing and breath-hold CT conditions compared to respiratory-synchronized acquisition. Chest 4D-CT images, divided into 10 respiratory phases, were used to generate lung, soft tissue, liver, and bone regions for each phase. A digital phantom was constructed via image processing using ImageJ. SPECT images were generated from these phantoms by employing the Prominence Processor to simulate projection data and reconstruct images. Simulations included a 'synchronized image,' where both SPECT and μMAP for attenuation correction were created in the same phase; a 'free-breathing image,' combining a free-breathing SPECT and μMAP; and a 'CT breath-hold image,' using phase-specific μMAPs with the free-breathing SPECT image for attenuation correction. Lung volumes and SPECT counts in the free-breathing and CT breath-hold images were compared with those in the synchronized image. By analyzing the relative errors caused by differences in the μMAPs, the study evaluated the impact of mismatch between SPECT and CT phases. Results indicated that lung volumes appeared reduced during inspiration and increased during expiration compared with synchronized images. No significant difference in the relative error was observed between the free-breathing and CT breath-hold images. Our findings revealed that in the quantitative evaluation of lung perfusion SPECT, varying the μ-map phase during free-breathing acquisition did not result in a significant improvement, suggesting that the mismatch between SPECT and CT had no statistically significant effect on quantitative accuracy. Compared with respiratory-gated SPECT, free-breathing acquisitions introduced potential errors of approximately 2.5% in lung volume measurement and 1.2% in SPECT counts. However, these errors were within acceptable tolerance limits for clinical diagnosis, indicating that free-breathing acquisition had minimal effects on diagnostic capability.