Transrectal Ultrasound Images Research Articles

Comparison of the Diagnostic Accuracy of Percutaneous Fistula Contrast-Enhanced Ultrasound Combined with Transrectal 360° 3-D Imaging and Conventional Transrectal Ultrasound in Complex Anal Fistula.

This study compared the diagnostic accuracy of percutaneous fistula contrast-enhanced ultrasound (CEUS) combined with 360° 3-D transrectal ultrasound (TRUS) imaging (CEUS+360°-TRUS) with that of conventional transrectal ultrasound in the diagnosis of complex anal fistulas. A total of 156 patients clinically diagnosed with complex anal fistula from January 2020 to December 2021 were studied and randomly divided into an experimental group (n=82) and a control group (n=74). Patients in the experimental group were examined by percutaneous fistula CEUS combined with CEUS+360°-TRUS, while patients in the control group were examined using TRUS. The detection of fistulas (main tract, branch and internal orifice) and the accuracy of Parks classification were compared between the two groups. Recurrences were followed up at 1, 3 and 6 mo after the surgery. A total of 156 patients were included, aged 23-68 y (average: 37.7 ± 18.2 y). In both groups, the course of disease was <1 mo in 128 cases, 1-2 mo in 22 cases and >3 mo in 6 cases. A total of 474 fistulas were confirmed by surgery in the aforementioned patients, including 224 main fistulas, 250 branch pipes and 254 internal orifices. The CEUS+360°-TRUS group had 96.87%, 90.41% and 90.14% diagnostic accuracy for the main tract, branch and internal orifice, which was statistically significant (p < 0.001) compared with the 85.00%, 70.00% and 72.46% for the TRUS group, respectively. The overall accuracy of Parks classification of anal fistula in the CEUS+360°-TRUS group was significantly higher than that in the TRUS group (90.24% vs. 78.38%, p < 0.001). After 6 mo of follow-up, the recurrence rate in the CEUS+360°-TRUS group was 4.87%, and the recurrence rate in the TRUS group was 18.91%. Percutaneous fistula CEUS combined with transrectal 360° 3-D imaging has significantly higher accuracy than conventional TRUS in the diagnosis of complex anal fistula, especially for anal fistula branches, internal openings and Parks classification and is beneficial in reducing post-operative occurrence of complex anal fistulas.

A novel design framework of synthetic radial aperture focusing for volumetric transrectal ultrasound imaging.

In this paper, we present a novel design framework of synthetic radial aperture focusing for three-dimensional (3D) transrectal ultrasound imaging (TRUS-rSAF), in which multiple transmittance/reception events at different scanning angles are synthesized to reconstruct a radial plane in the target volume, securing high spatial resolution and texture uniformity. A theory-based design approach has not been available to push the envelope of the 3D rSAF technique. Herein, a closed-form analytical description of the TRUS-rSAF method is presented for the first time, effectively delineating spatial resolution and grating lobe positions in the radial dimension of a TRUS transducer. We demonstrate a solid optimization workflow based on the theoretical foundation to improve its spatiotemporal resolution, grating lobe artifacts, and signal-to-noise ratio. A specific design criterion was considered to outperform a clinical 3D TRUS imaging as a reference (TRUS-REF), where each radial plane is reconstructed with a single transmittance/reception event using a motorized actuator. The optimized TRUS-rSAF method significantly enhanced spatial resolution up to 50% over the TRUS-REF method while providing clinically effective temporal resolution (2–8 volume/sec) with negligible grating lobe artifacts. The results indicate that the proposed design approach would enable a novel TRUS imaging solution in clinics.

The evolving role of contrast-enhanced ultrasound in urology: a review.

Ultrasound's versatility and ease of use has expanded its application in many clinical settings. Technological advancements with contrast-enhanced ultrasound (CEUS) have allowed high quality imaging similar to CT or MRI with lower risk of contrast toxicity and radiation exposure. In this review article we examine the development of CEUS and its vast applications in the field of urology. A PubMed literature search was performed using keywords: contrast enhanced ultrasound, prostate cancer, renal cancer, and multiparametric ultrasound. The development of CEUS has improved transrectal ultrasound imaging with increased detection of prostate cancer (PCa). Further enhancements of CEUS such as subharmonic imaging (SHI), flash replenishment imaging (FRI) and contrast ultrasound dispersion imaging (CUDI) allow improved PCa diagnosis. CEUS has also emerged as an important tool in characterizing suspicious renal mass without compromising renal function with contrast imaging. CEUS has modernized imaging and diagnosis of prostate and renal cancer. Future advancements and utilization of CEUS will allow its expansion into other urological subspecialties.

The role of multiparametric ultrasound in the detection of clinically significant prostate cancer.

Transrectal ultrasound (US) imaging is paramount to the successful completion of prostate biopsies. Certain US features have been associated with prostate cancer (PCa), but their utility remains controversial. We explored the role of multiparametric US (mpUS) in the detection of clinically significant PCa. We performed a retrospective cohort study to contrast the findings of prostate MRI and mpUS. Patients who underwent MRI, US and biopsy between 2015 and 2021 were included. Biopsies were performed using a systematic approach (12 cores), as well as with MRI (4 cores/lesion) and US (1 core/lesion) targeting. The US features analyzed consisted of: calcifications, hypoechoic lesions and power or color Doppler positivity. Gleason 3 + 4 or higher was used as to define true positives. Measures of diagnostic accuracy were calculated for the different imaging modalities. The final cohort included 74 patients, of which 24 (32.4%) had clinically significant PCa. The concordance between MRI and US was 63.5%. Seven individuals with discordant results had clinically significant PCa. MRI alone was more sensitive (87.5% vs 75%) but less specific (28% vs 32%) than US alone. An all-inclusive approach considering any suspicious US or MRI finding had a sensitivity of 95.8%. A more restrictive approach, targeting lesions noted in both US and MRI, yielded the highest specificity (50.0%) and accuracy (55.4%). Biopsy targeting based on US findings can provide additional diagnostic information that may increase sensitivity or specificity. Additional research into this topic could open the door to a more personalized approach to prostate biopsy.

Contrast-Specific Spherical Lesion Phantoms and Ancillary Analysis Software for the Objective Evaluation of Transrectal Ultrasound System Contrast Detectability.

Brachytherapy is an efficacious treatment option because of its benefits for patient recovery, dose localization and conformity, but these favorable outcomes can be ensured only if the transrectal ultrasound (TRUS) system is optimized for the specific application of ultrasound-guided prostate brachytherapy. The ability to delineate the prostate from surrounding tissue during TRUS-guided prostate brachytherapy is vital for treatment planning, and consequently, so is the contrast resolution. This study describes the development of task-specific contrast-detail phantoms with clinically relevant contrast and spherical target sizes for contrast-detail performance evaluation of TRUS systems used in the brachytherapy procedure. The procedure for objective assessment of the contrast detectability of the TRUS systems is also described; a program was developed in MATLAB (R2017a, The MathWorks, Natick, MA, USA) to quantitatively analyze image quality in terms of the lesion signal-to-noise ratio (LSNR) and validated with representative control test images. The LSNR of the Hitachi EUB-7500A (2013, Hitachi, Ltd, Tokyo, Japan) TRUS system was measured on sagittal and transverse TRUS images of the contrast-detail phantoms described in this work. Results revealed the efficacy of the device as an image quality evaluation tool and the impact of the size, depth and relative contrast of the targets to the surrounding tissue on the contrast detectability of a TRUS system for both transducer arrays. The MATLAB program objectively measured the contrast detectability of the TRUS system and has the potential to determine optimized imaging parameters that could be designed as part of standardization of the imaging protocol used in TRUS-guided prostate brachytherapy for prostate cancer.

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net.

In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface.

H-ProMed: Ultrasound image segmentation based on the evolutionary neural network and an improved principal curve

The purpose of this work is to develop a method for accurate and robust prostate segmentation in transrectal ultrasound (TRUS) images. These images are difficult to segment due to missing/ambiguous boundary between the prostate and neighboring structures, the presence of shadow artifacts, as well as the large variability in prostate shapes. This paper develops a novel hybrid method for TRUS prostate segmentation by combining an improved principal curve-based method with an evolutionary neural network; the former for achieving the data sequences while and the latter for improving the smoothness of the prostate contour. Both qualitative and quantitative experimental results showed that our proposed method achieved superior segmentation accuracy and robustness as compared to state-of-the-art methods. The average Dice similarity coefficient (DSC), Jaccard similarity coefficient (Ω), and accuracy (ACC) of prostate contours against ground-truths were 96.8%, 95.7%, and 96.4%, and the DSC of around 92% and 95% for other deep learning and hybrid methods, respectively.

Semi-Automatic Prostate Segmentation From Ultrasound Images Using Machine Learning and Principal Curve Based on Interpretable Mathematical Model Expression.

Accurate prostate segmentation in transrectal ultrasound (TRUS) is a challenging problem due to the low contrast of TRUS images and the presence of imaging artifacts such as speckle and shadow regions. To address this issue, we propose a semi-automatic model termed Hybrid Segmentation Model (H-SegMod) for prostate Region of Interest (ROI) segmentation in TRUS images. H-SegMod contains two cascaded stages. The first stage is to obtain the vertices sequences based on an improved principal curve-based model, where a few radiologist-selected seed points are used as prior. The second stage is to find a map function for describing the smooth prostate contour based on an improved machine learning model. Experimental results show that our proposed model achieved superior segmentation results compared with several other state-of-the-art models, achieving an average Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (Ω), and Accuracy (ACC) of 96.5%, 95.2%, and 96.3%, respectively.

Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation.

Prostate segmentation in transrectal ultrasound (TRUS) image is an essential prerequisite for many prostate-related clinical procedures, which, however, is also a long-standing problem due to the challenges caused by the low image quality and shadow artifacts. In this paper, we propose a Shadow-consistent Semi-supervised Learning (SCO-SSL) method with two novel mechanisms, namely shadow augmentation (Shadow-AUG) and shadow dropout (Shadow-DROP), to tackle this challenging problem. Specifically, Shadow-AUG enriches training samples by adding simulated shadow artifacts to the images to make the network robust to the shadow patterns. Shadow-DROP enforces the segmentation network to infer the prostate boundary using the neighboring shadow-free pixels. Extensive experiments are conducted on two large clinical datasets (a public dataset containing 1,761 TRUS volumes and an in-house dataset containing 662 TRUS volumes). In the fully-supervised setting, a vanilla U-Net equipped with our Shadow-AUG&Shadow-DROP outperforms the state-of-the-arts with statistical significance. In the semi-supervised setting, even with only 20% labeled training data, our SCO-SSL method still achieves highly competitive performance, suggesting great clinical value in relieving the labor of data annotation. Source code is released at https://github.com/DIAL-RPI/SCO-SSL.

Should asymptomatic small anteriorly located midline prostatic cyst in young adults be treated surgically or conservatively? A case report and review of the literature

Abstract Background Prostatic cysts are uncommon, typically asymptomatic and discovered by chance during imaging. Prostatic cysts in the midline are less prevalent and primarily seen in the posterior aspect of the prostate. Case presentation We describe a case of a 32-year-old man with a complaint of left loin pain and a little sensation of pelvic discomfort. Ultrasound was done revealing small pelvic cystic structure related to urinary bladder base and neck with possibility to be prostatic in origin. Transrectal ultrasound and pelvic magnetic resonance imaging were done and showed an anteriorly located midline prostatic cyst protruding into the bladder lumen, with no communication with the urethra on conventional ascending urethrogram. Conclusion The rare relationship between the cyst, bladder neck, and prostate make this case to some extent unique and further interesting. To our best knowledge, this is the eighth documented case in the literature to describe an anteriorly located midline intraprostatic cyst projecting at the bladder neck and base with illustration of different plans of treatment.

Polar transform network for prostate ultrasound segmentation with uncertainty estimation.

Automatic and accurate prostate ultrasound segmentation is a long-standing and challenging problem due to the severe noise and ambiguous/missing prostate boundaries. In this work, we propose a novel polar transform network (PTN) to handle this problem from a fundamentally new perspective, where the prostate is represented and segmented in the polar coordinate space rather than the original image grid space. This new representation gives a prostate volume, especially the most challenging apex and base sub-areas, much denser samples than the background and thus facilitate the learning of discriminative features for accurate prostate segmentation. Moreover, in the polar representation, the prostate surface can be efficiently parameterized using a 2D surface radius map with respect to a centroid coordinate, which allows the proposed PTN to obtain superior accuracy compared with its counterparts using convolutional neural networks while having significantly fewer (18%∼41%) trainable parameters. We also equip our PTN with a novel strategy of centroid perturbed test-time augmentation (CPTTA), which is designed to further improve the segmentation accuracy and quantitatively assess the model uncertainty at the same time. The uncertainty estimation function provides valuable feedback to clinicians when manual modifications or approvals are required for the segmentation, substantially improving the clinical significance of our work. We conduct a three-fold cross validation on a clinical dataset consisting of 315 transrectal ultrasound (TRUS) images to comprehensively evaluate the performance of the proposed method. The experimental results show that our proposed PTN with CPTTA outperforms the state-of-the-art methods with statistical significance on most of the metrics while exhibiting a much smaller model size. Source code of the proposed PTN is released at https://github.com/DIAL-RPI/PTN.

Texture analysis based on PI-RADS 4/5-scored magnetic resonance images combined with machine learning to distinguish benign lesions from prostate cancer.

BackgroundThe global morbidity and mortality of prostate cancer (PCa) increase sharply every year. Early diagnosis is essential; it determines survival and outcome. So, this study extracted the texture features of apparent diffusion coefficient images in multiparametric magnetic resonance imaging (mp-MRI) and built machine learning models based on radiomics texture analysis (TA) to determine its ability to distinguish benign from PCa lesions using the Prostate Imaging Reporting and Data System (PI-RADS) 4/5 score.MethodsWe enrolled 103 patients who underwent mp-MRI examinations and transrectal ultrasound and magnetic resonance fusion imaging (TRUS-MRI) targeted prostate biopsy and obtained pathological confirmation at our hospital from August 2017 to January 2020. We used ImageJ software to obtain texture feature parameters based on apparent diffusion coefficient (ADC) images, then standardized texture feature parameters, and used LASSO regression to reduce multiple feature parameters; 70% of the cases were randomly selected from the PCa group and the benign prostate hyperplasia group as the training set. The remaining 30% was used as the test set. The machine learning classification model for identifying benign and malignant prostate lesions was constructed using the feature parameters after dimensionality reduction. The clinical indicators were statistically analyzed, and we constructed a machine learning classification model based on clinical indicators of benign and malignant prostate lesions. Finally, we compared the model’s performance based on radiomics texture features and clinical indicators to identify benign and malignant prostate lesions in PI-RADS 4/5 score.ResultsThe area under the curve (AUC) of the R-logistic model test set was 0.838, higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-logistic classification model formula is as follow: Y_radiomics=9.396-7.464*median ADC-0.584*kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the corresponding C-logistic classification model formula is as follows: Y_clinical =-2.608+0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set was 0.860, smaller than the training set R-logistic classification model AUC value of 0.936.ConclusionsRadiomics combined with the machine learning classifier model has strong classification performance in identifying benign and PCa in PI-RADS 4/5 score. Various treatments and outcomes for PCa patients can be applied clinically.

Effect of dataset size, image quality, and image type on deep learning-based automatic prostate segmentation in 3D ultrasound

Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image quality grading method. The complete training dataset of 6761 2D images, resliced from 206 3D TRUS volumes acquired using end-fire and side-fire acquisition methods, was split to train two separate networks using either end-fire or side-fire images. Split datasets were reduced to 1000, 500, 250, and 100 2D images. For deep learning prediction, modified U-Net and U-Net++ architectures were implemented and compared using an unseen test dataset of 40 3D TRUS volumes. A 3D TRUS image quality grading scale with three factors (acquisition quality, artifact severity, and boundary visibility) was developed to assess the impact on segmentation performance. For the complete training dataset, U-Net and U-Net++ networks demonstrated equivalent performance, but when trained using split end-fire/side-fire datasets, U-Net++ significantly outperformed the U-Net. Compared to the complete training datasets, U-Net++ trained using reduced-size end-fire and side-fire datasets demonstrated equivalent performance down to 500 training images. For this dataset, image quality had no impact on segmentation performance for end-fire images but did have a significant effect for side-fire images, with boundary visibility having the largest impact. Our algorithm provided fast (<1.5 s) and accurate 3D segmentations across clinically diverse images, demonstrating generalizability and efficiency when employed on smaller datasets, supporting the potential for widespread use, even when data is scarce. The development of an image quality grading scale provides a quantitative tool for assessing segmentation performance.

H-ProSeg: Hybrid ultrasound prostate segmentation based on explainability-guided mathematical model

Background and ObjectiveAccurate and robust prostate segmentation in transrectal ultrasound (TRUS) images is of great interest for image-guided prostate interventions and prostate cancer diagnosis. However, it remains a challenging task for various reasons, including a missing or ambiguous boundary between the prostate and surrounding tissues, the presence of shadow artifacts, intra-prostate intensity heterogeneity, and anatomical variations. MethodsHere, we present a hybrid method for prostate segmentation (H-ProSeg) in TRUS images, using a small number of radiologist-defined seed points as the prior points. This method consists of three subnetworks. The first subnetwork uses an improved principal curve-based model to obtain data sequences consisting of seed points and their corresponding projection index. The second subnetwork uses an improved differential evolution-based artificial neural network for training to decrease the model error. The third subnetwork uses the parameters of the artificial neural network to explain the smooth mathematical description of the prostate contour. The performance of the H-ProSeg method was assessed in 55 brachytherapy patients using Dice similarity coefficient (DSC), Jaccard similarity coefficient (Ω), and accuracy (ACC) values. ResultsThe H-ProSeg method achieved excellent segmentation accuracy, with DSC, Ω, and ACC values of 95.8%, 94.3%, and 95.4%, respectively. Meanwhile, the DSC, Ω, and ACC values of the proposed method were as high as 93.3%, 91.9%, and 93%, respectively, due to the influence of Gaussian noise (standard deviation of Gaussian function, σ = 50). Although the σ increased from 10 to 50, the DSC, Ω, and ACC values fluctuated by a maximum of approximately 2.5%, demonstrating the excellent robustness of our method. ConclusionsHere, we present a hybrid method for accurate and robust prostate ultrasound image segmentation. The H-ProSeg method achieved superior performance compared with current state-of-the-art techniques. The knowledge of precise boundaries of the prostate is crucial for the conservation of risk structures. The proposed models have the potential to improve prostate cancer diagnosis and therapeutic outcomes.

Use of a novel anthropomorphic prostate simulator in a prostate brachytherapy transrectal ultrasound imaging workshop for medical physicists

PurposeUltrasound imaging training is not required as part of radiation oncology training programs nor does any objective competency measure exist to independently assess performance. Physical simulation training can provide a structured approach to this training but only if suitably challenging training simulators exist. This study describes the design and preliminary evaluation of a simulation-based transrectal ultrasound (TRUS) imaging training workshop developed for medical physicists involved in low-dose-rate (LDR) prostate brachytherapy (PBT). MethodsThe study incorporated novel high-fidelity anthropomorphic PBT TRUS training simulators and a TRUS imaging module with a blended-learning pedagogical approach, to address TRUS image optimisation and managing image quality. ResultsResults demonstrated a significant improvement in knowledge, with an average increase in multiple choice question score of 61% (P < 0.0002), and that there was a 46% (P < 0.0001) average increase in the participants perceived understanding of TRUS scanner operation, and an increase of 36% (P < 0.001) in participants readiness to optimise image quality and mitigate image artefacts. Focus group data explored participants’ experiences, perceptions and challenges with TRUS LDR PBT. ConclusionsThis study suggests a benefit in offering a simulation training workshop to medical physicists and the potential benefit to other healthcare professionals involved in prostate brachytherapy, by incorporating novel high-fidelity anthropomorphic PBT TRUS training simulators, in a simulated environment to practice ultrasound image optimisation for PBT image guidance. This approach to training would enable competency-based skill acquisition and continued proficiency or health professionals in the TRUS PBT procedure, outside of the surgical environment without direct exposure to patients.

Detection rates of urogenital cancers and benign pathology in men presenting with hematospermia

Background:Hematospermia, although often found to be a benign condition, can be an alarming sign. Consequently, patients can undergo multiple investigations with no current standardized pathway based on data from large series. The aim of this study was to evaluate the incidence of an underlying pathology and the value of diagnostic investigations performed in patients presenting with hematospermia.Materials and methods:A retrospective review of 393 consecutive men who underwent investigations for hematospermia was performed in a single tertiary center. Patient demographics, radiological and microbiological results were recorded together with symptoms of concomitant hematuria and clinical outcomes.Results:In this cohort, the overall prostate cancer detection rate was 5.3% and 7.2% in the ≥40 years group. One patient was diagnosed with testicular seminoma detected on scrotal ultrasound scan and one with G1pTa urothelial carcinoma of the bladder detected on flexible cystoscopy. In addition, 5.6% of patients were found to have a significant benign pathology for which intervention was proposed. A total of 288 patients underwent a transrectal ultrasound scan and 58.7% (n = 169) of these patients were found to have a positive finding. One hundred ten patients underwent a multiparametric magnetic resonance imaging and 73.6% (n = 81) had a positive finding.Conclusions:Apart from transrectal ultrasound and multiparametric magnetic resonance imaging, the remaining investigations have a low diagnostic yield. Prostate cancer detection was 5.3%; 7.2% in the ≥40 years group, and two further patients were diagnosed with testicular and bladder malignancy. Based on our results, we propose an algorithm for the management of hematospermia to limit unnecessary investigations with the majority requiring reassurance.

Weakly Supervised MR-TRUS Image Synthesis for Brachytherapy of Prostate Cancer.

Prostate magnetic resonance imaging (MRI) offers accurate details of structures and tumors for prostate cancer brachytherapy. However, it is unsuitable for routine treatment since MR images differ significantly from trans-rectal ultrasound (TRUS) images conventionally used for radioactive seed implants in brachytherapy. TRUS imaging is fast, convenient, and widely available in the operation room but is known for its low soft-tissue contrast and tumor visualization capability in the prostate area. Conventionally, practitioners usually rely on prostate segmentation to fuse the two imaging modalities with non-rigid registration. However, prostate delineation is often not available on diagnostic MR images. Besides, the high non-linear intensity relationship between two imaging modalities poses a challenge to non-rigid registration. Hence, we propose a method to generate a TRUS-styled image from a prostate MR image to replace the role of the TRUS image in radiation therapy dose pre-planning. We propose a structural constraint to handle non-linear projections of anatomical structures between MR and TRUS images. We further include an adversarial mechanism to enforce the model to preserve anatomical features in an MR image (such as prostate boundary and dominant intraprostatic lesion (DIL)) while synthesizing the TRUS-styled counterpart image. The proposed method is compared with other state-of-art methods with real TRUS images as the reference. The results demonstrate that the TRUS images synthesized by our method can be used for brachytherapy treatment planning for prostate cancer.

3-D Prostate MR and TRUS Images Detection and Segmentation for Puncture Biopsy

Prostate cancer is one of the male cancers with high mortality rate in the world. The mainstream diagnostic method is puncture biopsy. Accurate detection and segmentation of the prostate in MR (Magnetic Resonance) and TRUS (Transrectal Ultrasonography) images is an important prerequisite for image-guided puncture biopsy. Manual or semi-automatic detection and segmentation methods and traditional automatic detection and segmentation methods are difficult to achieve real-time and accurate detection and segmentation under the conditions of poor imaging effect or less labeled data. In this paper, a real-time and accurate automatic detection and segmentation algorithm DRC U-Net for 3D MR and TRUS images of prostate is proposed for prostate puncture biopsy. A novel convolution module that integrates residual connections, dense connections, and deep separable convolutions is proposed for multi-scale feature extraction and fusion. At the same time, deep supervision mechanism and a variety of attention mechanisms are integrated in the network to improve training efficiency and ensure segmentation effect. The experimental results on the public MR dataset Promise12 and the private TRUS dataset show that the real-time performance and accuracy of this method are better than the existing 3D medical image detection and segmentation methods, such as 3D U-Net and V-Net, and also have some advantages compared with the most advanced 3D medical image detection and segmentation methods, which proves that the proposed network structure has important clinical significance and practical value for image-guided puncture biopsy.

Advantages of TRUS-based delineation for high-dose-rate prostate brachytherapy planning.

PurposeTo evaluate the variability of prostate contours delineated on computed tomography (CT) and transrectal ultrasound (TRUS).Material and methodsA TRUS-based high-dose-rate (HDR) brachytherapy procedure was introduced in 2016 in our center. The first thirty patients were additionally imaged with CT immediately after the treatment. In 2018, four different radiation oncologists (ROs: 1, 2, 3, 4) contoured the prostate on both modalities. A volume comparison was performed between CT and TRUS imaging. Using prostate gold fiducial makers, a rigid registration between CT and TRUS was done in 20 of the 30 patients studied. Jaccard index (JI) was computed to evaluate the inter-observer volume delineation agreement.ResultsThe ratio of TRUS/CT volumes was 0.82 (95% CI: 0.79-0.87%). The mean JI was 87% for CT and 92% for TRUS, when comparing all four ROs; CT and TRUS JIs were significantly different (p < 0.001). The mean JI for the prostate on CT was significantly more consistent (p < 0.001) when comparing RO1, 2, and 3 together (RO1-2, RO1-3, and RO2-3; mean = 89%) than when comparing RO4 (newest to clinical practice) to others (RO1-4, RO2-4, and RO3-4; mean = 85%). For TRUS planning, the mean JI was not significantly different (p > 0.05) when comparing all ROs.ConclusionsThe inter-observer and intra-observer variability were statistically significantly smaller on TRUS compared to CT-based planning, despite varying ROs clinical experiences. The superior soft tissue contrast offered by TRUS obviates the effect of the ROs experience on prostate contour volumes and enables more reproducible prostate delineation.

Fabrication and Determination of Radiation Sensitive Features of Tissue Equivalent Gels in Radiation Dosimetry and Diagnostic Radiology

Various types of gel dosimeters have been developed so far with the names PVAL gel and MAGIC gel, have been abundantly applied in medical physics. This work is divided into two parts. First, this study aims to analyze an ultrasonic imaging device for accurate patient positioning and organ motion detection during prostate radiotherapy. The images of gel were taken before and after the irradiation, using CT, ultrasound and MRI and dose profiles were plotted. This gel will be used as a prostate phantom for the applications in trans-rectal ultrasound imaging. Secondly, this work aims to develop a realistic artificial breast Using PVAL Gel. The gel was X-rayed on a digital mammography machine which has 3 different programs which will select 3 different beam energies. Results for 2 sample thicknesses were compared against Perspex. PVAL gel was confirmed to be a close match to the Perspex for three beam energies.