A Molecular Image-directed, 3D Ultrasound-guided Biopsy System for the Prostate

Improving Prostate Cancer (PCa) Classification Performance by Using Three-Player Minimax Game to Reduce Data Source Heterogeneity

PCa is a disease with a wide range of tissue patterns and this adds to its classification difficulty. Moreover, the data source heterogeneity, i.e. inconsistent data collected using different machines, under different conditions, by different operators, from patients of different ethnic groups, etc., further hinders the effectiveness of training a generalized PCa classifier. In this paper, for the first time, a Generative Adversarial Network (GAN)-based three-player minimax game framework is used to tackle data source heterogeneity and to improve PCa classification performance, where a proposed modified U-Net is used as the encoder. Our dataset consists of novel high-frequency ExactVu ultrasound (US) data collected from 693 patients at five data centers. Gleason Scores (GSs) are assigned to the 12 prostatic regions of each patient. Two classification tasks: benign vs. malignant and low- vs. high-grade, are conducted and the classification results of different prostatic regions are compared. For benign vs. malignant classification, the three-player minimax game framework achieves an Area Under the Receiver Operating Characteristic (AUC) of 93.4%, a sensitivity of 95.1% and a specificity of 87.7%, respectively, representing significant improvements of 5.0%, 3.9%, and 6.0% compared to those of using heterogeneous data, which confirms its effectiveness in terms of PCa classification.

Biological principles and clinical application of positron emission tomography-tracers in prostate cancer: a review

Prostate carcinoma is the most common malignancy in men and the second cause of death by cancer in the western world. Currently, prostate carcinoma's diagnosis is achieved by transrectal ultrasound-guided biopsy (gold-standard), usually requested after an elevation of prostate specific antigen (PSA) levels or an abnormal digital rectal exam or transrectal ultrasound. Nevertheless, this diagnosis sequence sometimes presents with significant limitations. Therefore, there is a need of a diagnosis modality that improves the tumor detection rates and that offers information for its accurate staging, allowing the treatment's planning and administration. Molecular imaging by the means of positron emission tomography uses radiopharmaceuticals labeled with positron-emitting radioisotopes to detect metabolic changes that might be suggestive of cancer tissue. Recently, this technique has suffered a huge dynamic development, and researchers have been working on novel radiotracers agents to improve accuracy in targeting and detecting prostate tumors. On this review, it is highlighted that the most promising positron emission tomography-tracers that will, in a near future, not only improve diagnostic abilities for prostate carcinoma but also open new possibilities for theranostic approaches to treat this malignancy at a world level.

Feasibility and Initial Results: Fluciclovine Positron Emission Tomography/Ultrasound Fusion Targeted Biopsy of Recurrent Prostate Cancer

PURPOSE

We assessed the feasibility and cancer detection rate of fluciclovine (¹⁸F) positron emission tomography-ultrasound fusion targeted biopsy vs standard template biopsy in the same patient with biochemical failure after nonsurgical therapy for prostate cancer.

MATERIALS AND METHODS

A total of 21 patients with a mean ± SD prostate specific antigen of 7.4 ± 6.8 ng/ml and biochemical failure after nonoperative prostate cancer treatment underwent fluciclovine (¹⁸F) positron emission tomography-computerized tomography (mean 364.1 ± 37.7 MBq) and planning transrectal prostate ultrasound with 3-dimensional image reconstruction. Focal prostatic activity on positron emission tomography was delineated and co-registered with planning ultrasound. During the subsequent biopsy session computer generated 12-core template biopsies were performed and then fluciclovine defined targets were revealed and biopsied. Histological analysis of template and targeted cores were completed.

RESULTS

Template biopsy was positive for malignancy in 6 of 21 patients (28.6%), including 10 of 124 regions and 11 of 246 cores, vs targeted biopsy in 10 of 21 (47.6%), including 17 of 50 regions and 40 of 125 cores. Five of 21 patients had positive findings on targeted biopsy only and 1 of 21 had positive findings on template biopsy only. An additional case was upgraded from Grade Group 2 to 3 on targeted biopsy. Extraprostatic disease was detected in 8 of 21 men (38.1%) with histological confirmation in all 3 who underwent lesion biopsy.

CONCLUSIONS

Fluciclovine positron emission tomography real-time ultrasound fusion guidance for biopsy is feasible in patients with biochemical failure after nonsurgical therapy for prostate cancer. It identifies more recurrent prostate cancer using fewer cores compared with template biopsy in the same patient. Further study is required to determine in what manner targeted biopsy may augment template biopsy of recurrent prostate cancer.

Deformable registration of PET/CT and ultrasound for disease-targeted focal prostate brachytherapy

We propose a deformable registration algorithm for prostate-specific membrane antigen (PSMA) PET/CT and transrectal ultrasound (TRUS) fusion. Accurate registration of PSMA PET to intraoperative TRUS will allow physicians to customize dose planning based on the regions involved. The inputs to the registration algorithm are the PET/CT and TRUS volumes as well as the prostate segmentations. PET/CT and TRUS volumes are first rigidly registered by maximizing the overlap between the segmented prostate binary masks. Three-dimensional anatomical landmarks are then automatically extracted from the boundary as well as within the prostate. Then, a deformable registration is performed using a regularized thin plate spline where the landmark localization error is optimized between the extracted landmarks that are in correspondence. The proposed algorithm was evaluated on 25 prostate cancer patients treated with low-dose-rate brachytherapy. We registered the postimplant CT to TRUS using the proposed algorithm and computed target registration errors (TREs) by comparing implanted seed locations. Our approach outperforms state-of-the-art methods, with significantly lower ( mean ± standard deviation ) TRE of 1.96 ± 1.29    mm while being computationally efficient (mean computation time of 38 s). The proposed landmark-based PET/CT-TRUS deformable registration algorithm is simple, computationally efficient, and capable of producing quality registration of the prostate boundary as well as the internal gland.

Multimodal Imaging in Focal Therapy Planning and Assessment in Primary Prostate Cancer

PURPOSE

There is increasing interest in focal therapy (male lumpectomy) of localized low-intermediate risk prostate cancer. Focal therapy is typically associated with low morbidity and provides the possibility of retreatment. Imaging is pivotal in stratification of men with localized prostate cancer for active surveillance, focal therapy or radical intervention. This article provides a concise review of focal therapy and the evolving role of imaging in this clinical setting.

METHODS

We performed a narrative and critical literature review by searching PubMed/Medline database from January 1997 to January 2017 for articles in the English language and the use of search keywords "focal therapy", "prostate cancer", and "imaging".

RESULTS

Most imaging studies are based on multiparametric magnetic resonance imaging. Transrectal ultrasound is inadequate independently but multiparametric ultrasound may provide new prospects. Positron emission tomography with radiotracers targeted to various underlying tumor biological features may provide unprecedented new opportunities. Multimodal Imaging appears most useful in localization of intraprostatic dominant index lesions amenable to focal therapy, in early assessment of therapeutic efficacy and potential need for additional focal treatments or transition to whole-gland therapy, and in predicting short-term and long-term outcomes.

CONCLUSION

Multimodal imaging is anticipated to play an increasing role in the focal therapy planning and assessment of low-intermediate risk prostate cancer and thereby moving this form of treatment option forward in the clinic.

A random walk-based segmentation framework for 3D ultrasound images of the prostate

PURPOSE

Accurate segmentation of the prostate on ultrasound images has many applications in prostate cancer diagnosis and therapy. Transrectal ultrasound (TRUS) has been routinely used to guide prostate biopsy. This manuscript proposes a semiautomatic segmentation method for the prostate on three-dimensional (3D) TRUS images.

METHODS

The proposed segmentation method uses a context-classification-based random walk algorithm. Because context information reflects patient-specific characteristics and prostate changes in the adjacent slices, and classification information reflects population-based prior knowledge, we combine the context and classification information at the same time in order to define the applicable population and patient-specific knowledge so as to more accurately determine the seed points for the random walk algorithm. The method is initialized with the user drawing the prostate and non-prostate circles on the mid-gland slice and then automatically segments the prostate on other slices. To achieve reliable classification, we use a new adaptive k-means algorithm to cluster the training data and train multiple decision-tree classifiers. According to the patient-specific characteristics, the most suitable classifier is selected and combined with the context information in order to locate the seed points. By providing accuracy locations of the seed points, the random walk algorithm improves segmentation performance.

RESULTS

We evaluate the proposed segmentation approach on a set of 3D TRUS volumes of prostate patients. The experimental results show that our method achieved a Dice similarity coefficient of 91.0% ± 1.6% as compared to manual segmentation by clinically experienced radiologist.

CONCLUSIONS

The random walk-based segmentation framework, which combines patient-specific characteristics and population information, is effective for segmenting the prostate on ultrasound images. The segmentation method can have various applications in ultrasound-guided prostate procedures.

PET Molecular Imaging-Directed Biopsy: A Review

OBJECTIVE

The purpose of this review is to summarize the applications of PET molecular imaging-directed biopsy of a variety of organs in the management of various diseases with a focus on cancers.

CONCLUSION

PET can yield metabolic information at the cellular and molecular levels, and PET-directed biopsy is playing an increasing role in the diagnosis and staging of diseases.

Molecular imaging and fusion targeted biopsy of the prostate

PURPOSE

This paper provides a review on molecular imaging with positron emission tomography (PET) and magnetic resonance imaging (MRI) for prostate cancer detection and its applications in fusion targeted biopsy of the prostate.

METHODS

Literature search was performed through the PubMed database using the keywords "prostate cancer", "MRI/ultrasound fusion", "molecular imaging", and "targeted biopsy". Estimates in autopsy studies indicate that 50% of men older than 50 years of age have prostate cancer. Systematic transrectal ultrasound (TRUS) guided prostate biopsy is considered the standard method for prostate cancer detection and has a significant sampling error and a low sensitivity. Molecular imaging technology and new biopsy approaches are emerging to improve the detection of prostate cancer.

RESULTS

Molecular imaging with PET and MRI shows promising results in the early detection of prostate cancer. MRI/TRUS fusion targeted biopsy has become a new clinical standard for the diagnosis of prostate cancer. PET molecular image-directed, three-dimensional ultrasound-guided biopsy is a new technology that has great potential for improving prostate cancer detection rate and for distinguishing aggressive prostate cancer from indolent disease.

CONCLUSION

Molecular imaging and fusion targeted biopsy are active research areas in prostate cancer research.

PET Tracers Beyond FDG in Prostate Cancer

Conventional anatomical imaging with CT and MRI has limitations in the evaluation of prostate cancer. PET is a powerful imaging technique, which can be directed toward molecular targets as diverse as glucose metabolism, density of prostate-specific membrane antigen receptors, and skeletal osteoblastic activity. Although 2-deoxy-2-18F-FDG-PET is the mainstay of molecular imaging, FDG has limitations in typically indolent prostate cancer. Yet, there are many useful and emerging PET tracers beyond FDG, which provide added value. These include radiotracers interrogating prostate cancer via molecular mechanisms related to the biology of choline, acetate, amino acids, bombesin, and dihydrotestosterone, among others. Choline is used for cell membrane synthesis and its metabolism is upregulated in prostate cancer. 11C-choline and 18F-choline are in wide clinical use outside the United States, and they have proven most beneficial for detection of recurrent prostate cancer. 11C-acetate is an indirect biomarker of fatty acid synthesis, which is also upregulated in prostate cancer. Imaging of prostate cancer with 11C-acetate is overall similar to the choline radiotracers yet is not as widely used. Upregulation of amino acid transport in prostate cancer provides the biologic basis for amino acid-based radiotracers. Most recent progress has been made with the nonnatural alicyclic amino acid analogue radiotracer anti-1-amino-3-18F-fluorocyclobutane-1-carboxylic acid (FACBC or fluciclovine) also proven most useful for the detection of recurrent prostate cancer. Other emerging PET radiotracers for prostate cancer include the bombesin group directed to the gastrin-releasing peptide receptor, 16β-18F-fluoro-5α-dihydrotestosterone (FDHT) that binds to the androgen receptor, and those targeting the vasoactive intestinal polypeptide receptor 1 (VPAC-1) and urokinase plasminogen activator receptor (uPAR), which are also overexpressed in prostate cancer.

Combining Population and Patient-Specific Characteristics for Prostate Segmentation on 3D CT Images

Prostate segmentation on CT images is a challenging task. In this paper, we explore the population and patient-specific characteristics for the segmentation of the prostate on CT images. Because population learning does not consider the inter-patient variations and because patient-specific learning may not perform well for different patients, we are combining the population and patient-specific information to improve segmentation performance. Specifically, we train a population model based on the population data and train a patient-specific model based on the manual segmentation on three slice of the new patient. We compute the similarity between the two models to explore the influence of applicable population knowledge on the specific patient. By combining the patient-specific knowledge with the influence, we can capture the population and patient-specific characteristics to calculate the probability of a pixel belonging to the prostate. Finally, we smooth the prostate surface according to the prostate-density value of the pixels in the distance transform image. We conducted the leave-one-out validation experiments on a set of CT volumes from 15 patients. Manual segmentation results from a radiologist serve as the gold standard for the evaluation. Experimental results show that our method achieved an average DSC of 85.1% as compared to the manual segmentation gold standard. This method outperformed the population learning method and the patient-specific learning approach alone. The CT segmentation method can have various applications in prostate cancer diagnosis and therapy.

Random Walk Based Segmentation for the Prostate on 3D Transrectal Ultrasound Images

This paper proposes a new semi-automatic segmentation method for the prostate on 3D transrectal ultrasound images (TRUS) by combining the region and classification information. We use a random walk algorithm to express the region information efficiently and flexibly because it can avoid segmentation leakage and shrinking bias. We further use the decision tree as the classifier to distinguish the prostate from the non-prostate tissue because of its fast speed and superior performance, especially for a binary classification problem. Our segmentation algorithm is initialized with the user roughly marking the prostate and non-prostate points on the mid-gland slice which are fitted into an ellipse for obtaining more points. Based on these fitted seed points, we run the random walk algorithm to segment the prostate on the mid-gland slice. The segmented contour and the information from the decision tree classification are combined to determine the initial seed points for the other slices. The random walk algorithm is then used to segment the prostate on the adjacent slice. We propagate the process until all slices are segmented. The segmentation method was tested in 32 3D transrectal ultrasound images. Manual segmentation by a radiologist serves as the gold standard for the validation. The experimental results show that the proposed method achieved a Dice similarity coefficient of 91.37±0.05%. The segmentation method can be applied to 3D ultrasound-guided prostate biopsy and other applications.

3D in vivo imaging of rat hearts by high frequency ultrasound and its application in myofiber orientation wrapping

Cardiac ultrasound plays an important role in the imaging of hearts in basic cardiovascular research and clinical examinations. 3D ultrasound imaging can provide the geometry or motion information of the heart. Especially, the wrapping of cardiac fiber orientations to the ultrasound volume could supply useful information on the stress distributions and electric action spreading. However, how to acquire 3D ultrasound volumes of the heart of small animals in vivo for cardiac fiber wrapping is still a challenging problem. In this study, we provide an approach to acquire 3D ultrasound volumes of the rat hearts in vivo. The comparison between both in vivo and ex vivo geometries indicated 90.1% Dice similarity. In this preliminary study, the evaluations of the cardiac fiber orientation wrapping errors were 24.7° for the acute angle error and were 22.4° for the inclination angle error. This 3D ultrasound imaging and fiber orientation estimation technique have potential applications in cardiac imaging.

Prostate segmentation: an efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images

We propose a novel global optimization-based approach to segmentation of 3-D prostate transrectal ultrasound (TRUS) and T2 weighted magnetic resonance (MR) images, enforcing inherent axial symmetry of prostate shapes to simultaneously adjust a series of 2-D slice-wise segmentations in a "global" 3-D sense. We show that the introduced challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In this regard, we propose a novel coherent continuous max-flow model (CCMFM), which derives a new and efficient duality-based algorithm, leading to a GPU-based implementation to achieve high computational speeds. Experiments with 25 3-D TRUS images and 30 3-D T2w MR images from our dataset, and 50 3-D T2w MR images from a public dataset, demonstrate that the proposed approach can segment a 3-D prostate TRUS/MR image within 5-6 s including 4-5 s for initialization, yielding a mean Dice similarity coefficient of 93.2%±2.0% for 3-D TRUS images and 88.5%±3.5% for 3-D MR images. The proposed method also yields relatively low intra- and inter-observer variability introduced by user manual initialization, suggesting a high reproducibility, independent of observers.

Mapping Cardiac Fiber Orientations from High-Resolution DTI to High-Frequency 3D Ultrasound

The orientation of cardiac fibers affects the anatomical, mechanical, and electrophysiological properties of the heart. Although echocardiography is the most common imaging modality in clinical cardiac examination, it can only provide the cardiac geometry or motion information without cardiac fiber orientations. If the patient's cardiac fiber orientations can be mapped to his/her echocardiography images in clinical examinations, it may provide quantitative measures for diagnosis, personalized modeling, and image-guided cardiac therapies. Therefore, this project addresses the feasibility of mapping personalized cardiac fiber orientations to three-dimensional (3D) ultrasound image volumes. First, the geometry of the heart extracted from the MRI is translated to 3D ultrasound by rigid and deformable registration. Deformation fields between both geometries from MRI and ultrasound are obtained after registration. Three different deformable registration methods were utilized for the MRI-ultrasound registration. Finally, the cardiac fiber orientations imaged by DTI are mapped to ultrasound volumes based on the extracted deformation fields. Moreover, this study also demonstrated the ability to simulate electricity activations during the cardiac resynchronization therapy (CRT) process. The proposed method has been validated in two rat hearts and three canine hearts. After MRI/ultrasound image registration, the Dice similarity scores were more than 90% and the corresponding target errors were less than 0.25 mm. This proposed approach can provide cardiac fiber orientations to ultrasound images and can have a variety of potential applications in cardiac imaging.

[New puncture techniques in urology using 3D-assisted imaging]

The selective use of various puncture techniques for diagnostic or therapeutic purposes is a component of the daily routine of urologists. The aim of these interventions is always a safe and rapid puncture at the appropriate target point. Nowadays, imaging systems are increasingly being used in urology with the aim to achieve a more precise and safer planning and execution of punctures through an increased accuracy by the use of 3D representation. An approach to the solution to achieve this aim is the fusion of 3D reconstruction by magnetic resonance imaging (MRI) or computed tomography< (CT) with real-time imaging procedures, such as sonography or fluoroscopy.

Accuracy Evaluation of a 3D Ultrasound-guided Biopsy System

Early detection of prostate cancer is critical in maximizing the probability of successful treatment. Current systematic biopsy approach takes 12 or more randomly distributed core tissue samples within the prostate and can have a high potential, especially with early disease, for a false negative diagnosis. The purpose of this study is to determine the accuracy of a 3D ultrasound-guided biopsy system. Testing was conducted on prostate phantoms created from an agar mixture which had embedded markers. The phantoms were scanned and the 3D ultrasound system was used to direct the biopsy. Each phantom was analyzed with a CT scan to obtain needle deflection measurements. The deflection experienced throughout the biopsy process was dependent on the depth of the biopsy target. The results for markers at a depth of less than 20 mm, 20-30 mm, and greater than 30 mm were 3.3 mm, 4.7 mm, and 6.2 mm, respectively. This measurement encapsulates the entire biopsy process, from the scanning of the phantom to the firing of the biopsy needle. Increased depth of the biopsy target caused a greater deflection from the intended path in most cases which was due to an angular incidence of the biopsy needle. Although some deflection was present, this system exhibits a clear advantage in the targeted biopsy of prostate cancer and has the potential to reduce the number of false negative biopsies for large lesions.

3D ultrasound image segmentation using wavelet support vector machines

PURPOSE

Transrectal ultrasound (TRUS) imaging is clinically used in prostate biopsy and therapy. Segmentation of the prostate on TRUS images has many applications. In this study, a three-dimensional (3D) segmentation method for TRUS images of the prostate is presented for 3D ultrasound-guided biopsy.

METHODS

This segmentation method utilizes a statistical shape, texture information, and intensity profiles. A set of wavelet support vector machines (W-SVMs) is applied to the images at various subregions of the prostate. The W-SVMs are trained to adaptively capture the features of the ultrasound images in order to differentiate the prostate and nonprostate tissue. This method consists of a set of wavelet transforms for extraction of prostate texture features and a kernel-based support vector machine to classify the textures. The voxels around the surface of the prostate are labeled in sagittal, coronal, and transverse planes. The weight functions are defined for each labeled voxel on each plane and on the model at each region. In the 3D segmentation procedure, the intensity profiles around the boundary between the tentatively labeled prostate and nonprostate tissue are compared to the prostate model. Consequently, the surfaces are modified based on the model intensity profiles. The segmented prostate is updated and compared to the shape model. These two steps are repeated until they converge. Manual segmentation of the prostate serves as the gold standard and a variety of methods are used to evaluate the performance of the segmentation method.

RESULTS

The results from 40 TRUS image volumes of 20 patients show that the Dice overlap ratio is 90.3% ± 2.3% and that the sensitivity is 87.7% ± 4.9%.

CONCLUSIONS

The proposed method provides a useful tool in our 3D ultrasound image-guided prostate biopsy and can also be applied to other applications in the prostate.