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Proton magnetic resonance spectroscopic imaging (MRSI) of the prostate has been described at 1.5 T and 3 T as a means of localizing prostate cancers with high sensitivity and specificity. This technique could be improved by increasing the field strength further; however, it has not been described in detail above 3 T. To address the increase in B1 and SAR at high field strengths, a new protocol is described for reduced power STEAM MRSI of the prostate at 4.0 Tesla, using a pelvic surface coil array for RF transmission and reception, and a solid, reusable endorectal coil for reception only. The optimal STEAM sequence timing parameters for observation of the strongly coupled citrate spin system were determined through simulation to be echo time (TE) = 27 ms and mixing time (TM) = 27 ms, and the results were verified in vitro. Power reduction was achieved by applying the VERSE method to each of the three slice selective pulses in the STEAM sequence, and the B1max and SAR were reduced by 43% and 36%, respectively. Finally, in vivo spectroscopic imaging data were acquired from a prostate cancer patient, demonstrating the detection of citrate, choline, and creatine with 0.37 cc nominal resolution in a 10 minute scan. Magn Reson Med 61:273–281, 2009. © 2009 Wiley-Liss, Inc.

There are currently limitations associated with the prostate biopsy procedure, which is the most commonly used method for a definitive diagnosis of prostate cancer. With the use of two-dimensional (2D) transrectal ultrasound (TRUS) for needle-guidance in this procedure, the physician has restricted anatomical reference points for guiding the needle to target sites. Further, any motion of the physician's hand during the procedure may cause the prostate to move or deform to a prohibitive extent. These variations make it difficult to establish a consistent reference frame for guiding a needle. We have developed a 3D navigation system for prostate biopsy, which addresses these shortcomings. This system is composed of a 3D US imaging subsystem and a passive mechanical arm to minimize prostate motion. To validate our prototype, a series of experiments were performed on prostate phantoms. The 3D scan of the string phantom produced minimal geometric distortions, and the geometric error of the 3D imaging subsystem was 0.37 mm. The accuracy of 3D prostate segmentation was determined by comparing the known volume in a certified phantom to a reconstructed volume generated by our system and was shown to estimate the volume with less then 5% error. Biopsy needle guidance accuracy tests in agar prostate phantoms showed that the mean error was 2.1 mm and the 3D location of the biopsy core was recorded with a mean error of 1.8 mm. In this paper, we describe the mechanical design and validation of the prototype system using an in vitro prostate phantom. Preliminary results from an ongoing clinical trial show that prostate motion is small with an in-plane displacement of less than 1 mm during the biopsy procedure.

Purpose Defining prostate cancer (PCa) lesion clinical target volumes (CTVs) for multiparametric magnetic resonance imaging (mpMRI) could support focal boosting or treatment to improve outcomes or lower morbidity, necessitating appropriate CTV margins for mpMRI-defined gross tumor volumes (GTVs). This study aimed to identify CTV margins yielding 95% coverage of PCa tumors for prospective cases with high likelihood. Methods and Materials Twenty-five men with biopsy-confirmed clinical stage T1 or T2 PCa underwent pre-prostatectomy mpMRI, yielding T2-weighted, dynamic contrast-enhanced, and apparent diffusion coefficient images. Digitized whole-mount histology was contoured and registered to mpMRI scans (error ≤2 mm). Four observers contoured lesion GTVs on each mpMRI scan. CTVs were defined by isotropic and anisotropic expansion from these GTVs and from multiparametric (unioned) GTVs from 2 to 3 scans. Histologic coverage (proportions of tumor area on co-registered histology inside the CTV, measured for Gleason scores [GSs] ≥6 and ≥7) and prostate sparing (proportions of prostate volume outside the CTV) were measured. Nonparametric histologic-coverage prediction intervals defined minimal margins yielding 95% coverage for prospective cases with 78% to 92% likelihood. Results On analysis of 72 true-positive tumor detections, 95% coverage margins were 9 to 11 mm (GS ≥ 6) and 8 to 10 mm (GS ≥ 7) for single-sequence GTVs and were 8 mm (GS ≥ 6) and 6 mm (GS ≥ 7) for 3-sequence GTVs, yielding CTVs that spared 47% to 81% of prostate tissue for the majority of tumors. Inclusion of T2-weighted contours increased sparing for multiparametric CTVs with 95% coverage margins for GS ≥6, and inclusion of dynamic contrast-enhanced contours increased sparing for GS ≥7. Anisotropic 95% coverage margins increased the sparing proportions to 71% to 86%. Conclusions Multiparametric magnetic resonance imaging–defined GTVs expanded by appropriate margins may support focal boosting or treatment of PCa; however, these margins, accounting for interobserver and intertumoral variability, may preclude highly conformal CTVs. Multiparametric GTVs and anisotropic margins may reduce the required margins and improve prostate sparing.

Background Magnetic resonance imaging–guided transurethral ultrasound ablation (MRI-TULSA) is a novel minimally invasive technology for ablating prostate tissue, potentially offering good disease control of localized cancer and low morbidity. Objective To determine the clinical safety and feasibility of MRI-TULSA for whole-gland prostate ablation in a primary treatment setting of localized prostate cancer (PCa). Design, setting, and participants A single-arm prospective phase 1 study was performed at three tertiary referral centers in Canada, Germany, and the United States. Thirty patients (median age: 69 yr; interquartile range [IQR]: 67–71 yr) with biopsy-proven low-risk (80%) and intermediate-risk (20%) PCa were treated and followed for 12 mo. Intervention MRI-TULSA treatment was delivered with the therapeutic intent of conservative whole-gland ablation including 3-mm safety margins and 10% residual viable prostate expected around the capsule. Outcome measurements and statistical analysis Primary end points were safety (adverse events) and feasibility (technical accuracy and precision of conformal thermal ablation). Exploratory outcomes included quality of life, prostate-specific antigen (PSA), and biopsy at 12 mo. Results and limitations Median treatment time was 36min (IQR: 26–44) and prostate volume was 44ml (IQR: 38–48). Spatial control of thermal ablation was ±1.3mm on MRI thermometry. Common Terminology Criteria for Adverse Events included hematuria (43% grade [G] 1; 6.7% G2), urinary tract infections (33% G2), acute urinary retention (10% G1; 17% G2), and epididymitis (3.3% G3). There were no rectal injuries. Median pretreatment International Prostate Symptom Score 8 (IQR: 5–13) returned to 6 (IQR: 4–10) at 3 mo (mean change: −2; 95% confidence interval [CI], −4 to 1). Median pretreatment International Index of Erectile Function 13 (IQR: 6–28) recovered to 13 (IQR: 5–25) at 12 mo (mean change: −1; 95% CI, −5 to 3). Median PSA decreased 87% at 1 mo and was stable at 0.8 ng/ml (IQR: 0.6–1.1) to 12 mo. Positive biopsies showed 61% reduction in total cancer length, clinically significant disease in 9 of 29 patients (31%; 95% CI, 15–51), and any disease in 16 of 29 patients (55%; 95% CI, 36–74). Conclusions MRI-TULSA was feasible, safe, and technically precise for whole-gland prostate ablation in patients with localized PCa. Phase 1 data are sufficiently compelling to study MRI-TULSA further in a larger prospective trial with reduced safety margins. Patient summary We used magnetic resonance imaging–guided transurethral ultrasound to heat and ablate the prostate in men with prostate cancer. We showed that the treatment can be targeted within a narrow range (1mm) and has a well-tolerated side effect profile. A larger study is under way. Trial registration NCT01686958, DRKS00005311.

In surface-based registration for image-guided interventions, the presence of missing data can be a significant issue. This often arises with real-time imaging modalities such as ultrasound, where poor contrast can make tissue boundaries difficult to distinguish from surrounding tissue. Missing data poses two challenges: ambiguity in establishing correspondences; and extrapolation of the deformation field to those missing regions. To address these, we present a novel non-rigid registration method. For establishing correspondences, we use a probabilistic framework based on a Gaussian mixture model (GMM) that treats one surface as a potentially partial observation. To extrapolate and constrain the deformation field, we incorporate biomechanical prior knowledge in the form of a finite element model (FEM). We validate the algorithm, referred to as GMM-FEM, in the context of prostate interventions. Our method leads to a significant reduction in target registration error (TRE) compared to similar state-of-the-art registration algorithms in the case of missing data up to 30%, with a mean TRE of 2.6 mm. The method also performs well when full segmentations are available, leading to TREs that are comparable to or better than other surface-based techniques. We also analyze robustness of our approach, showing that GMM-FEM is a practical and reliable solution for surface-based registration.

This paper presents the results of a computer-aided intervention solution to demonstrate the application of RF time series for characterization of prostate cancer, in vivo. Methods: We pre-process RF time series features extracted from 14 patients using hierarchical clustering to remove possible outliers. Then, we demonstrate that the mean central frequency and wavelet features extracted from a group of patients can be used to build a nonlinear classifier which can be applied successfully to differentiate between cancerous and normal tissue regions of an unseen patient. Results: In a cross-validation strategy, we show an average area under receiver operating characteristic curve (AUC) of 0.93 and classification accuracy of 80%. To validate our results, we present a detailed ultrasound to histology registration framework. Conclusion: Ultrasound RF time series results in differentiation of cancerous and normal tissue with high AUC.

Ultrasound imaging is used extensively in diagnosis and image-guidance for interventions of human diseases. However, conventional 2D ultrasound suffers from limitations since it can only provide 2D images of 3-dimensional structures in the body. Thus, measurement of organ size is variable, and guidance of interventions is limited, as the physician is required to mentally reconstruct the 3-dimensional anatomy using 2D views. Over the past 20 years, a number of 3-dimensional ultrasound imaging approaches have been developed. We have developed an approach that is based on a mechanical mechanism to move any conventional ultrasound transducer while 2D images are collected rapidly and reconstructed into a 3D image. In this presentation, 3D ultrasound imaging approaches will be described for use in image-guided interventions.

Three-dimensional (3D) prostate image segmentation is useful for cancer diagnosis and therapy guidance, but can be time-consuming to perform manually and involves varying levels of difficulty and interoperator variability within the prostatic base, midgland (MG), and apex. In this study, the authors measured accuracy and interobserver variability in the segmentation of the prostate on T2-weighted endorectal magnetic resonance (MR) imaging within the whole gland (WG), and separately within the apex, midgland, and base regions.The authors collected MR images from 42 prostate cancer patients. Prostate border delineation was performed manually by one observer on all images and by two other observers on a subset of ten images. The authors used complementary boundary-, region-, and volume-based metrics [mean absolute distance (MAD), Dice similarity coefficient (DSC), recall rate, precision rate, and volume difference (ΔV)] to elucidate the different types of segmentation errors that they observed. Evaluation for expert manual and semiautomatic segmentation approaches was carried out. Compared to manual segmentation, the authors' semiautomatic approach reduces the necessary user interaction by only requiring an indication of the anteroposterior orientation of the prostate and the selection of prostate center points on the apex, base, and midgland slices. Based on these inputs, the algorithm identifies candidate prostate boundary points using learned boundary appearance characteristics and performs regularization based on learned prostate shape information.The semiautomated algorithm required an average of 30 s of user interaction time (measured for nine operators) for each 3D prostate segmentation. The authors compared the segmentations from this method to manual segmentations in a single-operator (mean whole gland MAD = 2.0 mm, DSC = 82%, recall = 77%, precision = 88%, and ΔV = - 4.6 cm(3)) and multioperator study (mean whole gland MAD = 2.2 mm, DSC = 77%, recall = 72%, precision = 86%, and ΔV = - 4.0 cm(3)). These results compared favorably with observed differences between manual segmentations and a simultaneous truth and performance level estimation reference for this data set (whole gland differences as high as MAD = 3.1 mm, DSC = 78%, recall = 66%, precision = 77%, and ΔV = 15.5 cm(3)). The authors found that overall, midgland segmentation was more accurate and repeatable than the segmentation of the apex and base, with the base posing the greatest challenge.The main conclusions of this study were that (1) the semiautomated approach reduced interobserver segmentation variability; (2) the segmentation accuracy of the semiautomated approach, as well as the accuracies of recently published methods from other groups, were within the range of observed expert variability in manual prostate segmentation; and (3) further efforts in the development of computer-assisted segmentation would be most productive if focused on improvement of segmentation accuracy and reduction of variability within the prostatic apex and base.

In targeted 3D transrectal ultrasound (TRUS)-guided biopsy, patient and prostate movement during the procedure can cause target misalignments that hinder accurate sampling of preplanned suspicious tissue locations. Multiple solutions have been proposed for displacement compensation via registration of intraprocedural TRUS images to a baseline 3D TRUS image acquired at the beginning of the biopsy procedure. While 2D TRUS images are widely used for intraprocedural guidance, some solutions utilize richer intraprocedural images such as bi- or multiplanar TRUS or 3D TRUS, acquired by specialized probes. In this work, the impact of such richer intraprocedural imaging on displacement compensation accuracy was measured to evaluate the tradeoff between cost and complexity of intraprocedural imaging versus improved displacement compensation.Baseline and intraprocedural 3D TRUS images were acquired from 29 patients at standard sextant-template biopsy locations. Planes extracted from 3D TRUS images acquired at sextant positions were used to simulate 2D and 3D intraprocedural information available in different potential clinically relevant scenarios for co-registration with the baseline 3D TRUS image. In practice, intraprocedural 3D information can be acquired either via the use of specialized ultrasound probes (e.g., multiplanar or 3D probes) or via axial rotation of a tracked 2D TRUS probe. Registration accuracy was evaluated by calculating the target registration error (TRE) using manually identified homologous intrinsic fiducial markers (microcalcifications). The TRE was analyzed separately at the base, mid-gland and apex regions of the prostate.The results indicate that TRE improved gradually as the number of intraprocedural imaging planes used in registration was increased, implying that 3D TRUS information assisted the registration algorithm to robustly converge to more accurate solutions. The acquisition of a partial volume up to the angle of rotation supported more accurate displacement compensation than acquiring biplane configurations. Additional intraprocedural 3D TRUS image information was more beneficial to registration accuracy in the base and apex regions as compared with the mid-gland region.While the majority of the registrations using 2D TRUS images provided a clinically desired level of accuracy, intraprocedural 3D imaging helped improve the overall registration accuracy and robustness, especially in the base and apex regions of the prostate. These results are helpful for devising image-based registration methods for displacement compensation when designing 3D TRUS-guided biopsy systems.

To develop and optimize radiofrequency (RF) hardware for the detection of endogenous sodium ((23) Na) by 3.0 Tesla (T) MRI in the human prostate.A transmit-only receive-only (TORO) RF system of resonators consisting of an unshielded, asymmetric, quadrature birdcage (transmit), and an endorectal (ER), linear, surface (receive) coil were developed and tested on a 3T MRI scanner. Two different ER receivers were constructed; a single-tuned ((23) Na) and a dual-tuned ((1) H/(23) Na). Both receivers were evaluated by the measurements of signal-to-noise ratio (SNR) and B1 homogeneity. For tissue sodium concentration (TSC) quantification, vials containing known sodium concentrations were incorporated into the ER. The system was used to measure the prostate TSC of three men (age 55 ± 5 years) with biopsy-proven prostate cancer.B1 field inhomogeneity of the asymmetric transmitter was estimated to be less than 5%. The mean SNR measured in a region of interest within the prostate using the single-tuned ER coil was 54.0 ± 4.6. The mean TSC in the central gland was 60.2 ± 5.7 mmol/L and in the peripheral gland was 70.5 ± 9.0 mmol/L.A TORO system was developed and optimized for (23) Na MRI of the human prostate which showed good sensitivity throughout the prostate for quantitative measurement of TSC.

Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided "fusion" prostate biopsy intends to reduce the ∼23% false negative rate of clinical two-dimensional TRUS-guided sextant biopsy. Although it has been reported to double the positive yield, MRI-targeted biopsies continue to yield false negatives. Therefore, the authors propose to investigate how biopsy system needle delivery error affects the probability of sampling each tumor, by accounting for uncertainties due to guidance system error, image registration error, and irregular tumor shapes.T2-weighted, dynamic contrast-enhanced T1-weighted, and diffusion-weighted prostate MRI and 3D TRUS images were obtained from 49 patients. A radiologist and radiology resident contoured 81 suspicious regions, yielding 3D tumor surfaces that were registered to the 3D TRUS images using an iterative closest point prostate surface-based method to yield 3D binary images of the suspicious regions in the TRUS context. The probabilityP of obtaining a sample of tumor tissue in one biopsy core was calculated by integrating a 3D Gaussian distribution over each suspicious region domain. Next, the authors performed an exhaustive search to determine the maximum root mean squared error (RMSE, in mm) of a biopsy system that gives P ≥ 95% for each tumor sample, and then repeated this procedure for equal-volume spheres corresponding to each tumor sample. Finally, the authors investigated the effect of probe-axis-direction error on measured tumor burden by studying the relationship between the error and estimated percentage of core involvement.Given a 3.5 mm RMSE for contemporary fusion biopsy systems,P ≥ 95% for 21 out of 81 tumors. The authors determined that for a biopsy system with 3.5 mm RMSE, one cannot expect to sample tumors of approximately 1 cm(3) or smaller with 95% probability with only one biopsy core. The predicted maximum RMSE giving P ≥ 95% for each tumor was consistently greater when using spherical tumor shapes as opposed to no shape assumption. However, an assumption of spherical tumor shape for RMSE = 3.5 mm led to a mean overestimation of tumor sampling probabilities of 3%, implying that assuming spherical tumor shape may be reasonable for many prostate tumors. The authors also determined that a biopsy system would need to have a RMS needle delivery error of no more than 1.6 mm in order to sample 95% of tumors with one core. The authors' experiments also indicated that the effect of axial-direction error on the measured tumor burden was mitigated by the 18 mm core length at 3.5 mm RMSE.For biopsy systems with RMSE ≥ 3.5 mm, more than one biopsy core must be taken from the majority of tumors to achieveP ≥ 95%. These observations support the authors' perspective that some tumors of clinically significant sizes may require more than one biopsy attempt in order to be sampled during the first biopsy session. This motivates the authors' ongoing development of an approach to optimize biopsy plans with the aim of achieving a desired probability of obtaining a sample from each tumor, while minimizing the number of biopsies. Optimized planning of within-tumor targets for MRI-3D TRUS fusion biopsy could support earlier diagnosis of prostate cancer while it remains localized to the gland and curable.

This paper presents the results of an in vivo clinical study to accurately characterize prostate cancer using new features of ultrasound RF time series.The mean central frequency and wavelet features of ultrasound RF time series from seven patients are used along with an elaborate framework of ultrasound to histology registration to identify and verify cancer in prostate tissue regions as small as 1.7 mm x 1.7 mm.In a leave-one-patient-out cross-validation strategy, an average classification accuracy of 76% and the area under ROC curve of 0.83 are achieved using two proposed RF time series features. The results statistically significantly outperform those achieved by previously reported features in the literature. The proposed features show the clinical relevance of RF time series for in vivo characterization of cancer.

In this study, we propose an efficient non-rigid MR-TRUS deformable registration method to improve the accuracy of targeting suspicious locations during a 3D ultrasound (US) guided prostate biopsy. The proposed deformable registration approach employs the multi-channel modality independent neighbourhood descriptor (MIND) as the local similarity feature across the two modalities of MR and TRUS, and a novel and efficient duality-based convex optimization based algorithmic scheme is introduced to extract the deformations which align the two MIND descriptors. The registration accuracy was evaluated using 10 patient images by measuring the TRE of manually identified corresponding intrinsic fiducials in the whole gland and peripheral zone, and performance metrics (DSC, MAD and MAXD) for the apex, mid-gland and base of the prostate were also calculated by comparing two manually segmented prostate surfaces in the registered 3D MR and TRUS images. Experimental results show that the proposed method yielded an overall mean TRE of 1.74 mm, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm.

Determining the intra-prostatic spatial distribution and grade of prostate cancer before treatment may support improved diagnosis, therapy selection, or guidance of intra-prostatic lesion-focused therapies (e.g., radiation boosting or ablative focal therapy). Several in vivo imaging modalities are showing promise for imaging the intra-prostatic distribution of cancer. Evaluations of such imaging modalities ideally include comparisons to registered histological examinations of prostatectomy specimens, the clinically accepted “gold standard” for staging and grading prostate cancer. The registration of histology to ex vivo magnetic resonance (MR) images supports these challenging in vivo registrations by reconstructing 3D spatial information that is lost during the process of acquiring histology sections. In the work described in this chapter, ex vivo MR and histology images were acquired from nine formalin-fixed radical prostatectomy specimens which had been marked with extrinsic fiducials designed to be visible in these modalities. The histology images were registered retrospectively to the MR images using a novel algorithm based on the minimization of fiducial registration error between fiducial cross-sections on histology images and parametric fiducial curves on MR images. The 3D target registration error (TRE) was quantified based on the post-registration misalignment of manually identified homologous landmarks (3–7 per section, 184 in total), and was compared to two previously developed methods: (1) a method based on the guidance of the coarse slicing of specimens and (2) a method based on additional imaging of the images of the thick tissue slices. The proposed method yielded a mean ± standard deviation TRE of 0.71 ± 0.38 mm, 0.38–0.63 mm lower [95 % confidence interval (CI)] than the image-guided-slicing-based method, and within 0.13 mm (95 % CI) of the tissue-slice-imaging-based method. One component of the proposed method was able to refine the result from the image-guided-slicing-based method to within 0.13 mm (95 % CI) of the proposed method. The proposed method also resulted in a 70 % decrease in specimen processing time compared to the image-guided-slicing-based approach previously implemented at our center.

Three-dimensional (3D) transrectal ultrasound (TRUS)-guided systems have been developed to improve targeting accuracy during prostate biopsy. However, prostate motion during the procedure is a potential source of error that can cause target misalignments. The authors present an image-based registration technique to compensate for prostate motion by registering the live two-dimensional (2D) TRUS images acquired during the biopsy procedure to a preacquired 3D TRUS image. The registration must be performed both accurately and quickly in order to be useful during the clinical procedure.The authors implemented an intensity-based 2D-3D rigid registration algorithm optimizing the normalized cross-correlation (NCC) metric using Powell's method. The 2D TRUS images acquired during the procedure prior to biopsy gun firing were registered to the baseline 3D TRUS image acquired at the beginning of the procedure. The accuracy was measured by calculating the target registration error (TRE) using manually identified fiducials within the prostate; these fiducials were used for validation only and were not provided as inputs to the registration algorithm. They also evaluated the accuracy when the registrations were performed continuously throughout the biopsy by acquiring and registering live 2D TRUS images every second. This measured the improvement in accuracy resulting from performing the registration, continuously compensating for motion during the procedure. To further validate the method using a more challenging data set, registrations were performed using 3D TRUS images acquired by intentionally exerting different levels of ultrasound probe pressures in order to measure the performance of our algorithm when the prostate tissue was intentionally deformed. In this data set, biopsy scenarios were simulated by extracting 2D frames from the 3D TRUS images and registering them to the baseline 3D image. A graphics processing unit (GPU)-based implementation was used to improve the registration speed. They also studied the correlation between NCC and TREs.The root-mean-square (RMS) TRE of registrations performed prior to biopsy gun firing was found to be 1.87 ± 0.81 mm. This was an improvement over 4.75 ± 2.62 mm before registration. When the registrations were performed every second during the biopsy, the RMS TRE was reduced to 1.63 ± 0.51 mm. For 3D data sets acquired under different probe pressures, the RMS TRE was found to be 3.18 ± 1.6 mm. This was an improvement from 6.89 ± 4.1 mm before registration. With the GPU based implementation, the registrations were performed with a mean time of 1.1 s. The TRE showed a weak correlation with the similarity metric. However, the authors measured a generally convex shape of the metric around the ground truth, which may explain the rapid convergence of their algorithm to accurate results.Registration to compensate for prostate motion during 3D TRUS-guided biopsy can be performed with a measured accuracy of less than 2 mm and a speed of 1.1 s, which is an important step toward improving the targeting accuracy of a 3D TRUS-guided biopsy system.

Introduction: This study evaluates the clinical benefit of magnetic resonance-transrectal ultrasound (MR-TRUS) fusion biopsy over systematic biopsy between first-time and repeat prostate biopsypatients with prior atypical small acinar proliferation (ASAP).Methods: 100 patients were enrolled in a single-centre prospective cohort study: 50 for first biopsy, 50 for repeat biopsy with prior ASAP. Multiparameteric magnetic resonance imaging (MP-MRI) and standard 12-core ultrasound biopsy (Std-Bx) were performed on all patients. Targeted biopsy using MRI-TRUS fusion (Fn-Bx) was performed f suspicious lesions were identified on the prebiopsyMP-MRI. Classification of clinically significant disease was assessed independently for the Std-Bx vs. Fn-Bx cores to compare the two approaches.Results: Adenocarcinoma was detected in 49/100 patients (26 first biopsy, 23 ASAP biopsy), with 25 having significant disease (17 first, 8 ASAP). Fn-Bx demonstrated significantly higher per-core cancer detection rates, cancer involvement, and Gleason scores for first-time and ASAP patients. However, Fn-Bx was significantly more likely to detect significant cancer missed on Std-Bx for ASAP patients than first-time biopsy patients. The addition of Fn-Bx to Std-Bx for ASAP patients had a 166.7% relative risk reduction for missing Gleason ≥ 3 + 4 disease (number needed to image with MP-MRI=10 patients) compared to 6.3% for first biopsy (number to image=50 patients). Negative predictive value of MP-MRI for negative biopsy was 79% for first-time and 100% for ASAP patients, with median followup of 32.1 ± 15.5 months.Conclusions: MR-TRUS Fn-Bx has a greater clinical impact for repeat biopsy patients with prior ASAP than biopsy-naïve patients by detecting more significant cancers that are missed on Std-Bx.

Purpose: This study investigates the performance of a 3 D Ultrasound (US) system in imaging elbow and wrist nerves. Materials and Methods: Twenty healthy volunteers with asymptomatic median, ulnar and radial nerves were prospectively investigated. Bilateral 3DUS scans of the elbows and wrists were acquired by using a commercially available US scanner (18 MHz, AplioXG, Toshiba) and stored as a 3 D volume by a dedicated software (CURE, Robarts Research Institute). Retrospectively, qualitative (image quality, atypical nerve location, findings potentially associated with compression neuropathy) and quantitative (cross-sectional area measurements) evaluations were performed. Results: In all 200 nerves 3DUS was feasible (100 %). Image quality was insufficient in 13.5 % (25 ulnar nerve elbow, 2 radial nerve) and sonomorphology was not assessable in those nerves. Measurement of cross sectional areas was feasible in all nerves (100 %). Median cross-sectional area (range) were: median nerve elbow 7 mm 2 (6 – 9), radial nerve 3 mm 2 (1 – 4), ulnar nerve elbow 8 mm 2 (5 – 11), median nerve wrist 8 mm 2 (5 – 10), and ulnar nerve wrist 4 mm 2 (2 – 6). No significant changes in nerve cross-sectional area along each nerve was found. Ulnar nerve subluxation was found in 2 nerves (6.7 %). No anconeus epitrochlearis muscle or osteophytes were found. Conclusion: 3DUS is a feasible method for assessing nerves of the upper extremity and has been shown to provide a good overview of the median, ulnar and radial nerve at the elbow and wrist, but is limited for evaluation of the ulnar nerve in the cubital tunnel. This technique enables reliable measurements at different locations along the nerve.

To ensure accurate targeting and repeatability, 3D TRUS-guided biopsies require registration to determine coordinate transformations to (1) incorporate pre-procedure biopsy plans and (2) compensate for inter-session prostate motion and deformation between repeat biopsy sessions. We evaluated prostate surface- and image-based 3D-to-3D TRUS registration by measuring the TRE of manually marked, corresponding, intrinsic fiducials in the whole gland and peripheral zone, and also evaluated the error anisotropy. The image-based rigid and non-rigid methods yielded the best results with mean TREs of 2.26 mm and 1.96 mm, respectively. These results compare favorably with a clinical need for an error of less than 2.5 mm.

Automatic identification of the biopsy-core tissue location during a prostate biopsy procedure would provide verification that targets were adequately sampled and would allow for appropriate intraprocedure biopsy target modification. Localization of the biopsy core requires accurate segmentation of the biopsy needle and needle tip from transrectal ultrasound (TRUS) biopsy images. A temporal-based TRUS needle segmentation algorithm was developed specifically for the prostate biopsy procedure to automatically identify the TRUS image containing the biopsy needle from a collection of 2D TRUS images and to segment the biopsy-core location from the 2D TRUS image.The temporal-based segmentation algorithm performs a temporal analysis on a series of biopsy TRUS images collected throughout needle insertion and withdrawal. Following the identification of points of needle insertion and retraction, the needle axis is segmented using a Hough transform-based algorithm, which is followed by a temporospectral TRUS analysis to identify the biopsy-needle tip. Validation of the temporal-based algorithm is performed on 108 TRUS biopsy sequences collected from the procedures of ten patients. The success of the temporal search to identify the proper images was manually assessed, while the accuracies of the needle-axis and needle-tip segmentations were quantitatively compared to implementations of two other needle segmentation algorithms within the literature.The needle segmentation algorithm demonstrated a99% accuracy in identifying the TRUS image at the moment of needle insertion from the collection of real-time TRUS images throughout the insertion and withdrawal of the biopsy needle. The segmented biopsy-needle axes were accurate to within 2.3 +/- 2.0 degrees and 0.48 +/- 0.42 mm of the gold standard. Identification of the needle tip to within half of the biopsy-core length (10 mm) was 95% successful with a mean error of 2.4 +/- 4.0 mm. Needle-tip detection using the temporal-based algorithm was significantly more accurate (p0.001) than the other two algorithms tested, while the segmentation of the needle axis was not significantly different between the three algorithms.The temporal-based needle segmentation algorithm accurately segments the location of the biopsy core from 2D TRUS images of clinical prostate biopsy procedures. The results for needle-tip localization demonstrated that the temporal-based algorithm is significantly more accurate than implementations of some existing needle segmentation algorithms within the literature.

There are currently limitations associated with the prostate biopsy procedure, which is the most commonly used method for a definitive diagnosis of prostate cancer. With the use of two-dimensional (2D) transrectal ultrasound (TRUS) for needle-guidance in this procedure, the physician has restricted anatomical reference points for guiding the needle to target sites. Further, any motion of the physician's hand during the procedure may cause the prostate to move or deform to a prohibitive extent. These variations make it difficult to establish a consistent reference frame for guiding a needle. We have developed a 3D navigation system for prostate biopsy, which addresses these shortcomings. This system is composed of a 3D US imaging subsystem and a passive mechanical arm to minimize prostate motion. To validate our prototype, a series of experiments were performed on prostate phantoms. The 3D scan of the string phantom produced minimal geometric distortions, and the geometric error of the 3D imaging subsystem was 0.37 mm. The accuracy of 3D prostate segmentation was determined by comparing the known volume in a certified phantom to a reconstructed volume generated by our system and was shown to estimate the volume with less then 5% error. Biopsy needle guidance accuracy tests in agar prostate phantoms showed that the mean error was 2.1 mm and the 3D location of the biopsy core was recorded with a mean error of 1.8 mm. In this paper, we describe the mechanical design and validation of the prototype system using an in vitro prostate phantom. Preliminary results from an ongoing clinical trial show that prostate motion is small with an in-plane displacement of less than 1 mm during the biopsy procedure.

Activities and interest in the sphere of university–industry interaction have grown rapidly in Italy over the past ten years, but cooperation between the sectors is still in its infancy and there is a lack of formal structures for liaison. With special reference to the research programme Progetto Impresa-Università of the Fondazione RUI, the author assesses recent developments in university–industry interaction in Italy and points up organizational and structural constraints. He suggests various policy and organizational measures to encourage and facilitate cooperative activities, while at the same time taking into account the necessity to avoid imposing cooperation on academic institutions.

18F-Fluorocholine (18F-FCH) Hybrid PET/MRI in the Evaluation of Men With Suspected Prostate Cancer Recurrence Following Definitive Local Therapy T.W. Zhang, Z. Kassam, I. Rachinsky, C. Romagnoli, D.P. D’Souza, B. Ahmad, V.M. Venkatesan, J. Chin, S. Pautler, J. Butler, J.D. Thiessen, A. Lozanski, P. Chung, C. Menard, T.Y. Lee, and G.S. Bauman; London Regional Cancer Program, London, ON, Canada, Western University, London, ON, Canada, Princess Margaret Cancer Centre / University of Toronto, Toronto, ON, Canada

Registration of three-dimensional (3-D) magnetic resonance (MR) to 3-D transrectal ultrasound (TRUS) prostate images is an important step in the planning and guidance of 3-D TRUS guided prostate biopsy. In order to accurately and efficiently perform the registration, a nonrigid landmark-based registration method is required to account for the different deformations of the prostate when using these two modalities. We describe a nonrigid landmark-based method for registration of 3-D TRUS to MR prostate images. The landmark-based registration method first makes use of an initial rigid registration of 3-D MR to 3-D TRUS images using six manually placed approximately corresponding landmarks in each image. Following manual initialization, the two prostate surfaces are segmented from 3-D MR and TRUS images and then nonrigidly registered using the following steps: (1) rotationally reslicing corresponding segmented prostate surfaces from both 3-D MR and TRUS images around a specified axis, (2) an approach to find point correspondences on the surfaces of the segmented surfaces, and (3) deformation of the surface of the prostate in the MR image to match the surface of the prostate in the 3-D TRUS image and the interior using a thin-plate spline algorithm. The registration accuracy was evaluated using 17 patient prostate MR and 3-D TRUS images by measuring the target registration error (TRE). Experimental results showed that the proposed method yielded an overall mean TRE of [Formula: see text] for the rigid registration and [Formula: see text] for the nonrigid registration, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm. A landmark-based nonrigid 3-D MR-TRUS registration approach is proposed, which takes into account the correspondences on the prostate surface, inside the prostate, as well as the centroid of the prostate. Experimental results indicate that the proposed method yields clinically sufficient accuracy.

Purpose: To measure the target registration error (TRE) of two approaches for the rigid registration of magnetic resonance(MR) prostate images to 3D transrectal ultrasound(TRUS)images, in order to improve biopsy planning and guidance for prostate cancer diagnosis. Methods: Six T2‐weighted MRimages and six 3D‐TRUS images from six different subjects were acquired during an MRI‐targeted, 3D TRUS‐guided biopsy procedure. All image pairs had identifiable calcifications and/or cysts which were used as fiducials for registration validation. A total of 78 fiducials were identified and all images were segmented using a semi‐automated method. The MRimages were designated as the source images, and the 3D‐TRUS images were designated as the target images. The source images were rigidly registered to the corresponding target images using 3 different methods. The first method was a visual alignment (VA) performed interactively by the operator, the second was a surface‐based iterative‐closest‐point (ICP) method, and the third was least‐squares best fit (LSF) transformation based on a set of identified homologous landmark pairs. We calculated the RMS TREs for each method and compared the results using a two‐tailed paired t‐test for each combination. Results: The RMS TREs for VA, ICP and LSF were 5.07mm, 5.87mm and 3.10mm, respectively (p 2.5mm). This is likely due to the substantial shape distortion of the prostate, caused mainly by transducer pressure in the 3D‐TRUS images, and the pressure induced by the endorectal coil in the MRimages. These preliminary results indicate that, at most, an ideal rigid registration algorithm based on the operator's initialization (i.e. VA) would able to reduce the TRE by 1.97mm. The remaining error of 3.10mm would require a non‐rigid registration to correct for the deformation.

volume is critical in treatment planning. Whether by expert guidelines, retrograde urethrography, bony landmark, ultrasound, or CT-based planning, there is discordant anatomic information regarding the distance between apex and the bulb, also known as the genitourinary diaphragm (GUD) or membranous urethra length (MUL). In this study, we use MRI with endorectal coil in a large series of patients to better characterize the MUL in radiation therapy planning. Materials/Methods: Patients with early stage, clinically localized prostate cancer (Gleason 6 and 7, cT1c-cT2b, PSA 85% interobserver agreement within 2 mm measurement. There was no association between MUL and patient age, T-stage, Gleason score, patient height, weight, or NCCN risk group. Conclusions: Although there remains variability, the median MUL, or GUD length, is 15 mm. This report likely represents the largest evaluation of the pre-treatment MUL in men with prostate cancer treated with external beam radiation. Our data challenges traditional guidelines between the apex and bulb from smaller series and adds to critical anatomic information of male urethral length and prostate apex anatomy. Prostate MRI with endorectal coil produces a reliable MUL and should be used to optimize target volume delineation at the prostate apex. Author Disclosure: N.K. Taunk: None. O. Akin: None. X. Pei: None. M. Zelefsky: None.

Purpose: In targeted 3D transrectal ultrasound (TRUS)-guided biopsy, patient and prostate movement during the procedure can cause target misalignments that hinder accurate sampling of preplanned suspicious tissue locations. Multiple solutions have been proposed for displacement compensation via registration of intraprocedural TRUS images to a baseline 3D TRUS image acquired at the beginning of the biopsy procedure. While 2D TRUS images are widely used for intraprocedural guidance, some solutions utilize richer intraprocedural images such as bi- or multiplanar TRUS or 3D TRUS, acquired by specialized probes. In this work, the impact of such richer intraprocedural imaging on displacement compensation accuracy was measured to evaluate the tradeoff between cost and complexity of intraprocedural imaging versus improved displacement compensation. Methods: Baseline and intraprocedural 3D TRUS images were acquired from 29 patients at standard sextant-template biopsy locations. Planes extracted from 3D TRUS images acquired at sextant positions were used to simulate 2D and 3D intraprocedural information available in different potential clinically relevant scenarios for co-registration with the baseline 3D TRUS image. In practice, intraprocedural 3D information can be acquired either via the use of specialized ultrasound probes (e.g., multiplanar or 3D probes) or via axial rotation of a tracked 2D TRUS probe. Registration accuracy was evaluated by calculating the target registration error (TRE) using manually identified homologous intrinsic fiducial markers (microcalcifications). The TRE was analyzed separately at the base, mid-gland and apex regions of the prostate. Results: The results indicate that TRE improved gradually as the number of intraprocedural imaging planes used in registration was increased, implying that 3D TRUS information assisted the registration algorithm to robustly converge to more accurate solutions. The acquisition of a partial volume up to the angle of rotation supported more accurate displacement compensation than acquiring biplane configurations. Additional intraprocedural 3D TRUS image information was more beneficial to registration accuracy in the base and apex regions as compared with the mid-gland region. Conclusions: While the majority of the registrations using 2D TRUS images provided a clinically desired level of accuracy, intraprocedural 3D imaging helped improve the overall registration accuracy and robustness, especially in the base and apex regions of the prostate. These results are helpful for devising image-based registration methods for displacement compensation when designing 3D TRUS-guided biopsy systems.

Purpose: Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided “fusion” prostate biopsy intends to reduce the ∼23% false negative rate of clinical two-dimensional TRUS-guided sextant biopsy. Although it has been reported to double the positive yield, MRI-targeted biopsies continue to yield false negatives. Therefore, the authors propose to investigate how biopsy system needle delivery error affects the probability of sampling each tumor, by accounting for uncertainties due to guidance system error, image registration error, and irregular tumor shapes. Methods: T2-weighted, dynamic contrast-enhanced T1-weighted, and diffusion-weighted prostate MRI and 3D TRUS images were obtained from 49 patients. A radiologist and radiology resident contoured 81 suspicious regions, yielding 3D tumor surfaces that were registered to the 3D TRUS images using an iterative closest point prostate surface-based method to yield 3D binary images of the suspicious regions in the TRUS context. The probabilityP of obtaining a sample of tumor tissue in one biopsy core was calculated by integrating a 3D Gaussian distribution over each suspicious region domain. Next, the authors performed an exhaustive search to determine the maximum root mean squared error (RMSE, in mm) of a biopsy system that gives P ≥ 95% for each tumor sample, and then repeated this proceduremore » for equal-volume spheres corresponding to each tumor sample. Finally, the authors investigated the effect of probe-axis-direction error on measured tumor burden by studying the relationship between the error and estimated percentage of core involvement. Results: Given a 3.5 mm RMSE for contemporary fusion biopsy systems,P ≥ 95% for 21 out of 81 tumors. The authors determined that for a biopsy system with 3.5 mm RMSE, one cannot expect to sample tumors of approximately 1 cm{sup 3} or smaller with 95% probability with only one biopsy core. The predicted maximum RMSE giving P ≥ 95% for each tumor was consistently greater when using spherical tumor shapes as opposed to no shape assumption. However, an assumption of spherical tumor shape for RMSE = 3.5 mm led to a mean overestimation of tumor sampling probabilities of 3%, implying that assuming spherical tumor shape may be reasonable for many prostate tumors. The authors also determined that a biopsy system would need to have a RMS needle delivery error of no more than 1.6 mm in order to sample 95% of tumors with one core. The authors’ experiments also indicated that the effect of axial-direction error on the measured tumor burden was mitigated by the 18 mm core length at 3.5 mm RMSE. Conclusions: For biopsy systems with RMSE ≥ 3.5 mm, more than one biopsy core must be taken from the majority of tumors to achieveP ≥ 95%. These observations support the authors’ perspective that some tumors of clinically significant sizes may require more than one biopsy attempt in order to be sampled during the first biopsy session. This motivates the authors’ ongoing development of an approach to optimize biopsy plans with the aim of achieving a desired probability of obtaining a sample from each tumor, while minimizing the number of biopsies. Optimized planning of within-tumor targets for MRI-3D TRUS fusion biopsy could support earlier diagnosis of prostate cancer while it remains localized to the gland and curable.« less

Since 1980 the privatization issue has been the focus of a big debate on the changing role of the state and its eventual substitution by the private sector as producer of goods and services. Since then more than eighty countries, with the two important exceptions of Italy and China, have started a privatization process, albeit adopting different criteria and typologies, according to a plurality of policy objectives pursued1. These privatization experiences favoured by the liberist ideological and political revival induced by British public failures have aroused world-wide interest and argument for emulation but have not produced general ideal solutions to complex problems dependent on different economic, social and political situations.

Purpose: Evaluation ofin vivo prostate imaging modalities for determining the spatial distribution and aggressiveness of prostate cancer ideally requires accurate registration of images to an accepted reference standard, such as histopathological examination of radical prostatectomy specimens. Three-dimensional (3D) reconstruction of prostate histology facilitates these registration-based evaluations by reintroducing 3D spatial information lost during histology processing. Because the reconstruction accuracy may constrain the clinical questions that can be answered with these data, it is important to assess the tradeoffs between minimally disruptive methods based on intrinsic image information and potentially more robust methods based on extrinsic fiducial markers. Methods: Ex vivo magnetic resonance (MR) images and digitized whole-mount histology images from 12 radical prostatectomy specimens were used to evaluate four 3D histology reconstruction algorithms. 3D reconstructions were computed by registering each histology image to the corresponding ex vivo MR image using one of two similarity metrics (mutual information or fiducial registration error) and one of two search domains (affine transformations or a constrained subset thereof). The algorithms were evaluated for accuracy using the mean target registration error (TRE) computed from homologous intrinsic point landmarks (3–16 per histology section; 232 total) identified on histology and MR images, and for the sensitivity of TRE to rotational, translational, and scaling initialization errors. Results: The algorithms using fiducial registration error and mutual information had mean ± standard deviation TREs of 0.7 ± 0.4 and 1.2 ± 0.7 mm, respectively, and one algorithm using fiducial registration error and affine transforms had negligible sensitivities to initialization errors. The postoptimization values of the mutual information-based metric showed evidence of errors due to both the optimizer and the similarity metric, and variation of parameters of the mutual information-based metric did not improve its performance. Conclusions: The extrinsic fiducial-based algorithm had lower mean TRE and lower sensitivity to initialization than the intrinsic intensity-based algorithm using mutual information. A model relating statistical power to registration error for certain imaging validation study designs estimated that a reconstruction algorithm with a mean TRE of 0.7 mm would require 27% fewer subjects than the method used to initialize the algorithms (mean TRE 1.3 ± 0.7 mm), suggesting the choice of reconstruction technique can have a substantial impact on the design of imaging validation studies, and on their overall cost.

Purpose: Three-dimensional (3D) transrectal ultrasound (TRUS)-guided systems have been developed to improve targeting accuracy during prostate biopsy. However, prostate motion during the procedure is a potential source of error that can cause target misalignments. The authors present an image-based registration technique to compensate for prostate motion by registering the live two-dimensional (2D) TRUS images acquired during the biopsy procedure to a preacquired 3D TRUS image. The registration must be performed both accurately and quickly in order to be useful during the clinical procedure. Methods: The authors implemented an intensity-based 2D-3D rigid registration algorithm optimizing the normalized cross-correlation (NCC) metric using Powell's method. The 2D TRUS images acquired during the procedure prior to biopsy gun firing were registered to the baseline 3D TRUS image acquired at the beginning of the procedure. The accuracy was measured by calculating the target registration error (TRE) using manually identified fiducials within the prostate; these fiducials were used for validation only and were not provided as inputs to the registration algorithm. They also evaluated the accuracy when the registrations were performed continuously throughout the biopsy by acquiring and registering live 2D TRUS images every second. This measured the improvement in accuracy resulting from performing the registration, continuously compensating for motion during the procedure. To further validate the method using a more challenging data set, registrations were performed using 3D TRUS images acquired by intentionally exerting different levels of ultrasound probe pressures in order to measure the performance of our algorithm when the prostate tissue was intentionally deformed. In this data set, biopsy scenarios were simulated by extracting 2D frames from the 3D TRUS images and registering them to the baseline 3D image. A graphics processing unit (GPU)-based implementation was used to improve the registration speed. They also studied the correlation between NCC and TREs. Results: The root-mean-square (RMS) TRE of registrations performed prior to biopsy gun firing was found to be 1.87 ± 0.81 mm. This was an improvement over 4.75 ± 2.62 mm before registration. When the registrations were performed every second during the biopsy, the RMS TRE was reduced to 1.63 ± 0.51 mm. For 3D data sets acquired under different probe pressures, the RMS TRE was found to be 3.18 ± 1.6 mm. This was an improvement from 6.89 ± 4.1 mm before registration. With the GPU based implementation, the registrations were performed with a mean time of 1.1 s. The TRE showed a weak correlation with the similarity metric. However, the authors measured a generally convex shape of the metric around the ground truth, which may explain the rapid convergence of their algorithm to accurate results. Conclusions: Registration to compensate for prostate motion during 3D TRUS-guided biopsy can be performed with a measured accuracy of less than 2 mm and a speed of 1.1 s, which is an important step toward improving the targeting accuracy of a 3D TRUS-guided biopsy system.

Purpose: To design, develop and test a semi‐automatic segmentation method for the prostate midgland on T2W magnetic resonance(MR)images acquired using an endorectal (ER) coil, based on inter‐subject prostate shape and local boundary appearance similarity.Method and Materials: We used T2W prostate MRimages of 33 subjects acquired using an ER coil. From each image, we extracted the closest mid‐gland axial slice to the verumontanum. We partitioned the images into training and test sets using leave‐one‐out cross‐validation. We used the training images to define a point distribution model (PDM) describing shape variability as well as 36 circular ‘mean intensity patches’ characterizing the inter‐subject local appearance at anatomically‐defined boundary locations. We chose a patch radius of 17mm based on our assessment of segmentation error, using the mean absolute boundary distance (MAD), on a subset of 13 images during a systematic radius search from 5mm to 25mm. For each test image, we defined 36 homologous rays emanating from the centre of the prostate. We used a radial‐based‐search strategy to translate each mean intensity patch along its corresponding ray and computed the normalized cross‐correlation (NCC) between the mean patch and the underlying patch in the test image at each point. We chose the point with the highest NCC along each ray. We then used the PDM to regularize the selected points. To compare the algorithm's segmentation to manual delineations performed by one operator, we calculated the MAD and Dice similarity coefficient (DSC). Results: We measured a MAD of 1.6 +/− 1.0mm, and a DSC of 89 +/− 6% between the semi‐automatically segmented contours and the manually‐delineated reference standard. Conclusions: NCC of local prostate boundary regions with a learned mean boundary appearance may be suitable for boundary localization and subsequent refinement using a PDM on T2W MRimages acquired using an ER coil.

Purpose: To evaluate the accuracy of our rigid and non‐rigid registration methods to align three‐dimensional (3D) transrectal ultrasound(TRUS)images of the prostate in order to improve repeat biopsy planning and guidance for prostate cancer diagnosis. Methods and Materials: Three 3D prostate ultrasoundimages were obtained from each of the 16 patients during 3D prostate ultrasound‐guided biopsy procedures. These 3D images had identifiable calcifications and cysts, which were used as fiducials for the registration validation. For each prostate, images at two different time points (to and t1) during the procedure were selected and the 3D coordinates of the identified fiducials were recorded. A total of 62 fiducials were identified. All images were then segmented manually and semi‐automatically. Images at to were registered to the corresponding images at t1 using rigid and non‐rigid registration. Rigid and non‐rigid registration was performed using boundary‐based iterative closest point and thin‐plate spline algorithms, respectively. We evaluated the registration methods by determining the target registration error (TRE), effect of segmentation variability on registration variability, the TRE dependence on fiducial distance from the transducer tip, and the fiducial localization error (FLE). Results: Tests showed that the FLE was 1.3mm. The mean TRE for manual and semi‐automatic segmentation was 2.2mm and 2.3mm respectively in both rigid and non‐rigid cases. There was little correlation between the TRE and the distance of the fiducials from the ultrasound transducer tip, and segmentation variability resulted in a 0.57mm standard deviation of the TRE. Conclusions: Our results suggest that both rigid and non‐rigid registration techniques for 3D TRUSimages during a biopsy procedure are accurate to within 2.3mm, which is less than the 5mm radius of the smallest tumors considered clinically significant. Future tests will evaluate registration accuracy for multimodal cases, specifically magnetic resonance to ultrasoundimages.

Gastrointestinal stromal tumors (GIST) have been suggested to be the most common neurofibromatosis 1-associated gastrointestinal tumors. This case report describes and compares US and CT findings of both abdominal neurofibromas and a gastrointestinal stromal tumor. On US, the GIST appeared as a well-defined inhomogeneous lesion with a target-like pattern similar to CT. The neurofibromas appeared as well-demarcated round nodules with a relatively homogeneous hypoechoic internal structure and were accompanied by subtle posterior acoustic enhancement. US and CT were able to differentiate between neurofibromatomas and GIST in this neurofibromatosis 1 patient; however, a biopsy of the suspicious mass was performed to clarify the diagnosis. © 2010 Wiley Periodicals, Inc. J Clin Ultrasound 2010

This extensive book explores in detail a wide range of topics within the public choice and constitutional political economy tradition, providing a comprehensive overview of current work across the field.

Nell'articolo di Caffè "Aspetti strutturali ed evolutivi del sistema imprenditoriale italiano" apparso nel 1974 e pubblicato due anni dopo nel volume "Un' Economia in Ritardo", egli sostiene che un tessuto produttivo troppo caratterizzato da imprese di piccola dimensione è frequentemente responsabile dell'insorgere di un fenomeno che chiama di "imprenditorialità precaria". Tale fenomeno, secondo Caffè, espone il sistema economico e il mercato del lavoro a un forte rischio ciclico. In questo lavoro si discute di come l'impresa "precaria" di Caffè abbia un identikit che appare sorprendentemente attuale, di quali sono le implicazione che ciò ha avuto nell'ultima crisi e di quali interventi di politica economica siano da considerarsi appropriati.