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Background PSMA-PET 1 has shown good concordance with histology, but there is a need to investigate the ability of PSMA-PET to delineate DIL 2 boundaries for guided biopsy and focal therapy planning. Objective To determine threshold and margin combinations that satisfy the following criteria: ≥95% sensitivity with max specificity and ≥95% specificity with max sensitivity. Design, setting and participants We registered pathologist-annotated whole-mount mid-gland prostatectomy histology sections cut in 4.4 mm intervals from 12 patients to pre-surgical PSMA-PET/MRI by mapping histology to ex-vivo imaging to in-vivo imaging. We generated PET-derived tumor volumes using boundaries defined by thresholded PET volumes from 1–100% of SUV 3 max in 1% intervals. At each interval, we applied margins of 0–30 voxels in one voxel increments, giving 3000 volumes/patient. Outcome measurements Mean and standard deviation of sensitivity and specificity for cancer detection within the 2D oblique histologic planes that intersected with the 3D PET volume for each patient. Results and limitations A threshold of 67% SUV max with an 8.4 mm margin achieved a (mean ± std.) sensitivity of 95.0 ± 7.8% and specificity of 76.4 ± 14.7%. A threshold of 81% SUV max with a 5.1 mm margin achieved sensitivity of 65.1 ± 28.4% and specificity of 95.1 ± 5.2%. Conclusions Preliminary evidence of thresholding and margin expansion of PSMA-PET images targeted at DILs validated with histopathology demonstrated excellent mean sensitivity and specificity in the setting of focal therapy/boosting and guided biopsy. These parameters can be used in a larger validation study supporting clinical translation.

Prostate cancer (PCa) is a common, serious form of cancer in men that is still prevalent despite ongoing developments in diagnostic oncology. Current detection methods lead to high rates of inaccurate diagnosis. We present a method to directly model and exploit temporal aspects of temporal enhanced ultrasound (TeUS) for tissue characterization, which improves malignancy prediction. We employ a probabilistic-temporal framework, namely, hidden Markov models (HMMs), for modeling TeUS data obtained from PCa patients. We distinguish malignant from benign tissue by comparing the respective log-likelihood estimates generated by the HMMs. We analyze 1100 TeUS signals acquired from 12 patients. Our results show improved malignancy identification compared to previous results, demonstrating over 85% accuracy and AUC of 0.95. Incorporating temporal information directly into the models leads to improved tissue differentiation in PCa. We expect our method to generalize and be applied to other types of cancer in which temporal-ultrasound can be recorded.

Multi-parametric magnetic resonance imaging (mp-MRI) is emerging as a useful tool for prostate cancer (PCa) detection but currently has unaddressed limitations. Computer aided diagnosis (CAD) systems have been developed to address these needs, but many approaches used to generate and validate the models have inherent biases.All clinically significant PCa on histology was mapped to mp-MRI using a previously validated registration algorithm. Shape and size matched non-PCa regions were selected using a proposed sampling algorithm to eliminate biases towards shape and size. Further analysis was performed to assess biases regarding inter-zonal variability.A 5-feature Naïve-Bayes classifier produced an area under the receiver operating characteristic curve (AUC) of 0.80 validated using leave-one-patient-out cross-validation. As mean inter-class area mismatch increased, median AUC trended towards positively biasing classifiers to producing higher AUCs. Classifiers were invariant to differences in shape between PCa and non-PCa lesions (AUC: 0.82 vs 0.82). Performance for models trained and tested only in the peripheral zone was found to be lower than in the central gland (AUC: 0.75 vs 0.95).We developed a radiomics based machine learning system to classify PCa vs non-PCa tissue on mp-MRI validated on accurately co-registered mid-gland histology with a measured target registration error. Potential biases involved in model development were interrogated to provide considerations for future work in this area.

PSMA-PETTo determine threshold and margin combinations that satisfy the following criteria: ≥95% sensitivity with max specificity and ≥95% specificity with max sensitivity.We registered pathologist-annotated whole-mount mid-gland prostatectomy histology sections cut in 4.4 mm intervals from 12 patients to pre-surgical PSMA-PET/MRI by mapping histology to ex-vivo imaging to in-vivo imaging. We generated PET-derived tumor volumes using boundaries defined by thresholded PET volumes from 1-100% of SUVMean and standard deviation of sensitivity and specificity for cancer detection within the 2D oblique histologic planes that intersected with the 3D PET volume for each patient.A threshold of 67% SUV max with an 8.4 mm margin achieved a (mean ± std.) sensitivity of 95.0 ± 7.8% and specificity of 76.4 ± 14.7%. A threshold of 81% SUV max with a 5.1 mm margin achieved sensitivity of 65.1 ± 28.4% and specificity of 95.1 ± 5.2%.Preliminary evidence of thresholding and margin expansion of PSMA-PET images targeted at DILs validated with histopathology demonstrated excellent mean sensitivity and specificity in the setting of focal therapy/boosting and guided biopsy. These parameters can be used in a larger validation study supporting clinical translation.

INTRODUCTION AND OBJECTIVE:Prostate specific membrane antigen positron emission tomography (PSMA PET) has shown good concordance with histology in involved sextants, but there is a need, addressed ...

BACKGROUND Overtreatment of prostate cancer (PCa) is a healthcare issue. Development of noninvasive imaging tools for improved characterization of prostate lesions might reduce overtreatment. PURPOSE To measure the distribution of tissue sodium concentration (TSC), proton T2 -weighted signal, and apparent diffusion coefficient (ADC) values in human PCa and to test the presence of a correlation between regional differences in imaging metrics and the Gleason grade of lesions determined from histopathology. STUDY TYPE Cross-sectional. SUBJECTS Ten men with biopsy-proven PCa. SEQUENCES/FIELD STRENGTH Sodium, proton T2 -weighted, and diffusion-weighted MRI data were acquired using Broad-Band 3D-Fast-Gradient-Recalled, 3D Cube (Isotropic 3D-Fast-Turbo-Spin-Echo acquisition) and 2D Spin-Echo sequences, respectively, with a 3.0T MR scanner. ASSESSMENT All imaging data were coregistered to Gleason-graded postprostatectomy histology, as the standard for prostate cancer lesion characterization. Regional TSC and T2 data were assessed using percent changes from healthy tissue of the same patient (denoted ΔTSC, ΔT2 ). STATISTICS Differences in ΔTSC, ADC, and ΔT2 as a function of Gleason score were analyzed for each imaging contrast using a one-way analysis of variance or a nonparametric t-test. Correlations between imaging data measures and Gleason score were assessed using a Spearman's ranked correlation. RESULTS Evaluation of the correlation of ΔTSC, ADC, and ΔT2 datasets with Gleason scoring revealed that only the correlation between ΔTSC and Gleason score was statistically significant (rs = 0.791, p

An ongoing prospective study is acquiring preoperative imaging data for men with prostate cancer (PCa) using the molecular imaging agent [ F]-DCFPyL targeted against prostate-specific membrane antigen (PSMA). To date, six men (of a planned accrual of 24) with clinically localized, biopsy-proven PCa have undergone preoperative [ F]-DCFPyL positron emission tomography (PET) imaging and multiparametric magnetic resonance imaging acquired using a hybrid PET/MRI system. Lesions identified by [ F]-DCFPyL uptake on PET/MRI were characterized in terms of maximum standardized uptake value (SUV ) and volume using a boundary threshold of 40% SUV . Following surgery, all prostatectomy specimens were processed using a whole-mount technique for accurate deformable co-registration and correlation with PCa foci defined on digitized pathology images. Well-defined intraprostatic dominant lesions were identified by [ F]-DCFPyL PET/MRI (mean SUV 11.4 ± 8.25; mean volume 2.2 ± 2.4 cm ) in all six men. Co-registered digitized whole-mount pathology for the first case revealed that intense [ F]-DCFPyL uptake (SUV 27 ± 1.1 cm ) and multiparametric MRI changes (Prostate Imaging Reporting and Data System score of 4) were highly correlated with a 0.5-cm dominant (largest) lesion with Gleason pattern 4 PCa in the right mid peripheral zone. A smaller focus (0.01 cm ) of lower-grade PCa (Gleason pattern 3) had much lower uptake (SUV 2.7). These early prospective data show that dominant intraprostatic lesions could be identified in all six men using [ F]-DCFPyL as an imaging probe. Trial accrual will continue to quantify in terms of spatial concordance the ability of [ F]-DCFPyL to identify the location and characterize the grade of intraprostatic cancer foci in clinically localized PCa. Patient summary: Positron emission tomography using a novel probe called [ F]-DCFPyL directed against the prostate-specific membrane antigen protein was able to identify locations of prostate cancer in the prostate glands of men undergoing imaging before surgery. In the future, such imaging may allow better targeting of treatment to the portion of the prostate containing the most aggressive components of cancer rather than treating the whole prostate in a uniform fashion. [ F]-DCFPyL positron emission tomography/magnetic resonance imaging shows promise for identifying high-grade intraprostatic foci of prostate cancer in a spatially accurate fashion to facilitate targeted biopsy and treatment. 18 18 18 18 3 18 3 3 3 18 18 18 18 max max max max

In-vivo imaging of the prostate has shown to be useful for prostate cancer (PCa) localization especially during biopsy procedures. Multi-parametric MRI (mp-MRI) is gaining rapid popularity amongst clinicians but is complex and difficult to interpret by even expert radiologists. Prostate specific membrane antigen positron emission tomography (PSMA PET) is emerging as a new tool for PCa detection and has shown promising results towards lesion identification. Both imaging procedures suffer from intra- and inter- observer variability in PCa detection. Computer-aided diagnosis (CAD) systems have been developed as a solution to mitigate observer variability and have shown to boost diagnostic accuracy. There are currently no studies published that assessed the benefit of incorporating PSMA PET imaging and mp-MRI into a CAD system for PCa detection. We compared the accuracy of CAD models trained and tested on features from mp-MRI+PSMA PET, mp-MRI and PSMA PET by training on 1-10 features chosen from three feature selection methods for 10 different classifiers for each of the three experiments. We found that models trained on mp-MRI provided lower overall error and greater specificity, and models trained on mp-MRI+PSMA PET and PSMA PET provided greater sensitivity to lesions in the central gland, which is a known area of difficulty for mp-MRI. Further validation using a larger dataset is required to prove the added benefit of PSMA PET imaging as a second modality to PCa CAD systems. Once fully validated, these results will demonstrate the added benefit of incorporating PSMA PET imaging into CAD models towards PCa detection.

Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reporting, potentially benefitting post-surgery prognosis, recurrence prediction, and treatment planning after RP. Promising results for detecting and grading prostate cancer on digital histopathology images have been reported using machine learning techniques. However, the importance and applicability of those methods have not been fully investigated. We computed three-class tissue component maps (TCMs) from the images, where each pixel was labeled as nuclei, lumina, or other. We applied seven different machine learning approaches: three non-deep learning classifiers with features extracted from TCMs, and four deep learning, using transfer learning with the 1) TCMs, 2) nuclei maps, 3) lumina maps, and 4) raw images for cancer detection and grading on whole-mount RP tissue sections. We performed leave-one-patient-out cross-validation against expert annotations using 286 whole-slide images from 68 patients. For both cancer detection and grading, transfer learning using TCMs performed best. Transfer learning using nuclei maps yielded slightly inferior overall performance, but the best performance for classifying higher-grade cancer. This suggests that 3-class TCMs provide the major cues for cancer detection and grading primarily using nucleus features, which are the most important information for identifying higher-grade cancer.

Prostate cancer (PCa) is a common, serious form of cancer in men that is still prevalent despite ongoing developments in diagnostic oncology. Current detection methods lead to high rates of inaccurate diagnosis. We present a method to directly model and exploit temporal aspects of temporal enhanced ultrasound (TeUS) for tissue characterization, which improves malignancy prediction. We employ a probabilistic-temporal framework, namely, hidden Markov models (HMMs), for modeling TeUS data obtained from PCa patients. We distinguish malignant from benign tissue by comparing the respective log-likelihood estimates generated by the HMMs. We analyze 1100 TeUS signals acquired from 12 patients. Our results show improved malignancy identification compared to previous results, demonstrating over 85% accuracy and AUC of 0.95. Incorporating temporal information directly into the models leads to improved tissue differentiation in PCa. We expect our method to generalize and be applied to other types of cancer in which temporal-ultrasound can be recorded.

Purpose: Automatic cancer detection on radical prostatectomy (RP) sections facilitates graphical and quantitative surgical pathology reporting, which can potentially benefit postsurgery follow-up care and treatment planning. It can also support imaging validation studies using a histologic reference standard and pathology research studies. This problem is challenging due to the large sizes of digital histopathology whole-mount whole-slide images (WSIs) of RP sections and staining variability across different WSIs. Approach: We proposed a calibration-free adaptive thresholding algorithm, which compensates for staining variability and yields consistent tissue component maps (TCMs) of the nuclei, lumina, and other tissues. We used and compared three machine learning methods for classifying each cancer versus noncancer region of interest (ROI) throughout each WSI: (1) conventional machine learning methods and 14 texture features extracted from TCMs, (2) transfer learning with pretrained AlexNet fine-tuned by TCM ROIs, and (3) transfer learning with pretrained AlexNet fine-tuned with raw image ROIs. Results: The three methods yielded areas under the receiver operating characteristic curve of 0.96, 0.98, and 0.98, respectively, in leave-one-patient-out cross validation using 1.3 million ROIs from 286 mid-gland whole-mount WSIs from 68 patients. Conclusion: Transfer learning with the use of TCMs demonstrated state-of-the-art overall performance and is more stable with respect to sample size across different tissue types. For the tissue types involving Gleason 5 (most aggressive) cancer, it achieved the best performance compared to the other tested methods. This tool can be translated to clinical workflow to assist graphical and quantitative pathology reporting for surgical specimens upon further multicenter validation.

Multi-parametric MRI (mp-MRI) has shown to be useful in contemporary prostate biopsy procedures. Unfortunately, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a potential solution and have been shown to boost diagnostic accuracy. We measured the accuracy of a CAD model using a systematic sampling algorithm to remove any spatial bias present in our input. We trained four classifiers with 1-10 features chosen by forward feature selection for each and reported the system with the highest AUC in both the peripheral zone and central gland. Furthermore, we investigated the effect on system performance by varying the minimum tumour size threshold and by varying the average difference in area between malignant and healthy tissue samples. The CAD model was able to classify malignant vs. benign tissue with accuracies competitive with those reported in the literature. Eroding healthy tissue ROIs positively biased the system's performance for the PZ, but no such trend was found in the CG. Once fully validated, this system has the potential to imp.

Objectives : Temporal enhanced ultrasound (TeUS) is a new ultrasound-based imaging technique that provides tissue-specific information. Recent studies have shown the potential of TeUS for improving tissue characterization in prostate cancer diagnosis. We study the temporal properties of TeUS—temporal order and length—and present a new framework to assess their impact on tissue information. Methods : We utilize a probabilistic modeling approach using hidden Markov models (HMMs) to capture the temporal signatures of malignant and benign tissues from TeUS signals of nine patients. We model signals of benign and malignant tissues (284 and 286 signals, respectively) in their original temporal order as well as under order permutations. We then compare the resulting models using the Kullback–Liebler divergence and assess their performance differences in characterization. Moreover, we train HMMs using TeUS signals of different durations and compare their model performance when differentiating tissue types. Results : Our findings demonstrate that models of order-preserved signals perform statistically significantly better (85% accuracy) in tissue characterization compared to models of order-altered signals (62% accuracy). The performance degrades as more changes in signal order are introduced. Additionally, models trained on shorter sequences perform as accurately as models of longer sequences. Conclusion : The work presented here strongly indicates that temporal order has substantial impact on TeUS performance; thus, it plays a significant role in conveying tissue-specific information. Furthermore, shorter TeUS signals can relay sufficient information to accurately distinguish between tissue types. Significance : Understanding the impact of TeUS properties facilitates the process of its adopting in diagnostic procedures and provides insights on improving its acquisition.

Objective: This paper presents the results of a new approach for selection of RF time series features based on joint independent component analysis for in vivo characterization of prostate cancer. Methods: We project three sets of RF time series features extracted from the spectrum, fractal dimension, and the wavelet transform of the ultrasound RF data on a space spanned by five joint independent components. Then, we demonstrate that the obtained mixing coefficients from a group of patients can be used to train a classifier, which can be applied to characterize cancerous regions of a test patient. Results: In a leave-one-patient-out cross validation, an area under receiver operating characteristic curve of 0.93 and classification accuracy of 84% are achieved. Conclusion: Ultrasound RF time series can be used to accurately characterize prostate cancer, in vivo without the need for exhaustive search in the feature space. Significance: We use joint independent component analysis for systematic fusion of multiple sets of RF time series features, within a machine learning framework, to characterize PCa in an in vivo study.

20 Background: Multi-focality and heterogeneity in prostate cancer can confound the selection of appropriate clinical management. Our study aimed to explore radio-genomic correlations using multiparametric magnetic resonance imaging (mpMRI) against a histopathologic reference standard. Methods: Eight men with prostate cancer who underwent mpMRI followed by prostatectomy were selected for this pilot. Whole-mount histopathology was digitized and co-registered to corresponding MRI slices using a validated high-fidelity methodology.(1) Foci, including central/transitional and peripheral zone lesions were identified by a pathologist, and contoured on digitized histopathology specimens and these digitized maps were used to guide macrodissection of the individual foci for genomic copy-number aberration (CNA) analysis. Correlation of radiomics signatures with the histologic findings and genomic analysis was performed. Results: We found a broad range of CNAs revealing inter-patient and intra-prostatic heterogeneity. Recurrently-altered loci ( e.g., 8p21) containing genes of known significance ( e.g., NKX3-1) were observed. Only radiomic features derived from apparent diffusion coefficient (ADC) independently correlated with both Gleason grade (Rho=-0.62, p=0.003) and median CNA burden (Rho=-0.68, p

Radical prostatectomy is performed on approximately 40% of men with organ-confined prostate cancer. Pathologic information obtained from the prostatectomy specimen provides important prognostic information and guides recommendations for adjuvant treatment. The current pathology protocol in most centers involves primarily qualitative assessment. In this paper, we describe and evaluate our system for automatic prostate cancer detection and grading on hematoxylin & eosin-stained tissue images. Our approach is intended to address the dual challenges of large data size and the need for high-level tissue information about the locations and grades of tumors. Our system uses two stages of AdaBoost-based classification. The first provides high-level tissue component labeling of a superpixel image partitioning. The second uses the tissue component labeling to provide a classification of cancer versus noncancer, and low-grade versus high-grade cancer. We evaluated our system using 991 sub-images extracted from digital pathology images of 50 whole-mount tissue sections from 15 prostatectomy patients. We measured accuracies of 90% and 85% for the cancer versus noncancer and high-grade versus low-grade classification tasks, respectively. This system represents a first step toward automated cancer quantification on prostate digital histopathology imaging, which could pave the way for more accurately informed postprostatectomy patient care.

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.

Recent studies highlight temporal ultrasound data as highly promising in differentiating between malignant and benign tissues in prostate cancer patients. Since Hidden Markov Models can be used for capturing order and patterns in time varying signals, we employ them to model temporal aspects of ultrasound data that are typically not incorporated in existing models. By comparing order-preserving and orderaltering models, we demonstrate that the order encoded in the series is necessary to model the variability in ultrasound data of prostate tissues. In future studies, we will investigate the influence of order on the differentiation between malignant and benign tissues.

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.

Purpose: MRI-targeted, 3D transrectal ultrasound (TRUS)-guided prostate biopsy aims to reduce the 21–47% false negative rate1 of clinical 2D TRUS-guided sextant biopsy, but still has a substantial false negative rate. This could be improved via biopsy needle target optimization, accounting for uncertainties due to guidance system errors and image registration errors. As an initial step toward this broader goal, we elucidated the impact of biopsy needle delivery error on the probability of obtaining tumour samples and on core involvement. These are both important parameters to patient risk stratification and treatment decision. Methods: We investigated this for cancer of all grades, and separately for intermediate/high grade (≥Gleason 4+3) cancer. We used expert-contoured gold-standard prostatectomy histology to simulate targeted biopsies using an isotropic Gaussian needle delivery error from 1 to 6 mm, and investigated the amount of cancer obtained in each biopsy core as determined by histology. Results: Needle delivery error resulted in core involvement variability that could influence treatment decisions; the presence or absence of cancer in 1/3 or more of each needle core can be attributed to needle delivery error of 4 mm (as observed in practice2). Conclusions: Repeated biopsies of the same tumour target can yield percent core involvement measures with sufficient variability to influence the decision between active surveillance and treatment. However, this may be mitigated by making more than one biopsy attempt at selected tumour targets.

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.

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.

Background: Guidelines for localizing prostate cancer on imaging are ideally informed by registered post-prostatectomy histology. 3D histology reconstruction methods can support this by reintroducing 3D spatial information lost during histology processing. The need to register small, high-grade foci drives a need for high accuracy. Accurate 3D reconstruction method design is impacted by the answers to the following central questions of this work. (1) How does prostate tissue deform during histology processing? (2) What spatial misalignment of the tissue sections is induced by microtome cutting? (3) How does the choice of reconstruction model affect histology reconstruction accuracy? Materials and Methods: Histology, paraffin block face and magnetic resonance images were acquired for 18 whole mid-gland tissue slices from six prostates. 7-15 homologous landmarks were identified on each image. Tissue deformation due to histology processing was characterized using the target registration error (TRE) after landmark-based registration under four deformation models (rigid, similarity, affine and thin-plate-spline [TPS]). The misalignment of histology sections from the front faces of tissue slices was quantified using manually identified landmarks. The impact of reconstruction models on the TRE after landmark-based reconstruction was measured under eight reconstruction models comprising one of four deformation models with and without constraining histology images to the tissue slice front faces. Results: Isotropic scaling improved the mean TRE by 0.8-1.0 mm (all results reported as 95% confidence intervals), while skew or TPS deformation improved the mean TRE by

The correlation of multiple in vivo imaging modalities with graded whole-mount histology ideally requires co-registration with appropriately quantified accuracy. This work presents a framework for the co-registration of multiple in vivo prostate images with histology as a composition of pair wise registrations. This work also proposes a methodology for the measurement of the accuracy of these registrations based on a composition of target registration errors calculated from homologous landmarks in registered pairs of images. Preliminary results from the implementation of components of the framework are reported.