On-Line Handwritten Formula Recognition using Hidden Markov Models and Context Dependent Graph Grammars

This paper presents an approach for the recognition of on-line handwritten mathematical expressions. The Hidden Markov Model (HMM) based system makes use of simultaneous segmentation and recognition capabilities, avoiding a crucial segmentation during pre-processing. With the segmentation and recognition results, obtained from the HMMrecognizer, it is possible to analyze and interpret the spatial two-dimensional arrangement of the symbols. We use a graph grammar approach for the structure recognition, also used in off-line recognition process, resulting in a general tree-structure of the underlying input-expression. The resulting constructed tree can be translated to any desired syntax (for example: Lisp, LaTeX, OpenMath . . . )

Associations between normal organs and tumor burden in patients imaged with fibroblast activation protein inhibitor-directed positron emission tomography

Several radiolabeled fibroblast activation protein targeted inhibitors (FAPI) have been developed for molecular imaging and therapy. A potential correlation of radiotracer uptake in normal organs and extent of tumor burden may have consequences for a theranostic approach using ligands structurally associated with [68Ga]Ga-FAPI, as one may anticipate decreased doses to normal organs in patients with extensive tumor load. In the present proof-of-concept study investigating patients with solid tumors, we aimed to quantitatively determine the normal organ biodistribution of [68Ga]Ga-FAPI-04, depending on the extent of tumor. Except for a trend towards significance in the myocardium, we did not observe any relevant associations between PET-based tumor burden and normal organs. Those preliminary findings may trigger future studies to determine possible implications for theranostic approaches and FAP-directed drugs, as one may expect an unchanged dose for normal organs even in patients with higher tumor load. Abstract (1) Background: We aimed to quantitatively investigate [68Ga]Ga-FAPI-04 uptake in normal organs and to assess a relationship with the extent of FAPI-avid tumor burden. (2) Methods: In this single-center retrospective analysis, thirty-four patients with solid cancers underwent a total of 40 [68Ga]Ga-FAPI-04 PET/CT scans. Mean standardized uptake values (SUVmean) for normal organs were established by placing volumes of interest (VOIs) in the heart, liver, spleen, pancreas, kidneys, and bone marrow. Total tumor burden was determined by manual segmentation of tumor lesions with increased uptake. For tumor burden, quantitative assessment included maximum SUV (SUVmax), tumor volume (TV), and fractional tumor activity (FTA = TV × SUVmean). Associations between uptake in normal organs and tumor burden were investigated by applying Spearman’s rank correlation coefficient. (3) Results: Median SUVmean values were 2.15 in the pancreas (range, 1.05–9.91), 1.42 in the right (range, 0.57–3.06) and 1.41 in the left kidney (range, 0.73–2.97), 1.2 in the heart (range, 0.46–2.59), 0.86 in the spleen (range, 0.55–1.58), 0.65 in the liver (range, 0.31–2.11), and 0.57 in the bone marrow (range, 0.26–0.94). We observed a trend towards significance for uptake in the myocardium and tumor-derived SUVmax (ρ = 0.29, p = 0.07) and TV (ρ = −0.30, p = 0.06). No significant correlation was achieved for any of the other organs: SUVmax (ρ ≤ 0.1, p ≥ 0.42), TV (ρ ≤ 0.11, p ≥ 0.43), and FTA (ρ ≤ 0.14, p ≥ 0.38). In a sub-analysis exclusively investigating patients with high tumor burden, significant correlations of myocardial uptake with tumor SUVmax (ρ = 0.44; p = 0.03) and tumor-derived FTA with liver uptake (ρ = 0.47; p = 0.02) were recorded. (4) Conclusions: In this proof-of-concept study, quantification of [68Ga]Ga-FAPI-04 PET showed no significant correlation between normal organs and tumor burden, except for a trend in the myocardium. Those preliminary findings may trigger future studies to determine possible implications for treatment with radioactive FAP-targeted drugs, as higher tumor load or uptake may not lead to decreased doses in the majority of normal organs

Test-retest repeatability of organ uptake on PSMA‐targeted 18F‐DCFPyL PET/CT in patients with prostate cancer

Objectives We evaluated 18F-DCFPyL test–retest repeatability of uptake in normal organs. Methods Twenty-two prostate cancer (PC) patients underwent two 18F-DCFPyL PET scans within 7 days within a prospective clinical trial (NCT03793543). In both PET scans, uptake in normal organs (kidneys, spleen, liver, and salivary and lacrimal glands) was quantified. Repeatability was determined by using within-subject coefficient of variation (wCOV), with lower values indicating improved repeatability. Results For SUVmean, repeatability was high for kidneys, spleen, liver, and parotid glands (wCOV, range: 9.0%–14.3%) and lower for lacrimal (23.9%) and submandibular glands (12.4%). For SUVmax, however, the lacrimal (14.4%) and submandibular glands (6.9%) achieved higher repeatability, while for large organs (kidneys, liver, spleen, and parotid glands), repeatability was low (range: 14.1%–45.2%). Conclusion We found acceptable repeatability of uptake on 18F-DCFPyL PET for normal organs, in particular for SUVmean in the liver or parotid glands. This may have implications for both PSMA-targeted imaging and treatment, as patient selection for radioligand therapy and standardized frameworks for scan interpretation (PROMISE, E-PSMA) rely on uptake in those reference organs

Lack of repeatability of radiomic features derived from PET scans: results from a 18F‐DCFPyL test–retest cohort

Objectives PET-based radiomic metrics are increasingly utilized as predictive image biomarkers. However, the repeatability of radiomic features on PET has not been assessed in a test–retest setting. The prostate-specific membrane antigen-targeted compound 18F-DCFPyL is a high-affinity, high-contrast PET agent that we utilized in a test-retest cohort of men with metastatic prostate cancer (PC). Methods Data of 21 patients enrolled in a prospective clinical trial with histologically proven PC underwent two 18F-DCFPyL PET scans within 7 days, using identical acquisition and reconstruction parameters. Sites of disease were segmented and a set of 29 different radiomic parameters were assessed on both scans. We determined repeatability of quantification by using Pearson's correlations, within-subject coefficient of variation (wCOV), and Bland–Altman analysis. Results In total, 230 lesions (177 bone, 38 lymph nodes, 15 others) were assessed on both scans. For all investigated radiomic features, a broad range of inter-scan correlation was found (r, 0.07–0.95), with acceptable reproducibility for entropy and homogeneity (wCOV, 16.0% and 12.7%, respectively). On Bland–Altman analysis, no systematic increase or decrease between the scans was observed for either parameter (±1.96 SD: 1.07/−1.30, 0.23/−0.18, respectively). The remaining 27 tested radiomic metrics, however, achieved unacceptable high wCOV (≥21.7%). Conclusion Many common radiomic features derived from a test–retest PET study had poor repeatability. Only Entropy and homogeneity achieved good repeatability, supporting the notion that those image biomarkers may be incorporated in future clinical trials. Those radiomic features based on high frequency aspects of images appear to lack the repeatability on PET to justify further study

HMM Based Online Handwriting Recognition: an Integrated Approach to Text- and Formula Recognition

This thesis deals with different aspects of automatic online handwriting recognition, comprising methods for the entire recognition process, such as pre-processing, handwriting normalization, feature extraction and Hidden Markov Model (HMM) based modeling techniques applied to text and formula recognition. The objectives of the developed pre-processing steps are basically the normalization of writer dependent writing characteristics (e.g. writing speed, character size and inclination). In order to achieve these objectives, a shape conserving re-sampling has been developed in combination with an entropy based slant and skew normalization. The normalizing scaling of the handwriting is based on an iterative region detection and a subsequent scaling to a standard character core height. The investigated feature extraction methods concern trajectory features as well as bitmap features. Several trajectory and bitmap based feature extraction methods have been developed and evaluated. Five trajectory and three bitmap features have finally been tested and presented in more detail and an optimal combination of the different feature types has been proposed. Considering the dynamic characteristics of online sampled handwriting, the HMM framework offers a couple of important advantages. Consequently, a further chapter is dedicated to the question of the optimal HMM paradigm for the modeling of handwriting. Another important aspect is the investigation of a context dependent impact on the handwritten characters. Significant character variations have been observed with varying adjacent characters. In order to cope with these inconsistencies, multiple models have been introduced for a single character, depending on it's context characters. A drawback of this approach is the sparseness of the available training data for most of the introduced contextual models, which requires an appropriate model tying. For the purpose of parameter tying, a selective approach, a data driven approach and a decision tree based approach have been proposed and compared. All considered aspects have been considered under the assumption that a large or very large vocabulary (up to 200000 words) has to be used for recognition. Although a very large vocabulary may yield high coverage rates, a closed vocabulary with a pre-defined set of words demands always a restriction in terms of system usability. In order to relax this restriction, a wide span statistical description of character strings has been proposed and tested as a substitution for a closed vocabulary. Beside a precise recognition and high vocabulary coverage, the flexible and efficient usage of a handwriting interface demands also the opportunity to enter not only characters, words and sentences, but also additional document elements such as figures, formulae and mathematical expressions. Consequently, an approach to the complex recognition and processing of mathematical formulae has been presented. This approach makes use of the automatic segmentation capabilities of the HMM framework. It has been presented of how the recognition results, combined with the segmentation information can be exploited for a subsequent parsing of the two dimensional structure of mathematical formulae. As a result, the system is capable of converting the handwritten input into a LaTeX document. Finally, the presented methods including text and formula recognition have been integrated into a realtime demonstration system